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

NASA Astrophysics Data System (ADS)

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

2005-05-01

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

Smoke detection

DOEpatents

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

2016-09-06

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDA training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.

Smoke detection

DOEpatents

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

2015-10-27

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDA training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.

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

PubMed

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

2007-01-01

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

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

A Hybrid Sensing Approach for Pure and Adulterated Honey Classification

PubMed Central

Subari, Norazian; Saleh, Junita Mohamad; Shakaff, Ali Yeon Md; Zakaria, Ammar

2012-01-01

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data. PMID:23202033

Palm vein recognition based on directional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

2014-04-01

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the 2LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based 2LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Spatial-temporal discriminant analysis for ERP-based brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Zhao, Qibin; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2013-03-01

Linear discriminant analysis (LDA) has been widely adopted to classify event-related potential (ERP) in brain-computer interface (BCI). Good classification performance of the ERP-based BCI usually requires sufficient data recordings for effective training of the LDA classifier, and hence a long system calibration time which however may depress the system practicability and cause the users resistance to the BCI system. In this study, we introduce a spatial-temporal discriminant analysis (STDA) to ERP classification. As a multiway extension of the LDA, the STDA method tries to maximize the discriminant information between target and nontarget classes through finding two projection matrices from spatial and temporal dimensions collaboratively, which reduces effectively the feature dimensionality in the discriminant analysis, and hence decreases significantly the number of required training samples. The proposed STDA method was validated with dataset II of the BCI Competition III and dataset recorded from our own experiments, and compared to the state-of-the-art algorithms for ERP classification. Online experiments were additionally implemented for the validation. The superior classification performance in using few training samples shows that the STDA is effective to reduce the system calibration time and improve the classification accuracy, thereby enhancing the practicability of ERP-based BCI.

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.

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

PubMed

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

2013-12-01

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

Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor

PubMed Central

Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Saad, Fathinul Syahir Ahmad; Adom, Abdul Hamid; Ahmad, Mohd Noor; Jaafar, Mahmad Nor; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

2012-01-01

In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied. PMID:22778629

Improved maturity and ripeness classifications of Magnifera Indica cv. Harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor.

PubMed

Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Saad, Fathinul Syahir Ahmad; Adom, Abdul Hamid; Ahmad, Mohd Noor; Jaafar, Mahmad Nor; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

2012-01-01

In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.

Sample-space-based feature extraction and class preserving projection for gene expression data.

PubMed

Wang, Wenjun

2013-01-01

In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.

Heating-induced glass-glass and glass-liquid transformations in computer simulations of water.

PubMed

Chiu, Janet; Starr, Francis W; Giovambattista, Nicolas

2014-03-21

Water exists in at least two families of glassy states, broadly categorized as the low-density (LDA) and high-density amorphous ice (HDA). Remarkably, LDA and HDA can be reversibly interconverted via appropriate thermodynamic paths, such as isothermal compression and isobaric heating, exhibiting first-order-like phase transitions. We perform out-of-equilibrium molecular dynamics simulations of glassy water using the ST2 model to study the evolution of LDA and HDA upon isobaric heating. Depending on pressure, glass-to-glass, glass-to-crystal, glass-to-vapor, as well as glass-to-liquid transformations are found. Specifically, heating LDA results in the following transformations, with increasing heating pressures: (i) LDA-to-vapor (sublimation), (ii) LDA-to-liquid (glass transition), (iii) LDA-to-HDA-to-liquid, (iv) LDA-to-HDA-to-liquid-to-crystal, and (v) LDA-to-HDA-to-crystal. Similarly, heating HDA results in the following transformations, with decreasing heating pressures: (a) HDA-to-crystal, (b) HDA-to-liquid-to-crystal, (c) HDA-to-liquid (glass transition), (d) HDA-to-LDA-to-liquid, and (e) HDA-to-LDA-to-vapor. A more complex sequence may be possible using lower heating rates. For each of these transformations, we determine the corresponding transformation temperature as function of pressure, and provide a P-T "phase diagram" for glassy water based on isobaric heating. Our results for isobaric heating dovetail with the LDA-HDA transformations reported for ST2 glassy water based on isothermal compression/decompression processes [Chiu et al., J. Chem. Phys. 139, 184504 (2013)]. The resulting phase diagram is consistent with the liquid-liquid phase transition hypothesis. At the same time, the glass phase diagram is sensitive to sample preparation, such as heating or compression rates. Interestingly, at least for the rates explored, our results suggest that the LDA-to-liquid (HDA-to-liquid) and LDA-to-HDA (HDA-to-LDA) transformation lines on heating are related, both being associated with the limit of kinetic stability of LDA (HDA).

Heating-induced glass-glass and glass-liquid transformations in computer simulations of water

NASA Astrophysics Data System (ADS)

Chiu, Janet; Starr, Francis W.; Giovambattista, Nicolas

2014-03-01

Water exists in at least two families of glassy states, broadly categorized as the low-density (LDA) and high-density amorphous ice (HDA). Remarkably, LDA and HDA can be reversibly interconverted via appropriate thermodynamic paths, such as isothermal compression and isobaric heating, exhibiting first-order-like phase transitions. We perform out-of-equilibrium molecular dynamics simulations of glassy water using the ST2 model to study the evolution of LDA and HDA upon isobaric heating. Depending on pressure, glass-to-glass, glass-to-crystal, glass-to-vapor, as well as glass-to-liquid transformations are found. Specifically, heating LDA results in the following transformations, with increasing heating pressures: (i) LDA-to-vapor (sublimation), (ii) LDA-to-liquid (glass transition), (iii) LDA-to-HDA-to-liquid, (iv) LDA-to-HDA-to-liquid-to-crystal, and (v) LDA-to-HDA-to-crystal. Similarly, heating HDA results in the following transformations, with decreasing heating pressures: (a) HDA-to-crystal, (b) HDA-to-liquid-to-crystal, (c) HDA-to-liquid (glass transition), (d) HDA-to-LDA-to-liquid, and (e) HDA-to-LDA-to-vapor. A more complex sequence may be possible using lower heating rates. For each of these transformations, we determine the corresponding transformation temperature as function of pressure, and provide a P-T "phase diagram" for glassy water based on isobaric heating. Our results for isobaric heating dovetail with the LDA-HDA transformations reported for ST2 glassy water based on isothermal compression/decompression processes [Chiu et al., J. Chem. Phys. 139, 184504 (2013)]. The resulting phase diagram is consistent with the liquid-liquid phase transition hypothesis. At the same time, the glass phase diagram is sensitive to sample preparation, such as heating or compression rates. Interestingly, at least for the rates explored, our results suggest that the LDA-to-liquid (HDA-to-liquid) and LDA-to-HDA (HDA-to-LDA) transformation lines on heating are related, both being associated with the limit of kinetic stability of LDA (HDA).

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

PubMed

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

2012-08-15

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

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

PubMed

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

2012-09-26

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

On the peculiarities of LDA method in two-phase flows with high concentrations of particles

NASA Astrophysics Data System (ADS)

Poplavski, S. V.; Boiko, V. M.; Nesterov, A. U.

2016-10-01

Popular applications of laser Doppler anemometry (LDA) in gas dynamics are reviewed. It is shown that the most popular method cannot be used in supersonic flows and two-phase flows with high concentrations of particles. A new approach to implementation of the known LDA method based on direct spectral analysis, which offers better prospects for such problems, is presented. It is demonstrated that the method is suitable for gas-liquid jets. Owing to the progress in laser engineering, digital recording of spectra, and computer processing of data, the method is implemented at a higher technical level and provides new prospects of diagnostics of high-velocity dense two-phase flows.

Robust linear discriminant models to solve financial crisis in banking sectors

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Differentiation of cumin seeds using a metal-oxide based gas sensor array in tandem with chemometric tools.

PubMed

Ghasemi-Varnamkhasti, Mahdi; Amiri, Zahra Safari; Tohidi, Mojtaba; Dowlati, Majid; Mohtasebi, Seyed Saeid; Silva, Adenilton C; Fernandes, David D S; Araujo, Mário C U

2018-01-01

Cumin is a plant of the Apiaceae family (umbelliferae) which has been used since ancient times as a medicinal plant and as a spice. The difference in the percentage of aromatic compounds in cumin obtained from different locations has led to differentiation of some species of cumin from other species. The quality and price of cumin vary according to the specie and may be an incentive for the adulteration of high value samples with low quality cultivars. An electronic nose simulates the human olfactory sense by using an array of sensors to distinguish complex smells. This makes it an alternative for the identification and classification of cumin species. The data, however, may have a complex structure, difficult to interpret. Given this, chemometric tools can be used to manipulate data with two-dimensional structure (sensor responses in time) obtained by using electronic nose sensors. In this study, an electronic nose based on eight metal oxide semiconductor sensors (MOS) and 2D-LDA (two-dimensional linear discriminant analysis), U-PLS-DA (Partial least square discriminant analysis applied to the unfolded data) and PARAFAC-LDA (Parallel factor analysis with linear discriminant analysis) algorithms were used in order to identify and classify different varieties of both cultivated and wild black caraway and cumin. The proposed methodology presented a correct classification rate of 87.1% for PARAFAC-LDA and 100% for 2D-LDA and U-PLS-DA, indicating a promising strategy for the classification different varieties of cumin, caraway and other seeds. Copyright © 2017 Elsevier B.V. All rights reserved.

Characterization and noninvasive diagnosis of bladder cancer with serum surface enhanced Raman spectroscopy and genetic algorithms

NASA Astrophysics Data System (ADS)

Li, Shaoxin; Li, Linfang; Zeng, Qiuyao; Zhang, Yanjiao; Guo, Zhouyi; Liu, Zhiming; Jin, Mei; Su, Chengkang; Lin, Lin; Xu, Junfa; Liu, Songhao

2015-05-01

This study aims to characterize and classify serum surface-enhanced Raman spectroscopy (SERS) spectra between bladder cancer patients and normal volunteers by genetic algorithms (GAs) combined with linear discriminate analysis (LDA). Two group serum SERS spectra excited with nanoparticles are collected from healthy volunteers (n = 36) and bladder cancer patients (n = 55). Six diagnostic Raman bands in the regions of 481-486, 682-687, 1018-1034, 1313-1323, 1450-1459 and 1582-1587 cm-1 related to proteins, nucleic acids and lipids are picked out with the GAs and LDA. By the diagnostic models built with the identified six Raman bands, the improved diagnostic sensitivity of 90.9% and specificity of 100% were acquired for classifying bladder cancer patients from normal serum SERS spectra. The results are superior to the sensitivity of 74.6% and specificity of 97.2% obtained with principal component analysis by the same serum SERS spectra dataset. Receiver operating characteristic (ROC) curves further confirmed the efficiency of diagnostic algorithm based on GA-LDA technique. This exploratory work demonstrates that the serum SERS associated with GA-LDA technique has enormous potential to characterize and non-invasively detect bladder cancer through peripheral blood.

Non-invasive optical detection of esophagus cancer based on urine surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Shaohua; Wang, Lan; Chen, Weiwei; Lin, Duo; Huang, Lingling; Wu, Shanshan; Feng, Shangyuan; Chen, Rong

2014-09-01

A surface-enhanced Raman spectroscopy (SERS) approach was utilized for urine biochemical analysis with the aim to develop a label-free and non-invasive optical diagnostic method for esophagus cancer detection. SERS spectrums were acquired from 31 normal urine samples and 47 malignant esophagus cancer (EC) urine samples. Tentative assignments of urine SERS bands demonstrated esophagus cancer specific changes, including an increase in the relative amounts of urea and a decrease in the percentage of uric acid in the urine of normal compared with EC. The empirical algorithm integrated with linear discriminant analysis (LDA) were employed to identify some important urine SERS bands for differentiation between healthy subjects and EC urine. The empirical diagnostic approach based on the ratio of the SERS peak intensity at 527 to 1002 cm-1 and 725 to 1002 cm-1 coupled with LDA yielded a diagnostic sensitivity of 72.3% and specificity of 96.8%, respectively. The area under the receive operating characteristic (ROC) curve was 0.954, which further evaluate the performance of the diagnostic algorithm based on the ratio of the SERS peak intensity combined with LDA analysis. This work demonstrated that the urine SERS spectra associated with empirical algorithm has potential for noninvasive diagnosis of esophagus cancer.

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.

Potential of non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Shaohua; Wang, Lan; Chen, Weisheng; Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Li, Buhong; Chen, Rong

2014-11-01

Non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy (SERS) analysis was presented. Urine SERS spectra were measured on esophagus cancer patients (n = 56) and healthy volunteers (n = 36) for control analysis. Tentative assignments of the urine SERS spectra indicated some interesting esophagus cancer-specific biomolecular changes, including a decrease in the relative content of urea and an increase in the percentage of uric acid in the urine of esophagus cancer patients compared to that of healthy subjects. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to analyze and differentiate the SERS spectra between normal and esophagus cancer urine. The diagnostic algorithms utilizing a multivariate analysis method achieved a diagnostic sensitivity of 89.3% and specificity of 83.3% for separating esophagus cancer samples from normal urine samples. These results from the explorative work suggested that silver nano particle-based urine SERS analysis coupled with PCA-LDA multivariate analysis has potential for non-invasive detection of esophagus cancer.

Overlapped Partitioning for Ensemble Classifiers of P300-Based Brain-Computer Interfaces

PubMed Central

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance. PMID:24695550

Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces.

PubMed

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.

Study of ice-related flow features around Tanaica Montes, Mars: Implications for late amazonian debris-covered glaciation

NASA Astrophysics Data System (ADS)

Sinha, Rishitosh K.; Vijayan, S.; Bharti, Rajiv R.

2017-11-01

Lobate debris aprons (LDA) and lineated valley fill (LVF) have been broadly recognized in the mid-latitudes of Mars and their subsequent analyses using data from the SHAllow RADar (SHARAD) instrument has suggested evidence for contemporary ice preserved beneath these features. In this study, we conduct detailed characterization of newly identified LDA flow units within the Tanaica Montes region (39.55˚ N, 269.17˚ E) of Mars to assess and understand the similarities in their emplacement with respect to LDA flow units mapped in other regions of Mars. We utilize the Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) images and SHAllow RADar (SHARAD) datasets for geomorphic and subsurface analysis and Mars Global Surveyor (MGS) Mars Orbiter Laser Altimeter (MOLA) point tracks for topographic analysis. Geomorphic observation of LDA flow units surrounding the montes flanks and massif walls reveal integrated pattern of convergence and divergence and evidence of bending and deflection within the flow lines that resulted in concentric, loop-like flow patterns in the downslope. Brain-terrain texture and craters with varying morphological characteristics (ring-mold type) is suggestive that LDAs may be similar to ice-rich, debris-covered glaciers. MOLA point track based convex-up topographic profiles of LDAs suggest that their thickness vary in the range of ∼100-200 m in both the northwestern and southeastern portions of study region. Further, the slope values of mapped LDA surfaces within the study region are within ∼0.1˚-4˚. The extent of mapped LDAs within the study region is such that some of the low elevation (∼0.8-1.3 km) portions of montes flanks are surrounded by relatively less extent (up to ∼0.5-0.8 km) of LDA flow units. Geomorphic and topographic evidence for flow units that appear to be superposed on the main LDA body collectively suggest the possibility of episodic glacial activity in the region. Furthermore, based on the alignment of subsurface reflectors with the surrounding plains when a permittivity of ice (3.2) is assumed and the radargram is depth-corrected, we infer that some of the portions of LDA flow units have preserved ice in their subsurface up to ∼300-500 m depth. Crater size frequency distribution of craters counted on LDA surface indicates that the best-fit age is ∼110 Ma. In addition, the LDA surfaces exhibit different best-fit ages for different types of crater morphologies (bowl-shaped, ring-mold and infilled craters) included in the crater count statistics. Together, these observations and the interpretations suggest that most, if not all, of the LDAs in the study region are like classical LDAs mapped in other regions of Mars (e.g. along the mid-latitude dichotomy boundary and eastern Hellas region). These results indicate that a widespread accumulation and preservation of ice has occurred during the Late Amazonian as suggested in previous studies.

Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

Orthogonal sparse linear discriminant analysis

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

[Vis-NIR spectroscopic pattern recognition combined with SG smoothing applied to breed screening of transgenic sugarcane].

PubMed

Liu, Gui-Song; Guo, Hao-Song; Pan, Tao; Wang, Ji-Hua; Cao, Gan

2014-10-01

Based on Savitzky-Golay (SG) smoothing screening, principal component analysis (PCA) combined with separately supervised linear discriminant analysis (LDA) and unsupervised hierarchical clustering analysis (HCA) were used for non-destructive visible and near-infrared (Vis-NIR) detection for breed screening of transgenic sugarcane. A random and stability-dependent framework of calibration, prediction, and validation was proposed. A total of 456 samples of sugarcane leaves planting in the elongating stage were collected from the field, which was composed of 306 transgenic (positive) samples containing Bt and Bar gene and 150 non-transgenic (negative) samples. A total of 156 samples (negative 50 and positive 106) were randomly selected as the validation set; the remaining samples (negative 100 and positive 200, a total of 300 samples) were used as the modeling set, and then the modeling set was subdivided into calibration (negative 50 and positive 100, a total of 150 samples) and prediction sets (negative 50 and positive 100, a total of 150 samples) for 50 times. The number of SG smoothing points was ex- panded, while some modes of higher derivative were removed because of small absolute value, and a total of 264 smoothing modes were used for screening. The pairwise combinations of first three principal components were used, and then the optimal combination of principal components was selected according to the model effect. Based on all divisions of calibration and prediction sets and all SG smoothing modes, the SG-PCA-LDA and SG-PCA-HCA models were established, the model parameters were optimized based on the average prediction effect for all divisions to produce modeling stability. Finally, the model validation was performed by validation set. With SG smoothing, the modeling accuracy and stability of PCA-LDA, PCA-HCA were signif- icantly improved. For the optimal SG-PCA-LDA model, the recognition rate of positive and negative validation samples were 94.3%, 96.0%; and were 92.5%, 98.0% for the optimal SG-PCA-LDA model, respectively. Vis-NIR spectro- scopic pattern recognition combined with SG smoothing could be used for accurate recognition of transgenic sugarcane leaves, and provided a convenient screening method for transgenic sugarcane breeding.

Proton pump inhibitors in prevention of low-dose aspirin-associated upper gastrointestinal injuries.

PubMed

Mo, Chen; Sun, Gang; Lu, Ming-Liang; Zhang, Li; Wang, Yan-Zhi; Sun, Xi; Yang, Yun-Sheng

2015-05-07

To determine the preventive effect and safety of proton pump inhibitors (PPIs) in low-dose aspirin (LDA)-associated gastrointestinal (GI) ulcers and bleeding. We searched MEDLINE, EMBASE and the Cochrane Controlled Trials Register from inception to December 2013, and checked conference abstracts of randomized controlled trials (RCTs) on the effect of PPIs in reducing adverse GI events (hemorrhage, ulcer, perforation, or obstruction) in patients taking LDA. The preventive effects of PPIs were compared with the control group [taking placebo, a cytoprotective agent, or an H2 receptor antagonist (H2RA)] in LDA-associated upper GI injuries. The meta-analysis was performed using RevMan 5.1 software. We evaluated 8780 participants in 10 RCTs. The meta-analysis showed that PPIs decreased the risk of LDA-associated upper GI ulcers (OR = 0.16; 95%CI: 0.12-0.23) and bleeding (OR = 0.27; 95%CI: 0.16-0.43) compared with control. For patients treated with dual anti-platelet therapy of LDA and clopidogrel, PPIs were able to prevent the LDA-associated GI bleeding (OR = 0.36; 95%CI: 0.15-0.87) without increasing the risk of major adverse cardiovascular events (MACE) (OR = 1.00; 95%CI: 0.76-1.31). PPIs were superior to H2RA in prevention of LDA-associated GI ulcers (OR = 0.12; 95%CI: 0.02-0.65) and bleeding (OR = 0.32; 95%CI: 0.13-0.79). PPIs are effective in preventing LDA-associated upper GI ulcers and bleeding. Concomitant use of PPI, LDA and clopidogrel did not increase the risk of MACE.

The Raman spectrum character of skin tumor induced by UVB

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Noninvasive detection of nasopharyngeal carcinoma based on saliva proteins using surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Lin, Xueliang; Lin, Duo; Ge, Xiaosong; Qiu, Sufang; Feng, Shangyuan; Chen, Rong

2017-10-01

The present study evaluated the capability of saliva analysis combining membrane protein purification with surface-enhanced Raman spectroscopy (SERS) for noninvasive detection of nasopharyngeal carcinoma (NPC). A rapid and convenient protein purification method based on cellulose acetate membrane was developed. A total of 659 high-quality SERS spectra were acquired from purified proteins extracted from the saliva samples of 170 patients with pathologically confirmed NPC and 71 healthy volunteers. Spectral analysis of those saliva protein SERS spectra revealed specific changes in some biochemical compositions, which were possibly associated with NPC transformation. Furthermore, principal component analysis combined with linear discriminant analysis (PCA-LDA) was utilized to analyze and classify the saliva protein SERS spectra from NPC and healthy subjects. Diagnostic sensitivity of 70.7%, specificity of 70.3%, and diagnostic accuracy of 70.5% could be achieved by PCA-LDA for NPC identification. These results show that this assay based on saliva protein SERS analysis holds promising potential for developing a rapid, noninvasive, and convenient clinical tool for NPC screening.

Q-mode versus R-mode principal component analysis for linear discriminant analysis (LDA)

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2017-05-01

Many literature apply Principal Component Analysis (PCA) as either preliminary visualization or variable con-struction methods or both. Focus of PCA can be on the samples (R-mode PCA) or variables (Q-mode PCA). Traditionally, R-mode PCA has been the usual approach to reduce high-dimensionality data before the application of Linear Discriminant Analysis (LDA), to solve classification problems. Output from PCA composed of two new matrices known as loadings and scores matrices. Each matrix can then be used to produce a plot, i.e. loadings plot aids identification of important variables whereas scores plot presents spatial distribution of samples on new axes that are also known as Principal Components (PCs). Fundamentally, the scores matrix always be the input variables for building classification model. A recent paper uses Q-mode PCA but the focus of analysis was not on the variables but instead on the samples. As a result, the authors have exchanged the use of both loadings and scores plots in which clustering of samples was studied using loadings plot whereas scores plot has been used to identify important manifest variables. Therefore, the aim of this study is to statistically validate the proposed practice. Evaluation is based on performance of external error obtained from LDA models according to number of PCs. On top of that, bootstrapping was also conducted to evaluate the external error of each of the LDA models. Results show that LDA models produced by PCs from R-mode PCA give logical performance and the matched external error are also unbiased whereas the ones produced with Q-mode PCA show the opposites. With that, we concluded that PCs produced from Q-mode is not statistically stable and thus should not be applied to problems of classifying samples, but variables. We hope this paper will provide some insights on the disputable issues.

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

NASA Astrophysics Data System (ADS)

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

2013-06-01

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

Small-bowel mucosal injuries in low-dose aspirin users with obscure gastrointestinal bleeding

PubMed Central

Iwamoto, Junichi; Mizokami, Yuji; Saito, Yoshifumi; Shimokobe, Koichi; Honda, Akira; Ikegami, Tadashi; Matsuzaki, Yasushi

2014-01-01

AIM: To investigate the clinical differences between small intestinal injuries in low-dose aspirin (LDA) users and in non-steroidal anti-inflammatory drug (NSAID) users who were examined by capsule endoscopy (CE) for obscure gastrointestinal bleeding (OGIB). METHODS: A total of 181 patients who underwent CE for OGIB were included in this study. Based on clinical records, laboratory data such as hemoglobin levels, major symptoms, underlying diseases, the types and duration of LDA and NSAID use, and endoscopic characteristics of CE were reviewed. RESULTS: Out of a total of 45 cases of erosive lesions, 27 cases were taking LDA or NSAIDs (7 were on NSAIDs, 9 were on LDA alone, 9 were on LDA and thienopyridine, and 2 were on LDA and warfarin).The prevalence of ulcers or erosion during chronic use of LDA, LDA and the anti-platelet drug thienopyridine (clopidogrel or ticlopidine), and NSAIDs were 64.3%, 80.0%, and 75.0%, respectively. Erosive lesions were observed predominantly in chronic LDA users, while ulcerative lesions were detected mainly in NSAID users. However, concomitant use of thienopyridine such as clopidogrel with LDA increased the proportion of ulcers. The erosive lesions were located in the whole of the small intestine (jejunum and ileum), whereas ulcerative lesions were mainly observed in the ileum (P < 0.05). CONCLUSION: Our CE findings indicate that chronic LDA users and NSAID users show different types and locations of small-bowel mucosal injuries. The concomitant use of anti-platelet drugs with LDA tends to exacerbate the injuries from LDA-type to NSAID-type injuries. PMID:25278707

Comparison of Fecal Microbiota between German Holstein Dairy Cows with and without Left-Sided Displacement of the Abomasum

PubMed Central

Song, Eun-Sik; Jung, Sang Il; Park, Hyung-Jin; Seo, Kyoung-Won; Son, Jeong-Hoon; Hong, Sanghyun; Shim, Minkyung

2016-01-01

One of the most common diseases in high-performance German Holstein dairy cows is left-sided displacement of the abomasum (LDA). Hypomotility of the abomasum is detrimental during the pathogenesis of LDA. It is known that improper interactions between the gut microbiota and the enteric nervous system contribute to dysfunctions of gastrointestinal motility. Therefore, we hypothesized that the gut microbial composition will be different between German Holstein dairy cows with and without LDA. We used 16S rRNA gene analysis to evaluate whether there are any differences in bacterial composition between German Holstein dairy cows with and without LDA. Even though our data are limited to being used to correlate compositional changes with corresponding functional aspects in the pathogenesis of LDA, results from this study show that the fecal microbial compositions of German Holstein dairy cows with LDA shifted and were less diverse than those in normal cows. In particular, Spirochaetes were absent in cows with LDA. PMID:26842700

Preconception Low-Dose Aspirin Restores Diminished Pregnancy and Live Birth Rates in Women With Low-Grade Inflammation: A Secondary Analysis of a Randomized Trial.

PubMed

Sjaarda, Lindsey A; Radin, Rose G; Silver, Robert M; Mitchell, Emily; Mumford, Sunni L; Wilcox, Brian; Galai, Noya; Perkins, Neil J; Wactawski-Wende, Jean; Stanford, Joseph B; Schisterman, Enrique F

2017-05-01

Inflammation is linked to causes of infertility. Low-dose aspirin (LDA) may improve reproductive success in women with chronic, low-grade inflammation. To investigate the effect of preconception-initiated LDA on pregnancy rate, pregnancy loss, live birth rate, and inflammation during pregnancy. Stratified secondary analysis of a multicenter, block-randomized, double-blind, placebo-controlled trial. Four US academic medical centers, 2007 to 2012. Healthy women aged 18 to 40 years (N = 1228) with one to two prior pregnancy losses actively attempting to conceive. Preconception-initiated, daily LDA (81 mg) or matching placebo taken up to six menstrual cycles attempting pregnancy and through 36 weeks' gestation in women who conceived. Confirmed pregnancy, live birth, and pregnancy loss were compared between LDA and placebo, stratified by tertile of preconception, preintervention serum high-sensitivity C-reactive protein (hsCRP) (low, <0.70 mg/L; middle, 0.70 to <1.95 mg/L; high, ≥1.95 mg/L). Live birth occurred in 55% of women overall. The lowest pregnancy and live birth rates occurred among the highest hsCRP tertile receiving placebo (44% live birth). LDA increased live birth among high-hsCRP women to 59% (relative risk, 1.35; 95% confidence interval, 1.08 to 1.67), similar to rates in the lower and mid-CRP tertiles. LDA did not affect clinical pregnancy or live birth in the low (live birth: 59% LDA, 54% placebo) or midlevel hsCRP tertiles (live birth: 59% LDA, 59% placebo). In women attempting conception with elevated hsCRP and prior pregnancy loss, LDA may increase clinical pregnancy and live birth rates compared with women without inflammation and reduce hsCRP elevation during pregnancy. Copyright © 2017 by the Endocrine Society

Proton pump inhibitors in prevention of low-dose aspirin-associated upper gastrointestinal injuries

PubMed Central

Mo, Chen; Sun, Gang; Lu, Ming-Liang; Zhang, Li; Wang, Yan-Zhi; Sun, Xi; Yang, Yun-Sheng

2015-01-01

AIM: To determine the preventive effect and safety of proton pump inhibitors (PPIs) in low-dose aspirin (LDA)-associated gastrointestinal (GI) ulcers and bleeding. METHODS: We searched MEDLINE, EMBASE and the Cochrane Controlled Trials Register from inception to December 2013, and checked conference abstracts of randomized controlled trials (RCTs) on the effect of PPIs in reducing adverse GI events (hemorrhage, ulcer, perforation, or obstruction) in patients taking LDA. The preventive effects of PPIs were compared with the control group [taking placebo, a cytoprotective agent, or an H2 receptor antagonist (H2RA)] in LDA-associated upper GI injuries. The meta-analysis was performed using RevMan 5.1 software. RESULTS: We evaluated 8780 participants in 10 RCTs. The meta-analysis showed that PPIs decreased the risk of LDA-associated upper GI ulcers (OR = 0.16; 95%CI: 0.12-0.23) and bleeding (OR = 0.27; 95%CI: 0.16-0.43) compared with control. For patients treated with dual anti-platelet therapy of LDA and clopidogrel, PPIs were able to prevent the LDA-associated GI bleeding (OR = 0.36; 95%CI: 0.15-0.87) without increasing the risk of major adverse cardiovascular events (MACE) (OR = 1.00; 95%CI: 0.76-1.31). PPIs were superior to H2RA in prevention of LDA-associated GI ulcers (OR = 0.12; 95%CI: 0.02-0.65) and bleeding (OR = 0.32; 95%CI: 0.13-0.79). CONCLUSION: PPIs are effective in preventing LDA-associated upper GI ulcers and bleeding. Concomitant use of PPI, LDA and clopidogrel did not increase the risk of MACE. PMID:25954113

Prediction of Potential Hit Song and Musical Genre Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Monterola, Christopher; Abundo, Cheryl; Tugaff, Jeric; Venturina, Lorcel Ericka

Accurately quantifying the goodness of music based on the seemingly subjective taste of the public is a multi-million industry. Recording companies can make sound decisions on which songs or artists to prioritize if accurate forecasting is achieved. We extract 56 single-valued musical features (e.g. pitch and tempo) from 380 Original Pilipino Music (OPM) songs (190 are hit songs) released from 2004 to 2006. Based on an effect size criterion which measures a variable's discriminating power, the 20 highest ranked features are fed to a classifier tasked to predict hit songs. We show that regardless of musical genre, a trained feed-forward neural network (NN) can predict potential hit songs with an average accuracy of ΦNN = 81%. The accuracy is about +20% higher than those of standard classifiers such as linear discriminant analysis (LDA, ΦLDA = 61%) and classification and regression trees (CART, ΦCART = 57%). Both LDA and CART are above the proportional chance criterion (PCC, ΦPCC = 50%) but are slightly below the suggested acceptable classifier requirement of 1.25*ΦPCC = 63%. Utilizing a similar procedure, we demonstrate that different genres (ballad, alternative rock or rock) of OPM songs can be automatically classified with near perfect accuracy using LDA or NN but only around 77% using CART.

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.

Comparative Chemometric Analysis for Classification of Acids and Bases via a Colorimetric Sensor Array.

PubMed

Kangas, Michael J; Burks, Raychelle M; Atwater, Jordyn; Lukowicz, Rachel M; Garver, Billy; Holmes, Andrea E

2018-02-01

With the increasing availability of digital imaging devices, colorimetric sensor arrays are rapidly becoming a simple, yet effective tool for the identification and quantification of various analytes. Colorimetric arrays utilize colorimetric data from many colorimetric sensors, with the multidimensional nature of the resulting data necessitating the use of chemometric analysis. Herein, an 8 sensor colorimetric array was used to analyze select acid and basic samples (0.5 - 10 M) to determine which chemometric methods are best suited for classification quantification of analytes within clusters. PCA, HCA, and LDA were used to visualize the data set. All three methods showed well-separated clusters for each of the acid or base analytes and moderate separation between analyte concentrations, indicating that the sensor array can be used to identify and quantify samples. Furthermore, PCA could be used to determine which sensors showed the most effective analyte identification. LDA, KNN, and HQI were used for identification of analyte and concentration. HQI and KNN could be used to correctly identify the analytes in all cases, while LDA correctly identified 95 of 96 analytes correctly. Additional studies demonstrated that controlling for solvent and image effects was unnecessary for all chemometric methods utilized in this study.

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

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

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDAmore » training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.« less

Detection of a gravitropism phenotype in glutamate receptor-like 3.3 mutants of Arabidopsis thaliana using machine vision and computation.

PubMed

Miller, Nathan D; Durham Brooks, Tessa L; Assadi, Amir H; Spalding, Edgar P

2010-10-01

Gene disruption frequently produces no phenotype in the model plant Arabidopsis thaliana, complicating studies of gene function. Functional redundancy between gene family members is one common explanation but inadequate detection methods could also be responsible. Here, newly developed methods for automated capture and processing of time series of images, followed by computational analysis employing modified linear discriminant analysis (LDA) and wavelet-based differentiation, were employed in a study of mutants lacking the Glutamate Receptor-Like 3.3 gene. Root gravitropism was selected as the process to study with high spatiotemporal resolution because the ligand-gated Ca(2+)-permeable channel encoded by GLR3.3 may contribute to the ion fluxes associated with gravity signal transduction in roots. Time series of root tip angles were collected from wild type and two different glr3.3 mutants across a grid of seed-size and seedling-age conditions previously found to be important to gravitropism. Statistical tests of average responses detected no significant difference between populations, but LDA separated both mutant alleles from the wild type. After projecting the data onto LDA solution vectors, glr3.3 mutants displayed greater population variance than the wild type in all four conditions. In three conditions the projection means also differed significantly between mutant and wild type. Wavelet analysis of the raw response curves showed that the LDA-detected phenotypes related to an early deceleration and subsequent slower-bending phase in glr3.3 mutants. These statistically significant, heritable, computation-based phenotypes generated insight into functions of GLR3.3 in gravitropism. The methods could be generally applicable to the study of phenotypes and therefore gene function.

Detection of a Gravitropism Phenotype in glutamate receptor-like 3.3 Mutants of Arabidopsis thaliana Using Machine Vision and Computation

PubMed Central

Miller, Nathan D.; Durham Brooks, Tessa L.; Assadi, Amir H.; Spalding, Edgar P.

2010-01-01

Gene disruption frequently produces no phenotype in the model plant Arabidopsis thaliana, complicating studies of gene function. Functional redundancy between gene family members is one common explanation but inadequate detection methods could also be responsible. Here, newly developed methods for automated capture and processing of time series of images, followed by computational analysis employing modified linear discriminant analysis (LDA) and wavelet-based differentiation, were employed in a study of mutants lacking the Glutamate Receptor-Like 3.3 gene. Root gravitropism was selected as the process to study with high spatiotemporal resolution because the ligand-gated Ca2+-permeable channel encoded by GLR3.3 may contribute to the ion fluxes associated with gravity signal transduction in roots. Time series of root tip angles were collected from wild type and two different glr3.3 mutants across a grid of seed-size and seedling-age conditions previously found to be important to gravitropism. Statistical tests of average responses detected no significant difference between populations, but LDA separated both mutant alleles from the wild type. After projecting the data onto LDA solution vectors, glr3.3 mutants displayed greater population variance than the wild type in all four conditions. In three conditions the projection means also differed significantly between mutant and wild type. Wavelet analysis of the raw response curves showed that the LDA-detected phenotypes related to an early deceleration and subsequent slower-bending phase in glr3.3 mutants. These statistically significant, heritable, computation-based phenotypes generated insight into functions of GLR3.3 in gravitropism. The methods could be generally applicable to the study of phenotypes and therefore gene function. PMID:20647506

Learning topic models by belief propagation.

PubMed

Zeng, Jia; Cheung, William K; Liu, Jiming

2013-05-01

Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representations.

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

NASA Astrophysics Data System (ADS)

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

2009-02-01

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

Glass polymorphism in glycerol-water mixtures: I. A computer simulation study.

PubMed

Jahn, David A; Wong, Jessina; Bachler, Johannes; Loerting, Thomas; Giovambattista, Nicolas

2016-04-28

We perform out-of-equilibrium molecular dynamics (MD) simulations of water-glycerol mixtures in the glass state. Specifically, we study the transformations between low-density (LDA) and high-density amorphous (HDA) forms of these mixtures induced by compression/decompression at constant temperature. Our MD simulations reproduce qualitatively the density changes observed in experiments. Specifically, the LDA-HDA transformation becomes (i) smoother and (ii) the hysteresis in a compression/decompression cycle decreases as T and/or glycerol content increase. This is surprising given the fast compression/decompression rates (relative to experiments) accessible in MD simulations. We study mixtures with glycerol molar concentration χ(g) = 0-13% and find that, for the present mixture models and rates, the LDA-HDA transformation is detectable up to χ(g) ≈ 5%. As the concentration increases, the density of the starting glass (i.e., LDA at approximately χ(g) ≤ 5%) rapidly increases while, instead, the density of HDA remains practically constant. Accordingly, the LDA state and hence glass polymorphism become inaccessible for glassy mixtures with approximately χ(g) > 5%. We present an analysis of the molecular-level changes underlying the LDA-HDA transformation. As observed in pure glassy water, during the LDA-to-HDA transformation, water molecules within the mixture approach each other, moving from the second to the first hydration shell and filling the first interstitial shell of water molecules. Interestingly, similar changes also occur around glycerol OH groups. It follows that glycerol OH groups contribute to the density increase during the LDA-HDA transformation. An analysis of the hydrogen bond (HB)-network of the mixtures shows that the LDA-HDA transformation is accompanied by minor changes in the number of HBs of water and glycerol. Instead, large changes in glycerol and water coordination numbers occur. We also perform a detailed analysis of the effects that the glycerol force field (FF) has on our results. By comparing MD simulations using two different glycerol models, we find that glycerol conformations indeed depend on the FF employed. Yet, the thermodynamic and microscopic mechanisms accompanying the LDA-HDA transformation and hence, our main results, do not. This work is accompanied by an experimental report where we study the glass polymorphism in glycerol-water mixtures prepared by isobaric cooling at 1 bar.

Application of mass spectrometry based electronic nose and chemometrics for fingerprinting radiation treatment

NASA Astrophysics Data System (ADS)

Gupta, Sumit; Variyar, Prasad S.; Sharma, Arun

2015-01-01

Volatile compounds were isolated from apples and grapes employing solid phase micro extraction (SPME) and subsequently analyzed by GC/MS equipped with a transfer line without stationary phase. Single peak obtained was integrated to obtain total mass spectrum of the volatile fraction of samples. A data matrix having relative abundance of all mass-to-charge ratios was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) to identify radiation treatment. PCA results suggested that there is sufficient variability between control and irradiated samples to build classification models based on supervised techniques. LDA successfully aided in segregating control from irradiated samples at all doses (0.1, 0.25, 0.5, 1.0, 1.5, 2.0 kGy). SPME-MS with chemometrics was successfully demonstrated as simple screening method for radiation treatment.

Decoding magnetoencephalographic rhythmic activity using spectrospatial information.

PubMed

Kauppi, Jukka-Pekka; Parkkonen, Lauri; Hari, Riitta; Hyvärinen, Aapo

2013-12-01

We propose a new data-driven decoding method called Spectral Linear Discriminant Analysis (Spectral LDA) for the analysis of magnetoencephalography (MEG). The method allows investigation of changes in rhythmic neural activity as a result of different stimuli and tasks. The introduced classification model only assumes that each "brain state" can be characterized as a combination of neural sources, each of which shows rhythmic activity at one or several frequency bands. Furthermore, the model allows the oscillation frequencies to be different for each such state. We present decoding results from 9 subjects in a four-category classification problem defined by an experiment involving randomly alternating epochs of auditory, visual and tactile stimuli interspersed with rest periods. The performance of Spectral LDA was very competitive compared with four alternative classifiers based on different assumptions concerning the organization of rhythmic brain activity. In addition, the spectral and spatial patterns extracted automatically on the basis of trained classifiers showed that Spectral LDA offers a novel and interesting way of analyzing spectrospatial oscillatory neural activity across the brain. All the presented classification methods and visualization tools are freely available as a Matlab toolbox. © 2013.

Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review.

PubMed

Fusco, Roberta; Sansone, Mario; Filice, Salvatore; Carone, Guglielmo; Amato, Daniela Maria; Sansone, Carlo; Petrillo, Antonella

2016-01-01

We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), and Bayesian classifiers (BC), and features used for classification are described. The findings of a systematic review of 26 studies are presented. The sensitivity and specificity are respectively 91 and 83 % for ANN, 85 and 82 % for SVM, 96 and 85 % for LDA, 92 and 87 % for TC, and 82 and 85 % for BC. The sensitivity and specificity are respectively 82 and 74 % for dynamic features, 93 and 60 % for morphological features, 88 and 81 % for textural features, 95 and 86 % for a combination of dynamic and morphological features, and 88 and 84 % for a combination of dynamic, morphological, and other features. LDA and TC have the best performance. A combination of dynamic and morphological features gives the best performance.

Lithium Diisopropylamide-Mediated Reactions of Imines, Unsaturated Esters, Epoxides, and Aryl Carbamates: Influence of Hexamethylphosphoramide and Ethereal Cosolvents on Reaction Mechanisms

PubMed Central

Ma, Yun

2010-01-01

Several reactions mediated by lithium diisopropylamide (LDA) with added hex-amethylphosphoramide (HMPA) are described. The N-isopropylimine of cyclohex-anone lithiates via an ensemble of monomer-based pathways. Conjugate addition of LDA/HMPA to an unsaturated ester proceeds via di- and tetra-HMPA-solvated dimers. Deprotonation of norbornene epoxide by LDA/HMPA proceeds via an intermediate metalated epoxide as a mixed dimer with LDA. Ortholithiation of an aryl carbamate proceeds via a mono-HMPA-solvated monomer-based pathway. Dependencies on THF and other ethereal cosolvents suggest that secondary-shell solvation effects are important in some instances. The origins of the inordinate mechanistic complexity are discussed. PMID:17985891

Analysis and design of fiber-coupled high-power laser diode array

NASA Astrophysics Data System (ADS)

Zhou, Chongxi; Liu, Yinhui; Xie, Weimin; Du, Chunlei

2003-11-01

A conclusion that a single conventional optical system could not realize fiber coupled high-power laser diode array is drawn based on the BPP of laser beam. According to the parameters of coupled fiber, a method to couple LDA beams into a single multi-mode fiber including beams collimating, shaping, focusing and coupling is present. The divergence angles after collimating are calculated and analyzed; the shape equation of the collimating micro-lenses array is deprived. The focusing lens is designed. A fiber coupled LDA result with the core diameter of 800 um and numeric aperture of 0.37 is gotten.

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2014-09-24

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

Long-range-corrected Rung 3.5 density functional approximations

NASA Astrophysics Data System (ADS)

Janesko, Benjamin G.; Proynov, Emil; Scalmani, Giovanni; Frisch, Michael J.

2018-03-01

Rung 3.5 functionals are a new class of approximations for density functional theory. They provide a flexible intermediate between exact (Hartree-Fock, HF) exchange and semilocal approximations for exchange. Existing Rung 3.5 functionals inherit semilocal functionals' limitations in atomic cores and density tails. Here we address those limitations using range-separated admixture of HF exchange. We present three new functionals. LRC-ωΠLDA combines long-range HF exchange with short-range Rung 3.5 ΠLDA exchange. SLC-ΠLDA combines short- and long-range HF exchange with middle-range ΠLDA exchange. LRC-ωΠLDA-AC incorporates a combination of HF, semilocal, and Rung 3.5 exchange in the short range, based on an adiabatic connection. We test these in a new Rung 3.5 implementation including up to analytic fourth derivatives. LRC-ωΠLDA and SLC-ΠLDA improve atomization energies and reaction barriers by a factor of 8 compared to the full-range ΠLDA. LRC-ωΠLDA-AC brings further improvement approaching the accuracy of standard long-range corrected schemes LC-ωPBE and SLC-PBE. The new functionals yield highest occupied orbital energies closer to experimental ionization potentials and describe correctly the weak charge-transfer complex of ethylene and dichlorine and the hole-spin distribution created by an Al defect in quartz. This study provides a framework for more flexible range-separated Rung 3.5 approximations.

Chemometric classification of Chinese lager beers according to manufacturer based on data fusion of fluorescence, UV and visible spectroscopies.

PubMed

Tan, Jin; Li, Rong; Jiang, Zi-Tao

2015-10-01

We report an application of data fusion for chemometric classification of 135 canned samples of Chinese lager beers by manufacturer based on the combination of fluorescence, UV and visible spectroscopies. Right-angle synchronous fluorescence spectra (SFS) at three wavelength difference Δλ=30, 60 and 80 nm and visible spectra in the range 380-700 nm of undiluted beers were recorded. UV spectra in the range 240-400 nm of diluted beers were measured. A classification model was built using principal component analysis (PCA) and linear discriminant analysis (LDA). LDA with cross-validation showed that the data fusion could achieve 78.5-86.7% correct classification (sensitivity), while those rates using individual spectroscopies ranged from 42.2% to 70.4%. The results demonstrated that the fluorescence, UV and visible spectroscopies complemented each other, yielding higher synergic effect. Copyright © 2015 Elsevier Ltd. All rights reserved.

A comparison of effectiveness among frequent treatments of recurrent spontaneous abortion: A Bayesian network meta-analysis.

PubMed

Lv, Sha; Yu, Jing; Xu, Xiaoxiao

2018-04-30

A comprehensive network meta-analysis was designed to clarify contradictions and offer valuable clinical guidance in the treatment of recurrent spontaneous abortion (RSA). The included clinical trials were selected from the relevant medical journal databases and screened. Treatments were ranked by the surface under the cumulative ranking curve. Heat plots were constructed to analyze the inconsistency between direct data and network results, and adjusted funnel plots were constructed to assess publication bias. Forty-nine randomized controlled trials involving a total of 8496 RSA patients were selected. With placebo as control, corticosteroid plus low dose aspirin (LDA) plus unfractionated heparin (UFH), granulocyte colony-stimulating factor (G-CSF) alone, and LDA plus low molecular weight heparin (LMWH) all demonstrated effectiveness in increasing successful live birth rates and reducing the incidences of miscarriage. However, no treatment was demonstrably superior to placebo in terms of pregnancy success. For all 3 endpoints (live birth, abortion and success pregnancy), the adjusted funnel plots were symmetric to zero and indicated no publication bias. In terms of live birth and abortion rates, no treatment outperformed placebo in patients with antiphospholipid syndrome. In consideration of live birth and abortion rates, corticosteroid plus LDA plus UFH appeared to be the optimum treatment strategy; G-CSF was second, followed by LDA with LMWH, LDA plus LMWH plus intravenous immunoglobulin, corticosteroid with LDA and others. Subgroup analysis demonstrated no benefit of antithrombotic therapy in patients with antiphospholipid syndrome. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Time-trends in the prescribing of gastroprotective agents to primary care patients initiating low-dose aspirin or non-steroidal anti-inflammatory drugs: a population-based cohort study

PubMed Central

Warlé-van Herwaarden, Margaretha F; Koffeman, Aafke R; Valkhoff, Vera E; ’t Jong, Geert W; Kramers, Cornelis; Sturkenboom, Miriam C; De Smet, Peter A G M

2015-01-01

Aims Low-dose aspirin (LDA) and non-steroidal-anti-inflammatory drugs (NSAIDs) both increase the risk of upper gastrointestinal events (UGIEs). In the Netherlands, recommendations regarding the prescription of gastroprotective agents (GPAs) in LDA users were first issued in 2009 in the HARM-Wrestling consensus. National guidelines on gastroprotective strategies (GPSs) in NSAID users were issued in the first part of the preceding. The aim of the present study was to examine time-trends in GPSs in patients initiating LDA and those initiating NSAIDs between 2000 and 2012. Methods Within a large electronic primary healthcare database, two cohorts were selected: (i) patients newly prescribed LDA and (ii) patients newly prescribed NSAIDs between 2000 and 2012. Patients who had been prescribed a GPA in the previous six months were excluded. For both cohorts, patients’ risk of a UGIE was classified as low, moderate or high, based on the HARM-Wrestling consensus, and the presence of an adequate GPSwas determined. Results A total of 37 578 patients were included in the LDA cohort and 352 025 patients in the NSAID cohort. In both cohorts, an increase in GPSs was observed over time, but prescription of GPAs was lower in the LDA cohort. By 2012, an adequate GPS was present in 31.8% of high-risk LDA initiators, vs. 48.0% of high-risk NSAID initiators. Conclusions Despite a comparable risk of UGIEs, GPSs are prescribed less in high-risk LDA initiators than in high-risk NSAID initiators. For both groups of patients, there is still room for improvement in guideline adherence. PMID:25777983

Time-trends in the prescribing of gastroprotective agents to primary care patients initiating low-dose aspirin or non-steroidal anti-inflammatory drugs: a population-based cohort study.

PubMed

Warlé-van Herwaarden, Margaretha F; Koffeman, Aafke R; Valkhoff, Vera E; 't Jong, Geert W; Kramers, Cornelis; Sturkenboom, Miriam C; De Smet, Peter A G M

2015-09-01

Low-dose aspirin (LDA) and non-steroidal-anti-inflammatory drugs (NSAIDs) both increase the risk of upper gastrointestinal events (UGIEs). In the Netherlands, recommendations regarding the prescription of gastroprotective agents (GPAs) in LDA users were first issued in 2009 in the HARM-Wrestling consensus. National guidelines on gastroprotective strategies (GPSs) in NSAID users were issued in the first part of the preceding. The aim of the present study was to examine time-trends in GPSs in patients initiating LDA and those initiating NSAIDs between 2000 and 2012. Within a large electronic primary healthcare database, two cohorts were selected: (i) patients newly prescribed LDA and (ii) patients newly prescribed NSAIDs between 2000 and 2012. Patients who had been prescribed a GPA in the previous six months were excluded. For both cohorts, patients' risk of a UGIE was classified as low, moderate or high, based on the HARM-Wrestling consensus, and the presence of an adequate GPSwas determined. A total of 37 578 patients were included in the LDA cohort and 352 025 patients in the NSAID cohort. In both cohorts, an increase in GPSs was observed over time, but prescription of GPAs was lower in the LDA cohort. By 2012, an adequate GPS was present in 31.8% of high-risk LDA initiators, vs. 48.0% of high-risk NSAID initiators. Despite a comparable risk of UGIEs, GPSs are prescribed less in high-risk LDA initiators than in high-risk NSAID initiators. For both groups of patients, there is still room for improvement in guideline adherence. © 2015 The British Pharmacological Society.

Volatile-Compound Fingerprinting by Headspace-Gas-Chromatography Ion-Mobility Spectrometry (HS-GC-IMS) as a Benchtop Alternative to 1H NMR Profiling for Assessment of the Authenticity of Honey.

PubMed

Gerhardt, Natalie; Birkenmeier, Markus; Schwolow, Sebastian; Rohn, Sascha; Weller, Philipp

2018-02-06

This work describes a simple approach for the untargeted profiling of volatile compounds for the authentication of the botanical origins of honey based on resolution-optimized HS-GC-IMS combined with optimized chemometric techniques, namely PCA, LDA, and kNN. A direct comparison of the PCA-LDA models between the HS-GC-IMS and 1 H NMR data demonstrated that HS-GC-IMS profiling could be used as a complementary tool to NMR-based profiling of honey samples. Whereas NMR profiling still requires comparatively precise sample preparation, pH adjustment in particular, HS-GC-IMS fingerprinting may be considered an alternative approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. It was demonstrated that all tested honey samples could be distinguished on the basis of their botanical origins. Loading plots revealed the volatile compounds responsible for the differences among the monofloral honeys. The HS-GC-IMS-based PCA-LDA model was composed of two linear functions of discrimination and 10 selected PCs that discriminated canola, acacia, and honeydew honeys with a predictive accuracy of 98.6%. Application of the LDA model to an external test set of 10 authentic honeys clearly proved the high predictive ability of the model by correctly classifying them into three variety groups with 100% correct classifications. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other food types.

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

PubMed

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

2012-04-01

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

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

PubMed Central

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

2011-01-01

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

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

PubMed

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

2011-01-01

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

Aided diagnosis methods of breast cancer based on machine learning

NASA Astrophysics Data System (ADS)

Zhao, Yue; Wang, Nian; Cui, Xiaoyu

2017-08-01

In the field of medicine, quickly and accurately determining whether the patient is malignant or benign is the key to treatment. In this paper, K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression were applied to predict the classification of thyroid,Her-2,PR,ER,Ki67,metastasis and lymph nodes in breast cancer, in order to recognize the benign and malignant breast tumors and achieve the purpose of aided diagnosis of breast cancer. The results showed that the highest classification accuracy of LDA was 88.56%, while the classification effect of KNN and Logistic Regression were better than that of LDA, the best accuracy reached 96.30%.

[Validity evidence of the Health-Related Quality of Life for Drug Abusers Test based on the Biaxial Model of Addiction].

PubMed

Lozano, Oscar M; Rojas, Antonio J; Pérez, Cristino; González-Sáiz, Francisco; Ballesta, Rosario; Izaskun, Bilbao

2008-05-01

The aim of this work is to show evidence of the validity of the Health-Related Quality of Life for Drug Abusers Test (HRQoLDA Test). This test was developed to measure specific HRQoL for drugs abusers, within the theoretical addiction framework of the biaxial model. The sample comprised 138 patients diagnosed with opiate drug dependence. In this study, the following constructs and variables of the biaxial model were measured: severity of dependence, physical health status, psychological adjustment and substance consumption. Results indicate that the HRQoLDA Test scores are related to dependency and consumption-related problems. Multiple regression analysis reveals that HRQoL can be predicted from drug dependence, physical health status and psychological adjustment. These results contribute empirical evidence of the theoretical relationships established between HRQoL and the biaxial model, and they support the interpretation of the HRQoLDA Test to measure HRQoL in drug abusers, thus providing a test to measure this specific construct in this population.

Anion-Driven Self-Assembly Processes Based on Halogen-Bonding

DTIC Science & Technology

2007-07-10

23993041 Fax: +39-02-23993180 E-mail: pierangelo.metrangolo@polimi.it Sede Leonardo : Piazza L.Da Vinci , 32 – 20133 Milano Tel. ++39-02...02-23993180 E-mail: pierangelo.metrangolo@polimi.it Sede Leonardo : Piazza L.Da Vinci , 32 – 20133 Milano Tel. ++39-02 2399.3200 Fax ++39-02...Pierangelo Metrangolo Ph. +39-02-23993041 Fax: +39-02-23993180 E-mail: pierangelo.metrangolo@polimi.it Sede Leonardo : Piazza L.Da Vinci , 32

Glass polymorphism in amorphous germanium probed by first-principles computer simulations

NASA Astrophysics Data System (ADS)

Mancini, G.; Celino, M.; Iesari, F.; Di Cicco, A.

2016-01-01

The low-density (LDA) to high-density (HDA) transformation in amorphous Ge at high pressure is studied by first-principles molecular dynamics simulations in the framework of density functional theory. Previous experiments are accurately reproduced, including the presence of a well-defined LDA-HDA transition above 8 GPa. The LDA-HDA density increase is found to be about 14%. Pair and bond-angle distributions are obtained in the 0-16 GPa pressure range and allowed us a detailed analysis of the transition. The local fourfold coordination is transformed in an average HDA sixfold coordination associated with different local geometries as confirmed by coordination number analysis and shape of the bond-angle distributions.

Ms2lda.org: web-based topic modelling for substructure discovery in mass spectrometry.

PubMed

Wandy, Joe; Zhu, Yunfeng; van der Hooft, Justin J J; Daly, Rónán; Barrett, Michael P; Rogers, Simon

2017-09-14

We recently published MS2LDA, a method for the decomposition of sets of molecular fragment data derived from large metabolomics experiments. To make the method more widely available to the community, here we present ms2lda.org, a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualisations. Ms2lda.org takes tandem mass spectrometry data in many standard formats and allows the user to infer the sets of fragment and neutral loss features that co-occur together (Mass2Motifs). As an alternative workflow, the user can also decompose a dataset onto predefined Mass2Motifs. This is accomplished through the web interface or programmatically from our web service. The website can be found at http://ms2lda.org , while the source code is available at https://github.com/sdrogers/ms2ldaviz under the MIT license. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Analysis of Flavonoid in Medicinal Plant Extract Using Infrared Spectroscopy and Chemometrics

PubMed Central

Retnaningtyas, Yuni; Nuri; Lukman, Hilmia

2016-01-01

Infrared (IR) spectroscopy combined with chemometrics has been developed for simple analysis of flavonoid in the medicinal plant extract. Flavonoid was extracted from medicinal plant leaves by ultrasonication and maceration. IR spectra of selected medicinal plant extract were correlated with flavonoid content using chemometrics. The chemometric method used for calibration analysis was Partial Last Square (PLS) and the methods used for classification analysis were Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogies (SIMCA), and Support Vector Machines (SVM). In this study, the calibration of NIR model that showed best calibration with R 2 and RMSEC value was 0.9916499 and 2.1521897, respectively, while the accuracy of all classification models (LDA, SIMCA, and SVM) was 100%. R 2 and RMSEC of calibration of FTIR model were 0.8653689 and 8.8958149, respectively, while the accuracy of LDA, SIMCA, and SVM was 86.0%, 91.2%, and 77.3%, respectively. PLS and LDA of NIR models were further used to predict unknown flavonoid content in commercial samples. Using these models, the significance of flavonoid content that has been measured by NIR and UV-Vis spectrophotometry was evaluated with paired samples t-test. The flavonoid content that has been measured with both methods gave no significant difference. PMID:27529051

Partial Membership Latent Dirichlet Allocation for Soft Image Segmentation.

PubMed

Chen, Chao; Zare, Alina; Trinh, Huy N; Omotara, Gbenga O; Cobb, James Tory; Lagaunne, Timotius A

2017-12-01

Topic models [e.g., probabilistic latent semantic analysis, latent Dirichlet allocation (LDA), and supervised LDA] have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there are many images in which some regions cannot be assigned a crisp categorical label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In these cases, a visual word is best represented with partial memberships across multiple topics. To address this, we present a partial membership LDA (PM-LDA) model and an associated parameter estimation algorithm. This model can be useful for imagery, where a visual word may be a mixture of multiple topics. Experimental results on visual and sonar imagery show that PM-LDA can produce both crisp and soft semantic image segmentations; a capability previous topic modeling methods do not have.

Transcription Factor Binding Site Polymorphism in the Motilin Gene Associated with Left-Sided Displacement of the Abomasum in German Holstein Cattle

PubMed Central

Mömke, Stefanie; Sickinger, Marlene; Rehage, Jürgen; Doll, Klaus; Distl, Ottmar

2012-01-01

Left-sided displacement of the abomasum (LDA) is a common disease in many dairy cattle breeds. A genome-wide screen for QTL for LDA in German Holstein (GH) cows indicated motilin (MLN) as a candidate gene on bovine chromosome 23. Genomic DNA sequence analysis of MLN revealed a total of 32 polymorphisms. All informative polymorphisms used for association analyses in a random sample of 1,136 GH cows confirmed MLN as a candidate for LDA. A single nucleotide polymorphism (FN298674:g.90T>C) located within the first non-coding exon of bovine MLN affects a NKX2-5 transcription factor binding site and showed significant associations (ORallele = 0.64; −log10Pallele = 6.8, −log10Pgenotype = 7.0) with LDA. An expression study gave evidence of a significantly decreased MLN expression in cows carrying the mutant allele (C). In individuals heterozygous or homozygous for the mutation, MLN expression was decreased by 89% relative to the wildtype. FN298674:g.90T>C may therefore play a role in bovine LDA via the motility of the abomasum. This MLN SNP appears useful to reduce the incidence of LDA in German Holstein cattle and provides a first step towards a deeper understanding of the genetics of LDA. PMID:22536407

Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features

NASA Astrophysics Data System (ADS)

Naghibi, Seyed Amir; Moradi Dashtpagerdi, Mostafa

2017-01-01

One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.

Investigation of laser Doppler anemometry in developing a velocity-based measurement technique

NASA Astrophysics Data System (ADS)

Jung, Ki Won

2009-12-01

Acoustic properties, such as the characteristic impedance and the complex propagation constant, of porous materials have been traditionally characterized based on pressure-based measurement techniques using microphones. Although the microphone techniques have evolved since their introduction, the most general form of the microphone technique employs two microphones in characterizing the acoustic field for one continuous medium. The shortcomings of determining the acoustic field based on only two microphones can be overcome by using numerous microphones. However, the use of a number of microphones requires a careful and intricate calibration procedure. This dissertation uses laser Doppler anemometry (LDA) to establish a new measurement technique which can resolve issues that microphone techniques have: First, it is based on a single sensor, thus the calibration is unnecessary when only overall ratio of the acoustic field is required for the characterization of a system. This includes the measurements of the characteristic impedance and the complex propagation constant of a system. Second, it can handle multiple positional measurements without calibrating the signal at each position. Third, it can measure three dimensional components of velocity even in a system with a complex geometry. Fourth, it has a flexible adaptability which is not restricted to a certain type of apparatus only if the apparatus is transparent. LDA is known to possess several disadvantages, such as the requirement of a transparent apparatus, high cost, and necessity of seeding particles. The technique based on LDA combined with a curvefitting algorithm is validated through measurements on three systems. First, the complex propagation constant of the air is measured in a rigidly terminated cylindrical pipe which has very low dissipation. Second, the radiation impedance of an open-ended pipe is measured. These two parameters can be characterized by the ratio of acoustic field measured at multiple locations. Third, the power dissipated in a variable RLC load is measured. The three experiments validate the LDA technique proposed. The utility of the LDA method is then extended to the measurement of the complex propagation constant of the air inside a 100 ppi reticulated vitreous carbon (RVC) sample. Compared to measurements in the available studies, the measurement with the 100 ppi RVC sample supports the LDA technique in that it can achieve a low uncertainty in the determined quantity. This dissertation concludes with using the LDA technique for modal decomposition of the plane wave mode and the (1,1) mode that are driven simultaneously. This modal decomposition suggests that the LDA technique surpasses microphone-based techniques, because they are unable to determine the acoustic field based on an acoustic model with unconfined propagation constants for each modal component.

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

PubMed

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

2016-07-01

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

Velocity precision measurements using laser Doppler anemometry

NASA Astrophysics Data System (ADS)

Dopheide, D.; Taux, G.; Narjes, L.

1985-07-01

A Laser Doppler Anemometer (LDA) was calibrated to determine its applicability to high pressure measurements (up to 10 bars) for industrial purposes. The measurement procedure with LDA and the experimental computerized layouts are presented. The calibration procedure is based on absolute accuracy of Doppler frequency and calibration of interference strip intervals. A four-quadrant detector allows comparison of the interference strip distance measurements and computer profiles. Further development of LDA is recommended to increase accuracy (0.1% inaccuracy) and to apply the method industrially.

Simple many-body based screening mixing ansatz for improvement of G W /Bethe-Salpeter equation excitation energies of molecular systems

NASA Astrophysics Data System (ADS)

Ziaei, Vafa; Bredow, Thomas

2017-11-01

We propose a simple many-body based screening mixing strategy to considerably enhance the performance of the Bethe-Salpeter equation (BSE) approach for prediction of excitation energies of molecular systems. This strategy enables us to closely reproduce results of highly correlated equation of motion coupled cluster singles and doubles (EOM-CCSD) through optimal use of cancellation effects. We start from the Hartree-Fock (HF) reference state and take advantage of local density approximation (LDA) based random phase approximation (RPA) screening, denoted as W0-RPA@LDA with W0 as the dynamically screened interaction built upon LDA wave functions and energies. We further use this W0-RPA@LDA screening as an initial screening guess for calculation of quasiparticle energies in the framework of G0W0 @HF. The W0-RPA@LDA screening is further injected into the BSE. By applying such an approach on a set of 22 molecules for which the traditional G W /BSE approaches fail, we observe good agreement with respect to EOM-CCSD references. The reason for the observed good accuracy of this mixing ansatz (scheme A) lies in an optimal damping of HF exchange effect through the W0-RPA@LDA strong screening, leading to substantial decrease of typically overestimated HF electronic gap, and hence to better excitation energies. Further, we present a second multiscreening ansatz (scheme B), which is similar to scheme A with the exception that now the W0-RPA@HF screening is used in the BSE in order to further improve the overestimated excitation energies of carbonyl sulfide (COS) and disilane (Si2H6 ). The reason for improvement of the excitation energies in scheme B lies in the fact that W0-RPA@HF screening is less effective (and weaker than W0-RPA@LDA), which gives rise to stronger electron-hole effects in the BSE.

The classification of lung cancers and their degree of malignancy by FTIR, PCA-LDA analysis, and a physics-based computational model.

PubMed

Kaznowska, E; Depciuch, J; Łach, K; Kołodziej, M; Koziorowska, A; Vongsvivut, J; Zawlik, I; Cholewa, M; Cebulski, J

2018-08-15

Lung cancer has the highest mortality rate of all malignant tumours. The current effects of cancer treatment, as well as its diagnostics, are unsatisfactory. Therefore it is very important to introduce modern diagnostic tools, which will allow for rapid classification of lung cancers and their degree of malignancy. For this purpose, the authors propose the use of Fourier Transform InfraRed (FTIR) spectroscopy combined with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) and a physics-based computational model. The results obtained for lung cancer tissues, adenocarcinoma and squamous cell carcinoma FTIR spectra, show a shift in wavenumbers compared to control tissue FTIR spectra. Furthermore, in the FTIR spectra of adenocarcinoma there are no peaks corresponding to glutamate or phospholipid functional groups. Moreover, in the case of G2 and G3 malignancy of adenocarcinoma lung cancer, the absence of an OH groups peak was noticed. Thus, it seems that FTIR spectroscopy is a valuable tool to classify lung cancer and to determine the degree of its malignancy. Copyright © 2018 Elsevier B.V. All rights reserved.

Prostate lesion detection and localization based on locality alignment discriminant analysis

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE PAGES

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; ...

2017-04-03

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

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

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species.

PubMed

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

PubMed Central

Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J.; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species. PMID:28369059

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

PubMed

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

2011-06-01

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

Non-equilibrium thermodynamics theory of econometric source discovery for large data analysis

NASA Astrophysics Data System (ADS)

van Bergem, Rutger; Jenkins, Jeffrey; Benachenhou, Dalila; Szu, Harold

2014-05-01

Almost all consumer and firm transactions are achieved using computers and as a result gives rise to increasingly large amounts of data available for analysts. The gold standard in Economic data manipulation techniques matured during a period of limited data access, and the new Large Data Analysis (LDA) paradigm we all face may quickly obfuscate most tools used by Economists. When coupled with an increased availability of numerous unstructured, multi-modal data sets, the impending 'data tsunami' could have serious detrimental effects for Economic forecasting, analysis, and research in general. Given this reality we propose a decision-aid framework for Augmented-LDA (A-LDA) - a synergistic approach to LDA which combines traditional supervised, rule-based Machine Learning (ML) strategies to iteratively uncover hidden sources in large data, the artificial neural network (ANN) Unsupervised Learning (USL) at the minimum Helmholtz free energy for isothermal dynamic equilibrium strategies, and the Economic intuitions required to handle problems encountered when interpreting large amounts of Financial or Economic data. To make the ANN USL framework applicable to economics we define the temperature, entropy, and energy concepts in Economics from non-equilibrium molecular thermodynamics of Boltzmann viewpoint, as well as defining an information geometry, on which the ANN can operate using USL to reduce information saturation. An exemplar of such a system representation is given for firm industry equilibrium. We demonstrate the traditional ML methodology in the economics context and leverage firm financial data to explore a frontier concept known as behavioral heterogeneity. Behavioral heterogeneity on the firm level can be imagined as a firm's interactions with different types of Economic entities over time. These interactions could impose varying degrees of institutional constraints on a firm's business behavior. We specifically look at behavioral heterogeneity for firms that are operating with the label of `Going-Concern' and firms labeled according to institutional influence they may be experiencing, such as constraints on firm hiring/spending while in a Bankruptcy or a Merger procedure. Uncovering invariant features, or behavioral data metrics from observable firm data in an economy can greatly benefit the FED, World Bank, etc. We find that the ML/LDA communities can benefit from Economic intuitions just as much as Economists can benefit from generic data exploration tools. The future of successful Economic data understanding, modeling, simulation, and visualization can be amplified by new A-LDA models and approaches for new and analogous models of Economic system dynamics. The potential benefits of improved economic data analysis and real time decision aid tools are numerous for researchers, analysts, and federal agencies who all deal with increasingly large amounts of complex data to support their decision making.

Identification of fungal phytopathogens using Fourier transform infrared-attenuated total reflection spectroscopy and advanced statistical methods

NASA Astrophysics Data System (ADS)

Salman, Ahmad; Lapidot, Itshak; Pomerantz, Ami; Tsror, Leah; Shufan, Elad; Moreh, Raymond; Mordechai, Shaul; Huleihel, Mahmoud

2012-01-01

The early diagnosis of phytopathogens is of a great importance; it could save large economical losses due to crops damaged by fungal diseases, and prevent unnecessary soil fumigation or the use of fungicides and bactericides and thus prevent considerable environmental pollution. In this study, 18 isolates of three different fungi genera were investigated; six isolates of Colletotrichum coccodes, six isolates of Verticillium dahliae and six isolates of Fusarium oxysporum. Our main goal was to differentiate these fungi samples on the level of isolates, based on their infrared absorption spectra obtained using the Fourier transform infrared-attenuated total reflection (FTIR-ATR) sampling technique. Advanced statistical and mathematical methods: principal component analysis (PCA), linear discriminant analysis (LDA), and k-means were applied to the spectra after manipulation. Our results showed significant spectral differences between the various fungi genera examined. The use of k-means enabled classification between the genera with a 94.5% accuracy, whereas the use of PCA [3 principal components (PCs)] and LDA has achieved a 99.7% success rate. However, on the level of isolates, the best differentiation results were obtained using PCA (9 PCs) and LDA for the lower wavenumber region (800-1775 cm-1), with identification success rates of 87%, 85.5%, and 94.5% for Colletotrichum, Fusarium, and Verticillium strains, respectively.

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.

PubMed

Wang, Shunfang; Liu, Shuhui

2015-12-19

An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

PubMed Central

Wang, Shunfang; Liu, Shuhui

2015-01-01

An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one. PMID:26703574

Effect of starvation and refeeding on the hepatopancreas of whiteleg shrimp Penaeus vannamei (Boone) using computer-assisted image analysis.

PubMed

Cervellione, F; McGurk, C; Berger Eriksen, T; Van den Broeck, W

2017-11-01

Under normal farming conditions, shrimp can experience starvation periods attributable to disease outbreaks or adverse environmental conditions. Starvation leads to significant morphological changes in the hepatopancreas (HP), being the main organ for absorption and storage of nutrients. In the literature, limited research has described the effect on the HP of periods of starvation followed by refeeding and none in whiteleg shrimp (Penaeus vannamei) using computer-assisted image analysis (CAIA). This study describes the effect of starvation and starvation followed by refeeding on the HP of whiteleg shrimp using CAIA. Visiopharm ® software was used to quantify the following morphological parameters, measured as ratio to the total tissue area (TLA): total lumen area (TLA:TTA), haemocytic infiltration area in the intertubular spaces (HIA:TTA), B-cell vacuole area (VBA:TTA), lipid droplet area within R cells (LDA:TTA) and F-cell area (FCA:TTA). Significant changes were measured for HIA:TTA and LDA:TTA during starvation (increase in HIA:TTA associated with decrease in LDA:TTA) and starvation followed by refeeding (decrease in HIA:TTA associated with increase in LDA:TTA). In the future, HIA:TTA and LDA:TTA have the potential to be used in a pre-emptive manner to monitor the health of the HP, facilitate early diagnosis of diseases and study the pathophysiology of the organ. © 2017 John Wiley & Sons Ltd.

Influence of warfarin and low-dose aspirin on the outcomes of geriatric patients with traumatic intracranial hemorrhage resulting from ground-level fall.

PubMed

Inamasu, Joji; Nakatsukasa, Masashi; Miyatake, Satoru; Hirose, Yuichi

2012-10-01

Ground-level fall is the most common cause of traumatic intracranial hemorrhage (TICH) in the elderly, and is a major cause of morbidity and mortality in that population. A retrospective study was carried out to evaluate whether the use of warfarin/low-dose aspirin (LDA) is predictive of unfavorable outcomes in geriatric patients who sustain a fall-induced TICH. Charts of 76 geriatric patients (≥ 65 years-of-age) with fall-induced TICH were reviewed. The number of patients taking warfarin and LDA was 12 and 21, respectively, whereas the other 43 took neither medication (non-user group). The frequency of patients with unfavorable outcomes (Glasgow Outcome Scale score of 1-3) at discharge was calculated. Furthermore, variables predictive of unfavorable outcomes were identified by logistic regression analysis. The frequency of patients with unfavorable outcomes was 75% in the warfarin group, 33% in the LDA group and 27% in the non-user group, respectively. The risk of having unfavorable outcomes was significantly higher in the warfarin group compared with the LDA group (P = 0.03) and non-user group (P < 0.01). Logistic regression analysis showed that variables predictive of unfavorable outcomes were: age, initial Glasgow Coma Scale score ≤ 13 and presence of midline shift ≥ 5 mm. The use of warfarin, but not of LDA, might be associated with unfavorable outcomes in elderly with fall-induced TICH. The risk of TICH should be communicated properly to elderly taking warfarin. The information might be important not only to trauma surgeons who take care of injured elderly, but also to geriatric physicians who prescribe warfarin/LDA to them. © 2012 Japan Geriatrics Society.

Eigenspace-based fuzzy c-means for sensing trending topics in Twitter

NASA Astrophysics Data System (ADS)

Muliawati, T.; Murfi, H.

2017-07-01

As the information and communication technology are developed, the fulfillment of information can be obtained through social media, like Twitter. The enormous number of internet users has triggered fast and large data flow, thus making the manual analysis is difficult or even impossible. An automated methods for data analysis is needed, one of which is the topic detection and tracking. An alternative method other than latent Dirichlet allocation (LDA) is a soft clustering approach using Fuzzy C-Means (FCM). FCM meets the assumption that a document may consist of several topics. However, FCM works well in low-dimensional data but fails in high-dimensional data. Therefore, we propose an approach where FCM works on low-dimensional data by reducing the data using singular value decomposition (SVD). Our simulations show that this approach gives better accuracies in term of topic recall than LDA for sensing trending topic in Twitter about an event.

Classification of smoke tainted wines using mid-infrared spectroscopy and chemometrics.

PubMed

Fudge, Anthea L; Wilkinson, Kerry L; Ristic, Renata; Cozzolino, Daniel

2012-01-11

In this study, the suitability of mid-infrared (MIR) spectroscopy, combined with principal component analysis (PCA) and linear discriminant analysis (LDA), was evaluated as a rapid analytical technique to identify smoke tainted wines. Control (i.e., unsmoked) and smoke-affected wines (260 in total) from experimental and commercial sources were analyzed by MIR spectroscopy and chemometrics. The concentrations of guaiacol and 4-methylguaiacol were also determined using gas chromatography-mass spectrometry (GC-MS), as markers of smoke taint. LDA models correctly classified 61% of control wines and 70% of smoke-affected wines. Classification rates were found to be influenced by the extent of smoke taint (based on GC-MS and informal sensory assessment), as well as qualitative differences in wine composition due to grape variety and oak maturation. Overall, the potential application of MIR spectroscopy combined with chemometrics as a rapid analytical technique for screening smoke-affected wines was demonstrated.

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

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classificationmore » rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.« less

Diagnostic potential for gold nanoparticle-based surface-enhanced Raman spectroscopy to provide colorectal cancer screening using blood serum sample

NASA Astrophysics Data System (ADS)

Lin, Duo; Feng, Shangyuan; Pan, Jianji; Chen, Yanping; Lin, Juqiang; Sun, Liqing; Chen, Rong

2011-11-01

Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique that is capable of probing the biomolecular changes associated with diseased transformation. The objective of our study was to explore gold nanoparticle based SERS to obtain blood serum biochemical information for non-invasive colorectal cancer detection. SERS measurements were performed on two groups of blood serum samples: one group from patients (n = 38) with pathologically confirmed colorectal cancer and the other group from healthy volunteers (control subjects, n = 45). Tentative assignments of the Raman bands in the measured SERS spectra suggested interesting cancer specific biomolecular changes, including an increase in the relative amounts of nucleic acid, a decrease in the percentage of saccharide and proteins contents in the blood serum of colorectal cancer patients as compared to that of healthy subjects. Principal component analysis (PCA) of the measured SERS spectra separated the spectral features of the two groups into two distinct clusters with little overlaps. Linear discriminate analysis (LDA) based on the PCA generated features differentiated the nasopharyngeal cancer SERS spectra from normal SERS spectra with high sensitivity (97.4%) and specificity (100%). The results from this exploratory study demonstrated that gold nanoparticle based SERS serum analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of colorectal cancers.

Diagnostic potential for gold nanoparticle-based surface-enhanced Raman spectroscopy to provide colorectal cancer screening using blood serum sample

NASA Astrophysics Data System (ADS)

Lin, Duo; Feng, Shangyuan; Pan, Jianji; Chen, Yanping; Lin, Juqiang; Sun, Liqing; Chen, Rong

2012-03-01

Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique that is capable of probing the biomolecular changes associated with diseased transformation. The objective of our study was to explore gold nanoparticle based SERS to obtain blood serum biochemical information for non-invasive colorectal cancer detection. SERS measurements were performed on two groups of blood serum samples: one group from patients (n = 38) with pathologically confirmed colorectal cancer and the other group from healthy volunteers (control subjects, n = 45). Tentative assignments of the Raman bands in the measured SERS spectra suggested interesting cancer specific biomolecular changes, including an increase in the relative amounts of nucleic acid, a decrease in the percentage of saccharide and proteins contents in the blood serum of colorectal cancer patients as compared to that of healthy subjects. Principal component analysis (PCA) of the measured SERS spectra separated the spectral features of the two groups into two distinct clusters with little overlaps. Linear discriminate analysis (LDA) based on the PCA generated features differentiated the nasopharyngeal cancer SERS spectra from normal SERS spectra with high sensitivity (97.4%) and specificity (100%). The results from this exploratory study demonstrated that gold nanoparticle based SERS serum analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of colorectal cancers.

An open-label randomized-controlled trial of low dose aspirin with an early screening test for pre-eclampsia and growth restriction (TEST): Trial protocol.

PubMed

Mone, Fionnuala; Mulcahy, Cecilia; McParland, Peter; Stanton, Alice; Culliton, Marie; Downey, Paul; McCormack, Dorothy; Tully, Elizabeth; Dicker, Patrick; Breathnach, Fionnuala; Malone, Fergal D; McAuliffe, Fionnuala M

2016-07-01

Pre-eclampsia remains a worldwide cause of maternal and perinatal morbidity and mortality. Low dose aspirin (LDA) can reduce the occurrence of pre-eclampsia in women with identifiable risk factors. Emerging screening tests can determine the maternal risk of developing placental disease, such as pre-eclampsia from the first trimester of pregnancy. The aim of this study is to determine if it is more beneficial in terms of efficacy and acceptability to routinely prescribe LDA to nulliparous low-risk women compared to test indicated LDA on the basis of a positive screening test for placental disease. We propose a three armed multi-center open-labeled randomized control trial of; (i) routine LDA, (ii) no aspirin, and (iii) LDA on the basis of a positive first trimester pre-eclampsia screening test. LDA (75mg once daily) shall be given from the first trimester until 36-week gestation. The primary outcome measures include; (i) the proportion of eligible women that agree to participate (acceptability), (ii) compliance with study protocol (acceptability and feasibility), (iii) the proportion of women in whom it is possible to obtain first trimester trans-abdominal uterine artery Doppler examination (feasibility) and (iv) the proportion of women with a completed screening test that are issued the screening result within one week of having the test performed (feasibility). This will be the first clinical trial to determine the efficacy and acceptability in low-risk women of taking routine LDA versus no aspirin versus LDA based on a positive first trimester screening test for the prevention of placental disease. Copyright © 2016 Elsevier Inc. All rights reserved.

First-principles modeling of localized d states with the GW@LDA+U approach

NASA Astrophysics Data System (ADS)

Jiang, Hong; Gomez-Abal, Ricardo I.; Rinke, Patrick; Scheffler, Matthias

2010-07-01

First-principles modeling of systems with localized d states is currently a great challenge in condensed-matter physics. Density-functional theory in the standard local-density approximation (LDA) proves to be problematic. This can be partly overcome by including local Hubbard U corrections (LDA+U) but itinerant states are still treated on the LDA level. Many-body perturbation theory in the GW approach offers both a quasiparticle perspective (appropriate for itinerant states) and an exact treatment of exchange (appropriate for localized states), and is therefore promising for these systems. LDA+U has previously been viewed as an approximate GW scheme. We present here a derivation that is simpler and more general, starting from the static Coulomb-hole and screened exchange approximation to the GW self-energy. Following our previous work for f -electron systems [H. Jiang, R. I. Gomez-Abal, P. Rinke, and M. Scheffler, Phys. Rev. Lett. 102, 126403 (2009)10.1103/PhysRevLett.102.126403] we conduct a systematic investigation of the GW method based on LDA+U(GW@LDA+U) , as implemented in our recently developed all-electron GW code FHI-gap (Green’s function with augmented plane waves) for a series of prototypical d -electron systems: (1) ScN with empty d states, (2) ZnS with semicore d states, and (3) late transition-metal oxides (MnO, FeO, CoO, and NiO) with partially occupied d states. We show that for ZnS and ScN, the GW band gaps only weakly depend on U but for the other transition-metal oxides the dependence on U is as strong as in LDA+U . These different trends can be understood in terms of changes in the hybridization and screening. Our work demonstrates that GW@LDA+U with “physical” values of U provides a balanced and accurate description of both localized and itinerant states.

Automatic Detection of Lung and Liver Lesions in 3-D Positron Emission Tomography Images: A Pilot Study

NASA Astrophysics Data System (ADS)

Lartizien, Carole; Marache-Francisco, Simon; Prost, Rémy

2012-02-01

Positron emission tomography (PET) using fluorine-18 deoxyglucose (18F-FDG) has become an increasingly recommended tool in clinical whole-body oncology imaging for the detection, diagnosis, and follow-up of many cancers. One way to improve the diagnostic utility of PET oncology imaging is to assist physicians facing difficult cases of residual or low-contrast lesions. This study aimed at evaluating different schemes of computer-aided detection (CADe) systems for the guided detection and localization of small and low-contrast lesions in PET. These systems are based on two supervised classifiers, linear discriminant analysis (LDA) and the nonlinear support vector machine (SVM). The image feature sets that serve as input data consisted of the coefficients of an undecimated wavelet transform. An optimization study was conducted to select the best combination of parameters for both the SVM and the LDA. Different false-positive reduction (FPR) methods were evaluated to reduce the number of false-positive detections per image (FPI). This includes the removal of small detected clusters and the combination of the LDA and SVM detection maps. The different CAD schemes were trained and evaluated based on a simulated whole-body PET image database containing 250 abnormal cases with 1230 lesions and 250 normal cases with no lesion. The detection performance was measured on a separate series of 25 testing images with 131 lesions. The combination of the LDA and SVM score maps was shown to produce very encouraging detection performance for both the lung lesions, with 91% sensitivity and 18 FPIs, and the liver lesions, with 94% sensitivity and 10 FPIs. Comparison with human performance indicated that the different CAD schemes significantly outperformed human detection sensitivities, especially regarding the low-contrast lesions.

Authenticity assessment of banknotes using portable near infrared spectrometer and chemometrics.

PubMed

da Silva Oliveira, Vanessa; Honorato, Ricardo Saldanha; Honorato, Fernanda Araújo; Pereira, Claudete Fernandes

2018-05-01

Spectra recorded using a portable near infrared (NIR) spectrometer, Soft Independent Modeling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) associated to Successive Projections Algorithm (SPA) models were applied to identify counterfeit and authentic Brazilian Real (R$20, R$50 and R$100) banknotes, enabling a simple field analysis. NIR spectra (950-1650nm) were recorded from seven different areas of the banknotes (two with fluorescent ink, one over watermark, three with intaglio printing process and one over the serial numbers with typography printing). SIMCA and SPA-LDA models were built using 1st derivative preprocessed spectral data from one of the intaglio areas. For the SIMCA models, all authentic (300) banknotes were correctly classified and the counterfeits (227) were not classified. For the two classes SPA-LDA models (authentic and counterfeit currencies), all the test samples were correctly classified into their respective class. The number of selected variables by SPA varied from two to nineteen for R$20, R$50 and R$100 currencies. These results show that the use of the portable near-infrared with SIMCA or SPA-LDA models can be a completely effective, fast, and non-destructive way to identify authenticity of banknotes as well as permitting field analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

p53-Based Strategy for Protection of Bone Marrow From Y-90 Ibritumomab Tiuxetan

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

Su, Hang, E-mail: suh3@uthscsa.edu; Ganapathy, Suthakar; Li, Xiaolei

Purpose: The main drawbacks of radioimmunotherapy have been severe hematological toxicity and potential development of myelodysplastic syndrome and secondary leukemia. Activation of p53 follows a major pathway by which normal tissues respond to DNA-damaging agents, such as chemotherapy and radiation therapy, that result in injuries and pathological consequences. This pathway is separate from the tumor suppressor pathway of p53. We have previously reported that use of low-dose arsenic (LDA) temporarily and reversibly suppresses p53 activation, thereby ameliorating normal tissue toxicity from exposure to 5-fluorouracil and X rays. We have also demonstrated that LDA-mediated protection requires functional p53 and thus ismore » selective to normal tissues, as essentially every cancer cell has dysfunctional p53. Here we tested the protective efficacy of LDA for bone marrow tissue against radioimmunotherapy through animal experiments. Methods and Materials: Mice were subjected to LDA pretreatment for 3 days, followed by treatment with Y-90 ibritumomab tiuxetan. Both dose course (10, 25, 50, 100, and 200 μCi) and time course (6, 24, and 72 hours and 1 and 2 weeks) experiments were performed. The response of bone marrow cells to LDA was determined by examining the expression of NFκB, Glut1, and Glut3. Staining with hematoxylin and eosin, γ-H2AX, and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) was used to examine morphology, DNA damage response, and apoptotic cell populations. Results: Elevated levels of NFκB, Glut1, and Glut3 were observed in bone marrow cells after LDA treatment. Bone marrow damage levels induced by Y-90 ibritumomab tiuxetan were greatly reduced by LDA pretreatment. Consistent with this observation, significantly less DNA damage and fewer apoptotic cells were accumulated after Y-90 ibritumomab tiuxetan treatment in LDA-pretreated mice. Furthermore, in the mouse xenograft model implanted with human Karpas-422 lymphoma cells, LDA pretreatment did not have any detectable effect on either tumor growth or Y-90 ibritumomab tiuxetan (200 μCi)-induced tumor suppression. Conclusions: LDA pretreatment protected bone marrow without compromising tumor control caused by Y-90 ibritumomab tiuxetan.« less

Hyperspectral Imaging and SPA-LDA Quantitative Analysis for Detection of Colon Cancer Tissue

NASA Astrophysics Data System (ADS)

Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.

2018-05-01

Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis-NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis-NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.

Non-invasive optical detection of HBV based on serum surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Zheng, Zuci; Wang, Qiwen; Weng, Cuncheng; Lin, Xueliang; Lin, Yao; Feng, Shangyuan

2016-10-01

An optical method of surface-enhanced Raman spectroscopy (SERS) was developed for non-invasive detection of hepatitis B surface virus (HBV). Hepatitis B virus surface antigen (HBsAg) is an established serological marker that is routinely used for the diagnosis of acute or chronic hepatitis B virus(HBV) infection. Utilizing SERS to analyze blood serum for detecting HBV has not been reported in previous literature. SERS measurements were performed on two groups of serum samples: one group for 50 HBV patients and the other group for 50 healthy volunteers. Blood serum samples are collected from healthy control subjects and patients diagnosed with HBV. Furthermore, principal components analysis (PCA) combined with linear discriminant analysis (LDA) were employed to differentiate HBV patients from healthy volunteer and achieved sensitivity of 80.0% and specificity of 74.0%. This exploratory work demonstrates that SERS serum analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of HBV.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Influence of sample preparation on the transformation of low-density to high-density amorphous ice: An explanation based on the potential energy landscape

NASA Astrophysics Data System (ADS)

Giovambattista, Nicolas; Starr, Francis W.; Poole, Peter H.

2017-07-01

Experiments and computer simulations of the transformations of amorphous ices display different behaviors depending on sample preparation methods and on the rates of change of temperature and pressure to which samples are subjected. In addition to these factors, simulation results also depend strongly on the chosen water model. Using computer simulations of the ST2 water model, we study how the sharpness of the compression-induced transition from low-density amorphous ice (LDA) to high-density amorphous ice (HDA) is influenced by the preparation of LDA. By studying LDA samples prepared using widely different procedures, we find that the sharpness of the LDA-to-HDA transformation is correlated with the depth of the initial LDA sample in the potential energy landscape (PEL), as characterized by the inherent structure energy. Our results show that the complex phenomenology of the amorphous ices reported in experiments and computer simulations can be understood and predicted in a unified way from knowledge of the PEL of the system.

Estimation of the Age and Amount of Brown Rice Plant Hoppers Based on Bionic Electronic Nose Use

PubMed Central

Xu, Sai; Zhou, Zhiyan; Lu, Huazhong; Luo, Xiwen; Lan, Yubin; Zhang, Yang; Li, Yanfang

2014-01-01

The brown rice plant hopper (BRPH), Nilaparvata lugens (Stal), is one of the most important insect pests affecting rice and causes serious damage to the yield and quality of rice plants in Asia. This study used bionic electronic nose technology to sample BRPH volatiles, which vary in age and amount. Principal component analysis (PCA), linear discrimination analysis (LDA), probabilistic neural network (PNN), BP neural network (BPNN) and loading analysis (Loadings) techniques were used to analyze the sampling data. The results indicate that the PCA and LDA classification ability is poor, but the LDA classification displays superior performance relative to PCA. When a PNN was used to evaluate the BRPH age and amount, the classification rates of the training set were 100% and 96.67%, respectively, and the classification rates of the test set were 90.67% and 64.67%, respectively. When BPNN was used for the evaluation of the BRPH age and amount, the classification accuracies of the training set were 100% and 48.93%, respectively, and the classification accuracies of the test set were 96.67% and 47.33%, respectively. Loadings for BRPH volatiles indicate that the main elements of BRPHs' volatiles are sulfur-containing organics, aromatics, sulfur- and chlorine-containing organics and nitrogen oxides, which provide a reference for sensors chosen when exploited in specialized BRPH identification devices. This research proves the feasibility and broad application prospects of bionic electronic noses for BRPH recognition. PMID:25268913

Classification of Malaysia aromatic rice using multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

2015-05-01

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

PubMed

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

2018-04-20

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

Application of FT-IR spectroscopy on breast cancer serum analysis

NASA Astrophysics Data System (ADS)

Elmi, Fatemeh; Movaghar, Afshin Fayyaz; Elmi, Maryam Mitra; Alinezhad, Heshmatollah; Nikbakhsh, Novin

2017-12-01

Breast cancer is regarded as the most malignant tumor among women throughout the world. Therefore, early detection and proper diagnostic methods have been known to help save women's lives. Fourier Transform Infrared (FT-IR) spectroscopy, coupled with PCA-LDA analysis, is a new technique to investigate the characteristics of serum in breast cancer. In this study, 43 breast cancer and 43 healthy serum samples were collected, and the FT-IR spectra were recorded for each one. Then, PCA analysis and linear discriminant analysis (LDA) were used to analyze the spectral data. The results showed that there were differences between the spectra of the two groups. Discriminating wavenumbers were associated with several spectral differences over the 950-1200 cm- 1(sugar), 1190-1350 cm- 1 (collagen), 1475-1710 cm- 1 (protein), 1710-1760 cm- 1 (ester), 2800-3000 cm- 1 (stretching motions of -CH2 & -CH3), and 3090-3700 cm- 1 (NH stretching) regions. PCA-LDA performance on serum IR could recognize changes between the control and the breast cancer cases. The diagnostic accuracy, sensitivity, and specificity of PCA-LDA analysis for 3000-3600 cm- 1 (NH stretching) were found to be 83%, 84%, 74% for the control and 80%, 76%, 72% for the breast cancer cases, respectively. The results showed that the major spectral differences between the two groups were related to the differences in protein conformation in serum samples. It can be concluded that FT-IR spectroscopy, together with multivariate data analysis, is able to discriminate between breast cancer and healthy serum samples.

Laguerre-based method for analysis of time-resolved fluorescence data: application to in-vivo characterization and diagnosis of atherosclerotic lesions.

PubMed

Jo, Javier A; Fang, Qiyin; Papaioannou, Thanassis; Baker, J Dennis; Dorafshar, Amir H; Reil, Todd; Qiao, Jian-Hua; Fishbein, Michael C; Freischlag, Julie A; Marcu, Laura

2006-01-01

We report the application of the Laguerre deconvolution technique (LDT) to the analysis of in-vivo time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data and the diagnosis of atherosclerotic plaques. TR-LIFS measurements were obtained in vivo from normal and atherosclerotic aortas (eight rabbits, 73 areas), and subsequently analyzed using LDT. Spectral and time-resolved features were used to develop four classification algorithms: linear discriminant analysis (LDA), stepwise LDA (SLDA), principal component analysis (PCA), and artificial neural network (ANN). Accurate deconvolution of TR-LIFS in-vivo measurements from normal and atherosclerotic arteries was provided by LDT. The derived Laguerre expansion coefficients reflected changes in the arterial biochemical composition, and provided a means to discriminate lesions rich in macrophages with high sensitivity (>85%) and specificity (>95%). Classification algorithms (SLDA and PCA) using a selected number of features with maximum discriminating power provided the best performance. This study demonstrates the potential of the LDT for in-vivo tissue diagnosis, and specifically for the detection of macrophages infiltration in atherosclerotic lesions, a key marker of plaque vulnerability.

Laguerre-based method for analysis of time-resolved fluorescence data: application to in-vivo characterization and diagnosis of atherosclerotic lesions

NASA Astrophysics Data System (ADS)

Jo, Javier A.; Fang, Qiyin; Papaioannou, Thanassis; Baker, J. Dennis; Dorafshar, Amir; Reil, Todd; Qiao, Jianhua; Fishbein, Michael C.; Freischlag, Julie A.; Marcu, Laura

2006-03-01

We report the application of the Laguerre deconvolution technique (LDT) to the analysis of in-vivo time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data and the diagnosis of atherosclerotic plaques. TR-LIFS measurements were obtained in vivo from normal and atherosclerotic aortas (eight rabbits, 73 areas), and subsequently analyzed using LDT. Spectral and time-resolved features were used to develop four classification algorithms: linear discriminant analysis (LDA), stepwise LDA (SLDA), principal component analysis (PCA), and artificial neural network (ANN). Accurate deconvolution of TR-LIFS in-vivo measurements from normal and atherosclerotic arteries was provided by LDT. The derived Laguerre expansion coefficients reflected changes in the arterial biochemical composition, and provided a means to discriminate lesions rich in macrophages with high sensitivity (>85%) and specificity (>95%). Classification algorithms (SLDA and PCA) using a selected number of features with maximum discriminating power provided the best performance. This study demonstrates the potential of the LDT for in-vivo tissue diagnosis, and specifically for the detection of macrophages infiltration in atherosclerotic lesions, a key marker of plaque vulnerability.

Laguerre-based method for analysis of time-resolved fluorescence data: application to in-vivo characterization and diagnosis of atherosclerotic lesions

PubMed Central

Jo, Javier A.; Fang, Qiyin; Papaioannou, Thanassis; Baker, J. Dennis; Dorafshar, Amir H.; Reil, Todd; Qiao, Jian-Hua; Fishbein, Michael C.; Freischlag, Julie A.; Marcu, Laura

2007-01-01

We report the application of the Laguerre deconvolution technique (LDT) to the analysis of in-vivo time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data and the diagnosis of atherosclerotic plaques. TR-LIFS measurements were obtained in vivo from normal and atherosclerotic aortas (eight rabbits, 73 areas), and subsequently analyzed using LDT. Spectral and time-resolved features were used to develop four classification algorithms: linear discriminant analysis (LDA), stepwise LDA (SLDA), principal component analysis (PCA), and artificial neural network (ANN). Accurate deconvolution of TR-LIFS in-vivo measurements from normal and atherosclerotic arteries was provided by LDT. The derived Laguerre expansion coefficients reflected changes in the arterial biochemical composition, and provided a means to discriminate lesions rich in macrophages with high sensitivity (>85%) and specificity (>95%). Classification algorithms (SLDA and PCA) using a selected number of features with maximum discriminating power provided the best performance. This study demonstrates the potential of the LDT for in-vivo tissue diagnosis, and specifically for the detection of macrophages infiltration in atherosclerotic lesions, a key marker of plaque vulnerability. PMID:16674179

Close-To-Practice Assessment Of Meat Freshness With Metal Oxide Sensor Microarray Electronic Nose

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

Musatov, V. Yu.; Sysoev, V. V.; Sommer, M.

In this report we estimate the ability of KAMINA e-nose, based on a metal oxide sensor (MOS) microarray and Linear Discriminant Analysis (LDA) pattern recognition, to evaluate meat freshness. The received results show that, 1) one or two exposures of standard meat samples to the e-nose are enough for the instrument to recognize the fresh meat prepared by the same supplier with 100% probability; 2) the meat samples of two kinds, stored at 4 deg. C and 25 deg. C, are mutually recognized at early stages of decay with the help of the LDA model built independently under the e-nosemore » training to each kind of meat; 3) the 3-4 training cycles of exposure to meat from different suppliers are necessary for the e-nose to build a reliable LDA model accounting for the supplier factor. This study approves that the MOS e-nose is ready to be currently utilised in food industry for evaluation of product freshness. The e-nose performance is characterized by low training cost, a confident recognition power of various product decay conditions and easy adjustment to changing conditions.« less

Improving Efficiency in Multi-Strange Baryon Reconstruction in d-Au at STAR

NASA Astrophysics Data System (ADS)

Leight, William

2003-10-01

We report preliminary multi-strange baryon measurements for d-Au collisions recorded at RHIC by the STAR experiment. After using classical topological analysis, in which cuts for each discriminating variable are adjusted by hand, we investigate improvements in signal-to-noise optimization using Linear Discriminant Analysis (LDA). LDA is an algorithm for finding, in the n-dimensional space of the n discriminating variables, the axis on which the signal and noise distributions are most separated. LDA is the first step in moving towards more sophisticated techniques for signal-to-noise optimization, such as Artificial Neural Nets. Due to the relatively low background and sufficiently high yields of d-Au collisions, they form an ideal system to study these possibilities for improving reconstruction methods. Such improvements will be extremely important for forthcoming Au-Au runs in which the size of the combinatoric background is a major problem in reconstruction efforts.

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

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy trainingmore » time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.« less

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

NASA Astrophysics Data System (ADS)

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

2011-07-01

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

Assessing College-Level Learning Difficulties and "At Riskness" for Learning Disabilities and ADHD: Development and Validation of the Learning Difficulties Assessment

ERIC Educational Resources Information Center

Kane, Steven T.; Walker, John H.; Schmidt, George R.

2011-01-01

This article describes the development and validation of the "Learning Difficulties Assessment" (LDA), a normed and web-based survey that assesses perceived difficulties with reading, writing, spelling, mathematics, listening, concentration, memory, organizational skills, sense of control, and anxiety in college students. The LDA is designed to…

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

Effects of Achieving Target Measures in Rheumatoid Arthritis on Functional Status, Quality of Life, and Resource Utilization: Analysis of Clinical Practice Data.

PubMed

Alemao, Evo; Joo, Seongjung; Kawabata, Hugh; Al, Maiwenn J; Allison, Paul D; Rutten-van Mölken, Maureen P M H; Frits, Michelle L; Iannaccone, Christine K; Shadick, Nancy A; Weinblatt, Michael E

2016-03-01

To evaluate associations between achieving guideline-recommended targets of disease activity, defined by the Disease Activity Score in 28 joints using C-reactive protein level (DAS28-CRP) <2.6, the Simplified Disease Activity Index (SDAI) ≤3.3, or the Clinical Disease Activity Index (CDAI) ≤2.8, and other health outcomes in a longitudinal observational study. Other defined thresholds included low disease activity (LDA), moderate (MDA), or severe disease activity (SDA). To control for intraclass correlation and estimate effects of independent variables on outcomes of the modified Health Assessment Questionnaire (M-HAQ), the EuroQol 5-domain (EQ-5D; a quality-of-life measure), hospitalization, and durable medical equipment (DME) use, we employed mixed models for continuous outcomes and generalized estimating equations for binary outcomes. Among 1,297 subjects, achievement (versus nonachievement) of recommended disease targets was associated with enhanced physical functioning and lower health resource utilization. After controlling for baseline covariates, achievement of disease targets (versus LDA) was associated with significantly enhanced physical functioning based on SDAI ≤3.3 (ΔM-HAQ -0.047; P = 0.0100) and CDAI ≤2.8 (-0.073; P = 0.0003) but not DAS28-CRP <2.6 (-0.022; P = 0.1735). Target attainment was associated with significantly improved EQ-5D (0.022-0.096; P < 0.0030 versus LDA, MDA, or SDA). Patients achieving guideline-recommended disease targets were 36-45% less likely to be hospitalized (P < 0.0500) and 23-45% less likely to utilize DME (P < 0.0100). Attaining recommended target disease-activity measures was associated with enhanced physical functioning and health-related quality of life. Some health outcomes were similar in subjects attaining guideline targets versus LDA. Achieving LDA is a worthy clinical objective in some patients. © 2016 The Authors. Arthritis Care & Research published by Wiley Periodicals, Inc. on behalf of the American College of Rheumatology.

Detection And Identification Of Inflammatory Bowel Disease Electronic Nose

NASA Astrophysics Data System (ADS)

Covington, J. A.; Ouaret, N.; Gardner, J. W.; Nwokolo, C.; Bardhan, K. D.; Arasaradnam, R. P.

2011-11-01

Inflammatory bowel disease (IBD) is an inflammation of the lining of the human bowel and a major health issue in Europe. IBD carries with it significant morbidity from toxic treatment, surgery and a risk of developing bowel cancer. Thus there is a need for early identification of the disease using non-invasive tests. Present diagnostic techniques are based around invasive tests (i.e. endoscopy) and laboratory culture; the latter is limited as only 50% of the gut bacteria can be identified. Here we explore the use of an e-nose as a tool to detect and identify two IBDs (i.e. Crohn's disease (CD) & Ulcerative Colitis (UC)) based on headspace analysis from urine samples. We believe that the gut bacterial flora is altered by disease (due to fermentation) that in-turn modulates the gas composition within urine samples. 24 samples (9 CD, 6 UC, 9 controls) were analysed with an in-house e-nose and an Owlstone IMS instrument. Data analysis was performed using linear discriminant analysis (LDA and principal components analysis (PCA). Using the e-nose, LDA separates both disease groups and control, whilst PCA shows a small overlap of classes. The IMS data are more complex but shows some disease/control separation. We are presently collecting further samples for a larger study using more advanced data processing methods.

Maintenance of Remission with Etanercept-DMARD Combination Therapy Compared with DMARDs Alone in African and Middle Eastern Patients with Active Rheumatoid Arthritis.

PubMed

Bassiouni, Hassan; Spargo, Catherine Elizabeth; Vlahos, Bonnie; Jones, Heather E; Pedersen, Ron; Shirazy, Khalid

2018-06-01

To compare etanercept (ETN) and placebo (PBO) for maintaining low disease activity (LDA) achieved with ETN in patients with rheumatoid arthritis (RA) from Africa and the Middle East. In this subset analysis of the Treat-to-Target trial (ClinicalTrials.gov identifier NCT01981473), 53 adult patients with moderate-to-severe RA nonresponsive to methotrexate were treated with 50 mg ETN/week for 24 weeks (Period 1). Patients achieving LDA were randomized to continue ETN treatment or switched to PBO for an additional 28 weeks (Period 2). The proportion of patients maintaining LDA or remission in each arm at the end of Period 2 was determined. Additional efficacy and patient-reported outcomes (PROs) were also evaluated. During Period 1, 51 patients achieved LDA according to the disease activity score-28 joints-erythrocyte sedimentation rate (DAS28-ESR LDA) and 30 achieved remission. At week 52, nine of 22 and eight of 29 in the ETN and PBO groups, respectively, remained in DAS28-ESR LDA without experiencing a flare. Additionally, six of 14 and five of 16 in the ETN and PBO groups, respectively, remained in remission. Among patients experiencing a flare during Period 2, 13 of 22 and 21 of 29 received ETN or PBO, respectively. The median time to flare was 193 and 87 days in the ETN and PBO groups, respectively. At week 52, consistently more patients in the ETN group than in the PBO group achieved predetermined efficacy and PRO endpoints. These data suggest continuing ETN maintenance therapy is beneficial to patients after they have achieved their treatment target. However, this subset analysis is limited by the small patient population and must be interpreted with caution. Pfizer. ClinicalTrials.gov identifier, NCT0198147.

Detecting Hotspot Information Using Multi-Attribute Based Topic Model

PubMed Central

Wang, Jing; Li, Li; Tan, Feng; Zhu, Ying; Feng, Weisi

2015-01-01

Microblogging as a kind of social network has become more and more important in our daily lives. Enormous amounts of information are produced and shared on a daily basis. Detecting hot topics in the mountains of information can help people get to the essential information more quickly. However, due to short and sparse features, a large number of meaningless tweets and other characteristics of microblogs, traditional topic detection methods are often ineffective in detecting hot topics. In this paper, we propose a new topic model named multi-attribute latent dirichlet allocation (MA-LDA), in which the time and hashtag attributes of microblogs are incorporated into LDA model. By introducing time attribute, MA-LDA model can decide whether a word should appear in hot topics or not. Meanwhile, compared with the traditional LDA model, applying hashtag attribute in MA-LDA model gives the core words an artificially high ranking in results meaning the expressiveness of outcomes can be improved. Empirical evaluations on real data sets demonstrate that our method is able to detect hot topics more accurately and efficiently compared with several baselines. Our method provides strong evidence of the importance of the temporal factor in extracting hot topics. PMID:26496635

Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF-RTFDDA

NASA Astrophysics Data System (ADS)

Wang, Haoliang; Liu, Yubao; Cheng, William Y. Y.; Zhao, Tianliang; Xu, Mei; Liu, Yuewei; Shen, Si; Calhoun, Kristin M.; Fierro, Alexandre O.

2017-11-01

In this study, a lightning data assimilation (LDA) scheme was developed and implemented in the National Center for Atmospheric Research Weather Research and Forecasting-Real-Time Four-Dimensional Data Assimilation system. In this LDA method, graupel mixing ratio (qg) is retrieved from observed total lightning. To retrieve qg on model grid boxes, column-integrated graupel mass is first calculated using an observation-based linear formula between graupel mass and total lightning rate. Then the graupel mass is distributed vertically according to the empirical qg vertical profiles constructed from model simulations. Finally, a horizontal spread method is utilized to consider the existence of graupel in the adjacent regions of the lightning initiation locations. Based on the retrieved qg fields, latent heat is adjusted to account for the latent heat releases associated with the formation of the retrieved graupel and to promote convection at the observed lightning locations, which is conceptually similar to the method developed by Fierro et al. Three severe convection cases were studied to evaluate the LDA scheme for short-term (0-6 h) lightning and precipitation forecasts. The simulation results demonstrated that the LDA was effective in improving the short-term lightning and precipitation forecasts by improving the model simulation of the qg fields, updrafts, cold pool, and front locations. The improvements were most notable in the first 2 h, indicating a highly desired benefit of the LDA in lightning and convective precipitation nowcasting (0-2 h) applications.

Modeling virtual organizations with Latent Dirichlet Allocation: a case for natural language processing.

PubMed

Gross, Alexander; Murthy, Dhiraj

2014-10-01

This paper explores a variety of methods for applying the Latent Dirichlet Allocation (LDA) automated topic modeling algorithm to the modeling of the structure and behavior of virtual organizations found within modern social media and social networking environments. As the field of Big Data reveals, an increase in the scale of social data available presents new challenges which are not tackled by merely scaling up hardware and software. Rather, they necessitate new methods and, indeed, new areas of expertise. Natural language processing provides one such method. This paper applies LDA to the study of scientific virtual organizations whose members employ social technologies. Because of the vast data footprint in these virtual platforms, we found that natural language processing was needed to 'unlock' and render visible latent, previously unseen conversational connections across large textual corpora (spanning profiles, discussion threads, forums, and other social media incarnations). We introduce variants of LDA and ultimately make the argument that natural language processing is a critical interdisciplinary methodology to make better sense of social 'Big Data' and we were able to successfully model nested discussion topics from forums and blog posts using LDA. Importantly, we found that LDA can move us beyond the state-of-the-art in conventional Social Network Analysis techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Glass Transitions in a Monatomic Liquid with Two Glassy States

NASA Astrophysics Data System (ADS)

Gordon, Andrew; Giovambattista, Nicolas

2014-04-01

We perform out-of-equilibrium molecular dynamics simulations of a monatomic liquid that exhibits liquid and glass polymorphism, with two distinct glasses, low- (LDA) and high-density (HDA) amorphous solids. By performing isobaric heating simulations of LDA and HDA at different pressures, we determine (a) the glass transition temperature of LDA and HDA, TgLDA(P) and TgHDA(P), as well as (b) the corresponding glass-glass transformation temperatures, TLDA-HDA(P) and THDA-LDA(P). It is found that TgLDA(P) is anomalous; i.e., it decreases with increasing pressure, while TgHDA(P) increases with increasing pressure. Interestingly, the TgLDA(P) and TLDA-HDA(P) loci, as well as the TgHDA(P) and THDA-LDA(P) loci, constitute smooth single lines in the P -T plane, suggesting that heating-induced glass-glass and glass transitions are related. We discuss the present results in the context of water experiments and simulations.

Why Does Rebalancing Class-Unbalanced Data Improve AUC for Linear Discriminant Analysis?

PubMed

Xue, Jing-Hao; Hall, Peter

2015-05-01

Many established classifiers fail to identify the minority class when it is much smaller than the majority class. To tackle this problem, researchers often first rebalance the class sizes in the training dataset, through oversampling the minority class or undersampling the majority class, and then use the rebalanced data to train the classifiers. This leads to interesting empirical patterns. In particular, using the rebalanced training data can often improve the area under the receiver operating characteristic curve (AUC) for the original, unbalanced test data. The AUC is a widely-used quantitative measure of classification performance, but the property that it increases with rebalancing has, as yet, no theoretical explanation. In this note, using Gaussian-based linear discriminant analysis (LDA) as the classifier, we demonstrate that, at least for LDA, there is an intrinsic, positive relationship between the rebalancing of class sizes and the improvement of AUC. We show that the largest improvement of AUC is achieved, asymptotically, when the two classes are fully rebalanced to be of equal sizes.

Habits with killer instincts: in vivo analysis on the severity of oral mucosal alterations using autofluorescence spectroscopy

NASA Astrophysics Data System (ADS)

Nazeer Shaiju, S.; Ariya, Saraswathy; Asish, Rajasekharan; Salim Haris, Padippurakkakath; Anita, Balan; Arun Kumar, Gupta; Jayasree, Ramapurath S.

2011-08-01

Oral habits like chewing and smoking are main causes of oral cancer, which has a higher mortality rate than many other cancer forms. Currently, the long term survival rate of oral cancer is less than 50%, as a majority of cases are detected very late. The clinician's main challenge is to differentiate among a multitude of red, white, or ulcerated lesions. Hence, new noninvasive, reliable, and fast techniques for the discrimination of oral cavity disorders are to be developed. This study includes autofluorescence spectroscopic screening of normal volunteers with and without lifestyle oral habits and patients with oral submucous fibrosis (OSF). The spectra from different sites of habitués, non-habitués, and OSF patients were analyzed using the intensity ratio, redox ratio, and linear discriminant analysis (LDA). The spectral disparities among these groups are well demonstrated in the emission regions of collagen and Flavin adenine dinucleotide. We observed that LDA gives better efficiency of classification than the intensity ratio technique. Even the differentiation of habitués and non-habitués could be well established with LDA. The study concludes that the clinical application of autofluorescence spectroscopy along with LDA, yields spontaneous screening among individuals, facilitating better patient management for clinicians and better quality of life for patients.

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining.

PubMed

Xu, Xiao; Jin, Tao; Wei, Zhijie; Wang, Jianmin

2017-01-01

Clinical pathways are widely used around the world for providing quality medical treatment and controlling healthcare cost. However, the expert-designed clinical pathways can hardly deal with the variances among hospitals and patients. It calls for more dynamic and adaptive process, which is derived from various clinical data. Topic-based clinical pathway mining is an effective approach to discover a concise process model. Through this approach, the latent topics found by latent Dirichlet allocation (LDA) represent the clinical goals. And process mining methods are used to extract the temporal relations between these topics. However, the topic quality is usually not desirable due to the low performance of the LDA in clinical data. In this paper, we incorporate topic assignment constraint and topic correlation limitation into the LDA to enhance the ability of discovering high-quality topics. Two real-world datasets are used to evaluate the proposed method. The results show that the topics discovered by our method are with higher coherence, informativeness, and coverage than the original LDA. These quality topics are suitable to represent the clinical goals. Also, we illustrate that our method is effective in generating a comprehensive topic-based clinical pathway model.

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining

PubMed Central

Xu, Xiao; Wei, Zhijie

2017-01-01

Clinical pathways are widely used around the world for providing quality medical treatment and controlling healthcare cost. However, the expert-designed clinical pathways can hardly deal with the variances among hospitals and patients. It calls for more dynamic and adaptive process, which is derived from various clinical data. Topic-based clinical pathway mining is an effective approach to discover a concise process model. Through this approach, the latent topics found by latent Dirichlet allocation (LDA) represent the clinical goals. And process mining methods are used to extract the temporal relations between these topics. However, the topic quality is usually not desirable due to the low performance of the LDA in clinical data. In this paper, we incorporate topic assignment constraint and topic correlation limitation into the LDA to enhance the ability of discovering high-quality topics. Two real-world datasets are used to evaluate the proposed method. The results show that the topics discovered by our method are with higher coherence, informativeness, and coverage than the original LDA. These quality topics are suitable to represent the clinical goals. Also, we illustrate that our method is effective in generating a comprehensive topic-based clinical pathway model. PMID:29065617

Extending information retrieval methods to personalized genomic-based studies of disease.

PubMed

Ye, Shuyun; Dawson, John A; Kendziorski, Christina

2014-01-01

Genomic-based studies of disease now involve diverse types of data collected on large groups of patients. A major challenge facing statistical scientists is how best to combine the data, extract important features, and comprehensively characterize the ways in which they affect an individual's disease course and likelihood of response to treatment. We have developed a survival-supervised latent Dirichlet allocation (survLDA) modeling framework to address these challenges. Latent Dirichlet allocation (LDA) models have proven extremely effective at identifying themes common across large collections of text, but applications to genomics have been limited. Our framework extends LDA to the genome by considering each patient as a "document" with "text" detailing his/her clinical events and genomic state. We then further extend the framework to allow for supervision by a time-to-event response. The model enables the efficient identification of collections of clinical and genomic features that co-occur within patient subgroups, and then characterizes each patient by those features. An application of survLDA to The Cancer Genome Atlas ovarian project identifies informative patient subgroups showing differential response to treatment, and validation in an independent cohort demonstrates the potential for patient-specific inference.

Information theoretic partitioning and confidence based weight assignment for multi-classifier decision level fusion in hyperspectral target recognition applications

NASA Astrophysics Data System (ADS)

Prasad, S.; Bruce, L. M.

2007-04-01

There is a growing interest in using multiple sources for automatic target recognition (ATR) applications. One approach is to take multiple, independent observations of a phenomenon and perform a feature level or a decision level fusion for ATR. This paper proposes a method to utilize these types of multi-source fusion techniques to exploit hyperspectral data when only a small number of training pixels are available. Conventional hyperspectral image based ATR techniques project the high dimensional reflectance signature onto a lower dimensional subspace using techniques such as Principal Components Analysis (PCA), Fisher's linear discriminant analysis (LDA), subspace LDA and stepwise LDA. While some of these techniques attempt to solve the curse of dimensionality, or small sample size problem, these are not necessarily optimal projections. In this paper, we present a divide and conquer approach to address the small sample size problem. The hyperspectral space is partitioned into contiguous subspaces such that the discriminative information within each subspace is maximized, and the statistical dependence between subspaces is minimized. We then treat each subspace as a separate source in a multi-source multi-classifier setup and test various decision fusion schemes to determine their efficacy. Unlike previous approaches which use correlation between variables for band grouping, we study the efficacy of higher order statistical information (using average mutual information) for a bottom up band grouping. We also propose a confidence measure based decision fusion technique, where the weights associated with various classifiers are based on their confidence in recognizing the training data. To this end, training accuracies of all classifiers are used for weight assignment in the fusion process of test pixels. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target recognition accuracies.

Carbon diffusion in molten uranium: an ab initio molecular dynamics study

NASA Astrophysics Data System (ADS)

Garrett, Kerry E.; Abrecht, David G.; Kessler, Sean H.; Henson, Neil J.; Devanathan, Ram; Schwantes, Jon M.; Reilly, Dallas D.

2018-04-01

In this work we used ab initio molecular dynamics within the framework of density functional theory and the projector-augmented wave method to study carbon diffusion in liquid uranium at temperatures above 1600 K. The electronic interactions of carbon and uranium were described using the local density approximation (LDA). The self-diffusion of uranium based on this approach is compared with literature computational and experimental results for liquid uranium. The temperature dependence of carbon and uranium diffusion in the melt was evaluated by fitting the resulting diffusion coefficients to an Arrhenius relationship. We found that the LDA calculated activation energy for carbon was nearly twice that of uranium: 0.55 ± 0.03 eV for carbon compared to 0.32 ± 0.04 eV for uranium. Structural analysis of the liquid uranium-carbon system is also discussed.

Triacylglycerol stereospecific analysis and linear discriminant analysis for milk speciation.

PubMed

Blasi, Francesca; Lombardi, Germana; Damiani, Pietro; Simonetti, Maria Stella; Giua, Laura; Cossignani, Lina

2013-05-01

Product authenticity is an important topic in dairy sector. Dairy products sold for public consumption must be accurately labelled in accordance with the contained milk species. Linear discriminant analysis (LDA), a common chemometric procedure, has been applied to fatty acid% composition to classify pure milk samples (cow, ewe, buffalo, donkey, goat). All original grouped cases were correctly classified, while 90% of cross-validated grouped cases were correctly classified. Another objective of this research was the characterisation of cow-ewe milk mixtures in order to reveal a common fraud in dairy field, that is the addition of cow to ewe milk. Stereospecific analysis of triacylglycerols (TAG), a method based on chemical-enzymatic procedures coupled with chromatographic techniques, has been carried out to detect fraudulent milk additions, in particular 1, 3, 5% cow milk added to ewe milk. When only TAG composition data were used for the elaboration, 75% of original grouped cases were correctly classified, while totally correct classified samples were obtained when both total and intrapositional TAG data were used. Also the results of cross validation were better when TAG stereospecific analysis data were considered as LDA variables. In particular, 100% of cross-validated grouped cases were obtained when 5% cow milk mixtures were considered.

A novel procedure on next generation sequencing data analysis using text mining algorithm.

PubMed

Zhao, Weizhong; Chen, James J; Perkins, Roger; Wang, Yuping; Liu, Zhichao; Hong, Huixiao; Tong, Weida; Zou, Wen

2016-05-13

Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure. The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data. The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.

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.

Discrimination of wine from grape cultivated in Japan, imported wine, and others by multi-elemental analysis.

PubMed

Shimizu, Hideaki; Akamatsu, Fumikazu; Kamada, Aya; Koyama, Kazuya; Okuda, Masaki; Fukuda, Hisashi; Iwashita, Kazuhiro; Goto-Yamamoto, Nami

2018-04-01

Differences in mineral concentrations were examined among three types of wine in the Japanese market place: Japan wine, imported wine, and domestically produced wine mainly from foreign ingredients (DWF), where Japan wine has been recently defined by the National Tax Agency as domestically produced wine from grapes cultivated in Japan. The main objective of this study was to examine the possibility of controlling the authenticity of Japan wine. The concentrations of 18 minerals (Li, B, Na, Mg, Si, P, S, K, Ca, Mn, Co, Ni, Ga, Rb, Sr, Mo, Ba, and Pb) in 214 wine samples were determined by inductively coupled-plasma mass spectrometry (ICP-MS) and ICP-atomic emission spectrometry (ICP-AES). In general, Japan wine had a higher concentration of potassium and lower concentrations of eight elements (Li, B, Na, Si, S, Co, Sr, and Pb) as compared with the other two groups of wine. Linear discriminant analysis (LDA) models based on concentrations of the 18 minerals facilitated the identification of three wine groups: Japan wine, imported wine, and DWF with a 91.1% classification score and 87.9% prediction score. In addition, an LDA model for discrimination of wine from four domestic geographic origins (Yamanashi, Nagano, Hokkaido, and Yamagata Prefectures) using 18 elements gave a classification score of 93.1% and a prediction score of 76.4%. In summary, we have shown that an LDA model based on mineral concentrations is useful for distinguishing Japan wine from other wine groups, and can contribute to classification of the four main domestic wine-producing regions of Japan. Copyright © 2017 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Development of a prototype sensor system for ultra-high-speed LDA-PIV

NASA Astrophysics Data System (ADS)

Griffiths, Jennifer A.; Royle, Gary J.; Bohndiek, Sarah E.; Turchetta, Renato; Chen, Daoyi

2008-04-01

Laser Doppler Anemometry (LDA) and Particle Image Velocimetry (PIV) are commonly used in the analysis of particulates in fluid flows. Despite the successes of these techniques, current instrumentation has placed limitations on the size and shape of the particles undergoing measurement, thus restricting the available data for the many industrial processes now utilising nano/micro particles. Data for spherical and irregularly shaped particles down to the order of 0.1 µm is now urgently required. Therefore, an ultra-fast LDA-PIV system is being constructed for the acquisition of this data. A key component of this instrument is the PIV optical detection system. Both the size and speed of the particles under investigation place challenging constraints on the system specifications: magnification is required within the system in order to visualise particles of the size of interest, but this restricts the corresponding field of view in a linearly inverse manner. Thus, for several images of a single particle in a fast fluid flow to be obtained, the image capture rate and sensitivity of the system must be sufficiently high. In order to fulfil the instrumentation criteria, the optical detection system chosen is a high-speed, lensed, digital imaging system based on state-of-the-art CMOS technology - the 'Vanilla' sensor developed by the UK based MI3 consortium. This novel Active Pixel Sensor is capable of high frame rates and sparse readout. When coupled with an image intensifier, it will have single photon detection capabilities. An FPGA based DAQ will allow real-time operation with minimal data transfer.

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

Shephard, Jacob J.; Vickers, Martin; Salzmann, Christoph G., E-mail: c.salzmann@ucl.ac.uk

Low-density amorphous (LDA) ice is involved in critical cosmological processes and has gained prominence as one of the at least two distinct amorphous forms of ice. Despite these accolades, we still have an incomplete understanding of the structural diversity that is encompassed within the LDA state and the dynamic processes that take place upon heating LDA. Heating the high-pressure ice VIII phase at ambient pressure is a remarkable example of temperature-induced amorphisation yielding LDA. We investigate this process in detail using X-ray diffraction and Raman spectroscopy and show that the LDA obtained from ice VIII is structurally different from themore » more “traditional” states of LDA which are approached upon thermal annealing. This new structural relaxation pathway involves an increase of structural order on the intermediate range length scale. In contrast with other LDA materials the local structure is more ordered initially and becomes slightly more disordered upon annealing. We also show that the cascade of phase transitions upon heating ice VIII at ambient pressure includes the formation of ice IX which may be connected with the structural peculiarities of LDA from ice VIII. Overall, this study shows that LDA is a structurally more diverse material than previously appreciated.« less

Quaternized magnetic nanoparticles-fluorescent polymer system for detection and identification of bacteria.

PubMed

Wan, Yi; Sun, Yan; Qi, Peng; Wang, Peng; Zhang, Dun

2014-05-15

Nanomaterial-based 'chemical nose' sensor with sufficient sensing specificity is a useful analytical tool for the detection of toxicologically important substances in complicated biological systems. A sensor array containing three quaternized magnetic nanoparticles (q-MNPs)-fluorescent polymer systems has been designed to identify and quantify bacteria. The bacterial cell membranes disrupt the q-MNP-fluorescent polymer, generating unique fluorescence response array. The response intensity of the array is dependent on the level of displacement determined by the relative q-MNP-fluorescent polymer binding strength and bacteria cells-MNP interaction. These characteristic responses show a highly repeatable bacteria cells and can be differentiated by linear discriminant analysis (LDA). Based on the array response matrix from LDA, our approach has been used to measure bacteria with an accuracy of 87.5% for 10(7) cfu mL(-1) within 20 min. Combined with UV-vis measurement, the method can be successfully performed to identify and detect eight different pathogen samples with an accuracy of 96.8%. The measurement system has a potential for further applications and provides a facile and simple method for the rapid analysis of protein, DNA, and pathogens. Copyright © 2013 Elsevier B.V. All rights reserved.

Face sketch recognition based on edge enhancement via deep learning

NASA Astrophysics Data System (ADS)

Xie, Zhenzhu; Yang, Fumeng; Zhang, Yuming; Wu, Congzhong

2017-11-01

In this paper,we address the face sketch recognition problem. Firstly, we utilize the eigenface algorithm to convert a sketch image into a synthesized sketch face image. Subsequently, considering the low-level vision problem in synthesized face sketch image .Super resolution reconstruction algorithm based on CNN(convolutional neural network) is employed to improve the visual effect. To be specific, we uses a lightweight super-resolution structure to learn a residual mapping instead of directly mapping the feature maps from the low-level space to high-level patch representations, which making the networks are easier to optimize and have lower computational complexity. Finally, we adopt LDA(Linear Discriminant Analysis) algorithm to realize face sketch recognition on synthesized face image before super resolution and after respectively. Extensive experiments on the face sketch database(CUFS) from CUHK demonstrate that the recognition rate of SVM(Support Vector Machine) algorithm improves from 65% to 69% and the recognition rate of LDA(Linear Discriminant Analysis) algorithm improves from 69% to 75%.What'more,the synthesized face image after super resolution can not only better describer image details such as hair ,nose and mouth etc, but also improve the recognition accuracy effectively.

The impact of low-dose aspirin on preterm birth: secondary analysis of a randomized controlled trial.

PubMed

Allshouse, A A; Jessel, R H; Heyborne, K D

2016-06-01

The objective of this study is to determine whether low-dose aspirin (LDA) reduced the rate of preterm birth (PTB) in a cohort of women at high risk for preeclampsia. Secondary analysis of the Maternal-Fetal Medicine Units High-Risk Aspirin trial. Preterm births were categorized by phenotype: indicated, spontaneous or due to preterm premature rupture of membranes (PPROMs). Of 1789 randomized women, 30.5% delivered before 37 weeks (18.5% indicated, 5.8% spontaneous and 6.2% following preterm PPROMs). Among women randomized to LDA, we observed a trend favoring fewer PTBs due to spontaneous preterm labor and preterm PPROMs, odds ratio (OR: 0.826 (0.620, 1.099)); the incidence of indicated PTBs appeared unchanged, OR: 0.999 (0.787, 1.268). Although not reaching significance, we observed an effect size similar to other studies of both low- and high-risk women. These results support findings from other studies assessing LDA as a PTB prevention strategy.

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

PubMed

Zheng, Wenming; Lin, Zhouchen; Wang, Haixian

2014-04-01

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

Spectral Learning for Supervised Topic Models.

PubMed

Ren, Yong; Wang, Yining; Zhu, Jun

2018-03-01

Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on variational approximation or Monte Carlo sampling, which often suffers from the local minimum defect. Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees. This paper investigates the possibility of applying spectral methods to recover the parameters of supervised LDA (sLDA). We first present a two-stage spectral method, which recovers the parameters of LDA followed by a power update method to recover the regression model parameters. Then, we further present a single-phase spectral algorithm to jointly recover the topic distribution matrix as well as the regression weights. Our spectral algorithms are provably correct and computationally efficient. We prove a sample complexity bound for each algorithm and subsequently derive a sufficient condition for the identifiability of sLDA. Thorough experiments on synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the spectral algorithms. In fact, our results on a large-scale review rating dataset demonstrate that our single-phase spectral algorithm alone gets comparable or even better performance than state-of-the-art methods, while previous work on spectral methods has rarely reported such promising performance.

Toward improving fine needle aspiration cytology by applying Raman microspectroscopy

NASA Astrophysics Data System (ADS)

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

2013-04-01

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

Using UV-Vis spectroscopy for simultaneous geographical and varietal classification of tea infusions simulating a home-made tea cup.

PubMed

Diniz, Paulo Henrique Gonçalves Dias; Barbosa, Mayara Ferreira; de Melo Milanez, Karla Danielle Tavares; Pistonesi, Marcelo Fabián; de Araújo, Mário César Ugulino

2016-02-01

In this work we proposed a method to verify the differentiating characteristics of simple tea infusions prepared in boiling water alone (simulating a home-made tea cup), which represents the final product as ingested by the consumers. For this purpose we used UV-Vis spectroscopy and variable selection through the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA) for simultaneous classification of the teas according to their variety and geographic origin. For comparison, KNN, CART, SIMCA, PLS-DA and PCA-LDA were also used. SPA-LDA and PCA-LDA provided significantly better results for tea classification of the five studied classes (Argentinean green tea; Brazilian green tea; Argentinean black tea; Brazilian black tea; and Sri Lankan black tea). The proposed methodology provides a simpler, faster and more affordable classification of simple tea infusions, and can be used as an alternative approach to traditional tea quality evaluation as made by skilful tasters, which is evidently partial and cannot assess geographic origins. Copyright © 2015 Elsevier Ltd. All rights reserved.

Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features.

PubMed

McDonald, Linda S; Panozzo, Joseph F; Salisbury, Phillip A; Ford, Rebecca

2016-01-01

Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective.

Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features

PubMed Central

McDonald, Linda S.; Panozzo, Joseph F.; Salisbury, Phillip A.; Ford, Rebecca

2016-01-01

Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective. PMID:27176469

On the difference between the pyroxenes LiFeSi2O6 and LiFeGe2O6 in their magnetic structures and spin orientations

NASA Astrophysics Data System (ADS)

Lee, Changhoon; Hong, Jisook; Shim, Ji Hoon; Whangbo, Myung-Hwan

2014-03-01

The clinopyroxenes LiFeSi2O6 and LiFeGe2O6, crystallizing in a monoclinic space group P21/c, are isostructural and isoelectronic Their crystal structures are made up of zigzag chains of edge-sharing FeO6 octahedra containing high-spin Fe3 + ions, which run along the c direction. Despite this structural similarity, the two have quite different magnetic structures and spin orientations. In LiFeSi2O6 the Fe spins have a ferromagnetic coupling within the zigzag chains along c and such FM chains have an antiferromagnetic coupling along a. In contrast, in LiFeGe2O6, the spins have an AFM coupling within the zigzag chains along c and such FM chains have an ↑ ↑ ↓ ↓ coupling along a. In addition, the spin orientation is parallel to c in LiFeSi2O6, but is perpendicular to c in LiFeGe2O6. To explain these differences in the magnetic structure and spin orientation, we evaluated the spin exchange parameters by performing energy mapping analysis based on LDA +U and GGA +U calculations and also by evaluating the magnetocrystalline anisotropy energies in terms of GGA +U +SOC and LDA +U +SOC calculations. Our study show that the magnetic structures and spin orientations of LiFeSi2O6 and LiFeGe2O6 are better described by LDA +U and LDA +U +SOC calculations. This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2013R1A1A2060341).

A study on native defects and magnetic properties in undoped rutile TiO2 using LDA and LDA+UO p+UTi d methods

NASA Astrophysics Data System (ADS)

Shi, Li-Bin; Wang, Yong Ping

2016-05-01

The native defects and magnetic properties in undoped rutile TiO2 are studied using local density approximation (LDA) and LDA adding Hubbard parameters (U) schemes. The band gap is adjusted to experimental value of 3.0 eV by combination of UTi d=4.2 eV and UO p=4.8 eV. This LDA+U methodology overcomes the band-gap problem and renders the approach more predictive. The formation energies of oxygen vacancy (VO), oxygen interstitial (Oi), titanium vacancy (VTi), titanium interstitial (Tii), oxygen anti-sites (OTi), and titanium anti-sites (TiO) are investigated by the LDA and LDA+U methods. In addition, some ground state configurations can be obtained by optimization of total spin. It is found that native defects can induce spin polarization and produce magnetic moment.

Carbon diffusion in molten uranium: an ab initio molecular dynamics study

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

Garrett, Kerry E.; Abrecht, David G.; Kessler, Sean H.

In this work we used ab initio molecular dynamics (AIMD) within the framework of density functional theory (DFT) and the projector-augmented wave (PAW) method to study carbon diffusion in liquid uranium at temperatures above 1600 K. The electronic interactions of carbon and uranium were described using the local density approximation (LDA). The self-diffusion of uranium based on this approach is compared with literature computational and experimental results for liquid uranium. The temperature dependence of carbon and uranium diffusion in the melt was evaluated by fitting the resulting diffusion coefficients to an Arrhenius relationship. We found that the LDA calculated activationmore » energy for carbon was nearly twice that of uranium: 0.55±0.03 eV for carbon compared to 0.32±0.04 eV for uranium. Structural analysis of the liquid uranium-carbon system is also discussed.« less

Self-interaction corrected LDA + U investigations of BiFeO3 properties: plane-wave pseudopotential method

NASA Astrophysics Data System (ADS)

Yaakob, M. K.; Taib, M. F. M.; Lu, L.; Hassan, O. H.; Yahya, M. Z. A.

2015-11-01

The structural, electronic, elastic, and optical properties of BiFeO3 were investigated using the first-principles calculation based on the local density approximation plus U (LDA + U) method in the frame of plane-wave pseudopotential density functional theory. The application of self-interaction corrected LDA + U method improved the accuracy of the calculated properties. Results of structural, electronic, elastic, and optical properties of BiFeO3, calculated using the LDA + U method were in good agreement with other calculation and experimental data; the optimized choice of on-site Coulomb repulsion U was 3 eV for the treatment of strong electronic localized Fe 3d electrons. Based on the calculated band structure and density of states, the on-site Coulomb repulsion U had a significant effect on the hybridized O 2p and Fe 3d states at the valence and the conduction band. Moreover, the elastic stiffness tensor, the longitudinal and shear wave velocities, bulk modulus, Poisson’s ratio, and the Debye temperature were calculated for U = 0, 3, and 6 eV. The elastic stiffness tensor, bulk modulus, sound velocities, and Debye temperature of BiFeO3 consistently decreased with the increase of the U value.

Electronic properties of 3R-CuAlO2 under pressure: Three theoretical approaches

NASA Astrophysics Data System (ADS)

Christensen, N. E.; Svane, A.; Laskowski, R.; Palanivel, B.; Modak, P.; Chantis, A. N.; van Schilfgaarde, M.; Kotani, T.

2010-01-01

The pressure variation in the structural parameters, u and c/a , of the delafossite CuAlO2 is calculated within the local-density approximation (LDA). Further, the electronic structures as obtained by different approximations are compared: LDA, LDA+U , and a recently developed “quasiparticle self-consistent GW ” (QSGW) approximation. The structural parameters obtained by the LDA agree very well with experiments but, as expected, gaps in the formal band structure are underestimated as compared to optical experiments. The (in LDA too high lying) Cu3d states can be down shifted by LDA+U . The magnitude of the electric field gradient (EFG) as obtained within the LDA is far too small. It can be “fitted” to experiments in LDA+U but a simultaneous adjustment of the EFG and the gap cannot be obtained with a single U value. QSGW yields reasonable values for both quantities. LDA and QSGW yield significantly different values for some of the band-gap deformation potentials but calculations within both approximations predict that 3R-CuAlO2 remains an indirect-gap semiconductor at all pressures in its stability range 0-36 GPa, although the smallest direct gap has a negative pressure coefficient.

A randomized, double-blind, placebo-controlled study of rebamipide for gastric mucosal injury taking aspirin with or without clopidogrel.

PubMed

Tozawa, Katsuyuki; Oshima, Tadayuki; Okugawa, Takuya; Ogawa, Tomohiro; Ohda, Yoshio; Tomita, Toshihiko; Hida, Nobuyuki; Fukui, Hirokazu; Hori, Kazutoshi; Watari, Jiro; Nakamura, Shiro; Miwa, Hiroto

2014-08-01

Antithrombotic drugs, such as low-dose aspirin (LDA) and clopidogrel, can cause upper gastrointestinal complications. The goal of the present study was to investigate whether a mucosal-protective agent, rebamipide, could prevent gastric mucosal injuries induced by LDA with or without clopidogrel in healthy subjects. A randomized, double-blind, placebo-controlled trial was performed with 32 healthy male volunteers. Subjects were randomly assigned to a 14-day course of one of the following regimens: group A, placebo (tid) + LDA; group B, rebamipide (100 mg tid) + LDA (100 mg once-daily); group C, placebo + LDA + clopidogrel (75 mg once-daily); or group D, rebamipide + LDA + clopidogrel. The grade of gastric mucosal injuries was evaluated by esophagogastroduodenoscopy before and after dosing (on day 0 and day 14), and the grade of gastric mucosal injury was assessed according to the modified Lanza score. Subjective symptoms were assessed using the Gastrointestinal Symptom Rating Scale (GSRS). A rapid urease test was performed on day 0, and blood tests were performed on day 0 and day 14. Rebamipide significantly inhibited gastric mucosal injury induced by LDA alone or by LDA plus clopidogrel when compared with placebo in healthy subjects. GSRS score and hemoglobin level were not significantly different among the four groups. Rebamipide is useful for the primary prevention of gastric mucosal injury induced by LDA alone or by LDA plus clopidogrel in healthy subjects.

Application of class-modelling techniques to infrared spectra for analysis of pork adulteration in beef jerkys.

PubMed

Kuswandi, Bambang; Putri, Fitra Karima; Gani, Agus Abdul; Ahmad, Musa

2015-12-01

The use of chemometrics to analyse infrared spectra to predict pork adulteration in the beef jerky (dendeng) was explored. In the first step, the analysis of pork in the beef jerky formulation was conducted by blending the beef jerky with pork at 5-80 % levels. Then, they were powdered and classified into training set and test set. The second step, the spectra of the two sets was recorded by Fourier Transform Infrared (FTIR) spectroscopy using atenuated total reflection (ATR) cell on the basis of spectral data at frequency region 4000-700 cm(-1). The spectra was categorised into four data sets, i.e. (a) spectra in the whole region as data set 1; (b) spectra in the fingerprint region (1500-600 cm(-1)) as data set 2; (c) spectra in the whole region with treatment as data set 3; and (d) spectra in the fingerprint region with treatment as data set 4. The third step, the chemometric analysis were employed using three class-modelling techniques (i.e. LDA, SIMCA, and SVM) toward the data sets. Finally, the best result of the models towards the data sets on the adulteration analysis of the samples were selected and the best model was compared with the ELISA method. From the chemometric results, the LDA model on the data set 1 was found to be the best model, since it could classify and predict 100 % accuracy of the sample tested. The LDA model was applied toward the real samples of the beef jerky marketed in Jember, and the results showed that the LDA model developed was in good agreement with the ELISA method.

Population differences in the postcrania of modern South Africans and the implications for ancestry estimation.

PubMed

Liebenberg, Leandi; L'Abbé, Ericka N; Stull, Kyra E

2015-12-01

The cranium is widely recognized as the most important skeletal element to use when evaluating population differences and estimating ancestry. However, the cranium is not always intact or available for analysis, which emphasizes the need for postcranial alternatives. The purpose of this study was to quantify postcraniometric differences among South Africans that can be used to estimate ancestry. Thirty-nine standard measurements from 11 postcranial bones were collected from 360 modern black, white and coloured South Africans; the sex and ancestry distribution were equal. Group differences were explored with analysis of variance (ANOVA) and Tukey's honestly significant difference (HSD) test. Linear and flexible discriminant analysis (LDA and FDA, respectively) were conducted with bone models as well as numerous multivariate subsets to identify the model and method that yielded the highest correct classifications. Leave-one-out (LDA) and k-fold (k=10; FDA) cross-validation with equal priors were used for all models. ANOVA and Tukey's HSD results reveal statistically significant differences between at least two of the three groups for the majority of the variables, with varying degrees of group overlap. Bone models, which consisted of all measurements per bone, resulted in low accuracies that ranged from 46% to 63% (LDA) and 41% to 66% (FDA). In contrast, the multivariate subsets, which consisted of different variable combinations from all elements, achieved accuracies as high as 85% (LDA) and 87% (FDA). Thus, when using a multivariate approach, the postcranial skeleton can distinguish among three modern South African groups with high accuracy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A multiple maximum scatter difference discriminant criterion for facial feature extraction.

PubMed

Song, Fengxi; Zhang, David; Mei, Dayong; Guo, Zhongwei

2007-12-01

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart--multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD out-performs state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.

PubMed

Khondoker, Mizanur; Dobson, Richard; Skirrow, Caroline; Simmons, Andrew; Stahl, Daniel

2016-10-01

Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms. We compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbour (kNN). Using massively parallel processing on high-performance supercomputers, we compare the generalisation errors at various combinations of levels of several factors: number of features, training sample size, biological variation, experimental variation, effect size, replication and correlation between features. For smaller number of correlated features, number of features not exceeding approximately half the sample size, LDA was found to be the method of choice in terms of average generalisation errors as well as stability (precision) of error estimates. SVM (with RBF kernel) outperforms LDA as well as RF and kNN by a clear margin as the feature set gets larger provided the sample size is not too small (at least 20). The performance of kNN also improves as the number of features grows and outplays that of LDA and RF unless the data variability is too high and/or effect sizes are too small. RF was found to outperform only kNN in some instances where the data are more variable and have smaller effect sizes, in which cases it also provide more stable error estimates than kNN and LDA. Applications to a number of real datasets supported the findings from the simulation study. © The Author(s) 2013.

Clinical progress of human papillomavirus genotypes and their persistent infection in subjects with atypical squamous cells of undetermined significance cytology: Statistical and latent Dirichlet allocation analysis

PubMed Central

Kim, Yee Suk; Lee, Sungin; Zong, Nansu; Kahng, Jimin

2017-01-01

The present study aimed to investigate differences in prognosis based on human papillomavirus (HPV) infection, persistent infection and genotype variations for patients exhibiting atypical squamous cells of undetermined significance (ASCUS) in their initial Papanicolaou (PAP) test results. A latent Dirichlet allocation (LDA)-based tool was developed that may offer a facilitated means of communication to be employed during patient-doctor consultations. The present study assessed 491 patients (139 HPV-positive and 352 HPV-negative cases) with a PAP test result of ASCUS with a follow-up period ≥2 years. Patients underwent PAP and HPV DNA chip tests between January 2006 and January 2009. The HPV-positive subjects were followed up with at least 2 instances of PAP and HPV DNA chip tests. The most common genotypes observed were HPV-16 (25.9%, 36/139), HPV-52 (14.4%, 20/139), HPV-58 (13.7%, 19/139), HPV-56 (11.5%, 16/139), HPV-51 (9.4%, 13/139) and HPV-18 (8.6%, 12/139). A total of 33.3% (12/36) patients positive for HPV-16 had cervical intraepithelial neoplasia (CIN)2 or a worse result, which was significantly higher than the prevalence of CIN2 of 1.8% (8/455) in patients negative for HPV-16 (P<0.001), while no significant association was identified for other genotypes in terms of genotype and clinical progress. There was a significant association between clearance and good prognosis (P<0.001). Persistent infection was higher in patients aged ≥51 years (38.7%) than in those aged ≤50 years (20.4%; P=0.036). Progression from persistent infection to CIN2 or worse (19/34, 55.9%) was higher than clearance (0/105, 0.0%; P<0.001). In the LDA analysis, using symmetric Dirichlet priors α=0.1 and β=0.01, and clusters (k)=5 or 10 provided the most meaningful groupings. Statistical and LDA analyses produced consistent results regarding the association between persistent infection of HPV-16, old age and long infection period with a clinical progression of CIN2 or worse. Therefore, LDA results may be presented as explanatory evidence during time-constrained patient-doctor consultations in order to deliver information regarding the patient's status. PMID:28587376

Changes of hematological and biochemical parameters and levels of pepsinogen, histamine and prostaglandins in dairy cows affected with left displacement of the abomasum.

PubMed

Al-Rawashdeh, O; Ismail, Z Bani; Talafha, A; Al-Momani, A

2017-03-28

The aims of this study were to determine the serum levels of pepsinogen, histamine, and prostaglandins F2α and E2 in lactating dairy cows affected with left displacement of the abomasum (LDA). In addition, the hematological and serum biochemical parameters were also determined in cows affected with LDA. A total of 52 adult lactating Holstein-Friesian cows affected with LDA and 30 normal cows (control) were used. In LDA cows, the average age, BCS and body weight were 4.9 ± 1.2 years, 2.5 ± 0.75, and 525 ± 150kg respectively. The average days-in-milk (DIM) in affected cows was 14 ± 6 with a range between 7 to 45 days. There were no significant differences in values of rectal temperature, heart rate and respiration rate between LDA cows and control. Rumen motility was significantly (p≤0.05) decreased in LDA cows. Cows affected with LDA had significantly (p≤0.05) increased glucose levels, and decreased levels of calcium and magnesium. There were significantly (p≤0.05) increased serum levels of pepsinogen and histamine in LDA cows while levels of prostaglandin E2 were significantly decreased in comparison to those in control cows. There were no significant changes in serum levels of prostaglandin F2α. In the hematology analyses, there were no significant changes in cows with LDA when compared to those in control cows. This study provides evidence of a possible role for pepsinogen, histamine and prostaglandin E2 in the etiopathophysiology of LDA in dairy cows.

Expanded findings from a randomized controlled trial of preconception low-dose aspirin and pregnancy loss

PubMed Central

Mumford, Sunni L.; Silver, Robert M.; Sjaarda, Lindsey A.; Wactawski-Wende, Jean; Townsend, Janet M.; Lynch, Anne M.; Galai, Noya; Lesher, Laurie L.; Faraggi, David; Perkins, Neil J.; Schliep, Karen C.; Zarek, Shvetha M.; Schisterman, Enrique F.

2016-01-01

STUDY QUESTION What is the association between daily preconception-initiated low-dose aspirin (LDA) treatment and very early pregnancy losses or euploid (chromosomally normal) losses among women with one to two prior losses? SUMMARY ANSWER Daily LDA initiated preconception was not associated with the rate or type of pregnancy loss among women with a history of one to two prior pregnancy losses. WHAT IS KNOWN ALREADY LDA is often used to treat recurrent pregnancy loss with reductions in pregnancy loss generally only observed among women with antiphospholipid antibodies, and null associations observed among women without antiphospholipid antibodies. We previously evaluated the association between LDA and pregnancy loss overall among women with one to two prior losses in the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial and found no association, though did not distinguish between potential effects at different stages of pregnancy loss, including implantation failure, or between euploid and aneuploid losses. STUDY DESIGN, SIZE, DURATION The EAGeR trial was a multi-site prospective block-randomized double-blind placebo-controlled trial. In total, 1228 women were randomized to daily LDA (81 mg/day) plus folic acid (400 mcg/day), or placebo plus folic acid. Participants were assigned study drug for less than or equal to six menstrual cycles or if they conceived, throughout pregnancy with study drug discontinued at 36 weeks gestation. This analysis includes additional outcome information obtained from chart abstractions after the completion of the trial, as well as testing of stored urine for measurement of hCG and detection of very early pregnancy losses, and karyotyping of the products of conception for assessment of aneuploidy of the losses. PARTICIPANTS, SETTING, METHODS Women aged 18–40 with a history of one to two prior losses and actively trying to conceive were randomized (n = 615 LDA and n = 613 placebo) at four clinical centers in the USA (2007–2011). Log-binomial regression was used to estimate risk ratios under the intent-to-treat approach. MAIN RESULTS AND THE ROLE OF CHANCE Daily LDA initiated preconception was not associated with clinically recognized pregnancy losses or implantation failures among women with proved fecundity and a history of one to two prior losses. Specifically, 1088 (88.6%) women completed the trial with 797 having an hCG detected pregnancy (64.9%). Overall there were 133 clinical losses (12.7% LDA versus 11.8% placebo, P = 0.71) and 55 implantation failures (5.2% LDA versus 4.9% placebo, P = 0.89). No differences were found in rate of euploid losses (RR 1.11, 95% confidence interval: 0.99, 1.26). LIMITATIONS, REASONS FOR CAUTION Generalizability of these findings is limited to women with a history of one to two prior losses, and may further be limited to women of white race with higher socioeconomic status as given the rigors of the study protocol participants tended to be white and have higher incomes and more education. We were also missing karyotype information on approximately one-third of the clinically recognized pregnancy losses, which may limit our power to detect effects on euploid losses, though detailed sensitivity analysis showed similar results. WIDER IMPLICATIONS OF THE FINDINGS Our data do not support the general use of LDA to decrease pregnancy loss and further demonstrate no increased risk of loss for women on LDA treatment. STUDY FUNDING/COMPETING INTERESTS This research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland (Contract Nos. HHSN267200603423, HHSN267200603424, HHSN267200603426). The authors have no conflicts of interest. TRIAL REGISTRATION NUMBER The trial was registered at ClinicalTrials.gov #NCT00467363. TRIAL REGISTRATION DATE 27 April 2007. DATE OF FIRST PATIENT'S ENROLLMENT 15 June 2007. PMID:26759138

Protein S deficiency complicated pregnancy in women with recurrent pregnancy loss.

PubMed

Shinozaki, Nanae; Ebina, Yasuhiko; Deguchi, Masashi; Tanimura, Kenji; Morizane, Mayumi; Yamada, Hideto

2016-08-01

This prospective study aimed to evaluate pregnancy outcome and complications in women with recurrent pregnancy loss (RPL) and protein S (PS) deficiency, who received low dose aspirin (LDA) or LDA plus heparin (LDA/H) therapies. Clinical characteristics, pregnancy outcome and complications of 38 women with two or more RPL and <60% of plasma free PS antigen were compared among three groups: antiphospholipid antibody (aPL)-negative women who received LDA (group A), aPL-negative women who received LDA/H (group B) and aPL-positive women who received LDA/H (group C). Gestational weeks (GW) at delivery in group C (median 32 GW) were earlier than 40 GW in group A and 38.5 GW in group B (p < 0.05). The birth weight in group C (median 1794 g) was less than 2855 g in group B (p < 0.05). The incidences of fetal growth restriction (37.5%), pregnancy-induced hypertension (37.5%), and preterm delivery (62.5%) in group C were higher than those (4.5%, 0%, and 4.5%, respectively) in group B (p<0.05). Women with RPL, PS deficiency, and positive aPL had high risks for adverse pregnancy outcome and complications, even when they received LDA/H therapy. Among women with RPL, PS, and negative aPL, there was no difference in these risks between LDA alone and LDA/H therapies.

FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications.

PubMed

Backenroth, Daniel; He, Zihuai; Kiryluk, Krzysztof; Boeva, Valentina; Pethukova, Lynn; Khurana, Ekta; Christiano, Angela; Buxbaum, Joseph D; Ionita-Laza, Iuliana

2018-05-03

We describe a method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell-type- and/or tissue-specific way (FUN-LDA). Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome in 127 different tissues and cell types. We demonstrate the usefulness of our predictions by using several validation experiments. Using eQTL data from several sources, including the GTEx project, Geuvadis project, and TwinsUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap. We further show how these integrated functional scores can be used for (1) deriving the most likely cell or tissue type causally implicated for a complex trait by using summary statistics from genome-wide association studies and (2) estimating a tissue-based correlation matrix of various complex traits. We found large enrichment of heritability in functional components of relevant tissues for various complex traits, and FUN-LDA yielded higher enrichment estimates than existing methods. Finally, using experimentally validated functional variants from the literature and variants possibly implicated in disease by previous studies, we rigorously compare FUN-LDA with state-of-the-art functional annotation methods and show that FUN-LDA has better prediction accuracy and higher resolution than these methods. In particular, our results suggest that tissue- and cell-type-specific functional prediction methods tend to have substantially better prediction accuracy than organism-level prediction methods. Scores for each position in the human genome and for each ENCODE and Roadmap tissue are available online (see Web Resources). Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Evaluation of burst-mode LDA spectra with implications

NASA Astrophysics Data System (ADS)

Velte, Clara; George, William

2009-11-01

Burst-mode LDA spectra, as described in [1], are compared to spectra obtained from corresponding HWA measurements using the FFT in a round jet and cylinder wake experiment. The phrase ``burst-mode LDA'' refers to an LDA which operates with at most one particle present in the measuring volume at a time. Due to the random sampling and velocity bias of the LDA signal, the Direct Fourier Transform with accompanying weighting by the measured residence times was applied to obtain a correct interpretation of the spectral estimate. Further, the self-noise was removed as described in [2]. In addition, resulting spectra from common interpolation and uniform resampling techniques are compared to the above mentioned estimates. The burst-mode LDA spectra are seen to concur well with the HWA spectra up to the emergence of the noise floor, caused mainly by the intermittency of the LDA signal. The interpolated and resampled counterparts yield unphysical spectra, which are buried in frequency dependent noise and step noise, except at very high LDA data rates where they perform well up to a limited frequency.[4pt] [1] Buchhave, P. PhD Thesis, SUNY/Buffalo, 1979.[0pt] [2] Velte, C.M. PhD Thesis, DTU/Copenhagen, 2009.

Optical diagnosis of the progression and reversal of CCl4-induced liver injury in rodent model using minimally invasive autofluorescence spectroscopy.

PubMed

Nazeer, Shaiju S; Sandhyamani, S; Jayasree, Ramapurath S

2015-06-07

Worldwide, liver cancer is the fifth most common cancer in men and seventh most common cancer in women. Intoxicant-induced liver injury is one of the major causes for severe structural damage with fibrosis and functional derangement of the liver leading to cancer in its later stages. This report focuses on the minimally invasive autofluorescence spectroscopic (AFS) studies on intoxicant, carbon tetrachloride (CCl4)-induced liver damage in a rodent model. Different stages of liver damage, including the reversed stage, on stoppage of the intoxicant are examined. Emission from prominent fluorophores, such as collagen, nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD), and variations in redox ratio have been studied. A direct correlation between the severity of the disease and the levels of collagen and redox ratio was observed. On withdrawal of the intoxicant, a gradual reversal of the disease to normal conditions was observed as indicated by the decrease in collagen levels and redox ratio. Multivariate statistical techniques and principal component analysis followed by linear discriminant analysis (PC-LDA) were used to develop diagnostic algorithms for distinguishing different stages of the liver disease based on spectral features. The PC-LDA modeling on a minimally invasive AFS dataset yielded diagnostic sensitivities of 93%, 87% and 87% and specificities of 90%, 98% and 98% for pairwise classification among normal, fibrosis, cirrhosis and reversal conditions. We conclude that AFS along with PC-LDA algorithm has the potential for rapid and accurate minimally invasive diagnosis and detection of structural changes due to liver injury resulting from various intoxicants.

Electronic properties of copper aluminate examined by three theoretical approaches

NASA Astrophysics Data System (ADS)

Christensen, Niels; Svane, Axel

2010-03-01

Electronic properties of 3R.CuAlO2 are derived vs. pressure from ab initio band structure calculations within the local-density approximation (LDA), LDA+U scheme as well as the quasiparticle self-consistent GW approximation (QSGW, van Schilfgaarde, Kotani, and Falaev). The LDA underestimates the gap and places the Cu-3d states at too high energies. An effective U value, 8.2 eV, can be selected so that LDA+U lowers the 3d states to match XPS data and such that the lowest gap agrees rather well with optical absorption experiments. The electrical field gradient (EFG) on Cu is in error when calculated within the LDA. The agreement with experiment can be improved by LDA+U, but a larger U, 13.5 eV, is needed for full adjustment. QSGW yields correct Cu-EFG and, when electron-hole correlations are included, also correct band gaps. The QSGW and LDA band gap deformation potential values differ significantly.

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

PubMed Central

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

2018-01-01

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

Computational materials design of attractive Fermion system with large negative effective Ueff in the hole-doped Delafossite of CuAlO2, AgAlO2 and AuAlO2: Charge-excitation induced Ueff < 0

NASA Astrophysics Data System (ADS)

Nakanishi, A.; Fukushima, T.; Uede, H.; Katayama-Yoshida, H.

2015-12-01

On the basis of general design rules for negative effective U(Ueff) systems by controlling purely-electronic and attractive Fermion mechanisms, we perform computational materials design (CMD®) for the negative Ueff system in hole-doped two-dimensional (2D) Delafossite CuAlO2, AgAlO2 and AuAlO2 by ab initio calculations with local density approximation (LDA) and self-interaction corrected-LDA (SIC-LDA). It is found that the large negative Ueff in the hole-doped attractive Fermion systems for CuAlO2 (UeffLDA = - 4.53 eV and UeffSIC-LDA = - 4.20 eV), AgAlO2 (UeffLDA = - 4.88 eV and UeffSIC-LDA = - 4.55 eV) and AuAlO2 (UeffLDA = - 4.14 eV and UeffSIC-LDA = - 3.55 eV). These values are 10 times larger than that in hole-doped three-dimensional (3D) CuFeS2 (Ueff = - 0.44 eV). For future calculations of Tc and phase diagram by quantum Monte Carlo simulations, we propose the negative Ueff Hubbard model with the anti-bonding single π-band model for CuAlO2, AgAlO2 and AuAlO2 using the mapped parameters obtained from ab initio electronic structure calculations. Based on the theory of negative Ueff Hubbard model (Noziéres and Schmitt-Rink, 1985), we discuss |Ueff| dependence of superconducting critical temperature (Tc) in the 2D Delafossite of CuAlO2, AgAlO2 and AuAlO2 and 3D Chalcopyrite of CuFeS2, which shows the interesting chemical trend, i.e., Tc increases exponentially (Tc ∝ exp [ - 1 / | Ueff | ]) in the weak coupling regime | Ueff(- 0.44 eV) | < W(∼ 2 eV) (where W is the band width of the negative Ueff Hubbard model) for the hole-doped CuFeS2, and then Tc goes through a maximum when | Ueff(- 4.88 eV , - 4.14 eV) | ∼ W(2.8 eV , 3.5 eV) for the hole-doped AgAlO2 and AuAlO2, and finally Tc decreases with increasing |Ueff| in the strong coupling regime, where | Ueff(- 4.53 eV) | > W(1.7 eV) , for the hole-doped CuAlO2.

Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites.

PubMed

Ielpo, Pierina; Leardi, Riccardo; Pappagallo, Giuseppe; Uricchio, Vito Felice

2017-06-01

In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project "Improvement of the Regional Agro-meteorological Monitoring Network (2004-2007)". LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture.

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

PubMed

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

2015-11-01

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

Electronic structure, magnetism, and exchange integrals in transition-metal oxides: Role of the spin polarization of the functional in DFT+U calculations

NASA Astrophysics Data System (ADS)

Keshavarz, Samara; Schött, Johan; Millis, Andrew J.; Kvashnin, Yaroslav O.

2018-05-01

Density functional theory augmented with Hubbard-U corrections (DFT+U ) is currently one of the most widely used methods for first-principles electronic structure modeling of insulating transition-metal oxides (TMOs). Since U is relatively large compared to bandwidths, the magnetic excitations in TMOs are expected to be well described by a Heisenberg model. However, in practice the calculated exchange parameters Ji j depend on the magnetic configuration from which they are extracted and on the functional used to compute them. In this work we investigate how the spin polarization dependence of the underlying exchange-correlation functional influences the calculated magnetic exchange constants of TMOs. We perform a systematic study of the predictions of calculations based on the local density approximation plus U (LDA+U ) and the local spin density approximation plus U (LSDA+U ) for the electronic structures, total energies, and magnetic exchange interactions Ji j extracted from ferromagnetic (FM) and antiferromagnetic (AFM) configurations of several transition-metal oxide materials. We report that for realistic choices of Hubbard U and Hund's J parameters, LSDA+U and LDA+U calculations result in different values of the magnetic exchange constants and band gap. The dependence of the band gap on the magnetic configuration is stronger in LDA+U than in LSDA+U and we argue that this is the main reason why the configuration dependence of Ji j is found to be systematically more pronounced in LDA+U than in LSDA+U calculations. We report a very good correspondence between the computed total energies and the parametrized Heisenberg model for LDA+U calculations, but not for LSDA+U , suggesting that LDA+U is a more appropriate method for estimating exchange interactions.

Mining Adverse Events of Dietary Supplements from Product Labels by Topic Modeling.

PubMed

Wang, Yefeng; Gunashekar, Divya R; Adam, Terrence J; Zhang, Rui

2017-01-01

The adverse events of the dietary supplements should be subject to scrutiny due to their growing clinical application and consumption among U.S. adults. An effective method for mining and grouping the adverse events of the dietary supplements is to evaluate product labeling for the rapidly increasing number of new products available in the market. In this study, the adverse events information was extracted from the product labels stored in the Dietary Supplement Label Data-base (DSLD) and analyzed by topic modeling techniques, specifically Latent Dirichlet Allocation (LDA). Among the 50 topics generated by LDA, eight topics were manually evaluated, with topic relatedness ranging from 58.8% to 100% on the product level, and 57.1% to 100% on the ingredient level. Five out of these eight topics were coherent groupings of the dietary supplements based on their adverse events. The results demonstrated that LDA is able to group supplements with similar adverse events based on the dietary supplement labels. Such information can be potentially used by consumers to more safely use dietary supplements.

Modeling and analysis of a large deployable antenna structure

NASA Astrophysics Data System (ADS)

Chu, Zhengrong; Deng, Zongquan; Qi, Xiaozhi; Li, Bing

2014-02-01

One kind of large deployable antenna (LDA) structure is proposed by combining a number of basic deployable units in this paper. In order to avoid vibration caused by fast deployment speed of the mechanism, a braking system is used to control the spring-actuated system. Comparisons between the LDA structure and a similar structure used by the large deployable reflector (LDR) indicate that the former has potential for use in antennas with up to 30 m aperture due to its lighter weight. The LDA structure is designed to form a spherical surface found by the least square fitting method so that it can be symmetrical. In this case, the positions of the terminal points in the structure are determined by two principles. A method to calculate the cable network stretched on the LDA structure is developed, which combines the original force density method and the parabolic surface constraint. Genetic algorithm is applied to ensure that each cable reaches a desired tension, which avoids the non-convergence issue effectively. We find that the pattern for the front and rear cable net must be the same when finding the shape of the rear cable net, otherwise anticlastic surface would generate.

AB INITIO STUDY OF OPTOELECTRONIC PROPERTIES OF SPINEL ZnAl2O4 BEYOND GGA AND LDA

NASA Astrophysics Data System (ADS)

Yousaf, Masood; Saeed, M. A.; Isa, Ahmad Radzi Mat; Rahnamaye Aliabad, H. A.; Noor, N. A.

2012-12-01

Electronic band structure and optical parameters of ZnAl2O4 are investigated by first-principles technique based on a new potential approximation, known as modified Becke-Johnson (mBJ). This method describes the excited states of insulators and semiconductors more accurately The recent direct band gap result by EV-GGA is underestimated by about 15% compared to our band gap value using mBJ-GGA. The value of the band gap of ZnAl2O4 decreases as follows: Eg(mBJ-GGA/LDA) > Eg(GGA) > Eg(LDA). The band structure base optical parametric quantities (dielectric constant, index of refraction, reflectivity and optical conductivity) are also calculated, and their variations with energy range are discussed. The first critical point (optical absorption's edge) in ZnAl2O4 occurs at about 5.26 eV in case of mBJ. This study about the optoelectronic properties indicates that ZnAl2O4 can be used in optical devices.

Calculations of Hubbard U from first-principles

NASA Astrophysics Data System (ADS)

Aryasetiawan, F.; Karlsson, K.; Jepsen, O.; Schönberger, U.

2006-09-01

The Hubbard U of the 3d transition metal series as well as SrVO3 , YTiO3 , Ce, and Gd has been estimated using a recently proposed scheme based on the random-phase approximation. The values obtained are generally in good accord with the values often used in model calculations but for some cases the estimated values are somewhat smaller than those used in the literature. We have also calculated the frequency-dependent U for some of the materials. The strong frequency dependence of U in some of the cases considered in this paper suggests that the static value of U may not be the most appropriate one to use in model calculations. We have also made comparison with the constrained local density approximation (LDA) method and found some discrepancies in a number of cases. We emphasize that our scheme and the constrained local density approximation LDA method theoretically ought to give similar results and the discrepancies may be attributed to technical difficulties in performing calculations based on currently implemented constrained LDA schemes.

Detection of nasopharyngeal cancer using confocal Raman spectroscopy and genetic algorithm technique

NASA Astrophysics Data System (ADS)

Li, Shao-Xin; Chen, Qiu-Yan; Zhang, Yan-Jiao; Liu, Zhi-Ming; Xiong, Hong-Lian; Guo, Zhou-Yi; Mai, Hai-Qiang; Liu, Song-Hao

2012-12-01

Raman spectroscopy (RS) and a genetic algorithm (GA) were applied to distinguish nasopharyngeal cancer (NPC) from normal nasopharyngeal tissue. A total of 225 Raman spectra are acquired from 120 tissue sites of 63 nasopharyngeal patients, 56 Raman spectra from normal tissue and 169 Raman spectra from NPC tissue. The GA integrated with linear discriminant analysis (LDA) is developed to differentiate NPC and normal tissue according to spectral variables in the selected regions of 792-805, 867-880, 996-1009, 1086-1099, 1288-1304, 1663-1670, and 1742-1752 cm-1 related to proteins, nucleic acids and lipids of tissue. The GA-LDA algorithms with the leave-one-out cross-validation method provide a sensitivity of 69.2% and specificity of 100%. The results are better than that of principal component analysis which is applied to the same Raman dataset of nasopharyngeal tissue with a sensitivity of 63.3% and specificity of 94.6%. This demonstrates that Raman spectroscopy associated with GA-LDA diagnostic algorithm has enormous potential to detect and diagnose nasopharyngeal cancer.

Optical diagnosis of cervical cancer by higher order spectra and boosting

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Implementation of an approximate self-energy correction scheme in the orthogonalized linear combination of atomic orbitals method of band-structure calculations

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

Gu, Z.; Ching, W.Y.

Based on the Sterne-Inkson model for the self-energy correction to the single-particle energy in the local-density approximation (LDA), we have implemented an approximate energy-dependent and [bold k]-dependent [ital GW] correction scheme to the orthogonalized linear combination of atomic orbital-based local-density calculation for insulators. In contrast to the approach of Jenkins, Srivastava, and Inkson, we evaluate the on-site exchange integrals using the LDA Bloch functions throughout the Brillouin zone. By using a [bold k]-weighted band gap [ital E][sub [ital g

Vision-based method for detecting driver drowsiness and distraction in driver monitoring system

NASA Astrophysics Data System (ADS)

Jo, Jaeik; Lee, Sung Joo; Jung, Ho Gi; Park, Kang Ryoung; Kim, Jaihie

2011-12-01

Most driver-monitoring systems have attempted to detect either driver drowsiness or distraction, although both factors should be considered for accident prevention. Therefore, we propose a new driver-monitoring method considering both factors. We make the following contributions. First, if the driver is looking ahead, drowsiness detection is performed; otherwise, distraction detection is performed. Thus, the computational cost and eye-detection error can be reduced. Second, we propose a new eye-detection algorithm that combines adaptive boosting, adaptive template matching, and blob detection with eye validation, thereby reducing the eye-detection error and processing time significantly, which is hardly achievable using a single method. Third, to enhance eye-detection accuracy, eye validation is applied after initial eye detection, using a support vector machine based on appearance features obtained by principal component analysis (PCA) and linear discriminant analysis (LDA). Fourth, we propose a novel eye state-detection algorithm that combines appearance features obtained using PCA and LDA, with statistical features such as the sparseness and kurtosis of the histogram from the horizontal edge image of the eye. Experimental results showed that the detection accuracies of the eye region and eye states were 99 and 97%, respectively. Both driver drowsiness and distraction were detected with a success rate of 98%.

Spotting the difference in molecular dynamics simulations of biomolecules

NASA Astrophysics Data System (ADS)

Sakuraba, Shun; Kono, Hidetoshi

2016-08-01

Comparing two trajectories from molecular simulations conducted under different conditions is not a trivial task. In this study, we apply a method called Linear Discriminant Analysis with ITERative procedure (LDA-ITER) to compare two molecular simulation results by finding the appropriate projection vectors. Because LDA-ITER attempts to determine a projection such that the projections of the two trajectories do not overlap, the comparison does not suffer from a strong anisotropy, which is an issue in protein dynamics. LDA-ITER is applied to two test cases: the T4 lysozyme protein simulation with or without a point mutation and the allosteric protein PDZ2 domain of hPTP1E with or without a ligand. The projection determined by the method agrees with the experimental data and previous simulations. The proposed procedure, which complements existing methods, is a versatile analytical method that is specialized to find the "difference" between two trajectories.

A chromatochemometric approach for evaluating and selecting the perfume maceration time.

PubMed

López-Nogueroles, Marina; Chisvert, Alberto; Salvador, Amparo

2010-04-30

A chemometric treatment of the data obtained by gas chromatography (GC) with flame ionization detector (FID) has been proposed to study the maceration time involved in perfumes manufacture with the final purpose of reducing this time but preserving the organoleptic characteristics of the perfume that is being elaborated. In this sense, GC-FID chromatograms were used as a fingerprint of perfume samples subjected to different maceration times, and data were treated by linear discriminant analysis (LDA), by comparing to a set of samples known to be macerated or not, which were used as calibration objects. The GC-FID methodology combined with the treatment of data by LDA has been applied successfully to seven different perfumes. The constructed LDA models exhibited excellent Wilks' lambdas (0.013-0.118, depending on the perfume), and up to a reduction of 57% has been achieved with respect to the maceration time initially established. 2010 Elsevier B.V. All rights reserved.

Key determinants of the fungal and bacterial microbiomes in homes.

PubMed

Kettleson, Eric M; Adhikari, Atin; Vesper, Stephen; Coombs, Kanistha; Indugula, Reshmi; Reponen, Tiina

2015-04-01

The microbiome of the home is of great interest because of its possible impact on health. Our goal was to identify some of the factors that determine the richness, evenness and diversity of the home's fungal and bacterial microbiomes. Vacuumed settled dust from homes (n=35) in Cincinnati, OH, were analyzed by pyrosequencing to determine the fungal and bacterial relative sequence occurrence. The correlation coefficients between home environmental characteristics, including age of home, Environmental Relative Moldiness Index (ERMI) values, occupant number, relative humidity and temperature, as well as pets (dog and cat) were evaluated for their influence on fungal and bacterial communities. In addition, linear discriminant analysis (LDA) was used for identifying fungal and bacterial genera and species associated with those housing determinants found to be significant. The fungal richness was found to be positively correlated with age of home (p=0.002), ERMI value (p=0.003), and relative humidity (p=0.015) in the home. However, fungal evenness and diversity were only correlated with the age of home (p=0.001). Diversity and evenness (not richness) of the bacterial microbiome in the homes were associated with dog ownership. Linear discriminant analysis showed total of 39 putative fungal genera/species with significantly higher LDA scores in high ERMI homes and 47 genera/species with significantly higher LDA scores in homes with high relative humidity. When categorized according to the age of the home, a total of 67 fungal genera/species had LDA scores above the significance threshold. Dog ownership appeared to have the most influence on the bacterial microbiome, since a total of 130 bacterial genera/species had significantly higher LDA scores in homes with dogs. Some key determinants of the fungal and bacterial microbiome appear to be excess moisture, age of the home and dog ownership. Copyright © 2015 Elsevier Inc. All rights reserved.

Constraints on the formation and properties of a Martian lobate debris apron: Insights from high-resolution topography, SHARAD radar data, and a numerical ice flow model

NASA Astrophysics Data System (ADS)

Parsons, Reid; Holt, John

2016-03-01

Lobate debris aprons (LDAs) are midlatitude deposits of debris-covered ice formed during one or more periods of glaciation during the Amazonian period. However, little is known about the climate conditions that led to LDA formation. We explore a hypothesis in which a single, extended period of precipitation of ice on the steep slopes of Euripus Mons (45°S, 105°E—east of the Hellas Basin) produced a flowing ice deposit which was protected from subsequent ablation to produce the LDA found at this location. We test this hypothesis with a numerical ice flow model using an ice rheology based on low-temperature ice deformation experiments. The model simulates ice accumulation and flow for the northern and southern lobes of the Euripus Mons LDA using basal topography constrained by data from the Shallow Radar (SHARAD) and a range of ice viscosities (determined by ice temperature and ice grain size). Simulations for the northern lobe of the Euripus LDA produce good fits to the surface topography. Assuming an LDA age of ˜60 Myr and an expected temperature range of 200 to 204 K (for various obliquities) gives an ice grain size of ≈2 mm. Simulations of the southern section produce poor fits to surface topography and result in much faster flow timescales unless multiple ice deposition events or higher ice viscosities are considered.

Redundancy-Aware Topic Modeling for Patient Record Notes

PubMed Central

Cohen, Raphael; Aviram, Iddo; Elhadad, Michael; Elhadad, Noémie

2014-01-01

The clinical notes in a given patient record contain much redundancy, in large part due to clinicians’ documentation habit of copying from previous notes in the record and pasting into a new note. Previous work has shown that this redundancy has a negative impact on the quality of text mining and topic modeling in particular. In this paper we describe a novel variant of Latent Dirichlet Allocation (LDA) topic modeling, Red-LDA, which takes into account the inherent redundancy of patient records when modeling content of clinical notes. To assess the value of Red-LDA, we experiment with three baselines and our novel redundancy-aware topic modeling method: given a large collection of patient records, (i) apply vanilla LDA to all documents in all input records; (ii) identify and remove all redundancy by chosing a single representative document for each record as input to LDA; (iii) identify and remove all redundant paragraphs in each record, leaving partial, non-redundant documents as input to LDA; and (iv) apply Red-LDA to all documents in all input records. Both quantitative evaluation carried out through log-likelihood on held-out data and topic coherence of produced topics and qualitative assessement of topics carried out by physicians show that Red-LDA produces superior models to all three baseline strategies. This research contributes to the emerging field of understanding the characteristics of the electronic health record and how to account for them in the framework of data mining. The code for the two redundancy-elimination baselines and Red-LDA is made publicly available to the community. PMID:24551060

Redundancy-aware topic modeling for patient record notes.

PubMed

Cohen, Raphael; Aviram, Iddo; Elhadad, Michael; Elhadad, Noémie

2014-01-01

The clinical notes in a given patient record contain much redundancy, in large part due to clinicians' documentation habit of copying from previous notes in the record and pasting into a new note. Previous work has shown that this redundancy has a negative impact on the quality of text mining and topic modeling in particular. In this paper we describe a novel variant of Latent Dirichlet Allocation (LDA) topic modeling, Red-LDA, which takes into account the inherent redundancy of patient records when modeling content of clinical notes. To assess the value of Red-LDA, we experiment with three baselines and our novel redundancy-aware topic modeling method: given a large collection of patient records, (i) apply vanilla LDA to all documents in all input records; (ii) identify and remove all redundancy by chosing a single representative document for each record as input to LDA; (iii) identify and remove all redundant paragraphs in each record, leaving partial, non-redundant documents as input to LDA; and (iv) apply Red-LDA to all documents in all input records. Both quantitative evaluation carried out through log-likelihood on held-out data and topic coherence of produced topics and qualitative assessment of topics carried out by physicians show that Red-LDA produces superior models to all three baseline strategies. This research contributes to the emerging field of understanding the characteristics of the electronic health record and how to account for them in the framework of data mining. The code for the two redundancy-elimination baselines and Red-LDA is made publicly available to the community.

Towards a Simple and Efficient Web Search Framework

DTIC Science & Technology

2014-11-01

any useful information about the various aspects of a topic. For example, for the query “ raspberry pi ”, it covers topics such as “what is raspberry pi ...topics generated by the LDA topic model for query ” raspberry pi ”. One simple explanation is that web texts are too noisy and unfocused for the LDA process...making a rasp- berry pi ”. However, the topics generated based on the 10 top ranked documents do not make much sense to us in terms of their keywords

Discrimination of rectal cancer through human serum using surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Zhang, Su; Jin, Lili

2015-05-01

In this paper, surface-enhanced Raman spectroscopy (SERS) was used to detect the changes in blood serum components that accompany rectal cancer. The differences in serum SERS data between rectal cancer patients and healthy controls were examined. Postoperative rectal cancer patients also participated in the comparison to monitor the effects of cancer treatments. The results show that there are significant variations at certain wavenumbers which indicates alteration of corresponding biological substances. Principal component analysis (PCA) and parameters of intensity ratios were used on the original SERS spectra for the extraction of featured variables. These featured variables then underwent linear discriminant analysis (LDA) and classification and regression tree (CART) for the discrimination analysis. Accuracies of 93.5 and 92.4 % were obtained for PCA-LDA and parameter-CART, respectively.

A new concept and a comprehensive evaluation of SYSMEX UF-1000i  flow cytometer to identify culture-negative urine specimens in patients with UTI.

PubMed

Monsen, T; Ryden, P

2017-09-01

Urinary tract infections (UTIs) are among the most common bacterial infections in men and urine culture is gold standard for diagnosis. Considering the high prevalence of culture-negative specimens, any method that identifies such specimens is of interest. The aim was to evaluate a new screening concept for flow cytometry analysis (FCA). The outcomes were evaluated against urine culture, uropathogen species and three conventional screening methods. A prospective, consecutive study examined 1,312 urine specimens, collected during January and February 2012. The specimens were analyzed using the Sysmex UF1000i FCA. Based on the FCA data culture negative specimens were identified in a new model by use of linear discriminant analysis (FCA-LDA). In total 1,312 patients were included. In- and outpatients represented 19.6% and 79.4%, respectively; 68.3% of the specimens originated from women. Of the 610 culture-positive specimens, Escherichia coli represented 64%, enterococci 8% and Klebsiella spp. 7%. Screening with FCA-LDA at 95% sensitivity identified 42% (552/1312) as culture negative specimens when UTI was defined according to European guidelines. The proposed screening method was either superior or similar in comparison to the three conventional screening methods. In conclusion, the proposed/suggested and new FCA-LDA screening method was superior or similar to three conventional screening methods. We recommend the proposed screening method to be used in clinic to exclude culture negative specimens, to reduce workload, costs and the turnaround time. In addition, the FCA data may add information that enhance handling and support diagnosis of patients with suspected UTI pending urine culture [corrected].

Three-dimensional passive sensing photon counting for object classification

NASA Astrophysics Data System (ADS)

Yeom, Seokwon; Javidi, Bahram; Watson, Edward

2007-04-01

In this keynote address, we address three-dimensional (3D) distortion-tolerant object recognition using photon-counting integral imaging (II). A photon-counting linear discriminant analysis (LDA) is discussed for classification of photon-limited images. We develop a compact distortion-tolerant recognition system based on the multiple-perspective imaging of II. Experimental and simulation results have shown that a low level of photons is sufficient to classify out-of-plane rotated objects.

PPIs Prevent Aspirin-Induced Gastrointestinal Bleeding Better than H2RAs. A Systematic Review and Meta-analysis.

PubMed

Szabó, Imre L; Mátics, Robert; Hegyi, Peter; Garami, Andras; Illés, Anita; Sarlós, Patricia; Bajor, Judit; Szűcs, Akos; Mosztbacher, Dora; Márta, Katalin; Szemes, Kata; Csekő, Kata; Kővári, Balint; Rumbus, Zoltan; Vincze, Áron

2017-12-01

Aspirin is one of the most widely used medication for its analgesic and anti-platelet properties and thus a major cause for gastrointestinal (GI) bleeding. This study compared the preventive effect of histamine-2 receptor antagonists (H2RAs) and proton-pump inhibitors (PPIs) against chronic low-dose aspirin (LDA)-related GI bleeding and ulcer formation. Electronic databases of Pubmed, Embase and Cochrane Central Register of Controlled Trials were searched for human observations (randomised controlled trials and observational studies) comparing the long term effects of PPIs and H2RAs treatment in the prevention of GI bleeding or ulcer formation in patients on chronic LDA treatment listed up till September 30, 2016. Two independent authors searched databases using PICO questions (aspirin, H2RA, PPI, GI bleeding or ulcer), and reviewed abstracts and articles for comprehensive studies keeping adequate study quality. Data of weighted odds ratios were statistically evaluated using Comprehensive Metaanalysis (Biostat, Inc., Engelwood, MJ, USA), potential bias was checked. Nine studies for GI bleeding and eight studies for ulcer formation were found meeting inclusion criteria, altogether 1,879 patients were included into review. The H2RAs prevented less effectively LDA-related GI bleeding (OR= 2.102, 95% CI: 1.008-4.385, p<0.048) and ulcer formation (OR= 2.257, 95% CI: 1.277-3.989, p<0.005) than PPIs. The meta-analysis showed that H2RAs were less effective in the prevention of LDA-related GI bleeding and ulcer formation suggesting the preferable usage of PPIs in case of tolerance.

Electronic-structure theory of plutonium chalcogenides

NASA Astrophysics Data System (ADS)

Shick, Alexander; Havela, Ladislav; Gouder, Thomas; Rebizant, Jean

2009-03-01

The correlated band theory methods, the around-mean-field LDA + U and dynamical LDA + HIA (Hubbard-I), are applied to investigate the electronic structure of Pu chalcogenides. The LDA + U calculations for PuX (X = S, Se, Te) provide non-magnetic ground state in agreement with the experimental data. Non-integer filling of 5 f-manifold (from approx. 5.6 in PuS to 5.7 PuTe). indicates a mixed valence ground state which combines f5 and f6 multiplets. Making use of the dynamical LDA+HIA method the photoelectron spectra are calculated in good agreement with experimental data. The three-peak feature near EF attributed to 5 f-manifold is well reproduced by LDA + HIA, and follows from mixed valence character of the ground state.

A Flow Cytometry-Based Assay for Quantifying Non-Plaque Forming Strains of Yellow Fever Virus

PubMed Central

Hammarlund, Erika; Amanna, Ian J.; Dubois, Melissa E.; Barron, Alex; Engelmann, Flora; Messaoudi, Ilhem; Slifka, Mark K.

2012-01-01

Primary clinical isolates of yellow fever virus can be difficult to quantitate by standard in vitro methods because they may not form discernable plaques or induce a measurable cytopathic effect (CPE) on cell monolayers. In our hands, the Dakar strain of yellow fever virus (YFV-Dakar) could not be measured by plaque assay (PA), focus-forming assay (FFA), or by measurement of CPE. For these reasons, we developed a YFV-specific monoclonal antibody (3A8.B6) and used it to optimize a highly sensitive flow cytometry-based tissue culture limiting dilution assay (TC-LDA) to measure levels of infectious virus. The TC-LDA was performed by incubating serial dilutions of virus in replicate wells of C6/36 cells and stained intracellularly for virus with MAb 3A8.B6. Using this approach, we could reproducibly quantitate YFV-Dakar in tissue culture supernatants as well as from the serum of viremic rhesus macaques experimentally infected with YFV-Dakar. Moreover, the TC-LDA approach was >10-fold more sensitive than standard plaque assay for quantitating typical plaque-forming strains of YFV including YFV-17D and YFV-FNV (French neurotropic vaccine). Together, these results indicate that the TC-LDA technique is effective for quantitating both plaque-forming and non-plaque-forming strains of yellow fever virus, and this methodology may be readily adapted for the study and quantitation of other non-plaque-forming viruses. PMID:23028428

A flow cytometry-based assay for quantifying non-plaque forming strains of yellow fever virus.

PubMed

Hammarlund, Erika; Amanna, Ian J; Dubois, Melissa E; Barron, Alex; Engelmann, Flora; Messaoudi, Ilhem; Slifka, Mark K

2012-01-01

Primary clinical isolates of yellow fever virus can be difficult to quantitate by standard in vitro methods because they may not form discernable plaques or induce a measurable cytopathic effect (CPE) on cell monolayers. In our hands, the Dakar strain of yellow fever virus (YFV-Dakar) could not be measured by plaque assay (PA), focus-forming assay (FFA), or by measurement of CPE. For these reasons, we developed a YFV-specific monoclonal antibody (3A8.B6) and used it to optimize a highly sensitive flow cytometry-based tissue culture limiting dilution assay (TC-LDA) to measure levels of infectious virus. The TC-LDA was performed by incubating serial dilutions of virus in replicate wells of C6/36 cells and stained intracellularly for virus with MAb 3A8.B6. Using this approach, we could reproducibly quantitate YFV-Dakar in tissue culture supernatants as well as from the serum of viremic rhesus macaques experimentally infected with YFV-Dakar. Moreover, the TC-LDA approach was >10-fold more sensitive than standard plaque assay for quantitating typical plaque-forming strains of YFV including YFV-17D and YFV-FNV (French neurotropic vaccine). Together, these results indicate that the TC-LDA technique is effective for quantitating both plaque-forming and non-plaque-forming strains of yellow fever virus, and this methodology may be readily adapted for the study and quantitation of other non-plaque-forming viruses.

Mining Adverse Events of Dietary Supplements from Product Labels by Topic Modeling

PubMed Central

Wang, Yefeng; Gunashekar, Divya R.; Adam, Terrence J.; Zhang, Rui

2018-01-01

The adverse events of the dietary supplements should be subject to scrutiny due to their growing clinical application and consumption among U.S. adults. An effective method for mining and grouping the adverse events of the dietary supplements is to evaluate product labeling for the rapidly increasing number of new products available in the market. In this study, the adverse events information was extracted from the product labels stored in the Dietary Supplement Label Database (DSLD) and analyzed by topic modeling techniques, specifically Latent Dirichlet Allocation (LDA). Among the 50 topics generated by LDA, eight topics were manually evaluated, with topic relatedness ranging from 58.8% to 100% on the product level, and 57.1% to 100% on the ingredient level. Five out of these eight topics were coherent groupings of the dietary supplements based on their adverse events. The results demonstrated that LDA is able to group supplements with similar adverse events based on the dietary supplement labels. Such information can be potentially used by consumers to more safely use dietary supplements. PMID:29295169

Novel 3D bio-macromolecular bilinear descriptors for protein science: Predicting protein structural classes.

PubMed

Marrero-Ponce, Yovani; Contreras-Torres, Ernesto; García-Jacas, César R; Barigye, Stephen J; Cubillán, Néstor; Alvarado, Ysaías J

2015-06-07

In the present study, we introduce novel 3D protein descriptors based on the bilinear algebraic form in the ℝ(n) space on the coulombic matrix. For the calculation of these descriptors, macromolecular vectors belonging to ℝ(n) space, whose components represent certain amino acid side-chain properties, were used as weighting schemes. Generalization approaches for the calculation of inter-amino acidic residue spatial distances based on Minkowski metrics are proposed. The simple- and double-stochastic schemes were defined as approaches to normalize the coulombic matrix. The local-fragment indices for both amino acid-types and amino acid-groups are presented in order to permit characterizing fragments of interest in proteins. On the other hand, with the objective of taking into account specific interactions among amino acids in global or local indices, geometric and topological cut-offs are defined. To assess the utility of global and local indices a classification model for the prediction of the major four protein structural classes, was built with the Linear Discriminant Analysis (LDA) technique. The developed LDA-model correctly classifies the 92.6% and 92.7% of the proteins on the training and test sets, respectively. The obtained model showed high values of the generalized square correlation coefficient (GC(2)) on both the training and test series. The statistical parameters derived from the internal and external validation procedures demonstrate the robustness, stability and the high predictive power of the proposed model. The performance of the LDA-model demonstrates the capability of the proposed indices not only to codify relevant biochemical information related to the structural classes of proteins, but also to yield suitable interpretability. It is anticipated that the current method will benefit the prediction of other protein attributes or functions. Copyright © 2015 Elsevier Ltd. All rights reserved.

A pattern recognition approach to transistor array parameter variance

NASA Astrophysics Data System (ADS)

da F. Costa, Luciano; Silva, Filipi N.; Comin, Cesar H.

2018-06-01

The properties of semiconductor devices, including bipolar junction transistors (BJTs), are known to vary substantially in terms of their parameters. In this work, an experimental approach, including pattern recognition concepts and methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), was used to experimentally investigate the variation among BJTs belonging to integrated circuits known as transistor arrays. It was shown that a good deal of the devices variance can be captured using only two PCA axes. It was also verified that, though substantially small variation of parameters is observed for BJT from the same array, larger variation arises between BJTs from distinct arrays, suggesting the consideration of device characteristics in more critical analog designs. As a consequence of its supervised nature, LDA was able to provide a substantial separation of the BJT into clusters, corresponding to each transistor array. In addition, the LDA mapping into two dimensions revealed a clear relationship between the considered measurements. Interestingly, a specific mapping suggested by the PCA, involving the total harmonic distortion variation expressed in terms of the average voltage gain, yielded an even better separation between the transistor array clusters. All in all, this work yielded interesting results from both semiconductor engineering and pattern recognition perspectives.

Classification of plum spirit drinks by synchronous fluorescence spectroscopy.

PubMed

Sádecká, J; Jakubíková, M; Májek, P; Kleinová, A

2016-04-01

Synchronous fluorescence spectroscopy was used in combination with principal component analysis (PCA) and linear discriminant analysis (LDA) for the differentiation of plum spirits according to their geographical origin. A total of 14 Czech, 12 Hungarian and 18 Slovak plum spirit samples were used. The samples were divided in two categories: colorless (22 samples) and colored (22 samples). Synchronous fluorescence spectra (SFS) obtained at a wavelength difference of 60 nm provided the best results. Considering the PCA-LDA applied to the SFS of all samples, Czech, Hungarian and Slovak colorless samples were properly classified in both the calibration and prediction sets. 100% of correct classification was also obtained for Czech and Hungarian colored samples. However, one group of Slovak colored samples was classified as belonging to the Hungarian group in the calibration set. Thus, the total correct classifications obtained were 94% and 100% for the calibration and prediction steps, respectively. The results were compared with those obtained using near-infrared (NIR) spectroscopy. Applying PCA-LDA to NIR spectra (5500-6000 cm(-1)), the total correct classifications were 91% and 92% for the calibration and prediction steps, respectively, which were slightly lower than those obtained using SFS. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cost-effectiveness of prize-based incentives for stimulant abusers in outpatient psychosocial treatment programs.

PubMed

Olmstead, Todd A; Sindelar, Jody L; Petry, Nancy M

2007-03-16

To evaluate the cost-effectiveness of a prize-based intervention as an addition to usual care for stimulant abusers. This cost-effectiveness analysis is based on a randomized clinical trial implemented within the National Drug Abuse Treatment Clinical Trials Network. The trial was conducted at eight community-based outpatient psychosocial drug abuse treatment clinics. Four hundred and fifteen stimulant abusers were assigned to usual care (N=206) or usual care plus abstinence-based incentives (N=209) for 12 weeks. Participants randomized to the incentive condition earned the chance to draw for prizes for submitting substance negative samples; the number of draws earned increased with continuous abstinence time. Incremental cost-effectiveness ratios were estimated to compare prize-based incentives relative to usual care. The primary patient outcome was longest duration of confirmed stimulant abstinence (LDA). Unit costs were obtained via surveys administered at the eight participating clinics. Resource utilizations and patient outcomes were obtained from the clinical trial. Acceptability curves are presented to illustrate the uncertainty due to the sample and to provide policy relevant information. The incremental cost to lengthen the LDA by 1 week was 258 US dollars (95% confidence interval, 191-401 US dollars). Sensitivity analyses on several key parameters show that this value ranges from 163 to 269 US dollars. Compared with the usual care group, the incentive group had significantly longer LDAs and significantly higher costs.

Methodological study of computational approaches to address the problem of strong correlations

NASA Astrophysics Data System (ADS)

Lee, Juho

The main focus of this thesis is the detailed investigation of computational methods to tackle strongly correlated materials in which a rich variety of exotic phenomena are found. A many-body problem with sizable electronic correlations can no longer be explained by independent-particle approximations such as density functional theory (DFT) or tight-binding approaches. The influence of an electron to the others is too strong for each electron to be treated as an independent quasiparticle and consequently those standard band-structure methods fail even at a qualitative level. One of the most powerful approaches for strong correlations is the dynamical mean-field theory (DMFT), which has enlightened the understanding of the Mott transition based on the Hubbard model. For realistic applications, the dynamical mean-field theory is combined with various independent-particles approaches. The most widely used one is the DMFT combined with the DFT in the local density approximation (LDA), so-called LDA+DMFT. In this approach, the electrons in the weakly correlated orbitals are calculated by LDA while others in the strongly correlated orbitals are treated by DMFT. Recently, the method combining DMFT with Hedin's GW approximation was also developed, in which the momentum-dependent self-energy is also added. In this thesis, we discuss the application of those methodologies based on DMFT. First, we apply the dynamical mean-field theory to solve the 3-dimensional Hubbard model in Chap. 3. In this application, we model the interface between the thermodynamically coexisting metal and Mott insulator. We show how to model the required slab geometry and extract the electronic spectra. We construct an effective Landau free energy and compute the variation of its parameters across the phase diagram. Finally, using a linear mixture of the density and double-occupancy, we identify a natural Ising order parameter which unifies the treatment of the bandwidth and filling controlled Mott transitions. Secondly, we study the double-counting problem, a subtle issue that arises in LDA+DMFT. We propose a highly precise double-counting functional, in which the intersection of LDA and DMFT is calculated exactly, and implement a parameter-free version of the LDA+DMFT that is tested on one of the simplest strongly correlated systems, the H2 molecule. We show that the exact double-counting treatment along with a good DMFT projector leads to very accurate and total energy and excitation spectrum of H2 molecule. Finally, we implement various versions of GW+DMFT, in its fully self-consistent way, one shot GW approximation, and quasiparticle self-consistent scheme, and studied how well these combined methods perform on H2 molecule as compared to more established methods such as LDA+DMFT. We found that most flavors of GW+DMFT break down in strongly correlated regime due to causality violation. Among GW+DMFT methods, only the self-consistent quasiparticle GW+DMFT with static double-counting, and a new method with causal double-counting, correctly recover the atomic limit at large H-atom separation. While some flavors of GW+DMFT improve the single-electron spectra of LDA+DMFT, the total energy is best predicted by LDA+DMFT, for which the exact double-counting is known, and is static.

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.

Age and Stratigraphic Relationships in Massif-Debris-Apron Terrain in Western Phlegra Montes, Mars

NASA Astrophysics Data System (ADS)

Kress, A.; Head, J. W.; Safaeinili, A.; Holt, J.; Plaut, J.; Posiolova, L.; Phillips, R.; Seu, R.; Sharad Team

2010-03-01

SHARAD returns from lobate debris aprons (LDA) near Phlegra Montes may show similarly high ice contents to other LDA on Mars; geomorphology and surface ages of the deposits confirm this detection and support a debris-covered-glacier origin for LDA.

Constraints on Lobate Debris Apron Evolution and Rheology from Numerical Modeling of Ice Flow

NASA Astrophysics Data System (ADS)

Parsons, R.; Nimmo, F.

2010-12-01

Recent radar observations of mid-latitude lobate debris aprons (LDAs) have confirmed the presence of ice within these deposits. Radar observations in Deuteronilus Mensae have constrained the concentration of dust found within the ice deposits to <30% by volume based on the strength of the returned signal. In addition to constraining the dust fraction, these radar observations can measure the ice thickness - providing an opportunity to more accurately estimate the flow behavior of ice responsible for the formation of LDAs. In order to further constrain the age and rheology of LDA ice, we developed a numerical model simulating ice flow under Martian conditions using results from ice deformation experiments, theory of ice grain growth based on terrestrial ice cores, and observational constraints from radar profiles and laser altimetry. This finite difference model calculates the LDA profile shape as it flows over time assuming no basal slip. In our model, the ice rheology is determined by the concentration of dust which influences the ice grain size by pinning the ice grain boundaries and halting ice grain growth. By varying the dust fraction (and therefore the ice grain size), the ice temperature, the subsurface slope, and the initial ice volume we are able to determine the combination of parameters that best reproduce the observed LDA lengths and thicknesses over a period of time comparable to crater age dates of LDA surfaces (90 - 300 My, see figure). Based on simulations using different combinations of ice temperature, ice grain size, and basal slope, we find that an ice temperature of 205 K, a dust volume fraction of 0.5% (resulting in an ice grain size of 5 mm), and a flat subsurface slope give reasonable model LDA ages for many LDAs in the northern mid-latitudes of Mars. However, we find that there is no single combination of dust fraction, temperature, and subsurface slope which can give realistic ages for all LDAs suggesting that all or some of these variables are spatially heterogeneous. We conclude that there are important regional differences in either the amount of dust mixed in with the ice, or in the presence of a basal slope below the LDA ice. Alternatively, the ice temperature and/or timing of ice deposition may vary significantly between different mid-latitude regions. a) Topographic profiles plotted every 200 My (thin, solid lines) from a 1 Gy simulation of ice flow for an initial ice deposit (thick, solid line) 5 km long and 1 km thick using an ice temperature of 205 K and a dust fraction, φ, of 0.047%. A MOLA profile of an LDA at 38.6oN, 24.3oE (dashed line) is shown for comparison. b) Final profiles for simulations lasting 100 My using temperatures of 195, 205 and 215 K illustrate the effect of both temperature and increasing the dust volume fraction to 1.2% (resulting in an ice grain size of 1 mm).

Exploring representations of protein structure for automated remote homology detection and mapping of protein structure space

PubMed Central

2014-01-01

Background Due to rapid sequencing of genomes, there are now millions of deposited protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a protein of interest to transfer functional information from the homologs to the given protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of protein structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the protein structure space. Methods Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of proteins, we propose higher-order LDA-obtained topic-based representations of protein structures to provide an alternative route for remote homology detection and organization of the protein structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the protein structure domain. Results We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of protein structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership. Conclusions This work opens exciting venues in designing novel representations to extract information about protein structures, as well as organizing and mining protein structure space with mature text mining tools. PMID:25080993

Fast mental states decoding in mixed reality.

PubMed

De Massari, Daniele; Pacheco, Daniel; Malekshahi, Rahim; Betella, Alberto; Verschure, Paul F M J; Birbaumer, Niels; Caria, Andrea

2014-01-01

The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.

Fast mental states decoding in mixed reality

PubMed Central

De Massari, Daniele; Pacheco, Daniel; Malekshahi, Rahim; Betella, Alberto; Verschure, Paul F. M. J.; Birbaumer, Niels; Caria, Andrea

2014-01-01

The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR. PMID:25505878

Emotion recognition based on physiological changes in music listening.

PubMed

Kim, Jonghwa; André, Elisabeth

2008-12-01

Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95\\% and 70\\% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.

Ultra-slow dynamics in low density amorphous ice revealed by deuteron NMR: indication of a glass transition.

PubMed

Löw, Florian; Amann-Winkel, Katrin; Loerting, Thomas; Fujara, Franz; Geil, Burkhard

2013-06-21

The postulated glass-liquid transition of low density amorphous ice (LDA) is investigated with deuteron NMR stimulated echo experiments. Such experiments give access to ultra-slow reorientations of water molecules on time scales expected for structural relaxation of glass formers close to the glass-liquid transition temperature. An involved data analysis is necessary to account for signal contributions originating from a gradual crystallization to cubic ice. Even if some ambiguities remain, our findings support the view that pressure amorphized LDA ices are of glassy nature and undergo a glass-liquid transition before crystallization.

Predicting binary, discrete and continued lncRNA-disease associations via a unified framework based on graph regression.

PubMed

Shi, Jian-Yu; Huang, Hua; Zhang, Yan-Ning; Long, Yu-Xi; Yiu, Siu-Ming

2017-12-21

In human genomes, long non-coding RNAs (lncRNAs) have attracted more and more attention because their dysfunctions are involved in many diseases. However, the associations between lncRNAs and diseases (LDA) still remain unknown in most cases. While identifying disease-related lncRNAs in vivo is costly, computational approaches are promising to not only accelerate the possible identification of associations but also provide clues on the underlying mechanism of various lncRNA-caused diseases. Former computational approaches usually only focus on predicting new associations between lncRNAs having known associations with diseases and other lncRNA-associated diseases. They also only work on binary lncRNA-disease associations (whether the pair has an association or not), which cannot reflect and reveal other biological facts, such as the number of proteins involved in LDA or how strong the association is (i.e., the intensity of LDA). To address abovementioned issues, we propose a graph regression-based unified framework (GRUF). In particular, our method can work on lncRNAs, which have no previously known disease association and diseases that have no known association with any lncRNAs. Also, instead of only a binary answer for the association, our method tries to uncover more biological relationship between a pair of lncRNA and disease, which may provide better clues for researchers. We compared GRUF with three state-of-the-art approaches and demonstrated the superiority of GRUF, which achieves 5%~16% improvement in terms of the area under the receiver operating characteristic curve (AUC). GRUF also provides a predicted confidence score for the predicted LDA, which reveals the significant correlation between the score and the number of RNA-Binding Proteins involved in LDAs. Lastly, three out of top-5 LDA candidates generated by GRUF in novel prediction are verified indirectly by medical literature and known biological facts. The proposed GRUF has two advantages over existing approaches. Firstly, it can be used to work on lncRNAs that have no known disease association and diseases that have no known association with any lncRNAs. Secondly, instead of providing a binary answer (with or without association), GRUF works for both discrete and continued LDA, which help revealing the pathological implications between lncRNAs and diseases.

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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

Content Coding of Psychotherapy Transcripts Using Labeled Topic Models.

PubMed

Gaut, Garren; Steyvers, Mark; Imel, Zac E; Atkins, David C; Smyth, Padhraic

2017-03-01

Psychotherapy represents a broad class of medical interventions received by millions of patients each year. Unlike most medical treatments, its primary mechanisms are linguistic; i.e., the treatment relies directly on a conversation between a patient and provider. However, the evaluation of patient-provider conversation suffers from critical shortcomings, including intensive labor requirements, coder error, nonstandardized coding systems, and inability to scale up to larger data sets. To overcome these shortcomings, psychotherapy analysis needs a reliable and scalable method for summarizing the content of treatment encounters. We used a publicly available psychotherapy corpus from Alexander Street press comprising a large collection of transcripts of patient-provider conversations to compare coding performance for two machine learning methods. We used the labeled latent Dirichlet allocation (L-LDA) model to learn associations between text and codes, to predict codes in psychotherapy sessions, and to localize specific passages of within-session text representative of a session code. We compared the L-LDA model to a baseline lasso regression model using predictive accuracy and model generalizability (measured by calculating the area under the curve (AUC) from the receiver operating characteristic curve). The L-LDA model outperforms the lasso logistic regression model at predicting session-level codes with average AUC scores of 0.79, and 0.70, respectively. For fine-grained level coding, L-LDA and logistic regression are able to identify specific talk-turns representative of symptom codes. However, model performance for talk-turn identification is not yet as reliable as human coders. We conclude that the L-LDA model has the potential to be an objective, scalable method for accurate automated coding of psychotherapy sessions that perform better than comparable discriminative methods at session-level coding and can also predict fine-grained codes.

Content Coding of Psychotherapy Transcripts Using Labeled Topic Models

PubMed Central

Gaut, Garren; Steyvers, Mark; Imel, Zac E; Atkins, David C; Smyth, Padhraic

2016-01-01

Psychotherapy represents a broad class of medical interventions received by millions of patients each year. Unlike most medical treatments, its primary mechanisms are linguistic; i.e., the treatment relies directly on a conversation between a patient and provider. However, the evaluation of patient-provider conversation suffers from critical shortcomings, including intensive labor requirements, coder error, non-standardized coding systems, and inability to scale up to larger data sets. To overcome these shortcomings, psychotherapy analysis needs a reliable and scalable method for summarizing the content of treatment encounters. We used a publicly-available psychotherapy corpus from Alexander Street press comprising a large collection of transcripts of patient-provider conversations to compare coding performance for two machine learning methods. We used the Labeled Latent Dirichlet Allocation (L-LDA) model to learn associations between text and codes, to predict codes in psychotherapy sessions, and to localize specific passages of within-session text representative of a session code. We compared the L-LDA model to a baseline lasso regression model using predictive accuracy and model generalizability (measured by calculating the area under the curve (AUC) from the receiver operating characteristic (ROC) curve). The L-LDA model outperforms the lasso logistic regression model at predicting session-level codes with average AUC scores of .79, and .70, respectively. For fine-grained level coding, L-LDA and logistic regression are able to identify specific talk-turns representative of symptom codes. However, model performance for talk-turn identification is not yet as reliable as human coders. We conclude that the L-LDA model has the potential to be an objective, scaleable method for accurate automated coding of psychotherapy sessions that performs better than comparable discriminative methods at session-level coding and can also predict fine-grained codes. PMID:26625437

Smart smoke alarm

DOEpatents

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

2015-04-28

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

Systematic identification of latent disease-gene associations from PubMed articles.

PubMed

Zhang, Yuji; Shen, Feichen; Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.

Systematic identification of latent disease-gene associations from PubMed articles

PubMed Central

Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research. PMID:29373609

Neural network classification of sweet potato embryos

NASA Astrophysics Data System (ADS)

Molto, Enrique; Harrell, Roy C.

1993-05-01

Somatic embryogenesis is a process that allows for the in vitro propagation of thousands of plants in sub-liter size vessels and has been successfully applied to many significant species. The heterogeneity of maturity and quality of embryos produced with this technique requires sorting to obtain a uniform product. An automated harvester is being developed at the University of Florida to sort embryos in vitro at different stages of maturation in a suspension culture. The system utilizes machine vision to characterize embryo morphology and a fluidic based separation device to isolate embryos associated with a pre-defined, targeted morphology. Two different backpropagation neural networks (BNN) were used to classify embryos based on information extracted from the vision system. One network utilized geometric features such as embryo area, length, and symmetry as inputs. The alternative network utilized polar coordinates of an embryo's perimeter with respect to its centroid as inputs. The performances of both techniques were compared with each other and with an embryo classification method based on linear discriminant analysis (LDA). Similar results were obtained with all three techniques. Classification efficiency was improved by reducing the dimension of the feature vector trough a forward stepwise analysis by LDA. In order to enhance the purity of the sample selected as harvestable, a reject to classify option was introduced in the model and analyzed. The best classifier performances (76% overall correct classifications, 75% harvestable objects properly classified, homogeneity improvement ratio 1.5) were obtained using 8 features in a BNN.

Using Fourier transform infrared spectroscopy to evaluate biological effects induced by photodynamic therapy.

PubMed

Lima, Cassio A; Goulart, Viviane P; Correa, Luciana; Zezell, Denise M

2016-07-01

Vibrational spectroscopic methods associated with multivariate statistical techniques have been succeeded in discriminating skin lesions from normal tissues. However, there is no study exploring the potential of these techniques to assess the alterations promoted by photodynamic effect in tissue. The present study aims to demonstrate the ability of Fourier Transform Infrared (FTIR) spectroscopy on Attenuated total reflection (ATR) sampling mode associated with principal component-linear discriminant analysis (PC-LDA) to evaluate the biochemical changes caused by photodynamic therapy (PDT) in skin neoplastic tissue. Cutaneous neoplastic lesions, precursors of squamous cell carcinoma (SCC), were chemically induced in Swiss mice and submitted to a single session of 5-aminolevulinic acid (ALA)-mediated PDT. Tissue sections with 5 μm thickness were obtained from formalin-fixed paraffin-embedded (FFPE) and processed prior to the histopathological analysis and spectroscopic measurements. Spectra were collected in mid-infrared region using a FTIR spectrometer on ATR sampling mode. Principal Component-Linear Discriminant Analysis (PC-LDA) was applied on preprocessed second derivatives spectra. Biochemical changes were assessed using PCA-loadings and accuracy of classification was obtained from PC-LDA . Sub-bands of Amide I (1,624 and 1,650 cm(-1) ) and Amide II (1,517 cm(-1) ) indicated a protein overexpression in non-treated and post-PDT neoplastic tissue compared with healthy skin, as well as a decrease in collagen fibers (1,204, 1,236, 1,282, and 1,338 cm(-1) ) and glycogen (1,028, 1,082, and 1,151 cm(-1) ) content. Photosensitized neoplastic tissue revealed shifted peak position and decreased β-sheet secondary structure of proteins (1,624 cm(-1) ) amount in comparison to non-treated neoplastic lesions. PC-LDA score plots discriminated non-treated neoplastic skin spectra from post-PDT cutaneous lesions with accuracy of 92.8%, whereas non-treated neoplastic skin was discriminated from healthy tissue with 93.5% accuracy and post-PDT cutaneous lesions was discriminated from healthy tissue with 89.7% accuracy. PC-LDA was able to discriminate ATR-FTIR spectra of non-treated and post-PDT neoplastic lesions, as well as from healthy skin. Thus, the method can be used for early diagnosis of premalignant skin lesions, as well as to evaluate the response to photodynamic treatment. Lasers Surg. Med. 48:538-545, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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.

Glass and liquid phase diagram of a polyamorphic monatomic system.

PubMed

Reisman, Shaina; Giovambattista, Nicolas

2013-02-14

We perform out-of-equilibrium molecular dynamics (MD) simulations of a monatomic system with Fermi-Jagla (FJ) pair potential interactions. This model system exhibits polyamorphism both in the liquid and glass state. The two liquids, low-density (LDL) and high-density liquid (HDL), are accessible in equilibrium MD simulations and can form two glasses, low-density (LDA) and high-density amorphous (HDA) solid, upon isobaric cooling. The FJ model exhibits many of the anomalous properties observed in water and other polyamorphic liquids and thus, it is an excellent model system to explore qualitatively the thermodynamic properties of such substances. The liquid phase behavior of the FJ model system has been previously characterized. In this work, we focus on the glass behavior of the FJ system. Specifically, we perform systematic isothermal compression and decompression simulations of LDA and HDA at different temperatures and determine "phase diagrams" for the glass state; these phase diagrams varying with the compression/decompression rate used. We obtain the LDA-to-HDA and HDA-to-LDA transition pressure loci, P(LDA-HDA)(T) and P(HDA-LDA)(T), respectively. In addition, the compression-induced amorphization line, at which the low-pressure crystal (LPC) transforms to HDA, P(LPC-HDA)(T), is determined. As originally proposed by Poole et al. [Phys. Rev. E 48, 4605 (1993)] simulations suggest that the P(LDA-HDA)(T) and P(HDA-LDA)(T) loci are extensions of the LDL-to-HDL and HDL-to-LDL spinodal lines into the glass domain. Interestingly, our simulations indicate that the P(LPC-HDA)(T) locus is an extension, into the glass domain, of the LPC metastability limit relative to the liquid. We discuss the effects of compression/decompression rates on the behavior of the P(LDA-HDA)(T), P(HDA-LDA)(T), P(LPC-HDA)(T) loci. The competition between glass polyamorphism and crystallization is also addressed. At our "fast rate," crystallization can be partially suppressed and the glass phase diagram can be related directly with the liquid phase diagram. However, at our "slow rate," crystallization cannot be prevented at intermediate temperatures, within the glass region. In these cases, multiple crystal-crystal transformations are found upon compression/decompression (polymorphism).

A paper-based cantilever array sensor: Monitoring volatile organic compounds with naked eye.

PubMed

Fraiwan, Arwa; Lee, Hankeun; Choi, Seokheun

2016-09-01

Volatile organic compound (VOC) detection is critical for controlling industrial and commercial emissions, environmental monitoring, and public health. Simple, portable, rapid and low-cost VOC sensing platforms offer the benefits of on-site and real-time monitoring anytime and anywhere. The best and most practically useful approaches to monitoring would include equipment-free and power-free detection by the naked eye. In this work, we created a novel, paper-based cantilever sensor array that allows simple and rapid naked-eye VOC detection without the need for power, electronics or readout interface/equipment. This simple VOC detection method was achieved using (i) low-cost paper materials as a substrate and (ii) swellable thin polymers adhered to the paper. Upon exposure to VOCs, the polymer swelling adhered to the paper-based cantilever, inducing mechanical deflection that generated a distinctive composite pattern of the deflection angles for a specific VOC. The angle is directly measured by the naked eye on a 3-D protractor printed on a paper facing the cantilevers. The generated angle patterns are subjected to statistical algorithms (linear discriminant analysis (LDA)) to classify each VOC sample and selectively detect a VOC. We classified four VOC samples with 100% accuracy using LDA. Copyright © 2016 Elsevier B.V. All rights reserved.

Ratiometric Decoding of Pheromones for a Biomimetic Infochemical Communication System.

PubMed

Wei, Guangfen; Thomas, Sanju; Cole, Marina; Rácz, Zoltán; Gardner, Julian W

2017-10-30

Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes information by producing volatile pheromone components and a chemoreceiver module that decodes the transmitted ratiometric information via polymer-coated piezoelectric Surface Acoustic Wave Resonator (SAWR) sensors. The inspiration for such a system is based on the pheromone-based communication between insects. Ten features are extracted from the SAWR sensor response and analysed using multi-variate classification techniques, i.e., Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), and Multilayer Perception Neural Network (MLPNN) methods, and an optimal feature subset is identified. A combination of steady state and transient features of the sensor signals showed superior performances with LDA and MLPNN. Although MLPNN gave excellent results reaching 100% recognition rate at 400 s, over all time stations PNN gave the best performance based on an expanded data-set with adjacent neighbours. In this case, 100% of the pheromone mixtures were successfully identified just 200 s after they were first injected into the wind tunnel. We believe that this approach can be used for future chemical communication employing simple mixtures of airborne molecules.

Ratiometric Decoding of Pheromones for a Biomimetic Infochemical Communication System

PubMed Central

Wei, Guangfen; Thomas, Sanju; Cole, Marina; Rácz, Zoltán

2017-01-01

Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes information by producing volatile pheromone components and a chemoreceiver module that decodes the transmitted ratiometric information via polymer-coated piezoelectric Surface Acoustic Wave Resonator (SAWR) sensors. The inspiration for such a system is based on the pheromone-based communication between insects. Ten features are extracted from the SAWR sensor response and analysed using multi-variate classification techniques, i.e., Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), and Multilayer Perception Neural Network (MLPNN) methods, and an optimal feature subset is identified. A combination of steady state and transient features of the sensor signals showed superior performances with LDA and MLPNN. Although MLPNN gave excellent results reaching 100% recognition rate at 400 s, over all time stations PNN gave the best performance based on an expanded data-set with adjacent neighbours. In this case, 100% of the pheromone mixtures were successfully identified just 200 s after they were first injected into the wind tunnel. We believe that this approach can be used for future chemical communication employing simple mixtures of airborne molecules. PMID:29084158

Potential energy landscape of the apparent first-order phase transition between low-density and high-density amorphous ice.

PubMed

Giovambattista, Nicolas; Sciortino, Francesco; Starr, Francis W; Poole, Peter H

2016-12-14

The potential energy landscape (PEL) formalism is a valuable approach within statistical mechanics to describe supercooled liquids and glasses. Here we use the PEL formalism and computer simulations to study the pressure-induced transformations between low-density amorphous ice (LDA) and high-density amorphous ice (HDA) at different temperatures. We employ the ST2 water model for which the LDA-HDA transformations are remarkably sharp, similar to what is observed in experiments, and reminiscent of a first-order phase transition. Our results are consistent with the view that LDA and HDA configurations are associated with two distinct regions (megabasins) of the PEL that are separated by a potential energy barrier. At higher temperature, we find that low-density liquid (LDL) configurations are located in the same megabasin as LDA, and that high-density liquid (HDL) configurations are located in the same megabasin as HDA. We show that the pressure-induced LDL-HDL and LDA-HDA transformations occur along paths that interconnect these two megabasins, but that the path followed by the liquid is different from the path followed by the amorphous solid. At higher pressure, we also study the liquid-to-ice-VII first-order phase transition, and find that the behavior of the PEL properties across this transition is qualitatively similar to the changes found during the LDA-HDA transformation. This similarity supports the interpretation that the LDA-HDA transformation is a first-order phase transition between out-of-equilibrium states. Finally, we compare the PEL properties explored during the LDA-HDA transformations in ST2 water with those reported previously for SPC/E water, for which the LDA-HDA transformations are rather smooth. This comparison illuminates the previous work showing that, at accessible computer times scales, a liquid-liquid phase transition occurs in the case of ST2 water, but not for SPC/E water.

Potential energy landscape of the apparent first-order phase transition between low-density and high-density amorphous ice

NASA Astrophysics Data System (ADS)

Giovambattista, Nicolas; Sciortino, Francesco; Starr, Francis W.; Poole, Peter H.

2016-12-01

The potential energy landscape (PEL) formalism is a valuable approach within statistical mechanics to describe supercooled liquids and glasses. Here we use the PEL formalism and computer simulations to study the pressure-induced transformations between low-density amorphous ice (LDA) and high-density amorphous ice (HDA) at different temperatures. We employ the ST2 water model for which the LDA-HDA transformations are remarkably sharp, similar to what is observed in experiments, and reminiscent of a first-order phase transition. Our results are consistent with the view that LDA and HDA configurations are associated with two distinct regions (megabasins) of the PEL that are separated by a potential energy barrier. At higher temperature, we find that low-density liquid (LDL) configurations are located in the same megabasin as LDA, and that high-density liquid (HDL) configurations are located in the same megabasin as HDA. We show that the pressure-induced LDL-HDL and LDA-HDA transformations occur along paths that interconnect these two megabasins, but that the path followed by the liquid is different from the path followed by the amorphous solid. At higher pressure, we also study the liquid-to-ice-VII first-order phase transition, and find that the behavior of the PEL properties across this transition is qualitatively similar to the changes found during the LDA-HDA transformation. This similarity supports the interpretation that the LDA-HDA transformation is a first-order phase transition between out-of-equilibrium states. Finally, we compare the PEL properties explored during the LDA-HDA transformations in ST2 water with those reported previously for SPC/E water, for which the LDA-HDA transformations are rather smooth. This comparison illuminates the previous work showing that, at accessible computer times scales, a liquid-liquid phase transition occurs in the case of ST2 water, but not for SPC/E water.

[Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

PubMed

Zhou, Jinzhi; Tang, Xiaofang

2015-08-01

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

Validity of the rheumatoid arthritis impact of disease (RAID) score and definition of cut-off points for disease activity states in a population-based European cohort of patients with rheumatoid arthritis.

PubMed

Salaffi, Fausto; Di Carlo, Marco; Vojinovic, Jelena; Tincani, Angela; Sulli, Alberto; Soldano, Stefano; Andreoli, Laura; Dall'Ara, Francesca; Ionescu, Ruxandra; Simić Pašalić, Katarina; Balčune, Ineta; Ferraz-Amaro, Iván; Tlustochowicz, Malgorzata; Butrimienė, Irena; Punceviciene, Egle; Toroptsova, Natalia; Grazio, Simeon; Morović-Vergles, Jadranka; Masaryk, Pavol; Otsa, Kati; Bernardes, Miguel; Boyadzhieva, Vladimira; Cutolo, Maurizio

2018-05-01

To assess the validity of the rheumatoid arthritis impact of disease (RAID) for measuring disease activity of rheumatoid arthritis (RA) and to determine cut-off values for defining the disease activity states. A total of 622 RA patients from an European database have been included. Cross-validation was based on assessment of convergent and discriminant validity. Optimal cut-offs were determined against external criteria by calculating the respective 25th and 75th percentiles mean values of RAID. External criteria included definitions for remission (REM), low disease activity (LDA), moderate disease activity (MDA) and high disease activity (HDA), cut-offs of the 28-joint disease activity score-C-reactive protein (DAS28-CRP) score. The RAID showed a moderate degree of correlation with respect to DAS28-CRP (rho=0.417; P<0.0001). The receiver operating characteristic (ROC) curves to discriminate the ability of RAID to distinguish patients with active and non-active disease was very good with an area under the curve (AUC) of 0.847 (95% confidence interval [CI]: 0.816 to 0.878; P<0.0001). Based on the distributions of RAID in the different disease activity groups, we propose the following cut-off values for REM: RAID ≤3; for LDA: RAID >3 and ≤4; for MDA: RAID >4 and ≤6; for HDA: RAID >6. Mean RAID differed significantly between patients classified as REM, LDA, MDA or HDA (P=0.001). The cut-offs revealed good measurement characteristics in cross-validation analysis, had great discriminatory performance in distinguishing patients with different levels of disease activity and are suited for widespread use in everyday practice application and research. Copyright © 2017 Société française de rhumatologie. Published by Elsevier SAS. All rights reserved.

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

PubMed

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

2017-11-01

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

A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis

PubMed Central

2011-01-01

Background Several computational candidate gene selection and prioritization methods have recently been developed. These in silico selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known. Results The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (>80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (http://main.g2.bx.psu.edu/). Nine genes (APLN, ZC4H2, MAGED4, MAGED4B, RAP2C, FAM156A, FAM156B, TBL1X, and UXT) were highlighted as highly-ranked XLMR methods. Conclusions The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR. Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi). PMID:21668950

Relationships between metabolite and IGF1 concentrations with fertility and production outcomes following left abomasal displacement.

PubMed

Lyons, N A; Cooke, J S; Wilson, S; van Winden, S C; Gordon, P J; Wathes, D C

2014-06-28

Left displacement of the abomasum (LDA) is an important periparturient disorder of dairy cows. This study evaluated differences in metabolic parameters between case-control pairs of cows (n=67) from 24 farms, and related these to outcomes in fertility and production. Cows with an assisted delivery were ×3 more likely to develop LDA, and affected cows tended to have had a longer dry period. At recruitment, cows with LDA tended to be in lower body condition accompanied by significantly higher circulating concentrations of β-hydroxybutyrate (BHB), non-esterified fatty acid (NEFA) and glucose and lower IGF1. Overall culling rate for all cows in the subsequent lactation was 22.5 per cent. Cows with LDA were not at increased odds of being culled but they produced, on average, 2272 l less milk and tended to have longer intervals to conception. Considering all cows irrespective of LDA status, the mean IGF1 level at recruitment was the only measured parameter associated with subsequent risk of culling (culled 11.7 ng/ml, not culled 23.5 ng/ml; P=0.005). Our findings support previous work indicating that poor insulin sensitivity through an uncoupling of the somatotrophic axis may be an important factor associated with LDA. Improved nutritional management of dry cows should reduce the incidence of both LDA and culling. British Veterinary Association.

Structural, electronic, and vibrational properties of high-density amorphous silicon: a first-principles molecular-dynamics study.

PubMed

Morishita, Tetsuya

2009-05-21

We report a first-principles study of the structural, electronic, and dynamical properties of high-density amorphous (HDA) silicon, which was found to be formed by pressurizing low-density amorphous (LDA) silicon (a normal amorphous Si) [T. Morishita, Phys. Rev. Lett. 93, 055503 (2004); P. F. McMillan, M. Wilson, D. Daisenberger, and D. Machon, Nature Mater. 4, 680 (2005)]. Striking structural differences between HDA and LDA are revealed. The LDA structure holds a tetrahedral network, while the HDA structure contains a highly distorted tetrahedral network. The fifth neighboring atom in HDA tends to be located at an interstitial position of a distorted tetrahedron composed of the first four neighboring atoms. Consequently, the coordination number of HDA is calculated to be approximately 5 unlike that of LDA. The electronic density of state (EDOS) shows that HDA is metallic, which is consistent with a recent experimental measurement of the electronic resistance of HDA Si. We find from local EDOS that highly distorted tetrahedral configurations enhance the metallic nature of HDA. The vibrational density of state (VDOS) also reflects the structural differences between HDA and LDA. Some of the characteristic vibrational modes of LDA are dematerialized in HDA, indicating the degradation of covalent bonds. The overall profile of the VDOS for HDA is found to be an intermediate between that for LDA and liquid Si under pressure (high-density liquid Si).

Potential Energy Landscape of the Liquid-Liquid Phase Transition in Water and the transformation between Low-Density and High-Density Amorphous Ice

NASA Astrophysics Data System (ADS)

Giovambattista, N.; Sciortino, F.; Starr, F. W.; Poole, P. H.

The potential energy landscape (PEL) formalism is a valuable approach within statistical mechanics for describing supercooled liquids and glasses. We use the PEL formalism and computer simulations to study the transformation between low-density (LDL) and high-density liquid (HDL) water, and between low-density (LDA) and high-density amorphous ice (HDA). We employ the ST2 water model that exhibits a LDL-HDL first-order phase transition and a sharp LDA-HDA transformation, as observed in experiments. Our results are consistent with the view that LDA and HDA configurations are associated with two distinct regions (megabasins) of the PEL that are separated by a potential energy barrier. At higher temperature, we find that LDL configurations are located in the same megabasin as LDA, and that HDL configurations are located in the same megabasin as HDA. We show that the pressure-induced LDL-HDL and LDA-HDA transformations occur along paths that interconnect these two megabasins, but that the path followed by the liquid and the amorphous ice differ. We also study the liquid-to-ice-VII first-order phase transition. The PEL properties across this transition are qualitatively similar to the changes found during the LDA-HDA transformation, supporting the interpretation that the LDA-HDA transformation is a first-order-like phase transition between out-of-equilibrium states.

Anti-citrullinated peptide antibodies are the strongest predictor of clinically relevant radiographic progression in rheumatoid arthritis patients achieving remission or low disease activity: A post hoc analysis of a nationwide cohort in Japan

PubMed Central

Okada, Akitomo; Fukuda, Takaaki; Hidaka, Toshihiko; Ishii, Tomonori; Ueki, Yukitaka; Kodera, Takao; Nakashima, Munetoshi; Takahashi, Yuichi; Honda, Seiyo; Horai, Yoshiro; Watanabe, Ryu; Okuno, Hiroshi; Aramaki, Toshiyuki; Izumiyama, Tomomasa; Takai, Osamu; Miyashita, Taiichiro; Sato, Shuntaro; Kawashiri, Shin-ya; Iwamoto, Naoki; Ichinose, Kunihiro; Tamai, Mami; Origuchi, Tomoki; Nakamura, Hideki; Aoyagi, Kiyoshi; Eguchi, Katsumi; Kawakami, Atsushi

2017-01-01

Objectives To determine prognostic factors of clinically relevant radiographic progression (CRRP) in patients with rheumatoid arthritis (RA) achieving remission or low disease activity (LDA) in clinical practice. Methods Using data from a nationwide, multicenter, prospective study in Japan, we evaluated 198 biological disease-modifying antirheumatic drug (bDMARD)-naïve RA patients who were in remission or had LDA at study entry after being treated with conventional synthetic DMARDs (csDMARDs). CRRP was defined as the yearly progression of modified total Sharp score (mTSS) >3.0 U. We performed a multiple logistic regression analysis to explore the factors to predict CRRP at 1 year. We used receiver operating characteristic (ROC) curve to estimate the performance of relevant variables for predicting CRRP. Results The mean Disease Activity Score in 28 joints-erythrocyte sedimentation rate (DAS28-ESR) was 2.32 ± 0.58 at study entry. During the 1-year observation, remission or LDA persisted in 72% of the patients. CRRP was observed in 7.6% of the patients. The multiple logistic regression analysis revealed that the independent variables to predict the development of CRRP were: anti-citrullinated peptide antibodies (ACPA) positivity at baseline (OR = 15.2, 95%CI 2.64–299), time-integrated DAS28-ESR during the 1 year post-baseline (7.85-unit increase, OR = 1.83, 95%CI 1.03–3.45), and the mTSS at baseline (13-unit increase, OR = 1.22, 95%CI 1.06–1.42). Conclusions ACPA positivity was the strongest independent predictor of CRRP in patients with RA in remission or LDA. Physicians should recognize ACPA as a poor-prognosis factor regarding the radiographic outcome of RA, even among patients showing a clinically favorable response to DMARDs. PMID:28505163

Density functional theory calculations of III-N based semiconductors with mBJLDA

NASA Astrophysics Data System (ADS)

Gürel, Hikmet Hakan; Akıncı, Özden; Ünlü, Hilmi

2017-02-01

In this work, we present first principles calculations based on a full potential linear augmented plane-wave method (FP-LAPW) to calculate structural and electronic properties of III-V based nitrides such as GaN, AlN, InN in a zinc-blende cubic structure. First principles calculation using the local density approximation (LDA) and generalized gradient approximation (GGA) underestimate the band gap. We proposed a new potential called modified Becke-Johnson local density approximation (MBJLDA) that combines modified Becke-Johnson exchange potential and the LDA correlation potential to get better band gap results compared to experiment. We compared various exchange-correlation potentials (LSDA, GGA, HSE, and MBJLDA) to determine band gaps and structural properties of semiconductors. We show that using MBJLDA density potential gives a better agreement with experimental data for band gaps III-V nitrides based semiconductors.

Maintenance of remission with combination etanercept-DMARD therapy versus DMARDs alone in active rheumatoid arthritis: results of an international treat-to-target study conducted in regions with limited biologic access.

PubMed

Pavelka, Karel; Akkoç, Nurullah; Al-Maini, Mustafa; Zerbini, Cristiano A F; Karateev, Dmitry E; Nasonov, Evgeny L; Rahman, Mahboob U; Pedersen, Ronald; Dinh, Andrew; Shen, Qi; Vasilescu, Radu; Kotak, Sameer; Mahgoub, Ehab; Vlahos, Bonnie

2017-09-01

In this transglobal, randomized, double-blind, placebo-controlled, treat-to-target study, the maintenance of efficacy was compared between biologic-and biologic-free-disease-modifying antirheumatic drug (DMARD) combination regimens after low disease activity (LDA) was achieved with biologic DMARD induction therapy. Patients with moderate-to-severe rheumatoid arthritis despite methotrexate therapy received open-label etanercept 50 mg subcutaneously once weekly plus methotrexate with or without other conventional synthetic (cs) DMARDs for 24 weeks. Patients achieving LDA [disease activity score in 28 joints based on erythrocyte sedimentation rate (DAS28-ESR) <3.2] at week 24 were randomized to receive etanercept-methotrexate combination therapy or placebo-methotrexate combination therapy, with or without other csDMARDs, for 28 weeks. In the open-label period, 72% of patients achieved DAS28-ESR LDA at week 24. Patients enrolled in the double-blind period had long-standing rheumatoid arthritis and high disease activity at baseline (mean duration, 8.1 years; DAS28-ESR, 6.4). In the etanercept and placebo combination groups, 44% versus 17% achieved DAS28-ESR LDA and 34 versus 13% achieved DAS28-ESR remission at week 52 (p < 0.001). Adverse events were reported in 37 and 43%, serious adverse events in 0 and 4%, and serious infections in 0 and 2% in these groups, respectively, in the double-blind period. After induction of response with etanercept combination therapy following a treat-to-target approach in patients with long-standing rheumatoid arthritis and high disease activity at baseline, the etanercept combination regimen was significantly more effective in maintaining LDA and remission than a biologic-free regimen. ClinicalTrials.gov identifier. NCT01578850.

The glass transition in high-density amorphous ice

PubMed Central

Loerting, Thomas; Fuentes-Landete, Violeta; Handle, Philip H.; Seidl, Markus; Amann-Winkel, Katrin; Gainaru, Catalin; Böhmer, Roland

2015-01-01

There has been a long controversy regarding the glass transition in low-density amorphous ice (LDA). The central question is whether or not it transforms to an ultraviscous liquid state above 136 K at ambient pressure prior to crystallization. Currently, the most widespread interpretation of the experimental findings is in terms of a transformation to a superstrong liquid above 136 K. In the last decade some work has also been devoted to the study of the glass transition in high-density amorphous ice (HDA) which is in the focus of the present review. At ambient pressure HDA is metastable against both ice I and LDA, whereas at > 0.2 GPa HDA is no longer metastable against LDA, but merely against high-pressure forms of crystalline ice. The first experimental observation interpreted as the glass transition of HDA was made using in situ methods by Mishima, who reported a glass transition temperature Tg of 160 K at 0.40 GPa. Soon thereafter Andersson and Inaba reported a much lower glass transition temperature of 122 K at 1.0 GPa. Based on the pressure dependence of HDA's Tg measured in Innsbruck, we suggest that they were in fact probing the distinct glass transition of very high-density amorphous ice (VHDA). Very recently the glass transition in HDA was also observed at ambient pressure at 116 K. That is, LDA and HDA show two distinct glass transitions, clearly separated by about 20 K at ambient pressure. In summary, this suggests that three glass transition lines can be defined in the p–T plane for LDA, HDA, and VHDA. PMID:25641986

The glass transition in high-density amorphous ice.

PubMed

Loerting, Thomas; Fuentes-Landete, Violeta; Handle, Philip H; Seidl, Markus; Amann-Winkel, Katrin; Gainaru, Catalin; Böhmer, Roland

2015-01-01

There has been a long controversy regarding the glass transition in low-density amorphous ice (LDA). The central question is whether or not it transforms to an ultraviscous liquid state above 136 K at ambient pressure prior to crystallization. Currently, the most widespread interpretation of the experimental findings is in terms of a transformation to a superstrong liquid above 136 K. In the last decade some work has also been devoted to the study of the glass transition in high-density amorphous ice (HDA) which is in the focus of the present review. At ambient pressure HDA is metastable against both ice I and LDA, whereas at > 0.2 GPa HDA is no longer metastable against LDA, but merely against high-pressure forms of crystalline ice. The first experimental observation interpreted as the glass transition of HDA was made using in situ methods by Mishima, who reported a glass transition temperature T g of 160 K at 0.40 GPa. Soon thereafter Andersson and Inaba reported a much lower glass transition temperature of 122 K at 1.0 GPa. Based on the pressure dependence of HDA's T g measured in Innsbruck, we suggest that they were in fact probing the distinct glass transition of very high-density amorphous ice (VHDA). Very recently the glass transition in HDA was also observed at ambient pressure at 116 K. That is, LDA and HDA show two distinct glass transitions, clearly separated by about 20 K at ambient pressure. In summary, this suggests that three glass transition lines can be defined in the p-T plane for LDA, HDA, and VHDA.

Pharmacokinetic Drug-Drug Interactions Between Vonoprazan and Low-Dose Aspirin or Nonsteroidal Anti-inflammatory Drugs: A Phase 2, Open-Label, Study in Healthy Japanese Men.

PubMed

Sakurai, Yuuichi; Shiino, Madoka; Horii, Sayako; Okamoto, Hiroyuki; Nakamura, Koki; Nishimura, Akira; Sakata, Yukikuni

2017-01-01

Gastroprotective agents are recommended for patients receiving low-dose aspirin (LDA) or nonsteroidal anti-inflammatory drugs (NSAIDs). Vonoprazan is a potassium-competitive acid blocker recently approved for the prevention of peptic ulcer recurrence in patients receiving LDA or NSAIDs. This phase 2, open-label, single-center study in healthy Japanese males evaluated drug-drug interactions between vonoprazan 40 mg and LDA (100 mg) or NSAIDs [loxoprofen sodium (60 mg), diclofenac sodium (25 mg), or meloxicam (10 mg)] and vice versa. Subjects were allocated to one of eight cohorts and received their orally administered treatment regimen (to assess the effect of vonoprazan vs. NSAID or LDA, or vice versa) once daily. Endpoints were the pharmacokinetics of plasma concentrations of the study drugs alone and in combination (primary), safety (secondary), and vonoprazan effects on aspirin-mediated inhibition of platelet-aggregation. Of 109 subjects screened, 64 were assigned to one of eight cohorts (n = 8 per cohort) and received treatment, one subject discontinued due to a treatment-emergent adverse event (TEAE), and 63 completed the study. There were few differences in the pharmacokinetics of vonoprazan when administered with LDA or NSAIDs, and few differences in the pharmacokinetics of LDA or NSAIDs when administered with vonoprazan. The differences were small and not clinically meaningful. Inhibition of arachidonic-induced platelet aggregation by LDA was not influenced by vonoprazan. Six patients experienced a TEAE, all were mild and were deemed unrelated to study drugs. One subject withdrew due to infection (tonsillitis). No clinically meaningful drug-drug interactions were observed and vonoprazan was well tolerated when administered with LDA or NSAIDs. JapicCTI-153100.

High-density amorphous ice: nucleation of nanosized low-density amorphous ice

NASA Astrophysics Data System (ADS)

Tonauer, Christina M.; Seidl-Nigsch, Markus; Loerting, Thomas

2018-01-01

The pressure dependence of the crystallization temperature of different forms of expanded high-density amorphous ice (eHDA) was scrutinized. Crystallization at pressures 0.05-0.30 GPa was followed using volumetry and powder x-ray diffraction. eHDA samples were prepared via isothermal decompression of very high-density amorphous ice at 140 K to different end pressures between 0.07-0.30 GPa (eHDA0.07-0.3). At 0.05-0.17 GPa the crystallization line T x (p) of all eHDA variants is the same. At pressures  >0.17 GPa, all eHDA samples decompressed to pressures  <0.20 GPa exhibit significantly lower T x values than eHDA0.2 and eHDA0.3. We rationalize our findings with the presence of nanoscaled low-density amorphous ice (LDA) seeds that nucleate in eHDA when it is decompressed to pressures  <0.20 GPa at 140 K. Below ~0.17 GPa, these nanosized LDA domains are latent within the HDA matrix, exhibiting no effect on T x of eHDA<0.2. Upon heating at pressures  ⩾0.17 GPa, these nanosized LDA nuclei transform to ice IX nuclei. They are favored sites for crystallization and, hence, lower T x . By comparing crystallization experiments of bulk LDA with the ones involving nanosized LDA we are able to estimate the Laplace pressure and radius of ~0.3-0.8 nm for the nanodomains of LDA. The nucleation of LDA in eHDA revealed here is evidence for the first-order-like nature of the HDA  →  LDA transition, supporting water’s liquid-liquid transition scenarios.

Comparison of diluents for holding cock semen six hours at 41 C.

PubMed

Howarth, B

1983-06-01

Beltsville Poultry Semen Extender (BPSE) and Lake's Diluent A (LDA) were compared with minimum essential medium (MEM) for their ability to maintain the fertilizing capacity of cock semen held 6 hr at 41 C. Motility significantly declined from the beginning to the end of the holding period for semen in BPSE and LDA. Only in LDA, however, were the number of live spermatozoa significantly reduced. Although there were no differences in oxygen (O2) consumption measured at 1 and 6 hr for semen in BPSE and MEM, a significant reduction in O2 consumption was observed between these time periods for semen in LDA. Fertility of semen held in MEM (90.3%) was significantly higher than the unstored control semen (82.9%) and semen held in either BPSE (3.5%) or LDA (1.9%). No differences in hatchability of fertile eggs were observed between the semen groups.

Assessing college-level learning difficulties and "at riskness" for learning disabilities and ADHD: development and validation of the learning difficulties assessment.

PubMed

Kane, Steven T; Walker, John H; Schmidt, George R

2011-01-01

This article describes the development and validation of the Learning Difficulties Assessment (LDA), a normed and web-based survey that assesses perceived difficulties with reading, writing, spelling, mathematics, listening, concentration, memory, organizational skills, sense of control, and anxiety in college students. The LDA is designed to (a) map individual learning strengths and weaknesses, (b) provide users with a comparative sense of their academic skills, (c) integrate research in user-interface design to assist those with reading and learning challenges, and (d) identify individuals who may be at risk for learning disabilities and attention-deficit/hyperactivity disorder (ADHD) and who should thus be further assessed. Data from a large-scale 5-year study describing the instrument's validity as a screening tool for learning disabilities and ADHD are presented. This article also describes unique characteristics of the LDA including its user-interface design, normative characteristics, and use as a no-cost screening tool for identifying college students at risk for learning disorders and ADHD.

Mapping annotations with textual evidence using an scLDA model.

PubMed

Jin, Bo; Chen, Vicky; Chen, Lujia; Lu, Xinghua

2011-01-01

Most of the knowledge regarding genes and proteins is stored in biomedical literature as free text. Extracting information from complex biomedical texts demands techniques capable of inferring biological concepts from local text regions and mapping them to controlled vocabularies. To this end, we present a sentence-based correspondence latent Dirichlet allocation (scLDA) model which, when trained with a corpus of PubMed documents with known GO annotations, performs the following tasks: 1) learning major biological concepts from the corpus, 2) inferring the biological concepts existing within text regions (sentences), and 3) identifying the text regions in a document that provides evidence for the observed annotations. When applied to new gene-related documents, a trained scLDA model is capable of predicting GO annotations and identifying text regions as textual evidence supporting the predicted annotations. This study uses GO annotation data as a testbed; the approach can be generalized to other annotated data, such as MeSH and MEDLINE documents.

Local self-energies for V and Pd emergent from a nonlocal LDA+FLEX implementation

NASA Astrophysics Data System (ADS)

Savrasov, Sergey Y.; Resta, Giacomo; Wan, Xiangang

2018-04-01

In the spirit of recently developed LDA+U and LDA+DMFT methods, we implement a combination of density functional theory in its local density approximation (LDA) with a k - and ω -dependent self-energy found from diagrammatic fluctuational exchange (FLEX) approximation. The active Hilbert space here is described by the correlated subset of electrons which allows one to tremendously reduce the sizes of the matrices needed to represent charge and spin susceptibilities. The method is perturbative in nature but accounts for both bubble and ladder diagrams and accumulates the physics of momentum-resolved spin fluctuations missing in such popular approach as GW. As an application, we study correlation effects on band structures in V and Pd. The d -electron self-energies emergent from this calculation are found to be remarkably k independent. However, when we compare our calculated electronic mass enhancements against LDA+DMFT, we find that for the longstanding problem of spin fluctuations in Pd, LDA+FLEX delivers a better agreement with experiment, although this conclusion depends on a particular value of the Hubbard U used in the simulation. We also discuss outcomes of a recently proposed combination of k -dependent FLEX with dynamical mean-field theory (DMFT).

Flow patterns of lobate debris aprons and lineated valley fill north of Ismeniae Fossae, Mars: Evidence for extensive mid-latitude glaciation in the Late Amazonian

NASA Astrophysics Data System (ADS)

Baker, David M. H.; Head, James W.; Marchant, David R.

2010-05-01

A variety of Late Amazonian landforms on Mars have been attributed to the dynamics of ice-related processes. Evidence for large-scale, mid-latitude glacial episodes existing within the last 100 million to 1 billion years on Mars has been presented from analyses of lobate debris aprons (LDA) and lineated valley fill (LVF) in the northern and southern mid-latitudes. We test the glacial hypothesis for LDA and LVF along the dichotomy boundary in the northern mid-latitudes by examining the morphological characteristics of LDA and LVF surrounding two large plateaus, proximal massifs, and the dichotomy boundary escarpment north of Ismeniae Fossae (centered at 45.3°N and 39.2°E). Lineations and flow directions within LDA and LVF were mapped using images from the Context (CTX) camera, the Thermal Emission Imaging Spectrometer (THEMIS), and the High Resolution Stereo Camera (HRSC). Flow directions were then compared to topographic contours derived from the Mars Orbiter Laser Altimeter (MOLA) to determine the down-gradient components of LDA and LVF flow. Observations indicate that flow patterns emerge from numerous alcoves within the plateau walls, are integrated over distances of up to tens of kilometers, and have down-gradient flow directions. Smaller lobes confined within alcoves and superposed on the main LDA and LVF represent a later, less extensive glacial phase. Crater size-frequency distributions of LDA and LVF suggest a minimum (youngest) age of 100 Ma. The presence of ring-mold crater morphologies is suggestive that LDA and LVF are formed of near-surface ice-rich bodies. From these observations, we interpret LDA and LVF within our study region to result from formerly active debris-covered glacial flow, consistent with similar observations in the northern mid-latitudes of Mars. Glacial flow was likely initiated from the accumulation and compaction of snow and ice on plateaus and in alcoves within the plateau walls as volatiles were mobilized to the mid-latitudes during higher obliquity excursions. Together with similar analyses elsewhere along the dichotomy boundary, these observations suggest that multiple glacial episodes occurred in the Late Amazonian and that LDA and LVF represent significant reservoirs of non-polar ice sequestered below a surface lag for hundreds of millions of years.

Using near infrared spectroscopy to classify soybean oil according to expiration date.

PubMed

da Costa, Gean Bezerra; Fernandes, David Douglas Sousa; Gomes, Adriano A; de Almeida, Valber Elias; Veras, Germano

2016-04-01

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard-Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA-LDA and PLS-DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Initiative Toward Reliable Long-Duration Operation of Diode Lasers in Space

NASA Technical Reports Server (NTRS)

Tratt, David M.; Amzajerdian, Farzin; Stephen, Mark A.; Shapiro, Andrew A.

2006-01-01

This viewgraph presentation reviews the workings of the Laser Diode Arrays (LDA) working group. The group facilitates focused interaction between the LDA user and provider communities and it will author standards document for the specification and qualification of LDA's for operation in the space environment. It also reviews the NASA test and evaluation facilities that are available to the community.

Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform

PubMed Central

Wei, Zhebo; Xiao, Xize

2017-01-01

In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R2 = 0.9942) worked much better than PLSR. PMID:29088076

Partial Least Squares for Discrimination in fMRI Data

PubMed Central

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

2011-01-01

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

Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform.

PubMed

Wei, Zhebo; Xiao, Xize; Wang, Jun; Wang, Hui

2017-10-31

In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages-all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R² = 0.9942) worked much better than PLSR.

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

PubMed

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

2018-01-01

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

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.

Launch Deployment Assembly Extravehicular Activity Neutral Buoyancy Development Test Report

NASA Technical Reports Server (NTRS)

Loughead, T.

1996-01-01

This test evaluated the Launch Deployment Assembly (LDA) design for Extravehicular Activity (EVA) work sites (setup, igress, egress), reach and visual access, and translation required for cargo item removal. As part of the LDA design, this document describes the method and results of the LDA EVA Neutral Buoyancy Development Test to ensure that the LDA hardware support the deployment of the cargo items from the pallet. This document includes the test objectives, flight and mockup hardware description, descriptions of procedures and data collection used in the testing, and the results of the development test at the National Aeronautics and Space Administrations (NASA) Marshall Space Flight Center (MSFC) Neutral Buoyancy Simulator (NBS).

Quantification of Coffea arabica and Coffea canephora var. robusta concentration in blends by means of synchronous fluorescence and UV-Vis spectroscopies.

PubMed

Dankowska, A; Domagała, A; Kowalewski, W

2017-09-01

The potential of fluorescence, UV-Vis spectroscopies as well as the low- and mid-level data fusion of both spectroscopies for the quantification of concentrations of roasted Coffea arabica and Coffea canephora var. robusta in coffee blends was investigated. Principal component analysis was used to reduce data multidimensionality. To calculate the level of undeclared addition, multiple linear regression (PCA-MLR) models were used with lowest root mean square error of calibration (RMSEC) of 3.6% and root mean square error of cross-validation (RMSECV) of 7.9%. LDA analysis was applied to fluorescence intensities and UV spectra of Coffea arabica, canephora samples, and their mixtures in order to examine classification ability. The best performance of PCA-LDA analysis was observed for data fusion of UV and fluorescence intensity measurements at wavelength interval of 60nm. LDA showed that data fusion can achieve over 96% of correct classifications (sensitivity) in the test set and 100% of correct classifications in the training set, with low-level data fusion. The corresponding results for individual spectroscopies ranged from 90% (UV-Vis spectroscopy) to 77% (synchronous fluorescence) in the test set, and from 93% to 97% in the training set. The results demonstrate that fluorescence, UV, and visible spectroscopies complement each other, giving a complementary effect for the quantification of roasted Coffea arabica and Coffea canephora var. robusta concentration in blends. Copyright © 2017 Elsevier B.V. All rights reserved.

Lineated valley fill (LVF) and lobate debris aprons (LDA) in the Deuteronilus Mensae northern dichotomy boundary region, Mars: Constraints on the extent, age and episodicity of Amazonian glacial events

NASA Astrophysics Data System (ADS)

Morgan, Gareth A.; Head, James W.; Marchant, David R.

2009-07-01

In order to assess the nature, degradational processes and history of the dichotomy boundary on Mars, we conducted a detailed morphological analysis of a 70,000 km2 region of its northern portion (north-central Deuteronilus Mensae, south of Lyot, in the vicinity of Sinton Crater). This region is characterized by the distinctive sinuous ∼2 km-high plateau scarp boundary, outlying massifs to the north, and extensive fretted valleys dissecting the plateau to the south. These features represent the first-order modification and retreat of the dichotomy boundary, and are further modified by processes that form lineated valley fill (LVF) in the fretted valleys, and lobate debris aprons (LDA) along the dichotomy scarp and surrounding the outlying massifs. We use new high-resolution image and topography data to examine the nature and origin of LVF and LDA and to investigate the climatic and accompanying degradational history of the escarpment. On the basis of our analysis, we conclude that: (1) LVF and LDA deposits within the study region are comprised of the same material, show integrated flow patterns, and originate as debris-covered valley glaciers; a significant amount of ice (hundreds of meters) is likely to remain today beneath a thin cover of sublimation till. (2) There is depositional evidence to suggest glacial highstands at least 800 m above the present level, implying previous conditions in which the distribution of ice was much more widespread; this is supported by similar deposits within many other areas across the dichotomy boundary. (3) The timing of the most recent large-scale activity of the LDA/LVF in this area is about 100-500 million years ago, similar to ages reported elsewhere along the dichotomy boundary. (4) There is evidence for a secondary, but significantly limited phase of glaciation; the deposits of which are limited to the vicinity of the alcoves; similar later phases have also been reported elsewhere along the dichotomy boundary. (5) Modification of the fretted valleys of the dichotomy boundary has been substantial locally, but we find no evidence that the Amazonian glacial epochs caused retreat of the dichotomy boundary of the scale of tens to hundreds of kilometers. Our findings support the results of an analysis just to the east of the study region and of studies carried out elsewhere along the dichotomy boundary that find further evidence for the remnants of debris-covered glaciers and extensive valley glacial land systems.

Computer-aided detection of bladder wall thickening in CT urography (CTU)

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Weizer, Alon Z.; Gordon, Marshall N.; Samala, Ravi K.

2018-02-01

We are developing a computer-aided detection system for bladder cancer in CT urography (CTU). Bladder wall thickening is a manifestation of bladder cancer and its detection is more challenging than the detection of bladder masses. We first segmented the inner and outer bladder walls using our method that combined deep-learning convolutional neural network with level sets. The non-contrast-enhanced region was separated from the contrast-enhanced region with a maximum-intensity-projection-based method. The non-contrast region was smoothed and gray level threshold was applied to the contrast and non-contrast regions separately to extract the bladder wall and potential lesions. The bladder wall was transformed into a straightened thickness profile, which was analyzed to identify regions of wall thickening candidates. Volume-based features of the wall thickening candidates were analyzed with linear discriminant analysis (LDA) to differentiate bladder wall thickenings from false positives. A data set of 112 patients, 87 with wall thickening and 25 with normal bladders, was collected retrospectively with IRB approval, and split into independent training and test sets. Of the 57 training cases, 44 had bladder wall thickening and 13 were normal. Of the 55 test cases, 43 had wall thickening and 12 were normal. The LDA classifier was trained with the training set and evaluated with the test set. FROC analysis showed that the system achieved sensitivities of 93.2% and 88.4% for the training and test sets, respectively, at 0.5 FPs/case.

Glass-to-cryogenic-liquid transitions in aqueous solutions suggested by crack healing

PubMed Central

Kim, Chae Un; Tate, Mark W.; Gruner, Sol M.

2015-01-01

Observation of theorized glass-to-liquid transitions between low-density amorphous (LDA) and high-density amorphous (HDA) water states had been stymied by rapid crystallization below the homogeneous water nucleation temperature (∼235 K at 0.1 MPa). We report optical and X-ray observations suggestive of glass-to-liquid transitions in these states. Crack healing, indicative of liquid, occurs when LDA ice transforms to cubic ice at 160 K, and when HDA ice transforms to the LDA state at temperatures as low as 120 K. X-ray diffraction study of the HDA to LDA transition clearly shows the characteristics of a first-order transition. Study of the glass-to-liquid transitions in nanoconfined aqueous solutions shows them to be independent of the solute concentrations, suggesting that they represent an intrinsic property of water. These findings support theories that LDA and HDA ice are thermodynamically distinct and that they are continuously connected to two different liquid states of water. PMID:26351671

Tofacitinib in Rheumatoid Arthritis: Lack of Early Change in Disease Activity Predicts a Low Probability of Achieving Low Disease Activity at Month 6.

PubMed

Van Vollenhoven, Ronald F; Lee, Eun Bong; Fallon, Lara; Zwillich, Samuel H; Wilkinson, Bethanie; Chapman, Douglass; Demasi, Ryan; Keystone, Edward

2018-04-26

Optimal targeted treatment in rheumatoid arthritis requires early identification of failure to respond. This post-hoc analysis explored the relationship between early disease activity changes and achievement of low disease activity (LDA) and remission targets with tofacitinib. Data were from two randomized, double-blind, Phase 3 studies. In ORAL Start (NCT01039688), methotrexate (MTX)-naïve patients received tofacitinib 5 or 10 mg BID, or MTX, for 24 months. In placebo-controlled ORAL Standard (NCT00853385), MTX-inadequate responder (MTX-IR) patients received tofacitinib 5 or 10 mg BID or adalimumab 40 mg Q2W, with MTX, for 12 months. Probabilities of achieving LDA (CDAI ≤10; DAS28-4[ESR] ≤3.2) at months 6 and 12 were calculated, given failure to achieve threshold improvement from baseline (change in CDAI ≥6; DAS28-4[ESR] ≥1.2) at month 1 or 3. In ORAL Start, 7.2% and 5.4% of patients receiving tofacitinib 5 and 10 mg BID, respectively, failed to improve CDAI ≥6 at month 3; of those who failed, 3.8% and 28.6%, respectively, achieved month 6 CDAI-defined LDA. In ORAL Standard, 18.8% and 17.5% of patients receiving tofacitinib 5 and 10 mg BID, respectively, failed to improve CDAI ≥6 at month 3; of those who failed, 0% and 2.9%, respectively, achieved month 6 CDAI-defined LDA. Findings were similar when considering month 1 improvements or DAS28-4(ESR) thresholds. In MTX-IR patients, lack of response to tofacitinib after 1 or 3 months predicted low probability of achieving LDA at month 6. Lack of early response may be considered when deciding whether to continue treatment with tofacitinib. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

New antitrichomonal drug-like chemicals selected by bond (edge)-based TOMOCOMD-CARDD descriptors.

PubMed

Meneses-Marcel, Alfredo; Rivera-Borroto, Oscar M; Marrero-Ponce, Yovani; Montero, Alina; Machado Tugores, Yanetsy; Escario, José Antonio; Gómez Barrio, Alicia; Montero Pereira, David; Nogal, Juan José; Kouznetsov, Vladimir V; Ochoa Puentes, Cristian; Bohórquez, Arnold R; Grau, Ricardo; Torrens, Francisco; Ibarra-Velarde, Froylán; Arán, Vicente J

2008-09-01

Bond-based quadratic indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel lead trichomonacidals. The obtained LDA-based quantitative structure-activity relationships (QSAR) models, using nonstochastic and stochastic indices, were able to classify correctly 87.91% (87.50%) and 89.01% (84.38%) of the chemicals in training (test) sets, respectively. They showed large Matthews correlation coefficients of 0.75 (0.71) and 0.78 (0.65) for the training (test) sets, correspondingly. Later, both models were applied to the virtual screening of 21 chemicals to find new lead antitrichomonal agents. Predictions agreed with experimental results to a great extent because a correct classification for both models of 95.24% (20 of 21) of the chemicals was obtained. Of the 21 compounds that were screened and synthesized, 2 molecules (chemicals G-1, UC-245) showed high to moderate cytocidal activity at the concentration of 10 microg/ml, another 2 compounds (G-0 and CRIS-148) showed high cytocidal activity only at the concentration of 100 microg/ml, and the remaining chemicals (from CRIS-105 to CRIS-153, except CRIS-148) were inactive at these assayed concentrations. Finally, the best candidate, G-1 (cytocidal activity of 100% at 10 microg/ml) was in vivo assayed in ovariectomized Wistar rats achieving promising results as a trichomonacidal drug-like compound.

A standardization model based on image recognition for performance evaluation of an oral scanner.

PubMed

Seo, Sang-Wan; Lee, Wan-Sun; Byun, Jae-Young; Lee, Kyu-Bok

2017-12-01

Accurate information is essential in dentistry. The image information of missing teeth is used in optically based medical equipment in prosthodontic treatment. To evaluate oral scanners, the standardized model was examined from cases of image recognition errors of linear discriminant analysis (LDA), and a model that combines the variables with reference to ISO 12836:2015 was designed. The basic model was fabricated by applying 4 factors to the tooth profile (chamfer, groove, curve, and square) and the bottom surface. Photo-type and video-type scanners were used to analyze 3D images after image capture. The scans were performed several times according to the prescribed sequence to distinguish the model from the one that did not form, and the results confirmed it to be the best. In the case of the initial basic model, a 3D shape could not be obtained by scanning even if several shots were taken. Subsequently, the recognition rate of the image was improved with every variable factor, and the difference depends on the tooth profile and the pattern of the floor surface. Based on the recognition error of the LDA, the recognition rate decreases when the model has a similar pattern. Therefore, to obtain the accurate 3D data, the difference of each class needs to be provided when developing a standardized model.

Heating- and pressure-induced transformations in amorphous and hexagonal ice: A computer simulation study using the TIP4P/2005 model

NASA Astrophysics Data System (ADS)

Engstler, Justin; Giovambattista, Nicolas

2017-08-01

We characterize the phase behavior of glassy water by performing extensive out-of-equilibrium molecular dynamics simulations using the TIP4P/2005 water model. Specifically, we study (i) the pressure-induced transformations between low-density (LDA) and high-density amorphous ice (HDA), (ii) the pressure-induced amorphization (PIA) of hexagonal ice (Ih), (iii) the heating-induced LDA-to-HDA transformation at high pressures, (iv) the heating-induced HDA-to-LDA transformation at low and negative pressures, (v) the glass transition temperatures of LDA and HDA as a function of pressure, and (vi) the limit of stability of LDA upon isobaric heating and isothermal decompression (at negative pressures). These transformations are studied systematically, over a wide range of temperatures and pressures, allowing us to construct a P-T phase diagram for glassy TIP4P/2005 water. Our results are in qualitative agreement with experimental observations and with the P-T phase diagram obtained for glassy ST2 water that exhibits a liquid-liquid phase transition and critical point. We also discuss the mechanism for PIA of ice Ih and show that this is a two-step process where first, the hydrogen-bond network (HBN) is distorted and then the HBN abruptly collapses. Remarkably, the collapse of the HB in ice Ih occurs when the average molecular orientations order, a measure of the tetrahedrality of the HBN, is of the same order as in LDA, suggesting a common mechanism for the LDA-to-HDA and Ih-to-HDA transformations.

Heating- and pressure-induced transformations in amorphous and hexagonal ice: A computer simulation study using the TIP4P/2005 model.

PubMed

Engstler, Justin; Giovambattista, Nicolas

2017-08-21

We characterize the phase behavior of glassy water by performing extensive out-of-equilibrium molecular dynamics simulations using the TIP4P/2005 water model. Specifically, we study (i) the pressure-induced transformations between low-density (LDA) and high-density amorphous ice (HDA), (ii) the pressure-induced amorphization (PIA) of hexagonal ice (I h ), (iii) the heating-induced LDA-to-HDA transformation at high pressures, (iv) the heating-induced HDA-to-LDA transformation at low and negative pressures, (v) the glass transition temperatures of LDA and HDA as a function of pressure, and (vi) the limit of stability of LDA upon isobaric heating and isothermal decompression (at negative pressures). These transformations are studied systematically, over a wide range of temperatures and pressures, allowing us to construct a P-T phase diagram for glassy TIP4P/2005 water. Our results are in qualitative agreement with experimental observations and with the P-T phase diagram obtained for glassy ST2 water that exhibits a liquid-liquid phase transition and critical point. We also discuss the mechanism for PIA of ice I h and show that this is a two-step process where first, the hydrogen-bond network (HBN) is distorted and then the HBN abruptly collapses. Remarkably, the collapse of the HB in ice I h occurs when the average molecular orientations order, a measure of the tetrahedrality of the HBN, is of the same order as in LDA, suggesting a common mechanism for the LDA-to-HDA and I h -to-HDA transformations.

Effect of finite sample size on feature selection and classification: a simulation study.

PubMed

Way, Ted W; Sahiner, Berkman; Hadjiiski, Lubomir M; Chan, Heang-Ping

2010-02-01

The small number of samples available for training and testing is often the limiting factor in finding the most effective features and designing an optimal computer-aided diagnosis (CAD) system. Training on a limited set of samples introduces bias and variance in the performance of a CAD system relative to that trained with an infinite sample size. In this work, the authors conducted a simulation study to evaluate the performances of various combinations of classifiers and feature selection techniques and their dependence on the class distribution, dimensionality, and the training sample size. The understanding of these relationships will facilitate development of effective CAD systems under the constraint of limited available samples. Three feature selection techniques, the stepwise feature selection (SFS), sequential floating forward search (SFFS), and principal component analysis (PCA), and two commonly used classifiers, Fisher's linear discriminant analysis (LDA) and support vector machine (SVM), were investigated. Samples were drawn from multidimensional feature spaces of multivariate Gaussian distributions with equal or unequal covariance matrices and unequal means, and with equal covariance matrices and unequal means estimated from a clinical data set. Classifier performance was quantified by the area under the receiver operating characteristic curve Az. The mean Az values obtained by resubstitution and hold-out methods were evaluated for training sample sizes ranging from 15 to 100 per class. The number of simulated features available for selection was chosen to be 50, 100, and 200. It was found that the relative performance of the different combinations of classifier and feature selection method depends on the feature space distributions, the dimensionality, and the available training sample sizes. The LDA and SVM with radial kernel performed similarly for most of the conditions evaluated in this study, although the SVM classifier showed a slightly higher hold-out performance than LDA for some conditions and vice versa for other conditions. PCA was comparable to or better than SFS and SFFS for LDA at small samples sizes, but inferior for SVM with polynomial kernel. For the class distributions simulated from clinical data, PCA did not show advantages over the other two feature selection methods. Under this condition, the SVM with radial kernel performed better than the LDA when few training samples were available, while LDA performed better when a large number of training samples were available. None of the investigated feature selection-classifier combinations provided consistently superior performance under the studied conditions for different sample sizes and feature space distributions. In general, the SFFS method was comparable to the SFS method while PCA may have an advantage for Gaussian feature spaces with unequal covariance matrices. The performance of the SVM with radial kernel was better than, or comparable to, that of the SVM with polynomial kernel under most conditions studied.

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

PubMed

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

2016-06-01

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

Multivariate pattern analysis for MEG: A comparison of dissimilarity measures.

PubMed

Guggenmos, Matthias; Sterzer, Philipp; Cichy, Radoslaw Martin

2018-06-01

Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis - LDA, Support Vector Machine - SVM, Weighted Robust Distance - WeiRD, Gaussian Naïve Bayes - GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA. Copyright © 2018 Elsevier Inc. All rights reserved.

A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning.

PubMed

Arguello Casteleiro, Mercedes; Maseda Fernandez, Diego; Demetriou, George; Read, Warren; Fernandez Prieto, Maria Jesus; Des Diz, Julio; Nenadic, Goran; Keane, John; Stevens, Robert

2017-01-01

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning. The evaluation conducted concentrates on sepsis, a major life-threatening condition, and shows that Deep Learning models outperform LSA and LDA with much higher precision.

Experimental validation of alternate integral-formulation method for predicting acoustic radiation based on particle velocity measurements.

PubMed

Ni, Zhi; Wu, Sean F

2010-09-01

This paper presents experimental validation of an alternate integral-formulation method (AIM) for predicting acoustic radiation from an arbitrary structure based on the particle velocities specified on a hypothetical surface enclosing the target source. Both the normal and tangential components of the particle velocity on this hypothetical surface are measured and taken as the input to AIM codes to predict the acoustic pressures in both exterior and interior regions. The results obtained are compared with the benchmark values measured by microphones at the same locations. To gain some insight into practical applications of AIM, laser Doppler anemometer (LDA) and double hotwire sensor (DHS) are used as measurement devices to collect the particle velocities in the air. Measurement limitations of using LDA and DHS are discussed.

A Method of Data Aggregation for Wearable Sensor Systems

PubMed Central

Shen, Bo; Fu, Jun-Song

2016-01-01

Data aggregation has been considered as an effective way to decrease the data to be transferred in sensor networks. Particularly for wearable sensor systems, smaller battery has less energy, which makes energy conservation in data transmission more important. Nevertheless, wearable sensor systems usually have features like frequently dynamic changes of topologies and data over a large range, of which current aggregating methods can’t adapt to the demand. In this paper, we study the system composed of many wearable devices with sensors, such as the network of a tactical unit, and introduce an energy consumption-balanced method of data aggregation, named LDA-RT. In the proposed method, we develop a query algorithm based on the idea of ‘happened-before’ to construct a dynamic and energy-balancing routing tree. We also present a distributed data aggregating and sorting algorithm to execute top-k query and decrease the data that must be transferred among wearable devices. Combining these algorithms, LDA-RT tries to balance the energy consumptions for prolonging the lifetime of wearable sensor systems. Results of evaluation indicate that LDA-RT performs well in constructing routing trees and energy balances. It also outperforms the filter-based top-k monitoring approach in energy consumption, load balance, and the network’s lifetime, especially for highly dynamic data sources. PMID:27347953

Powerful conveyer belt real-time online detection system based on x-ray

NASA Astrophysics Data System (ADS)

Rong, Feng; Miao, Chang-yun; Meng, Wei

2009-07-01

The powerful conveyer belt is widely used in the mine, dock, and so on. After used for a long time, internal steel rope of the conveyor belt may fracture, rust, joints moving, and so on .This would bring potential safety problems. A kind of detection system based on x-ray is designed in this paper. Linear array detector (LDA) is used. LDA cost is low, response fast; technology mature .Output charge of LDA is transformed into differential voltage signal by amplifier. This kind of signal have great ability of anti-noise, is suitable for long-distance transmission. The processor is FPGA. A IP core control 4-channel A/D convertor, achieve parallel output data collection. Soft-core processor MicroBlaze which process tcp/ip protocol is embedded in FPGA. Sampling data are transferred to a computer via Ethernet. In order to improve the image quality, algorithm of getting rid of noise from the measurement result and taking gain normalization for pixel value is studied and designed. Experiments show that this system work well, can real-time online detect conveyor belt of width of 2.0m and speed of 5 m/s, does not affect the production. Image is clear, visual and can easily judge the situation of conveyor belt.

Discriminant forest classification method and system

DOEpatents

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

2012-11-06

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

Formation of lobate debris aprons on Mars: Assessment of regional ice sheet collapse and debris-cover armoring

NASA Astrophysics Data System (ADS)

Fastook, James L.; Head, James W.; Marchant, David R.

2014-01-01

Lobate debris aprons (LDA) are lobate-shaped aprons surrounding scarps and isolated massifs that are concentrated in the vicinity of the northern Dichotomy Boundary on Mars. LDAs have been interpreted as (1) ice-cemented talus aprons undergoing viscous flow, (2) local debris-covered alpine-like glaciers, or (3) remnants of the collapse of a regional retreating ice sheet. We investigate the plausibility that LDAs are remnants of a more extensive regional ice sheet by modeling this process. We find that as a regional ice sheet collapses, the surface drops below cliff and massif bedrock margins, exposing bedrock and regolith, and initiating debris deposition on the surface of a cold-based glacier. Reduced sublimation due to debris-cover armoring of the proto-LDA surface produces a surface slope and consequent ice flow that carries the armoring debris away from the rock outcrops. As collapse and ice retreat continue the debris train eventually reaches the substrate surface at the front of the glacier, leaving the entire LDA armored by debris cover. Using a simplified ice flow model we are able to characterize the temperature and sublimation rate that would be necessary to produce LDAs with a wide range of specified lateral extents and thicknesses. We then apply this method to a database of documented LDA parameters (height, lateral extent) from the Dichotomy Boundary region, and assess the implications for predicted climate conditions during their formation and the range of formation times implied by the model. We find that for the population examined here, typical temperatures are in the range of -85 to -40 °C and typical sublimation rates lie in the range of 6-14 mm/a. Lobate debris apron formation times (from the point of bedrock exposure to complete debris cover) cluster near 400-500 ka. These results show that LDA length and thickness characteristics are consistent with climate conditions and a formation scenario typical of the collapse of a regional retreating ice sheet and exposure of bedrock cliffs. This scenario helps resolve many of the unusual characteristics of lobate debris aprons (LDA) and lineated valley fill (LVF). For example, the distribution of LVF is very consistent with extensive flow of glacial ice from plateau icefields, and the acquisition of a debris cover in the waning stages of retreat of the regional cover as the bedrock scarps are exposed. The typical concentric development of LDA around massifs is much more consistent with ice sheet retreat than insolation-related local accumulation and flow. We thus conclude that the retreating ice-sheet model is robust and should be investigated and tested in more detail. In addition, these results clearly show that the lobate debris aprons in the vicinity of the Dichotomy Boundary could not have attained temperatures near or above the ice melting point and retained their current shape, a finding that supports subzero temperatures for the last several hundred million years, the age of the LDA surfaces. A further implication is that the LDA ice has been preserved for at least several hundred million years, and could potentially contain the record of the climate of Mars, preserved since that time below a sublimation lag deposit.

Cheap streak camera based on the LD-S-10 intensifier tube

NASA Astrophysics Data System (ADS)

Dashevsky, Boris E.; Krutik, Mikhail I.; Surovegin, Alexander L.

1992-01-01

Basic properties of a new streak camera and its test results are reported. To intensify images on its screen, we employed modular G1 tubes, the LD-A-1.0 and LD-A-0.33, enabling magnification of 1.0 and 0.33, respectively. If necessary, the LD-A-0.33 tube may be substituted by any other image intensifier of the LDA series, the choice to be determined by the size of the CCD matrix with fiber-optical windows. The reported camera employs a 12.5- mm-long CCD strip consisting of 1024 pixels, each 12 X 500 micrometers in size. Registered radiation was imaged on a 5 X 0.04 mm slit diaphragm tightly connected with the LD-S- 10 fiber-optical input window. Electrons escaping the cathode are accelerated in a 5 kV electric field and focused onto a phosphor screen covering a fiber-optical plate as they travel between deflection plates. Sensitivity of the latter was 18 V/mm, which implies that the total deflecting voltage was 720 V per 40 mm of the screen surface, since reversed-polarity scan pulses +360 V and -360 V were applied across the deflection plate. The streak camera provides full scan times over the screen of 15, 30, 50, 100, 250, and 500 ns. Timing of the electrically or optically driven camera was done using a 10 ns step-controlled-delay (0 - 500 ns) circuit.

Better band gaps with asymptotically corrected local exchange potentials

NASA Astrophysics Data System (ADS)

Singh, Prashant; Harbola, Manoj K.; Hemanadhan, M.; Mookerjee, Abhijit; Johnson, D. D.

2016-02-01

We formulate a spin-polarized van Leeuwen and Baerends (vLB) correction to the local density approximation (LDA) exchange potential [R. van Leeuwen and E. J. Baerends, Phys. Rev. A 49, 2421 (1994), 10.1103/PhysRevA.49.2421] that enforces the ionization potential (IP) theorem following T. Stein et al. [Phys. Rev. Lett. 105, 266802 (2010), 10.1103/PhysRevLett.105.266802]. For electronic-structure problems, the vLB correction replicates the behavior of exact-exchange potentials, with improved scaling and well-behaved asymptotics, but with the computational cost of semilocal functionals. The vLB + IP correction produces a large improvement in the eigenvalues over those from the LDA due to correct asymptotic behavior and atomic shell structures, as shown in rare-gas, alkaline-earth, zinc-based oxides, alkali halides, sulfides, and nitrides. In half-Heusler alloys, this asymptotically corrected LDA reproduces the spin-polarized properties correctly, including magnetism and half-metallicity. We also consider finite-sized systems [e.g., ringed boron nitride (B12N12 ) and graphene (C24)] to emphasize the wide applicability of the method.

Better band gaps with asymptotically corrected local exchange potentials

DOE PAGES

Singh, Prashant; Harbola, Manoj K.; Hemanadhan, M.; ...

2016-02-22

In this study, we formulate a spin-polarized van Leeuwen and Baerends (vLB) correction to the local density approximation (LDA) exchange potential [R. van Leeuwen and E. J. Baerends, Phys. Rev. A 49, 2421 (1994)] that enforces the ionization potential (IP) theorem following T. Stein et al. [Phys. Rev. Lett. 105, 266802 (2010)]. For electronic-structure problems, the vLB correction replicates the behavior of exact-exchange potentials, with improved scaling and well-behaved asymptotics, but with the computational cost of semilocal functionals. The vLB + IP correction produces a large improvement in the eigenvalues over those from the LDA due to correct asymptotic behaviormore » and atomic shell structures, as shown in rare-gas, alkaline-earth, zinc-based oxides, alkali halides, sulfides, and nitrides. In half-Heusler alloys, this asymptotically corrected LDA reproduces the spin-polarized properties correctly, including magnetism and half-metallicity. We also consider finite-sized systems [e.g., ringed boron nitride (B 12N 12) and graphene (C 24)] to emphasize the wide applicability of the method.« less

Investigation of the spectral properties and magnetism of BiFeO3 by dynamical mean-field theory

NASA Astrophysics Data System (ADS)

Paul, Souvik; Iuşan, Diana; Thunström, Patrik; Kvashnin, Yaroslav O.; Hellsvik, Johan; Pereiro, Manuel; Delin, Anna; Knut, Ronny; Phuyal, Dibya; Lindblad, Andreas; Karis, Olof; Sanyal, Biplab; Eriksson, Olle

2018-03-01

Using the local density approximation plus dynamical mean-field theory (LDA+DMFT), we have computed the valence-band photoelectron spectra and magnetic excitation spectra of BiFeO3, one of the most studied multiferroics. Within the DMFT approach, the local impurity problem is tackled by the exact diagonalization solver. The solution of the impurity problem within the LDA+DMFT method for the paramagnetic and magnetically ordered phases produces result in agreement with the experimental data on electronic and magnetic structures. For comparison, we also present results obtained by the LDA +U approach which is commonly used to compute the physical properties of this compound. Our LDA+DMFT derived electronic spectra match adequately with the experimental hard x-ray photoelectron spectroscopy and resonant photoelectron spectroscopy for Fe 3 d states, whereas the LDA +U method fails to capture the general features of the measured spectra. This indicates the importance of accurately incorporating the dynamical aspect of electronic correlation among Fe 3 d orbitals to reproduce the experimental excitation spectra. Specifically, the LDA+DMFT derived density of states exhibits a significant amount of Fe 3 d states at the position of Bi lone pairs, implying that the latter are not alone in the spectral scenario. This fact might modify our interpretation about the origin of ferroelectric polarization in this material. Our study demonstrates that the combination of orbital cross sections for the constituent elements and broadening schemes for the spectral functions are crucial to explain the detailed structures of the experimental electronic spectra. Our magnetic excitation spectra computed from the LDA+DMFT result conform well with the inelastic neutron scattering data.

High-dose aspirin for Kawasaki disease: outdated myth or effective aid?

PubMed

Amarilyo, Gil; Koren, Yael; Brik Simon, Dafna; Bar-Meir, Maskit; Bahat, Hilla; Helou, Mona Hanna; Mendelson, Amir; Hezkelo, Nofar; Chodick, Gabriel; Berkun, Yackov; Eisenstein, Eli; Butbul Aviel, Yonatan; Barkai, Galia; Bolkier, Yoav; Padeh, Shai; Brik, Riva; Hashkes, Phillip J; Harel, Liora; Uziel, Yosef

2017-01-01

To compare the efficacy and safety of intravenous immunoglobulin (IVIG) plus high-dose aspirin (HDA) vs. IVIG plus low-dose aspirin (LDA) for the treatment of Kawasaki disease, with an emphasis on coronary artery outcomes. This study was a retrospective, medical record review of paediatric patients with Kawasaki disease comparing 6 centres that routinely used HAD for initial treatment and 2 that used LDA in 2004-2013. Treatment response and adverse events were compared. The primary outcome measure was the occurrence of coronary aneurysm at the subacute or convalescent stage. The cohort included 358 patients, of whom 315 were initially treated with adjunctive HDA and 43 with LDA. There were no demographic differences between the groups. Coronary aneurysms occurred in 10% (20/196) of the HDA group and 4% (1/24) of the LDA group (p=0.34). Equivalence tests indicate it is unlikely that the risk of coronary aneurysm in LDA exceeds HDA by more than 3.5%. There were no significant between-group differences in the need for glucocorticoid pulse therapy or disease recurrence. Coronary ectasia rate and hospitalisation time were significantly greater in the HDA group. Adverse events were similar in the two groups. We found no significant clinical benefit in using IVIG+HDA in Kawasaki disease compared to IVIG+LDA. The use of adjunctive HDA in this setting should be reconsidered.

Improved Classification of Orthosiphon stamineus by Data Fusion of Electronic Nose and Tongue Sensors

PubMed Central

Zakaria, Ammar; Shakaff, Ali Yeon Md.; Adom, Abdul Hamid; Ahmad, Mohd Noor; Masnan, Maz Jamilah; Aziz, Abdul Hallis Abdul; Fikri, Nazifah Ahmad; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

2010-01-01

An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together. PMID:22163381

Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors.

PubMed

Zakaria, Ammar; Shakaff, Ali Yeon Md; Adom, Abdul Hamid; Ahmad, Mohd Noor; Masnan, Maz Jamilah; Aziz, Abdul Hallis Abdul; Fikri, Nazifah Ahmad; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

2010-01-01

An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.

Urinary Volatile Organic Compounds for the Detection of Prostate Cancer

PubMed Central

Khalid, Tanzeela; Aggio, Raphael; White, Paul; De Lacy Costello, Ben; Persad, Raj; Al-Kateb, Huda; Jones, Peter; Probert, Chris S.; Ratcliffe, Norman

2015-01-01

The aim of this work was to investigate volatile organic compounds (VOCs) emanating from urine samples to determine whether they can be used to classify samples into those from prostate cancer and non-cancer groups. Participants were men referred for a trans-rectal ultrasound-guided prostate biopsy because of an elevated prostate specific antigen (PSA) level or abnormal findings on digital rectal examination. Urine samples were collected from patients with prostate cancer (n = 59) and cancer-free controls (n = 43), on the day of their biopsy, prior to their procedure. VOCs from the headspace of basified urine samples were extracted using solid-phase micro-extraction and analysed by gas chromatography/mass spectrometry. Classifiers were developed using Random Forest (RF) and Linear Discriminant Analysis (LDA) classification techniques. PSA alone had an accuracy of 62–64% in these samples. A model based on 4 VOCs, 2,6-dimethyl-7-octen-2-ol, pentanal, 3-octanone, and 2-octanone, was marginally more accurate 63–65%. When combined, PSA level and these four VOCs had mean accuracies of 74% and 65%, using RF and LDA, respectively. With repeated double cross-validation, the mean accuracies fell to 71% and 65%, using RF and LDA, respectively. Results from VOC profiling of urine headspace are encouraging and suggest that there are other metabolomic avenues worth exploring which could help improve the stratification of men at risk of prostate cancer. This study also adds to our knowledge on the profile of compounds found in basified urine, from controls and cancer patients, which is useful information for future studies comparing the urine from patients with other disease states. PMID:26599280

Using spectrotemporal indices to improve the fruit-tree crop classification accuracy

NASA Astrophysics Data System (ADS)

Peña, M. A.; Liao, R.; Brenning, A.

2017-06-01

This study assesses the potential of spectrotemporal indices derived from satellite image time series (SITS) to improve the classification accuracy of fruit-tree crops. Six major fruit-tree crop types in the Aconcagua Valley, Chile, were classified by applying various linear discriminant analysis (LDA) techniques on a Landsat-8 time series of nine images corresponding to the 2014-15 growing season. As features we not only used the complete spectral resolution of the SITS, but also all possible normalized difference indices (NDIs) that can be constructed from any two bands of the time series, a novel approach to derive features from SITS. Due to the high dimensionality of this "enhanced" feature set we used the lasso and ridge penalized variants of LDA (PLDA). Although classification accuracies yielded by the standard LDA applied on the full-band SITS were good (misclassification error rate, MER = 0.13), they were further improved by 23% (MER = 0.10) with ridge PLDA using the enhanced feature set. The most important bands to discriminate the crops of interest were mainly concentrated on the first two image dates of the time series, corresponding to the crops' greenup stage. Despite the high predictor weights provided by the red and near infrared bands, typically used to construct greenness spectral indices, other spectral regions were also found important for the discrimination, such as the shortwave infrared band at 2.11-2.19 μm, sensitive to foliar water changes. These findings support the usefulness of spectrotemporal indices in the context of SITS-based crop type classifications, which until now have been mainly constructed by the arithmetic combination of two bands of the same image date in order to derive greenness temporal profiles like those from the normalized difference vegetation index.

Role of non-local exchange in the electronic structure of correlated oxides

NASA Astrophysics Data System (ADS)

Iori, Federico; Gatti, Matteo; Rubio Secades, Angel

Transition-metal oxides (TMO) with partially filled d or f shells are a prototype of correlated materials. They exhibit very interesting properties, like metal-insulator phase transitions (MIT). In this work we consider several TMO insulators in which Kohn-Sham LDA band structures are metallic: VO2, V2O3, Ti2O3, LaTiO3 and YTiO3. In the past, this failure of LDA has been explained in terms of its inadequacy to capture the strong interactions taking place between correlated electrons. In the spirit of the Hubbard model, possible corrections to improve onsite correlation are the LDA +U and LDA +DMFT approaches. Here we make use of the HSE06 hybrid functional. We show that, without invoking strong-correlation effects, the contribution of the non-local Fock exchange is essential to correct the LDA results, by curing its delocalization error. In fact, HSE06 provides insulating band structures and correctly describes the MIT in all the considered compounds. We further discuss the advantages and the limitations of the HSE06 hybrid functional in correlated TMO

Launch Deployment Assembly Human Engineering Analysis

NASA Technical Reports Server (NTRS)

Loughead, T.

1996-01-01

This report documents the human engineering analysis performed by the Systems Branch in support of the 6A cargo element design. The human engineering analysis is limited to the extra vehicular activities (EVA) which are involved in removal of various cargo items from the LDA and specific activities concerning deployment of the Space Station Remote Manipulator System (SSRMS).

Design a New Strategy Based on Nanoparticle-Enhanced Chemiluminescence Sensor Array for Biothiols Discrimination

NASA Astrophysics Data System (ADS)

Shahrajabian, Maryam; Hormozi-Nezhad, M. Reza

2016-08-01

Array-based sensor is an interesting approach that suggests an alternative to expensive analytical methods. In this work, we introduce a novel, simple, and sensitive nanoparticle-based chemiluminescence (CL) sensor array for discrimination of biothiols (e.g., cysteine, glutathione and glutathione disulfide). The proposed CL sensor array is based on the CL efficiencies of four types of enhanced nanoparticle-based CL systems. The intensity of CL was altered to varying degrees upon interaction with biothiols, producing unique CL response patterns. These distinct CL response patterns were collected as “fingerprints” and were then identified through chemometric methods, including linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA). The developed array was able to successfully differentiate between cysteine, glutathione and glutathione disulfide in a wide concentration range. Moreover, it was applied to distinguish among the above analytes in human plasma.

Quartz tuning fork based sensor for detection of volatile organic compounds: towards breath analysis

NASA Astrophysics Data System (ADS)

Sampson, Abraham; Panchal, Suresh; Phadke, Apoorva; Kashyap, A.; Suman, Jilma; Unnikrishnan, G.; Datar, Suwarna

2018-04-01

Several volatile organic compounds (VOCs) are present in the exhaled human breath whose concentration can vary depending on the physiological changes occurring within a human being. These changes in the concentration or the occurrence of a particular VOC can be used as signature of a particular disease in a person. In the present work, a sensor has been developed to detect VOCs such as 1,4-dimethoxy-2,3-butanediol (BD), and cyclohexanone (CH), acetone, methanol and ethanol. Except for BD and CH, the rest of the VOCs are present in a healthy person in ppm levels. CH and BD have been reported to be present in the exhaled human breath of breast cancer patients in ppm levels and can be used to distinguish between a healthy person and a person with breast cancer. The selectivity of the sensor towards these two compounds in the presence of other VOCs commonly present in human breath like acetone, ethanol and methanol has been studied. The sensor has been developed using modified Quartz Tuning Forks (QTFs) with the intent of developing an array of such sensors identifying different VOCs present in a healthy human’s breath. Two differently modified QTFs have been used to detect 1 ppm of 1,4-dimethoxy-2,3-butanediol and 20 ppm of cyclohexanone. Linear Discriminants Analysis (LDA) has been used to classify seven different VOCs. For this purpose, features extracted from sensor responses -shift in resonant frequency, response time and recovery time of the sensors- have been used as features in the model. Differently modified array of QTFs along with the use of LDA can be a useful pathway towards development of a QTF based sensor array for human breath analysis.

Optical redox ratio differentiates early tissue transformations in DMBA-induced hamster oral carcinogenesis based on autofluorescence spectroscopy coupled with multivariate analysis

NASA Astrophysics Data System (ADS)

Sethupathi, R.; Gurushankar, K.; Krishnakumar, N.

2016-11-01

Fluorescence intensity measurements have the potential to facilitate the diagnoses of many pathological conditions. The changes in fluorescence intensity may be influenced by factors such as tissue architectures, endogenous fluorophores, cellular metabolism and light penetration depth in tissue. Two of the most diagnostically important endogenous fluorophores are reduced nicotinamide dinucleotide (NADH) and flavin adenine dinucleotide (FAD), which can be used to monitor dramatic metabolic changes in cells and tissues. The goal of this study is to investigate changes in the endogenous fluorophore emission and to quantify metabolic changes in the redox state of various tissue transformation conditions with respect to control tissues in dimethyl benz[a] anthracene (DMBA)-induced hamster oral carcinogenesis for measuring emission spectrum at 320 nm excitation. In the present study, collagen, NADH and FAD emission of well-differentiated squamous cell carcinoma (WDSCC) showed decreased intensity at ~385 nm, ~450 nm and ~520 nm compared to hyperplasia, dysplasia and control tissues. Furthermore, a significant decrease in the optical redox ratio is observed in WDSCC tissues, which indicates an increased metabolic activity compared to the control tissues. Moreover, the principal component linear discriminant analysis (PC-LDA) algorithm together with the leave-one-out cross-validation (LOOCV) method yield an overall diagnostic sensitivity of 77.7% and a specificity of 88.8% in the classification of control, hyperplasia, dysplasia and WDSCC tissues, respectively. The results from this study demonstrated that fluorescence-based tissue analysis combined with PC-LDA has tremendous potential for the effective discrimination of control from neoplastic tissues; furthermore it also detects early neoplastic changes prior to definite morphologic alteration.

Optical diagnosis of actinic cheilitis by infrared spectroscopy.

PubMed

das Chagas E Silva de Carvalho, Luis Felipe; Pereira, Thiago Martini; Magrini, Taciana Depra; Cavalcante, Ana Sueli Rodrigues; da Silva Martinho, Herculano; Almeida, Janete Dias

2016-12-01

Actinic cheilitis (AC) is considered a potentially malignant disorder of the lip. Biomolecular markers study is important to understand malignant transformation into squamous cell carcinoma. Fourier transform infra red (FT-IR) spectroscopy was used to analyze AC in this study. The aim of the study was to evaluate if FT-IR spectral regions of nucleic acids and collagen can help in early diagnosis of malignant transformation. Tissues biopsies of 14 patients diagnosed with AC and 14 normal tissues were obtained. FT-IR spectra were measured at five different points resulting in 70 spectra of each. Analysis of Principal components analysis (PCA) and linear discrimination analysis (LDA) model were also used. In order to verify the statistical difference in the spectra, Mann-Whitney U test was performed in each variable (wavenumber) with p-value <0.05. After the Mann-Whitney U test the vibrational modes of CO (Collagen 1), PO2 (Nucleic Acids) and CO asymmetric (Triglycerides/Lipids) were observed as a possible spectral biomarker. These bands were chosen because they represent the vibrational modes related to collagen and DNA, which are supposed to be changed in AC samples. Based on the PCA-LDA results, the predictive model corresponding to the area under the curve was 0.91 for the fingerprint region and 0.83 for the high wavenumber region, showing the greater accuracy of the test. FT-IR changes in collagen and nucleic acids could be used as molecular biomarkers for malignant transformation. Copyright © 2016 Elsevier B.V. All rights reserved.

Laser anemometry for hot flows

NASA Astrophysics Data System (ADS)

Kugler, P.; Langer, G.

1987-07-01

The fundamental principles, instrumentation, and practical operation of LDA and laser-transit-anemometry systems for measuring velocity profiles and the degree of turbulence in high-temperature flows are reviewed and illustrated with diagrams, drawings and graphs of typical data. Consideration is given to counter, tracker, spectrum-analyzer and correlation methods of LDA signal processing; multichannel analyzer and cross correlation methods for LTA data; LTA results for a small liquid fuel rocket motor; and experiments demonstrating the feasibility of an optoacoustic demodulation scheme for LDA signals from unsteady flows.

Processing techniques for correlation of LDA and thermocouple signals

NASA Astrophysics Data System (ADS)

Nina, M. N. R.; Pita, G. P. A.

1986-11-01

A technique was developed to enable the evaluation of the correlation between velocity and temperature, with laser Doppler anemometer (LDA) as the source of velocity signals and fine wire thermocouple as that of flow temperature. The discontinuous nature of LDA signals requires a special technique for correlation, in particular when few seeding particles are present in the flow. The thermocouple signal was analog compensated in frequency and the effect of the value of time constant on the velocity temperature correlation was studied.

Liquid contrabands classification based on energy dispersive X-ray diffraction and hybrid discriminant analysis

NASA Astrophysics Data System (ADS)

YangDai, Tianyi; Zhang, Li

2016-02-01

Energy dispersive X-ray diffraction (EDXRD) combined with hybrid discriminant analysis (HDA) has been utilized for classifying the liquid materials for the first time. The XRD spectra of 37 kinds of liquid contrabands and daily supplies were obtained using an EDXRD test bed facility. The unique spectra of different samples reveal XRD's capability to distinguish liquid contrabands from daily supplies. In order to create a system to detect liquid contrabands, the diffraction spectra were subjected to HDA which is the combination of principal components analysis (PCA) and linear discriminant analysis (LDA). Experiments based on the leave-one-out method demonstrate that HDA is a practical method with higher classification accuracy and lower noise sensitivity than the other methods in this application. The study shows the great capability and potential of the combination of XRD and HDA for liquid contrabands classification.

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

PubMed

Del Signore, Antonella

2003-01-01

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

High-Reproducibility and High-Accuracy Method for Automated Topic Classification

NASA Astrophysics Data System (ADS)

Lancichinetti, Andrea; Sirer, M. Irmak; Wang, Jane X.; Acuna, Daniel; Körding, Konrad; Amaral, Luís A. Nunes

2015-01-01

Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent searching, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state of the art in topic modeling. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results that are not accurate in inferring the most suitable model parameters. Adapting approaches from community detection in networks, we propose a new algorithm that displays high reproducibility and high accuracy and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure.

Adsorption and Electronic Structure of Sr and Ag Atoms on Graphite Surfaces: a First-Principles Study

NASA Astrophysics Data System (ADS)

Luo, Xiao-Feng; Fang, Chao; Li, Xin; Lai, Wen-Sheng; Sun, Li-Feng; Liang, Tong-Xiang

2013-06-01

The adsorption behaviors of radioactive strontium and silver nuclides on the graphite surface in a high-temperature gas-cooled reactor are studied by first-principles theory using generalized gradient approximation (GGA) and local density approximation (LDA) pseudo-potentials. It turns out that Sr prefers to be absorbed at the hollow of the carbon hexagonal cell by 0.54 eV (GGA), while Ag likes to sit right above the carbon atom with an adsorption energy of almost zero (GGA) and 0.45 eV (LDA). Electronic structure analysis reveals that Sr donates its partial electrons of the 4p and 5s states to the graphite substrate, while Ag on graphite is a physical adsorption without any electron transfer.

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

In vivo Raman spectroscopy of human uterine cervix: exploring the utility of vagina as an internal control

NASA Astrophysics Data System (ADS)

Shaikh, Rubina; Dora, Tapas Kumar; Chopra, Supriya; Maheshwari, Amita; Kedar K., Deodhar; Bharat, Rekhi; Krishna, C. Murali

2014-08-01

In vivo Raman spectroscopy is being projected as a new, noninvasive method for cervical cancer diagnosis. In most of the reported studies, normal areas in the cancerous cervix were used as control. However, in the Indian subcontinent, the majority of cervical cancers are detected at advanced stages, leaving no normal sites for acquiring control spectra. Moreover, vagina and ectocervix are reported to have similar biochemical composition. Thus, in the present study, we have evaluated the feasibility of classifying normal and cancerous conditions in the Indian population and we have also explored the utility of the vagina as an internal control. A total of 228 normal and 181 tumor in vivo Raman spectra were acquired from 93 subjects under clinical supervision. The spectral features in normal conditions suggest the presence of collagen, while DNA and noncollagenous proteins were abundant in tumors. Principal-component linear discriminant analysis (PC-LDA) yielded 97% classification efficiency between normal and tumor groups. An analysis of a normal cervix and vaginal controls of cancerous and noncancerous subjects suggests similar spectral features between these groups. PC-LDA of tumor, normal cervix, and vaginal controls further support the utility of the vagina as an internal control. Overall, findings of the study corroborate with earlier studies and facilitate objective, noninvasive, and rapid Raman spectroscopic-based screening/diagnosis of cervical cancers.

In vivo Raman spectroscopy of human uterine cervix: exploring the utility of vagina as an internal control.

PubMed

Shaikh, Rubina; Dora, Tapas Kumar; Chopra, Supriya; Maheshwari, Amita; Kedar K, Deodhar; Bharat, Rekhi; Krishna, C Murali

2014-08-01

In vivo Raman spectroscopy is being projected as a new, noninvasive method for cervical cancer diagnosis. In most of the reported studies, normal areas in the cancerous cervix were used as control. However, in the Indian subcontinent, the majority of cervical cancers are detected at advanced stages, leaving no normal sites for acquiring control spectra. Moreover, vagina and ectocervix are reported to have similar biochemical composition. Thus, in the present study, we have evaluated the feasibility of classifying normal and cancerous conditions in the Indian population and we have also explored the utility of the vagina as an internal control. A total of 228 normal and 181 tumor in vivo Raman spectra were acquired from 93 subjects under clinical supervision. The spectral features in normal conditions suggest the presence of collagen, while DNA and noncollagenous proteins were abundant in tumors. Principal-component linear discriminant analysis (PC-LDA) yielded 97% classification efficiency between normal and tumor groups. An analysis of a normal cervix and vaginal controls of cancerous and noncancerous subjects suggests similar spectral features between these groups. PC-LDA of tumor, normal cervix, and vaginal controls further support the utility of the vagina as an internal control. Overall, findings of the study corroborate with earlier studies and facilitate objective, noninvasive, and rapid Raman spectroscopic-based screening/diagnosis of cervical cancers.

First principles LDA + U and GGA + U study of protactinium and protactinium oxides: dependence on the effective U parameter

NASA Astrophysics Data System (ADS)

Obodo, K. O.; Chetty, N.

2013-04-01

The electronic structure and properties of protactinium and its oxides (PaO and PaO2) have been studied within the framework of the local density approximation (LDA), the Perdew-Burke-Ernzerhof generalized gradient approximation [GGA(PBE)], LDA + U and GGA(PBE) + U implementations of density functional theory. The dependence of selected observables of these materials on the effective U parameter has been investigated in detail. The examined properties include lattice constants, bulk moduli, the effect of charge density distributions, the hybridization of the 5f orbital and the energy of formation for PaO and PaO2. The LDA gives better agreement with experiment for the bulk modulus than the GGA for Pa but the GGA gives better structural properties. We found that PaO is metallic and PaO2 is a Mott-Hubbard insulator. This is consistent with observations for the other actinide oxides. We discover that GGA and LDA incorrectly give metallic behavior for PaO2. The GGA(PBE) + U calculated indirect band gap of 3.48 eV reported for PaO2 is a prediction and should stimulate further studies of this material.

The relation between high-density and very-high-density amorphous ice.

PubMed

Loerting, Thomas; Salzmann, Christoph G; Winkel, Katrin; Mayer, Erwin

2006-06-28

The exact nature of the relationship between high-density (HDA) and very-high-density (VHDA) amorphous ice is unknown at present. Here we review the relation between HDA and VHDA, concentrating on experimental aspects and discuss these with respect to the relation between low-density amorphous ice (LDA) and HDA. On compressing LDA at 125 K up to 1.5 GPa, two distinct density steps are observable in the pressure-density curves which correspond to the LDA --> HDA and HDA --> VHDA conversion. This stepwise formation process LDA --> HDA --> VHDA at 125 K is the first unambiguous observation of a stepwise amorphous-amorphous-amorphous transformation sequence. Density values of amorphous ice obtained in situ between 0.3 and 1.9 GPa on isobaric heating up to the temperatures of crystallization show a pronounced change of slope at ca. 0.8 GPa which could indicate formation of a distinct phase. We infer that the relation between HDA and VHDA is very similar to that between LDA and HDA except for a higher activation barrier between the former. We further discuss the two options of thermodynamic phase transition versus kinetic densification for the HDA --> VHDA conversion.

Single trial detection of hand poses in human ECoG using CSP based feature extraction.

PubMed

Kapeller, C; Schneider, C; Kamada, K; Ogawa, H; Kunii, N; Ortner, R; Pruckl, R; Guger, C

2014-01-01

Decoding brain activity of corresponding highlevel tasks may lead to an independent and intuitively controlled Brain-Computer Interface (BCI). Most of today's BCI research focuses on analyzing the electroencephalogram (EEG) which provides only limited spatial and temporal resolution. Derived electrocorticographic (ECoG) signals allow the investigation of spatially highly focused task-related activation within the high-gamma frequency band, making the discrimination of individual finger movements or complex grasping tasks possible. Common spatial patterns (CSP) are commonly used for BCI systems and provide a powerful tool for feature optimization and dimensionality reduction. This work focused on the discrimination of (i) three complex hand movements, as well as (ii) hand movement and idle state. Two subjects S1 and S2 performed single `open', `peace' and `fist' hand poses in multiple trials. Signals in the high-gamma frequency range between 100 and 500 Hz were spatially filtered based on a CSP algorithm for (i) and (ii). Additionally, a manual feature selection approach was tested for (i). A multi-class linear discriminant analysis (LDA) showed for (i) an error rate of 13.89 % / 7.22 % and 18.42 % / 1.17 % for S1 and S2 using manually / CSP selected features, where for (ii) a two class LDA lead to a classification error of 13.39 % and 2.33 % for S1 and S2, respectively.

Multigrid based First-Principles Molecular Dynamics

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

Fattebert, Jean-Luc; Osei-Kuffuor, Daniel; Dunn, Ian

2017-06-01

MGmol ls a First-Principles Molecular Dynamics code. It relies on the Born-Oppenheimer approximation and models the electronic structure using Density Functional Theory, either LDA or PBE. Norm-conserving pseudopotentials are used to model atomic cores.

Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex.

PubMed

Yeo, B T Thomas; Krienen, Fenna M; Chee, Michael W L; Buckner, Randy L

2014-03-01

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. © 2013.

Estimates of Segregation and Overlap of Functional Connectivity Networks in the Human Cerebral Cortex

PubMed Central

Yeo, BT Thomas; Krienen, Fenna M; Chee, Michael WL; Buckner, Randy L

2014-01-01

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1,000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. PMID:24185018

Serum amyloid A and haptoglobin concentrations and liver fat percentage in lactating dairy cows with abomasal displacement.

PubMed

Guzelbektes, H; Sen, I; Ok, M; Constable, P D; Boydak, M; Coskun, A

2010-01-01

There has been increased interest in measuring the serum concentration of acute phase reactants such as serum amyloid A [SAA] and haptoglobin [haptoglobin] in periparturient cattle in order to provide a method for detecting the presence of inflammation or bacterial infection. To determine whether [SAA] and [haptoglobin] are increased in cows with displaced abomasum as compared with healthy dairy cows. Fifty-four adult dairy cows in early lactation that had left displaced abomasum (LDA, n = 34), right displaced abomasum or abomasal volvulus (RDA/AV, n = 11), or were healthy on physical examination (control, n = 9). Inflammatory diseases or bacterial infections such as mastitis, metritis, or pneumonia were not clinically apparent in any animal. Jugular venous blood was obtained from all cows and analyzed. Liver samples were obtained by biopsy in cattle with abomasal displacement. [SAA] and [haptoglobin] concentrations were increased in cows with LDA or RDA/AV as compared with healthy controls. Cows with displaced abomasum had mild to moderate hepatic lipidosis, based on liver fat percentages of 9.3 +/- 5.3% (mean +/- SD, LDA) and 10.8 +/- 7.7% (RDA/AV). [SAA] and [haptoglobin] were most strongly associated with liver fat percentage, r(s) = +0.55 (P < .0001) and r(s) = +0.42 (P = .0041), respectively. An increase in [SAA] or [haptoglobin] in postparturient dairy cows with LDA or RDA/AV is not specific for inflammation or bacterial infection. An increase in [SAA] or [haptoglobin] may indicate the presence of hepatic lipidosis in cattle with abomasal displacement.

Fast Solution in Sparse LDA for Binary Classification

NASA Technical Reports Server (NTRS)

Moghaddam, Baback

2010-01-01

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic form along with the inherent sequential nature of greedy search itself. Together this enables the use of highly-efficient partitioned-matrix-inverse techniques that result in large speedups of computation in both the forward-selection and backward-elimination stages of greedy algorithms in general.

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

NASA Astrophysics Data System (ADS)

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

2014-11-01

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

Pressure-induced transformations in computer simulations of glassy water.

PubMed

Chiu, Janet; Starr, Francis W; Giovambattista, Nicolas

2013-11-14

Glassy water occurs in at least two broad categories: low-density amorphous (LDA) and high-density amorphous (HDA) solid water. We perform out-of-equilibrium molecular dynamics simulations to study the transformations of glassy water using the ST2 model. Specifically, we study the known (i) compression-induced LDA-to-HDA, (ii) decompression-induced HDA-to-LDA, and (iii) compression-induced hexagonal ice-to-HDA transformations. We study each transformation for a broad range of compression/decompression temperatures, enabling us to construct a "P-T phase diagram" for glassy water. The resulting phase diagram shows the same qualitative features reported from experiments. While many simulations have probed the liquid-state phase behavior, comparatively little work has examined the transitions of glassy water. We examine how the glass transformations relate to the (first-order) liquid-liquid phase transition previously reported for this model. Specifically, our results support the hypothesis that the liquid-liquid spinodal lines, between a low-density and high-density liquid, are extensions of the LDA-HDA transformation lines in the limit of slow compression. Extending decompression runs to negative pressures, we locate the sublimation lines for both LDA and hyperquenched glassy water (HGW), and find that HGW is relatively more stable to the vapor. Additionally, we observe spontaneous crystallization of HDA at high pressure to ice VII. Experiments have also seen crystallization of HDA, but to ice XII. Finally, we contrast the structure of LDA and HDA for the ST2 model with experiments. We find that while the radial distribution functions (RDFs) of LDA are similar to those observed in experiments, considerable differences exist between the HDA RDFs of ST2 water and experiment. The differences in HDA structure, as well as the formation of ice VII (a tetrahedral crystal), are a consequence of ST2 overemphasizing the tetrahedral character of water.

Pressure-induced transformations in computer simulations of glassy water

NASA Astrophysics Data System (ADS)

Chiu, Janet; Starr, Francis W.; Giovambattista, Nicolas

2013-11-01

Glassy water occurs in at least two broad categories: low-density amorphous (LDA) and high-density amorphous (HDA) solid water. We perform out-of-equilibrium molecular dynamics simulations to study the transformations of glassy water using the ST2 model. Specifically, we study the known (i) compression-induced LDA-to-HDA, (ii) decompression-induced HDA-to-LDA, and (iii) compression-induced hexagonal ice-to-HDA transformations. We study each transformation for a broad range of compression/decompression temperatures, enabling us to construct a "P-T phase diagram" for glassy water. The resulting phase diagram shows the same qualitative features reported from experiments. While many simulations have probed the liquid-state phase behavior, comparatively little work has examined the transitions of glassy water. We examine how the glass transformations relate to the (first-order) liquid-liquid phase transition previously reported for this model. Specifically, our results support the hypothesis that the liquid-liquid spinodal lines, between a low-density and high-density liquid, are extensions of the LDA-HDA transformation lines in the limit of slow compression. Extending decompression runs to negative pressures, we locate the sublimation lines for both LDA and hyperquenched glassy water (HGW), and find that HGW is relatively more stable to the vapor. Additionally, we observe spontaneous crystallization of HDA at high pressure to ice VII. Experiments have also seen crystallization of HDA, but to ice XII. Finally, we contrast the structure of LDA and HDA for the ST2 model with experiments. We find that while the radial distribution functions (RDFs) of LDA are similar to those observed in experiments, considerable differences exist between the HDA RDFs of ST2 water and experiment. The differences in HDA structure, as well as the formation of ice VII (a tetrahedral crystal), are a consequence of ST2 overemphasizing the tetrahedral character of water.

Analysis of select Dalbergia and trade timber using direct analysis in real time and time-of-flight mass spectrometry for CITES enforcement.

PubMed

Lancaster, Cady; Espinoza, Edgard

2012-05-15

International trade of several Dalbergia wood species is regulated by The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). In order to supplement morphological identification of these species, a rapid chemical method of analysis was developed. Using Direct Analysis in Real Time (DART) ionization coupled with Time-of-Flight (TOF) Mass Spectrometry (MS), selected Dalbergia and common trade species were analyzed. Each of the 13 wood species was classified using principal component analysis and linear discriminant analysis (LDA). These statistical data clusters served as reliable anchors for species identification of unknowns. Analysis of 20 or more samples from the 13 species studied in this research indicates that the DART-TOFMS results are reproducible. Statistical analysis of the most abundant ions gave good classifications that were useful for identifying unknown wood samples. DART-TOFMS and LDA analysis of 13 species of selected timber samples and the statistical classification allowed for the correct assignment of unknown wood samples. This method is rapid and can be useful when anatomical identification is difficult but needed in order to support CITES enforcement. Published 2012. This article is a US Government work and is in the public domain in the USA.

Economic analysis of HPAI control in the Netherlands II: comparison of control strategies.

PubMed

Longworth, N; Mourits, M C M; Saatkamp, H W

2014-06-01

A combined epidemiological-economic modelling approach was used to analyse strategies for highly pathogenic avian influenza (HPAI) control for the Netherlands. The modelling framework used was InterSpread Plus (ISP), a spatially based, stochastic and dynamic simulation model. A total of eight control strategies were analysed, including pre-emptive depopulation and vaccination strategies. The analysis was carried out for three different regions in the Netherlands: high-, medium- and low-density areas (HDA, MDA and LDA, respectively). The analysis included the veterinary impact (e.g. number of infected premises and duration), but was particularly focused on the impact on direct costs (DC) and direct consequential costs. The efficient set of control strategies for HDA and MDA included strategies based on either pre-emptive depopulation only or combined vaccination and pre-emptive depopulation: D2 (pre-emptive depopulation within a radius of 2 km), RV3 + D1 (ring vaccination within a radius of 3 km and additional pre-emptive depopulation within a radius of 1 km) and PV + D1 (preventive vaccination in non-affected HDAs and pre-emptive depopulation within a radius of 1 km in the affected HDA). Although control solely based on depopulation in most cases showed to be effective for LDA, pre-emptive depopulation showed to have an additional advantage in these areas, that is, prevention of 'virus jumps' to other areas. The pros and cons of the efficient control strategies were discussed, for example, public perception and risk of export restrictions. It was concluded that for the Netherlands control of HPAI preferably should be carried out using strategies including pre-emptive depopulation with or without vaccination. Particularly, the short- and long-term implications on export, that is, indirect consequential costs (ICC) and aftermath costs of these strategies, should be analysed further. © 2012 Blackwell Verlag GmbH.

Camera Network Topology Discovery Literature Review

DTIC Science & Technology

2011-11-01

essential for 21st century military, enviromental and surveillance applications [Devarajan, Cheng & Radke 2008]. Computer networks pose several research...starting and ending points of object trajectories into entry/exit regions [Makris & Ellis 2003]. 3LDA is a new standard for document analysis. The model

Analyses of absorption distribution of a rubidium cell side-pumped by a Laser-Diode-Array (LDA)

NASA Astrophysics Data System (ADS)

Yu, Hang; Han, Juhong; Rong, Kepeng; Wang, Shunyan; Cai, He; An, Guofei; Zhang, Wei; Yu, Qiang; Wu, Peng; Wang, Hongyuan; Wang, You

2018-01-01

A diode-pumped alkali laser (DPAL) has been regarded as one of the most potential candidates to achieve high power performances of next generation. In this paper, we investigate the physical properties of a rubidium cell side-pumped by a Laser-Diode-Array (LDA) in this study. As the saturated concentration of a gain medium inside a vapor cell is extremely sensitive to the temperature, the populations of every energy-level of the atomic alkali are strongly relying on the vapor temperature. Thus, the absorption characteristics of a DPAL are mainly dominated by the temperature distribution. In this paper, the temperature, absorption, and lasing distributions in the cross-section of a rubidium cell side-pumped by a LDA are obtained by means of a complicated mathematic procedure. Based on the original end-pumped mode we constructed before, a novel one-direction side-pumped theoretical mode has been established to explore the distribution properties in the transverse section of a rubidium vapor cell by combining the procedures of heat transfer and laser kinetics together. It has been thought the results might be helpful for design of a side-pumped configuration in a high-powered DPAL.

Magnetic properties of vanadium doped CdTe: Ab initio calculations

NASA Astrophysics Data System (ADS)

Goumrhar, F.; Bahmad, L.; Mounkachi, O.; Benyoussef, A.

2017-04-01

In this paper, we are applying the ab initio calculations to study the magnetic properties of vanadium doped CdTe. This study is based on the Korringa-Kohn-Rostoker method (KKR) combined with the coherent potential approximation (CPA), within the local density approximation (LDA). This method is called KKR-CPA-LDA. We have calculated and plotted the density of states (DOS) in the energy diagram for different concentrations of dopants. We have also investigated the magnetic and half-metallic properties of this compound and shown the mechanism of exchange interaction. Moreover, we have estimated the Curie temperature Tc for different concentrations. Finally, we have shown how the crystal field and the exchange splittings vary as a function of the concentrations.

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

PubMed

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

2017-12-15

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

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

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

PubMed

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

2013-07-15

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

[Different wavelengths selection methods for identification of early blight on tomato leaves by using hyperspectral imaging technique].

PubMed

Cheng, Shu-Xi; Xie, Chuan-Qi; Wang, Qiao-Nan; He, Yong; Shao, Yong-Ni

2014-05-01

Identification of early blight on tomato leaves by using hyperspectral imaging technique based on different effective wavelengths selection methods (successive projections algorithm, SPA; x-loading weights, x-LW; gram-schmidt orthogonaliza-tion, GSO) was studied in the present paper. Hyperspectral images of seventy healthy and seventy infected tomato leaves were obtained by hyperspectral imaging system across the wavelength range of 380-1023 nm. Reflectance of all pixels in region of interest (ROI) was extracted by ENVI 4. 7 software. Least squares-support vector machine (LS-SVM) model was established based on the full spectral wavelengths. It obtained an excellent result with the highest identification accuracy (100%) in both calibration and prediction sets. Then, EW-LS-SVM and EW-LDA models were established based on the selected wavelengths suggested by SPA, x-LW and GSO, respectively. The results showed that all of the EW-LS-SVM and EW-LDA models performed well with the identification accuracy of 100% in EW-LS-SVM model and 100%, 100% and 97. 83% in EW-LDA model, respectively. Moreover, the number of input wavelengths of SPA-LS-SVM, x-LW-LS-SVM and GSO-LS-SVM models were four (492, 550, 633 and 680 nm), three (631, 719 and 747 nm) and two (533 and 657 nm), respectively. Fewer input variables were beneficial for the development of identification instrument. It demonstrated that it is feasible to identify early blight on tomato leaves by using hyperspectral imaging, and SPA, x-LW and GSO were effective wavelengths selection methods.

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

PubMed

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

2001-03-01

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

Analysis of Fringe Field Formed Inside LDA Measurement Volume Using Compact Two Hololens Imaging Systems

NASA Astrophysics Data System (ADS)

Ghosh, Abhijit; Nirala, A. K.; Yadav, H. L.

2018-03-01

We have designed and fabricated four LDA optical setups consisting of aberration compensated four different compact two hololens imaging systems. We have experimentally investigated and realized a hololens recording geometry which is interferogram of converging spherical wavefront with mutually coherent planar wavefront. Proposed real time monitoring and actual fringe field analysis techniques allow complete characterizations of fringes formed at measurement volume and permit to evaluate beam quality, alignment and fringe uniformity with greater precision. After experimentally analyzing the fringes formed at measurement volume by all four imaging systems, it is found that fringes obtained using compact two hololens imaging systems get improved both qualitatively and quantitatively compared to that obtained using conventional imaging system. Results indicate qualitative improvement of non-uniformity in fringe thickness and micro intensity variations perpendicular to the fringes, and quantitative improvement of 39.25% in overall average normalized standard deviations of fringe width formed by compact two hololens imaging systems compare to that of conventional imaging system.

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

PubMed

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

2017-04-01

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

Experimental study of the polyamorphism of water. I. The isobaric transitions from amorphous ices to LDA at 4 MPa.

PubMed

Handle, Philip H; Loerting, Thomas

2018-03-28

The existence of more than one solid amorphous state of water is an extraordinary feature. Since polyamorphism might be connected to the liquid-liquid critical point hypothesis, it is particularly important to study the relations amongst the different amorphous ices. Here we study the polyamorphic transformations of several high pressure amorphous ices to low-density amorphous ice (LDA) at 4 MPa by isobaric heating utilising in situ volumetry and ex situ X-ray diffraction. We find that very-high density amorphous ice (VHDA) and unannealed high density amorphous ice (HDA) show significant relaxation before transforming to LDA, whereby VHDA is seen to relax toward HDA. By contrast, expanded HDA shows almost no relaxation prior to the transformation. The transition to LDA itself obeys criteria for a first-order-like transition in all cases. In the case of VHDA, even macroscopic phase separation is observed. These findings suggest that HDA and LDA are two clearly distinct polyamorphs. We further present evidence that HDA reaches the metastable equilibrium at 140 K and 0.1 GPa but only comes close to that at 140 K and 0.2 GPa. The most important is the path independence of the amorphous phase reached at 140 K and 0.1 GPa.

Jahn-Teller transition in TiF3 investigated using density-functional theory

NASA Astrophysics Data System (ADS)

Perebeinos, Vasili; Vogt, Tom

2004-03-01

We use first-principles density-functional theory to calculate the electronic and magnetic properties of TiF3 using the full-potential-linearized augmented-plane-wave method. The local density approximation (LDA) predicts a fully saturated ferromagnetic metal and finds degenerate energy minima for high- and low-symmetry structures. The experimentally observed Jahn-Teller phase transition at Tc=370 K cannot be driven by the electron-phonon interaction alone, which is usually described accurately by the LDA. Electron correlations beyond the LDA are essential to lift the degeneracy of the singly occupied Ti t2g orbital. Although the on-site Coulomb correlations are important, the direction of the t2g-level splitting is determined by dipole-dipole interactions. The LDA+U functional predicts an aniferromagnetic insulator with an orbitally ordered ground state. The input parameters U=8.1 eV and J=0.9 eV for the Ti 3d orbital were found by varying the total charge on the TiF2-6 ion using the molecular NRLMOL code. We estimate the Heisenberg exchange constant for spin 1/2 on a cubic lattice to be approximately 24 K. The symmetry lowering energy in LDA+U is about 900 K per TiF3 formula unit.

Experimental study of the polyamorphism of water. I. The isobaric transitions from amorphous ices to LDA at 4 MPa

NASA Astrophysics Data System (ADS)

Handle, Philip H.; Loerting, Thomas

2018-03-01

The existence of more than one solid amorphous state of water is an extraordinary feature. Since polyamorphism might be connected to the liquid-liquid critical point hypothesis, it is particularly important to study the relations amongst the different amorphous ices. Here we study the polyamorphic transformations of several high pressure amorphous ices to low-density amorphous ice (LDA) at 4 MPa by isobaric heating utilising in situ volumetry and ex situ X-ray diffraction. We find that very-high density amorphous ice (VHDA) and unannealed high density amorphous ice (HDA) show significant relaxation before transforming to LDA, whereby VHDA is seen to relax toward HDA. By contrast, expanded HDA shows almost no relaxation prior to the transformation. The transition to LDA itself obeys criteria for a first-order-like transition in all cases. In the case of VHDA, even macroscopic phase separation is observed. These findings suggest that HDA and LDA are two clearly distinct polyamorphs. We further present evidence that HDA reaches the metastable equilibrium at 140 K and 0.1 GPa but only comes close to that at 140 K and 0.2 GPa. The most important is the path independence of the amorphous phase reached at 140 K and 0.1 GPa.

Multielement fingerprinting as a tool in origin authentication of PGI food products: Tropea red onion.

PubMed

Furia, Emilia; Naccarato, Attilio; Sindona, Giovanni; Stabile, Gaetano; Tagarelli, Antonio

2011-08-10

Tropea red onion ( Allium cepa L. var. Tropea) is among the most highly appreciated Italian products. It is cultivated in specific areas of Calabria and, due to its characteristics, was recently awarded with the protected geographical indications (PGI) certification from the European Union. A reliable classification of onion samples in groups corresponding to "Tropea" and "non-Tropea" categories is now available to the producers. This important goal has been achieved through the evaluation of three supervised chemometric approaches. Onion samples with PGI brand (120) and onion samples not cultivated following the production regulations (80) were digested by a closed-vessel microwave oven system. ICP-MS equipped with a dynamic reaction cell was used to determine the concentrations of 25 elements (Al, Ba, Ca, Cd, Ce, Cr, Dy, Eu, Fe, Ga, Gd, Ho, La, Mg, Mn, Na, Nd, Ni, Pr, Rb, Sm, Sr, Tl, Y, and Zn). The multielement fingerprint was processed using linear discriminant analysis (LDA) (standard and stepwise), soft independent modeling of class analogy (SIMCA), and back-propagation artificial neural network (BP-ANN). The cross-validation procedure has shown good results in terms of the prediction ability for all of the chemometric models: standard LDA, 94.0%; stepwise LDA, 94.5%; SIMCA, 95.5%; and BP-ANN, 91.5%.

Glass polymorphism in glycerol–water mixtures: I. A computer simulation study

PubMed Central

Jahn, David A.; Wong, Jessina; Bachler, Johannes; Loerting, Thomas

2016-01-01

We perform out-of-equilibrium molecular dynamics (MD) simulations of water–glycerol mixtures in the glass state. Specifically, we study the transformations between low-density (LDA) and high-density amorphous (HDA) forms of these mixtures induced by compression/decompression at constant temperature. Our MD simulations reproduce qualitatively the density changes observed in experiments. Specifically, the LDA–HDA transformation becomes (i) smoother and (ii) the hysteresis in a compression/decompression cycle decreases as T and/or glycerol content increase. This is surprising given the fast compression/decompression rates (relative to experiments) accessible in MD simulations. We study mixtures with glycerol molar concentration χ g = 0–13% and find that, for the present mixture models and rates, the LDA–HDA transformation is detectable up to χ g ≈ 5%. As the concentration increases, the density of the starting glass (i.e., LDA at approximately χ g ≤ 5%) rapidly increases while, instead, the density of HDA remains practically constant. Accordingly, the LDA state and hence glass polymorphism become inaccessible for glassy mixtures with approximately χ g > 5%. We present an analysis of the molecular-level changes underlying the LDA–HDA transformation. As observed in pure glassy water, during the LDA-to-HDA transformation, water molecules within the mixture approach each other, moving from the second to the first hydration shell and filling the first interstitial shell of water molecules. Interestingly, similar changes also occur around glycerol OH groups. It follows that glycerol OH groups contribute to the density increase during the LDA–HDA transformation. An analysis of the hydrogen bond (HB)-network of the mixtures shows that the LDA–HDA transformation is accompanied by minor changes in the number of HBs of water and glycerol. Instead, large changes in glycerol and water coordination numbers occur. We also perform a detailed analysis of the effects that the glycerol force field (FF) has on our results. By comparing MD simulations using two different glycerol models, we find that glycerol conformations indeed depend on the FF employed. Yet, the thermodynamic and microscopic mechanisms accompanying the LDA–HDA transformation and hence, our main results, do not. This work is accompanied by an experimental report where we study the glass polymorphism in glycerol–water mixtures prepared by isobaric cooling at 1 bar. PMID:27063705

Application of inorganic element ratios to chemometrics for determination of the geographic origin of welsh onions.

PubMed

Ariyama, Kaoru; Horita, Hiroshi; Yasui, Akemi

2004-09-22

The composition of concentration ratios of 19 inorganic elements to Mg (hereinafter referred to as 19-element/Mg composition) was applied to chemometric techniques to determine the geographic origin (Japan or China) of Welsh onions (Allium fistulosum L.). Using a composition of element ratios has the advantage of simplified sample preparation, and it was possible to determine the geographic origin of a Welsh onion within 2 days. The classical technique based on 20 element concentrations was also used along with the new simpler one based on 19 elements/Mg in order to validate the new technique. Twenty elements, Na, P, K, Ca, Mg, Mn, Fe, Cu, Zn, Sr, Ba, Co, Ni, Rb, Mo, Cd, Cs, La, Ce, and Tl, in 244 Welsh onion samples were analyzed by flame atomic absorption spectroscopy, inductively coupled plasma atomic emission spectrometry, and inductively coupled plasma mass spectrometry. Linear discriminant analysis (LDA) on 20-element concentrations and 19-element/Mg composition was applied to these analytical data, and soft independent modeling of class analogy (SIMCA) on 19-element/Mg composition was applied to these analytical data. The results showed that techniques based on 19-element/Mg composition were effective. LDA, based on 19-element/Mg composition for classification of samples from Japan and from Shandong, Shanghai, and Fujian in China, classified 101 samples used for modeling 97% correctly and predicted another 119 samples excluding 24 nonauthentic samples 93% correctly. In discriminations by 10 times of SIMCA based on 19-element/Mg composition modeled using 101 samples, 220 samples from known production areas including samples used for modeling and excluding 24 nonauthentic samples were predicted 92% correctly.

Modulation of serpin reaction through stabilization of transient intermediate by ligands bound to alpha-helix F.

PubMed

Komissarov, Andrey A; Zhou, Aiwu; Declerck, Paul J

2007-09-07

Mechanism-based inhibition of proteinases by serpins involves enzyme acylation and fast insertion of the reactive center loop (RCL) into the central beta-sheet of the serpin, resulting in mechanical inactivation of the proteinase. We examined the effects of ligands specific to alpha-helix F (alphaHF) of plasminogen activator inhibitor-1 (PAI-1) on the stoichiometry of inhibition (SI) and limiting rate constant (k(lim)) of RCL insertion for reactions with beta-trypsin, tissue-type plasminogen activator (tPA), and urokinase. The somatomedin B domain of vitronectin (SMBD) did not affect SI for any proteinase or k(lim) for tPA but decreased the k(lim) for beta-trypsin. In contrast to SMBD, monoclonal antibodies MA-55F4C12 and MA-33H1F7, the epitopes of which are located at the opposite side of alphaHF, decreased k(lim) and increased SI for every enzyme. These effects were enhanced in the presence of SMBD. RCL insertion for beta-trypsin and tPA is limited by different subsequent steps of PAI-1 mechanism as follows: enzyme acylation and formation of a loop-displaced acyl complex (LDA), respectively. Stabilization of LDA through the disruption of the exosite interactions between PAI-1 and tPA induced an increase in the k(lim) but did not affect the SI. Thus it is unlikely that LDA contributes significantly to the outcome of the serpin reaction. These results demonstrate that the rate of RCL insertion is not necessarily correlated with SI and indicate that an intermediate, different from LDA, which forms during the late steps of PAI-1 mechanism, and could be stabilized by ligands specific to alphaHF, controls bifurcation between the inhibitory and the substrate pathways.

Deciphering the Routes of invasion of Drosophila suzukii by Means of ABC Random Forest.

PubMed

Fraimout, Antoine; Debat, Vincent; Fellous, Simon; Hufbauer, Ruth A; Foucaud, Julien; Pudlo, Pierre; Marin, Jean-Michel; Price, Donald K; Cattel, Julien; Chen, Xiao; Deprá, Marindia; François Duyck, Pierre; Guedot, Christelle; Kenis, Marc; Kimura, Masahito T; Loeb, Gregory; Loiseau, Anne; Martinez-Sañudo, Isabel; Pascual, Marta; Polihronakis Richmond, Maxi; Shearer, Peter; Singh, Nadia; Tamura, Koichiro; Xuéreb, Anne; Zhang, Jinping; Estoup, Arnaud

2017-04-01

Deciphering invasion routes from molecular data is crucial to understanding biological invasions, including identifying bottlenecks in population size and admixture among distinct populations. Here, we unravel the invasion routes of the invasive pest Drosophila suzukii using a multi-locus microsatellite dataset (25 loci on 23 worldwide sampling locations). To do this, we use approximate Bayesian computation (ABC), which has improved the reconstruction of invasion routes, but can be computationally expensive. We use our study to illustrate the use of a new, more efficient, ABC method, ABC random forest (ABC-RF) and compare it to a standard ABC method (ABC-LDA). We find that Japan emerges as the most probable source of the earliest recorded invasion into Hawaii. Southeast China and Hawaii together are the most probable sources of populations in western North America, which then in turn served as sources for those in eastern North America. European populations are genetically more homogeneous than North American populations, and their most probable source is northeast China, with evidence of limited gene flow from the eastern US as well. All introduced populations passed through bottlenecks, and analyses reveal five distinct admixture events. These findings can inform hypotheses concerning how this species evolved between different and independent source and invasive populations. Methodological comparisons indicate that ABC-RF and ABC-LDA show concordant results if ABC-LDA is based on a large number of simulated datasets but that ABC-RF out-performs ABC-LDA when using a comparable and more manageable number of simulated datasets, especially when analyzing complex introduction scenarios. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Impact of Soil Moisture Assimilation on Land Surface Model Spin-Up and Coupled LandAtmosphere Prediction

NASA Technical Reports Server (NTRS)

Santanello, Joseph A., Jr.; Kumar, Sujay V.; Peters-Lidard, Christa D.; Lawston, P.

2016-01-01

Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASAs Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spin-up can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux- PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

Metabolic Profile and Inflammatory Responses in Dairy Cows with Left Displaced Abomasum Kept under Small-Scaled Farm Conditions

PubMed Central

Klevenhusen, Fenja; Humer, Elke; Metzler-Zebeli, Barbara; Podstatzky-Lichtenstein, Leopold; Wittek, Thomas; Zebeli, Qendrim

2015-01-01

Simple Summary This research established an association between lactation number and milk production and metabolic and inflammatory responses in high-producing dairy cows affected by left abomasal displacement in small-scaled dairy farms. The study showed metabolic alterations, liver damage, and inflammation in the sick cows, which were further exacerbated with increasing lactation number and milk yield of the cows. Abstract Left displaced abomasum (LDA) is a severe metabolic disease of cattle with a strong negative impact on production efficiency of dairy farms. Metabolic and inflammatory alterations associated with this disease have been reported in earlier studies, conducted mostly in large dairy farms. This research aimed to: (1) evaluate metabolic and inflammatory responses in dairy cows affected by LDA in small-scaled dairy farms; and (2) establish an association between lactation number and milk production with the outcome of metabolic variables. The cows with LDA had lower serum calcium (Ca), but greater concentrations of non-esterified fatty acids (NEFA) and beta-hydroxy-butyrate (BHBA), in particular when lactation number was >2. Cows with LDA showed elevated levels of aspartate aminotransferase, glutamate dehydrogenase, and serum amyloid A (SAA), regardless of lactation number. In addition, this study revealed strong associations between milk yield and the alteration of metabolic profile but not with inflammation in the sick cows. Results indicate metabolic alterations, liver damage, and inflammation in LDA cows kept under small-scale farm conditions. Furthermore, the data suggest exacerbation of metabolic profile and Ca metabolism but not of inflammation and liver health with increasing lactation number and milk yield in cows affected by LDA. PMID:26479481

Attention-deficit hyperactivity disorder symptoms add risk to circadian rhythm sleep problems in depression and anxiety.

PubMed

Bron, T I; Bijlenga, D; Kooij, J J S; Vogel, S W N; Wynchank, D; Beekman, A T F; Penninx, B W J H

2016-08-01

Comorbid ADHD symptoms may partly account for circadian rhythm disturbances in depression and anxiety disorders. Self-reported sleep characteristics of 2090 participants in the Netherlands Study of Depression and Anxiety were assessed using the Munich Chronotype Questionnaire. We defined 3 groups: healthy controls (HC), persons with lifetime depression and/or anxiety disorders (LDA), and those with both LDA and high ADHD symptoms (LDA+ADHD), using the Conner's Adult ADHD Rating Scale. Sleep characteristics were least favorable in the LDA+ADHD group. Important group differences between LDA+ADHD, LDA and HC were found for extremely late chronotype (12% vs. 5% vs. 3%; p<.001), sleep duration <6h (15% vs. 5% vs. 4%; p<.001), and for an indication of the Delayed Sleep Phase Syndrome (DSPS; 16% vs. 8% vs. 5%; p<.001). After adjustment for covariates, including depression and anxiety, presence of ADHD symptoms increased the odds ratio for late chronotype (OR=2.6; p=.003), indication of DSPS (OR=2.4; p=.002), and sleep duration <6h (OR=2.7; p=.007). ADHD conceptually overlaps with symptom presentation of depression and anxiety. We used a cross-sectional study design, and used self reported sleep characteristics. High ADHD symptoms were associated with an increased rate of circadian rhythm sleep disturbances in an already at-risk population of people with depression and/or anxiety disorders. Circadian rhythm sleep disorders, as often seen in ADHD are not entirely due to any comorbid depression and/or anxiety disorder. Adequate treatment of such sleep problems is needed and may prevent serious health conditions in the long term. Copyright © 2016 Elsevier B.V. All rights reserved.

Excitation spectra of aromatic molecules within a real-space G W -BSE formalism: Role of self-consistency and vertex corrections

DOE PAGES

Hung, Linda; da Jornada, Felipe H.; Souto-Casares, Jaime; ...

2016-08-15

Here, we present first-principles calculations on the vertical ionization potentials (IPs), electron affinities (EAs), and singlet excitation energies on an aromatic-molecule test set (benzene, thiophene, 1,2,5-thiadiazole, naphthalene, benzothiazole, and tetrathiafulvalene) within the GW and Bethe-Salpeter equation (BSE) formalisms. Our computational framework, which employs a real-space basis for ground-state and a transition-space basis for excited-state calculations, is well suited for high-accuracy calculations on molecules, as we show by comparing against G0W0 calculations within a plane-wave-basis formalism. We then generalize our framework to test variants of the GW approximation that include a local density approximation (LDA)–derived vertex function (Γ LDA ) andmore » quasiparticle-self-consistent (QS) iterations. We find that Γ LDA and quasiparticle self-consistency shift IPs and EAs by roughly the same magnitude, but with opposite sign for IPs and the same sign for EAs. G0W0 and QS GWΓ LDA are more accurate for IPs, while G 0W 0Γ LDA and QS GW are best for EAs. For optical excitations, we find that perturbative GW-BSE underestimates the singlet excitation energy, while self-consistent GW-BSE results in good agreement with previous best-estimate values for both valence and Rydberg excitations. Finally, our work suggests that a hybrid approach, in which G0W0 energies are used for occupied orbitals and G0W0Γ LDA for unoccupied orbitals, also yields optical excitation energies in good agreement with experiment but at a smaller computational cost.« less

Lafutidine prevents low-dose aspirin and loxoprofen induced gastric injury: a randomized, double-blinded, placebo controlled study.

PubMed

Kato, Mototsugu; Kamada, Go; Yamamoto, Keiko; Nishida, Urara; Imai, Aki; Yoshida, Takeshi; Ono, Shouko; Nakagawa, Manabu; Nakagawa, Soichi; Shimizu, Yuichi; Asaka, Masahiro

2010-10-01

The concomitant use of non-steroidal anti-inflammatory drugs is a risk factor for low-dose aspirin (LDA)-associated upper gastrointestinal toxicity. Lafutidine is an H2-receptor antagonist with gastroprotective activity, produced by acting on capsaicin-sensitive afferent neurons. To evaluate the preventive effect of lafutidine on gastric damage caused by LDA alone and by the combination of both LDA and loxoprofen, we conducted a clinical study using healthy volunteers. A randomized, double-blinded, placebo-controlled, crossover study was carried out. Sixteen healthy volunteers without Helicobacter pylori infection were randomly assigned to two groups. Both groups received 81 mg of aspirin once daily for 14 days (on days 1 to 14) and 60 mg of loxoprofen three times daily for the last 7 days (on days 8 to 14). Placebo or 10 mg of lafutidine was administered twice daily for 14 days in each group. After a 2-week washout period, placebo and lafutidine were crossed over. Endoscopic findings of gastric mucosal damage were evaluated according to the modified Lanza score. The mean modified Lanza score was 2.19 ± 1.06 (SD) for aspirin plus placebo as compared with 0.50 ± 0.77 for aspirin plus lafutidine (P < 0.001), and 3.00 ± 1.56 for aspirin plus loxoprofen and placebo as compared with 1.25 ± 1.37 for aspirin plus loxoprofen and lafutidine (P < 0.01). The addition of loxoprofen to LDA increases gastric mucosal damage. Standard-dose lafutidine significantly prevents gastric mucosal damage induced by LDA alone or LDA plus loxoprofen in H. pylori-negative volunteers. Larger controlled studies are needed to strengthen these findings. © 2010 Journal of Gastroenterology and Hepatology Foundation and Blackwell Publishing Asia Pty Ltd.

Excitation spectra of aromatic molecules within a real-space G W -BSE formalism: Role of self-consistency and vertex corrections

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

Hung, Linda; da Jornada, Felipe H.; Souto-Casares, Jaime

Here, we present first-principles calculations on the vertical ionization potentials (IPs), electron affinities (EAs), and singlet excitation energies on an aromatic-molecule test set (benzene, thiophene, 1,2,5-thiadiazole, naphthalene, benzothiazole, and tetrathiafulvalene) within the GW and Bethe-Salpeter equation (BSE) formalisms. Our computational framework, which employs a real-space basis for ground-state and a transition-space basis for excited-state calculations, is well suited for high-accuracy calculations on molecules, as we show by comparing against G0W0 calculations within a plane-wave-basis formalism. We then generalize our framework to test variants of the GW approximation that include a local density approximation (LDA)–derived vertex function (Γ LDA ) andmore » quasiparticle-self-consistent (QS) iterations. We find that Γ LDA and quasiparticle self-consistency shift IPs and EAs by roughly the same magnitude, but with opposite sign for IPs and the same sign for EAs. G0W0 and QS GWΓ LDA are more accurate for IPs, while G 0W 0Γ LDA and QS GW are best for EAs. For optical excitations, we find that perturbative GW-BSE underestimates the singlet excitation energy, while self-consistent GW-BSE results in good agreement with previous best-estimate values for both valence and Rydberg excitations. Finally, our work suggests that a hybrid approach, in which G0W0 energies are used for occupied orbitals and G0W0Γ LDA for unoccupied orbitals, also yields optical excitation energies in good agreement with experiment but at a smaller computational cost.« less

Authentication of animal fats using direct analysis in real time (DART) ionization-mass spectrometry and chemometric tools.

PubMed

Vaclavik, Lukas; Hrbek, Vojtech; Cajka, Tomas; Rohlik, Bo-Anne; Pipek, Petr; Hajslova, Jana

2011-06-08

A combination of direct analysis in real time (DART) ionization coupled to time-of-flight mass spectrometry (TOFMS) and chemometrics was used for animal fat (lard and beef tallow) authentication. This novel instrumentation was employed for rapid profiling of triacylglycerols (TAGs) and polar compounds present in fat samples and their mixtures. Additionally, fat isolated from pork, beef, and pork/beef admixtures was analyzed. Mass spectral records were processed by principal component analysis (PCA) and stepwise linear discriminant analysis (LDA). DART-TOFMS profiles of TAGs were found to be more suitable for the purpose of discrimination among the examined fat types as compared to profiles of polar compounds. The LDA model developed using TAG data enabled not only reliable classification of samples representing neat fats but also detection of admixed lard and tallow at adulteration levels of 5 and 10% (w/w), respectively. The presented approach was also successfully applied to minced meat prepared from pork and beef with comparable fat content. Using the DART-TOFMS TAG profiles of fat isolated from meat mixtures, detection of 10% pork added to beef and vice versa was possible.

Detecting Spatial Patterns of Natural Hazards from the Wikipedia Knowledge Base

NASA Astrophysics Data System (ADS)

Fan, J.; Stewart, K.

2015-07-01

The Wikipedia database is a data source of immense richness and variety. Included in this database are thousands of geotagged articles, including, for example, almost real-time updates on current and historic natural hazards. This includes usercontributed information about the location of natural hazards, the extent of the disasters, and many details relating to response, impact, and recovery. In this research, a computational framework is proposed to detect spatial patterns of natural hazards from the Wikipedia database by combining topic modeling methods with spatial analysis techniques. The computation is performed on the Neon Cluster, a high performance-computing cluster at the University of Iowa. This work uses wildfires as the exemplar hazard, but this framework is easily generalizable to other types of hazards, such as hurricanes or flooding. Latent Dirichlet Allocation (LDA) modeling is first employed to train the entire English Wikipedia dump, transforming the database dump into a 500-dimension topic model. Over 230,000 geo-tagged articles are then extracted from the Wikipedia database, spatially covering the contiguous United States. The geo-tagged articles are converted into an LDA topic space based on the topic model, with each article being represented as a weighted multidimension topic vector. By treating each article's topic vector as an observed point in geographic space, a probability surface is calculated for each of the topics. In this work, Wikipedia articles about wildfires are extracted from the Wikipedia database, forming a wildfire corpus and creating a basis for the topic vector analysis. The spatial distribution of wildfire outbreaks in the US is estimated by calculating the weighted sum of the topic probability surfaces using a map algebra approach, and mapped using GIS. To provide an evaluation of the approach, the estimation is compared to wildfire hazard potential maps created by the USDA Forest service.

Use of density functional theory orbitals in the GVVPT2 variant of second-order multistate multireference perturbation theory.

PubMed

Hoffmann, Mark R; Helgaker, Trygve

2015-03-05

A new variation of the second-order generalized van Vleck perturbation theory (GVVPT2) for molecular electronic structure is suggested. In contrast to the established procedure, in which CASSCF or MCSCF orbitals are first obtained and subsequently used to define a many-electron model (or reference) space, the use of an orbital space obtained from the local density approximation (LDA) variant of density functional theory is considered. Through a final, noniterative diagonalization of an average Fock matrix within orbital subspaces, quasicanonical orbitals that are otherwise indistinguishable from quasicanonical orbitals obtained from a CASSCF or MCSCF calculation are obtained. Consequently, all advantages of the GVVPT2 method are retained, including use of macroconfigurations to define incomplete active spaces and rigorous avoidance of intruder states. The suggested variant is vetted on three well-known model problems: the symmetric stretching of the O-H bonds in water, the dissociation of N2, and the stretching of ground and excited states C2 to more than twice the equilibrium bond length of the ground state. It is observed that the LDA-based GVVPT2 calculations yield good results, of comparable quality to conventional CASSCF-based calculations. This is true even for the C2 model problem, in which the orbital space for each state was defined by the LDA orbitals. These results suggest that GVVPT2 can be applied to much larger problems than previously accessible.

Identification of pesticide varieties by detecting characteristics of Chlorella pyrenoidosa using Visible/Near infrared hyperspectral imaging and Raman microspectroscopy technology.

PubMed

Shao, Yongni; Li, Yuan; Jiang, Linjun; Pan, Jian; He, Yong; Dou, Xiaoming

2016-11-01

The main goal of this research is to examine the feasibility of applying Visible/Near-infrared hyperspectral imaging (Vis/NIR-HSI) and Raman microspectroscopy technology for non-destructive identification of pesticide varieties (glyphosate and butachlor). Both mentioned technologies were explored to investigate how internal elements or characteristics of Chlorella pyrenoidosa change when pesticides are applied, and in the meantime, to identify varieties of the pesticides during this procedure. Successive projections algorithm (SPA) was introduced to our study to identify seven most effective wavelengths. With those wavelengths suggested by SPA, a model of the linear discriminant analysis (LDA) was established to classify the pesticide varieties, and the correct classification rate of the SPA-LDA model reached as high as 100%. For the Raman technique, a few partial least squares discriminant analysis models were established with different preprocessing methods from which we also identified one processing approach that achieved the most optimal result. The sensitive wavelengths (SWs) which are related to algae's pigment were chosen, and a model of LDA was established with the correct identification reached a high level of 90.0%. The results showed that both Vis/NIR-HSI and Raman microspectroscopy techniques are capable to identify pesticide varieties in an indirect but effective way, and SPA is an effective wavelength extracting method. The SWs corresponding to microalgae pigments, which were influenced by pesticides, could also help to characterize different pesticide varieties and benefit the variety identification. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

An Anonymous Referee Report

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

Tobin, J. G.

2015-06-08

This is a combined experimental and theoretical study of the compound UO 2, UO 2(NO 3) 2(H 20) 6 and UO 0.75Pu 0.25O 2, using resonant inelastic x-ray scattering (RXIS), high resolution x-ray absorption (XAS) and LDA and LDA-U calculations.

Application of a Novel Tool for Diagnosing Bile Acid Diarrhoea

PubMed Central

Covington, James A.; Westenbrink, Eric W.; Ouaret, Nathalie; Harbord, Ruth; Bailey, Catherine; O'Connell, Nicola; Cullis, James; Williams, Nigel; Nwokolo, Chuka U.; Bardhan, Karna D.; Arasaradnam, Ramesh P.

2013-01-01

Bile acid diarrhoea (BAD) is a common disease that requires expensive imaging to diagnose. We have tested the efficacy of a new method to identify BAD, based on the detection of differences in volatile organic compounds (VOC) in urine headspace of BAD vs. ulcerative colitis and healthy controls. A total of 110 patients were recruited; 23 with BAD, 42 with ulcerative colitis (UC) and 45 controls. Patients with BAD also received standard imaging (Se75HCAT) for confirmation. Urine samples were collected and the headspace analysed using an AlphaMOS Fox 4000 electronic nose in combination with an Owlstone Lonestar Field Asymmetric Ion Mobility Spectrometer (FAIMS). A subset was also tested by gas chromatography, mass spectrometry (GCMS). Linear Discriminant Analysis (LDA) was used to explore both the electronic nose and FAIMS data. LDA showed statistical differences between the groups, with reclassification success rates (using an n-1 approach) at typically 83%. GCMS experiments confirmed these results and showed that patients with BAD had two chemical compounds, 2-propanol and acetamide, that were either not present or were in much reduced quantities in the ulcerative colitis and control samples. We believe that this work may lead to a new tool to diagnose BAD, which is cheaper, quicker and easier that current methods. PMID:24018955

Comparison of different methods for gender estimation from face image of various poses

NASA Astrophysics Data System (ADS)

Ishii, Yohei; Hongo, Hitoshi; Niwa, Yoshinori; Yamamoto, Kazuhiko

2003-04-01

Recently, gender estimation from face images has been studied for frontal facial images. However, it is difficult to obtain such facial images constantly in the case of application systems for security, surveillance and marketing research. In order to build such systems, a method is required to estimate gender from the image of various facial poses. In this paper, three different classifiers are compared in appearance-based gender estimation, which use four directional features (FDF). The classifiers are linear discriminant analysis (LDA), Support Vector Machines (SVMs) and Sparse Network of Winnows (SNoW). Face images used for experiments were obtained from 35 viewpoints. The direction of viewpoints varied +/-45 degrees horizontally, +/-30 degrees vertically at 15 degree intervals respectively. Although LDA showed the best performance for frontal facial images, SVM with Gaussian kernel was found the best performance (86.0%) for the facial images of 35 viewpoints. It is considered that SVM with Gaussian kernel is robust to changes in viewpoint when estimating gender from these results. Furthermore, the estimation rate was quite close to the average estimation rate at 35 viewpoints respectively. It is supposed that the methods are reasonable to estimate gender within the range of experimented viewpoints by learning face images from multiple directions by one class.

Dry selection and wet evaluation for the rational discovery of new anthelmintics

NASA Astrophysics Data System (ADS)

Marrero-Ponce, Yovani; Castañeda, Yeniel González; Vivas-Reyes, Ricardo; Vergara, Fredy Máximo; Arán, Vicente J.; Castillo-Garit, Juan A.; Pérez-Giménez, Facundo; Torrens, Francisco; Le-Thi-Thu, Huong; Pham-The, Hai; Montenegro, Yolanda Vera; Ibarra-Velarde, Froylán

2017-09-01

Helminths infections remain a major problem in medical and public health. In this report, atom-based 2D bilinear indices, a TOMOCOMD-CARDD (QuBiLs-MAS module) molecular descriptor family and linear discriminant analysis (LDA) were used to find models that differentiate among anthelmintic and non-anthelmintic compounds. Two classification models obtained by using non-stochastic and stochastic 2D bilinear indices, classified correctly 86.64% and 84.66%, respectively, in the training set. Equation 1(2) correctly classified 141(135) out of 165 [85.45%(81.82%)] compounds in external validation set. Another LDA models were performed in order to get the most likely mechanism of action of anthelmintics. The model shows an accuracy of 86.84% in the training set and 94.44% in the external prediction set. Finally, we carry out an experiment to predict the biological profile of our 'in-house' collections of indole, indazole, quinoxaline and cinnoline derivatives (∼200 compounds). Subsequently, we selected a group of nine of the theoretically most active structures. Then, these chemicals were tested in an in vitro assay and one good candidate (VA5-5c) as fasciolicide compound (100% of reduction at concentrations of 50 and 10 mg/L) was discovered.

Glass and liquid phase diagram of a polyamorphic monatomic system

NASA Astrophysics Data System (ADS)

Reisman, Shaina; Giovambattista, Nicolas

2013-02-01

We perform out-of-equilibrium molecular dynamics (MD) simulations of a monatomic system with Fermi-Jagla (FJ) pair potential interactions. This model system exhibits polyamorphism both in the liquid and glass state. The two liquids, low-density (LDL) and high-density liquid (HDL), are accessible in equilibrium MD simulations and can form two glasses, low-density (LDA) and high-density amorphous (HDA) solid, upon isobaric cooling. The FJ model exhibits many of the anomalous properties observed in water and other polyamorphic liquids and thus, it is an excellent model system to explore qualitatively the thermodynamic properties of such substances. The liquid phase behavior of the FJ model system has been previously characterized. In this work, we focus on the glass behavior of the FJ system. Specifically, we perform systematic isothermal compression and decompression simulations of LDA and HDA at different temperatures and determine "phase diagrams" for the glass state; these phase diagrams varying with the compression/decompression rate used. We obtain the LDA-to-HDA and HDA-to-LDA transition pressure loci, PLDA-HDA(T) and PHDA-LDA(T), respectively. In addition, the compression-induced amorphization line, at which the low-pressure crystal (LPC) transforms to HDA, PLPC-HDA(T), is determined. As originally proposed by Poole et al. [Phys. Rev. E 48, 4605 (1993)], 10.1103/PhysRevE.48.4605 simulations suggest that the PLDA-HDA(T) and PHDA-LDA(T) loci are extensions of the LDL-to-HDL and HDL-to-LDL spinodal lines into the glass domain. Interestingly, our simulations indicate that the PLPC-HDA(T) locus is an extension, into the glass domain, of the LPC metastability limit relative to the liquid. We discuss the effects of compression/decompression rates on the behavior of the PLDA-HDA(T), PHDA-LDA(T), PLPC-HDA(T) loci. The competition between glass polyamorphism and crystallization is also addressed. At our "fast rate," crystallization can be partially suppressed and the glass phase diagram can be related directly with the liquid phase diagram. However, at our "slow rate," crystallization cannot be prevented at intermediate temperatures, within the glass region. In these cases, multiple crystal-crystal transformations are found upon compression/decompression (polymorphism).

Pressure-induced transformations in glassy water: A computer simulation study using the TIP4P/2005 model

NASA Astrophysics Data System (ADS)

Wong, Jessina; Jahn, David A.; Giovambattista, Nicolas

2015-08-01

We study the pressure-induced transformations between low-density amorphous (LDA) and high-density amorphous (HDA) ice by performing out-of-equilibrium molecular dynamics (MD) simulations. We employ the TIP4P/2005 water model and show that this model reproduces qualitatively the LDA-HDA transformations observed experimentally. Specifically, the TIP4P/2005 model reproduces remarkably well the (i) structure (OO, OH, and HH radial distribution functions) and (ii) densities of LDA and HDA at P = 0.1 MPa and T = 80 K, as well as (iii) the qualitative behavior of ρ(P) during compression-induced LDA-to-HDA and decompression-induced HDA-to-LDA transformations. At the rates explored, the HDA-to-LDA transformation is less pronounced than in experiments. By studying the LDA-HDA transformations for a broad range of compression/decompression temperatures, we construct a "P-T phase diagram" for glassy water that is consistent with experiments and remarkably similar to that reported previously for ST2 water. This phase diagram is not inconsistent with the possibility of TIP4P/2005 water exhibiting a liquid-liquid phase transition at low temperatures. A comparison with previous MD simulation studies of SPC/E and ST2 water as well as experiments indicates that, overall, the TIP4P/2005 model performs better than the SPC/E and ST2 models. The effects of cooling and compression rates as well as aging on our MD simulations results are also discussed. The MD results are qualitatively robust under variations of cooling/compression rates (accessible in simulations) and are not affected by aging the hyperquenched glass for at least 1 μs. A byproduct of this work is the calculation of TIP4P/2005 water's diffusion coefficient D(T) at P = 0.1 MPa. It is found that, for T ≥ 210 K, D(T) ≈ (T - TMCT)-γ as predicted by mode coupling theory and in agreement with experiments. For TIP4P/2005 water, TMCT = 209 K and γ = 2.14, very close to the corresponding experimental values TMCT = 221 K and γ = 2.2.

Pressure-induced transformations in glassy water: A computer simulation study using the TIP4P/2005 model.

PubMed

Wong, Jessina; Jahn, David A; Giovambattista, Nicolas

2015-08-21

We study the pressure-induced transformations between low-density amorphous (LDA) and high-density amorphous (HDA) ice by performing out-of-equilibrium molecular dynamics (MD) simulations. We employ the TIP4P/2005 water model and show that this model reproduces qualitatively the LDA-HDA transformations observed experimentally. Specifically, the TIP4P/2005 model reproduces remarkably well the (i) structure (OO, OH, and HH radial distribution functions) and (ii) densities of LDA and HDA at P = 0.1 MPa and T = 80 K, as well as (iii) the qualitative behavior of ρ(P) during compression-induced LDA-to-HDA and decompression-induced HDA-to-LDA transformations. At the rates explored, the HDA-to-LDA transformation is less pronounced than in experiments. By studying the LDA-HDA transformations for a broad range of compression/decompression temperatures, we construct a "P-T phase diagram" for glassy water that is consistent with experiments and remarkably similar to that reported previously for ST2 water. This phase diagram is not inconsistent with the possibility of TIP4P/2005 water exhibiting a liquid-liquid phase transition at low temperatures. A comparison with previous MD simulation studies of SPC/E and ST2 water as well as experiments indicates that, overall, the TIP4P/2005 model performs better than the SPC/E and ST2 models. The effects of cooling and compression rates as well as aging on our MD simulations results are also discussed. The MD results are qualitatively robust under variations of cooling/compression rates (accessible in simulations) and are not affected by aging the hyperquenched glass for at least 1 μs. A byproduct of this work is the calculation of TIP4P/2005 water's diffusion coefficient D(T) at P = 0.1 MPa. It is found that, for T ≥ 210 K, D(T) ≈ (T - T(MCT))(-γ) as predicted by mode coupling theory and in agreement with experiments. For TIP4P/2005 water, T(MCT) = 209 K and γ = 2.14, very close to the corresponding experimental values T(MCT) = 221 K and γ = 2.2.

Clinical features of gastroduodenal injury associated with long-term low-dose aspirin therapy

PubMed Central

Iwamoto, Junichi; Saito, Yoshifumi; Honda, Akira; Matsuzaki, Yasushi

2013-01-01

Low-dose aspirin (LDA) is clinically used for the prevention of cardiovascular and cerebrovascular events with the advent of an aging society. On the other hand, a very low dose of aspirin (10 mg daily) decreases the gastric mucosal prostaglandin levels and causes significant gastric mucosal damage. The incidence of LDA-induced gastrointestinal mucosal injury and bleeding has increased. It has been noticed that the incidence of LDA-induced gastrointestinal hemorrhage has increased more than that of non-aspirin non-steroidal anti-inflammatory drug (NSAID)-induced lesions. The pathogenesis related to inhibition of cyclooxygenase (COX)-1 includes reduced mucosal flow, reduced mucus and bicarbonate secretion, and impaired platelet aggregation. The pathogenesis related to inhibition of COX-2 involves reduced angiogenesis and increased leukocyte adherence. The pathogenic mechanisms related to direct epithelial damage are acid back diffusion and impaired platelet aggregation. The factors associated with an increased risk of upper gastrointestinal (GI) complications in subjects taking LDA are aspirin dose, history of ulcer or upper GI bleeding, age > 70 years, concomitant use of non-aspirin NSAIDs including COX-2-selective NSAIDs, and Helicobacter pylori (H. pylori) infection. Moreover, no significant differences have been found between ulcer and non-ulcer groups in the frequency and severity of symptoms such as nausea, acid regurgitation, heartburn, and bloating. It has been shown that the ratios of ulcers located in the body, fundus and cardia are significantly higher in bleeding patients than the ratio of gastroduodenal ulcers in patients taking LDA. Proton pump inhibitors reduce the risk of developing gastric and duodenal ulcers. In contrast to NSAID-induced gastrointestinal ulcers, a well-tolerated histamine H2-receptor antagonist is reportedly effective in prevention of LDA-induced gastrointestinal ulcers. The eradication of H. pylori is equivalent to treatment with omeprazole in preventing recurrent bleeding. Continuous aspirin therapy for patients with gastrointestinal bleeding may increase the risk of recurrent bleeding but potentially reduces the mortality rates, as stopping aspirin therapy is associated with higher mortality rates. It is very important to prevent LDA-induced gastroduodenal ulcer complications including bleeding, and every effort should be exercised to prevent the bleeding complications. PMID:23555156

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.

Characterization of Thin Film Materials using SCAN meta-GGA, an Accurate Nonempirical Density Functional

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

Buda, I. G.; Lane, C.; Barbiellini, B.

We discuss self-consistently obtained ground-state electronic properties of monolayers of graphene and a number of ’beyond graphene’ compounds, including films of transition-metal dichalcogenides (TMDs), using the recently proposed strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) to the density functional theory. The SCAN meta-GGA results are compared with those based on the local density approximation (LDA) as well as the generalized gradient approximation (GGA). As expected, the GGA yields expanded lattices and softened bonds in relation to the LDA, but the SCAN meta-GGA systematically improves the agreement with experiment. Our study suggests the efficacy of the SCAN functionalmore » for accurate modeling of electronic structures of layered materials in high-throughput calculations more generally.« less

Fast detection of the fuzzy communities based on leader-driven algorithm

NASA Astrophysics Data System (ADS)

Fang, Changjian; Mu, Dejun; Deng, Zhenghong; Hu, Jun; Yi, Chen-He

2018-03-01

In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.

Characterization of Thin Film Materials using SCAN meta-GGA, an Accurate Nonempirical Density Functional

DOE PAGES

Buda, I. G.; Lane, C.; Barbiellini, B.; ...

2017-03-23

We discuss self-consistently obtained ground-state electronic properties of monolayers of graphene and a number of ’beyond graphene’ compounds, including films of transition-metal dichalcogenides (TMDs), using the recently proposed strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) to the density functional theory. The SCAN meta-GGA results are compared with those based on the local density approximation (LDA) as well as the generalized gradient approximation (GGA). As expected, the GGA yields expanded lattices and softened bonds in relation to the LDA, but the SCAN meta-GGA systematically improves the agreement with experiment. Our study suggests the efficacy of the SCAN functionalmore » for accurate modeling of electronic structures of layered materials in high-throughput calculations more generally.« less

Computer aided detection of clusters of microcalcifications on full field digital mammograms

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

Ge Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.

2006-08-15

We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual microcalcifications; regional clustering; and FP reduction for clustered microcalcifications. At the stage of FP reduction for individual microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered microcalcifications, morphological features and features derived from themore » artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was used to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case-based performance evaluation, a sensitivity of 70, 80, and 90 % can be achieved at 0.07, 0.17, and 0.65 FPs/image, respectively. We also used a data set of 216 mammograms negative for clustered microcalcifications to further estimate the FP rate of our CAD system. The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates.« less

Effect of intravenous amino acids on interdigestive antroduodenal motility and small bowel transit time.

PubMed

Gielkens, H A; van den Biggelaar, A; Vecht, J; Onkenhout, W; Lamers, C B; Masclee, A A

1999-02-01

Patients on total parenteral nutrition have an increased risk of developing gallstones because of gall bladder hypomotility. High dose amino acids may prevent biliary stasis by stimulating gall bladder emptying. To investigate whether intravenous amino acids also influence antroduodenal motility. Eight healthy volunteers received, on three separate occasions, intravenous saline (control), low dose amino acids (LDA), or high dose amino acids (HDA). Antroduodenal motility was recorded by perfusion manometry and duodenocaecal transit time (DCTT) using the lactulose breath hydrogen test. DCTT was significantly prolonged during LDA and HDA treatment compared with control. The interdigestive motor pattern was maintained and migrating motor complex (MMC) cycle length was significantly reduced during HDA compared with control and LDA due to a significant reduction in phase II duration. Significantly fewer phase IIIs originated in the gastric antrum during LDA and HDA compared with control. Duodenal phase II motility index was significantly reduced during HDA, but not during LDA, compared with control. Separate intravenous infusion of high doses of amino acids in healthy volunteers: (1) modulates interdigestive antroduodenal motility; (2) shortens MMC cycle length due to a reduced duration of phase II with a lower contractile incidence both in the antrum and duodenum (phase I remains unchanged whereas the effect on phase III is diverse: in the antrum phase III is suppressed and in the duodenum the frequency is increased); and (3) prolongs interdigestive DCTT.

Predicting structured metadata from unstructured metadata.

PubMed

Posch, Lisa; Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier

2016-01-01

Enormous amounts of biomedical data have been and are being produced by investigators all over the world. However, one crucial and limiting factor in data reuse is accurate, structured and complete description of the data or data about the data-defined as metadata. We propose a framework to predict structured metadata terms from unstructured metadata for improving quality and quantity of metadata, using the Gene Expression Omnibus (GEO) microarray database. Our framework consists of classifiers trained using term frequency-inverse document frequency (TF-IDF) features and a second approach based on topics modeled using a Latent Dirichlet Allocation model (LDA) to reduce the dimensionality of the unstructured data. Our results on the GEO database show that structured metadata terms can be the most accurately predicted using the TF-IDF approach followed by LDA both outperforming the majority vote baseline. While some accuracy is lost by the dimensionality reduction of LDA, the difference is small for elements with few possible values, and there is a large improvement over the majority classifier baseline. Overall this is a promising approach for metadata prediction that is likely to be applicable to other datasets and has implications for researchers interested in biomedical metadata curation and metadata prediction. © The Author(s) 2016. Published by Oxford University Press.

Predicting structured metadata from unstructured metadata

PubMed Central

Posch, Lisa; Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier

2016-01-01

Enormous amounts of biomedical data have been and are being produced by investigators all over the world. However, one crucial and limiting factor in data reuse is accurate, structured and complete description of the data or data about the data—defined as metadata. We propose a framework to predict structured metadata terms from unstructured metadata for improving quality and quantity of metadata, using the Gene Expression Omnibus (GEO) microarray database. Our framework consists of classifiers trained using term frequency-inverse document frequency (TF-IDF) features and a second approach based on topics modeled using a Latent Dirichlet Allocation model (LDA) to reduce the dimensionality of the unstructured data. Our results on the GEO database show that structured metadata terms can be the most accurately predicted using the TF-IDF approach followed by LDA both outperforming the majority vote baseline. While some accuracy is lost by the dimensionality reduction of LDA, the difference is small for elements with few possible values, and there is a large improvement over the majority classifier baseline. Overall this is a promising approach for metadata prediction that is likely to be applicable to other datasets and has implications for researchers interested in biomedical metadata curation and metadata prediction. Database URL: http://www.yeastgenome.org/ PMID:28637268

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

PubMed

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

2012-01-01

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

Quantification of wind flow in the European Mars Simulation Wind Tunnel Facility

NASA Astrophysics Data System (ADS)

Holstein-Rathlou, C.; Merrison, J. P.; Iversen, J. J.; Nornberg, P.

2012-04-01

We present the European Mars Simulation Wind Tunnel facility, a unique prototype facility capable of simulating a wide range of environmental conditions, such as those which can be found at the surface of Earth or Mars. The chamber complements several other large-scale simulation facilities at Aarhus University, Denmark. The facility consists of a 50 m3 environmental chamber capable of operating at low pressure (0.02 - 1000 mbar) and cryogenic temperatures (-130 °C up to +60 °C). This chamber houses a re-circulating wind tunnel capable of generating wind speeds up to 25 m/s and has a dust injection system that can produce suspended particulates (aerosols). It employs a unique LED based optical illumination system (solar simulator) and an advanced network based control system. Laser based optoelectronic instrumentation is used to quantify and monitor wind flow, dust suspension and deposition. This involves a commercial Laser Doppler Anemometer (LDA) and a Particle Dynamics Analysis receiver (PDA), which are small laser based instruments specifically designed for measuring wind speed and sizes of particles situated in a wind flow. Wind flow calibrations will be performed with the LDA system and presented. Pressure and temperature calibrations will follow in order to enable the facility to be used for the testing, development, calibration and comparison of e.g. meteorological sensors under a wide range of environmental conditions as well as multi-disciplinary scientific studies. The wind tunnel is accessible to international collaborators and space agencies for instrument testing, calibration and qualification. It has been financed by the European Space Agency (ESA) as well as the Aarhus University Science Faculty and the Villum Kann Rasmussen Foundation.

LDA Assessments. Learning Times. Volume 7, Number 1

ERIC Educational Resources Information Center

LDA Minnesota, 2008

2008-01-01

This issue of "Learning Times" discusses how LDA assessments for children and adults provide self-understanding, renewed hope, and improved achievement. It also includes: (1) Where do Your Candidates Stand on Controlling Toxic Chemicals?; (2) ADHD (Attention Deficit Hyperactivity Disorder) Tips: Back-to-School (Ty Sassaman); (3) Healthy…

LDA-Mediated Synthesis of Triarylmethanes by Arylation of Diarylmethanes with Fluoroarenes at Room Temperature.

PubMed

Ji, Xinfei; Huang, Tao; Wu, Wei; Liang, Fang; Cao, Song

2015-10-16

A practical and convenient approach for the secondary C(sp(3))-H arylation of diarylmethanes with various fluoroarenes is described. The reaction proceeds smoothly in the presence of LDA (lithium diisopropylamide) at room temperature and affords triarylmethanes in moderate to high yields.

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

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.

A LDA-based approach to promoting ranking diversity for genomics information retrieval.

PubMed

Chen, Yan; Yin, Xiaoshi; Li, Zhoujun; Hu, Xiaohua; Huang, Jimmy Xiangji

2012-06-11

In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc. Hence, it is important of a biomedical IR system to be able to provide relevant and diverse answers to fulfill biologists' information needs. However traditional IR model only concerns with the relevance between retrieved documents and user query, but does not take redundancy between retrieved documents into account. This will lead to high redundancy and low diversity in the retrieval ranked lists. In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. We perform our approach on TREC 2007 Genomics collection and two distinctive IR baseline runs, which can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track. The proposed method is the first study of adopting topic model to genomics information retrieval, and demonstrates its effectiveness in promoting ranking diversity as well as in improving relevance of ranked lists of genomics search. Moreover, we proposes a distance measure to quantify how much a passage can increase topical diversity by considering both topical importance and topical coefficient by LDA, and the distance measure is a modified Euclidean distance.

User intent prediction with a scaled conjugate gradient trained artificial neural network for lower limb amputees using a powered prosthesis.

PubMed

Woodward, Richard B; Spanias, John A; Hargrove, Levi J

2016-08-01

Powered lower limb prostheses have the ability to provide greater mobility for amputee patients. Such prostheses often have pre-programmed modes which can allow activities such as climbing stairs and descending ramps, something which many amputees struggle with when using non-powered limbs. Previous literature has shown how pattern classification can allow seamless transitions between modes with a high accuracy and without any user interaction. Although accurate, training and testing each subject with their own dependent data is time consuming. By using subject independent datasets, whereby a unique subject is tested against a pooled dataset of other subjects, we believe subject training time can be reduced while still achieving an accurate classification. We present here an intent recognition system using an artificial neural network (ANN) with a scaled conjugate gradient learning algorithm to classify gait intention with user-dependent and independent datasets for six unilateral lower limb amputees. We compare these results against a linear discriminant analysis (LDA) classifier. The ANN was found to have significantly lower classification error (P<;0.05) than LDA with all user-dependent step-types, as well as transitional steps for user-independent datasets. Both types of classifiers are capable of making fast decisions; 1.29 and 2.83 ms for the LDA and ANN respectively. These results suggest that ANNs can provide suitable and accurate offline classification in prosthesis gait prediction.

Determination of colonoscopy indication from administrative claims data.

PubMed

Ko, Cynthia W; Dominitz, Jason A; Neradilek, Moni; Polissar, Nayak; Green, Pam; Kreuter, William; Baldwin, Laura-Mae

2014-04-01

Colonoscopy outcomes, such as polyp detection or complication rates, may differ by procedure indication. To develop methods to classify colonoscopy indications from administrative data, facilitating study of colonoscopy quality and outcomes. We linked 14,844 colonoscopy reports from the Clinical Outcomes Research Initiative, a national repository of endoscopic reports, to the corresponding Medicare Carrier and Outpatient File claims. Colonoscopy indication was determined from the procedure reports. We developed algorithms using classification and regression trees and linear discriminant analysis (LDA) to classify colonoscopy indication. Predictor variables included ICD-9CM and CPT/HCPCS codes present on the colonoscopy claim or in the 12 months prior, patient demographics, and site of colonoscopy service. Algorithms were developed on a training set of 7515 procedures, then validated using a test set of 7329 procedures. Sensitivity was lowest for identifying average-risk screening colonoscopies, varying between 55% and 86% for the different algorithms, but specificity for this indication was consistently over 95%. Sensitivity for diagnostic colonoscopy varied between 77% and 89%, with specificity between 55% and 87%. Algorithms with classification and regression trees with 7 variables or LDA with 10 variables had similar overall accuracy, and generally lower accuracy than the algorithm using LDA with 30 variables. Algorithms using Medicare claims data have moderate sensitivity and specificity for colonoscopy indication, and will be useful for studying colonoscopy quality in this population. Further validation may be needed before use in alternative populations.

UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization.

PubMed

Choo, Jaegul; Lee, Changhyun; Reddy, Chandan K; Park, Haesun

2013-12-01

Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets.

Latent Dirichlet Allocation (LDA) for Sentiment Analysis Toward Tourism Review in Indonesia

NASA Astrophysics Data System (ADS)

Putri, IR; Kusumaningrum, R.

2017-01-01

The tourism industry is one of foreign exchange sector, which has considerable potential development in Indonesia. Compared to other Southeast Asia countries such as Malaysia with 18 million tourists and Singapore 20 million tourists, Indonesia which is the largest Southeast Asia’s country have failed to attract higher tourist numbers compared to its regional peers. Indonesia only managed to attract 8,8 million foreign tourists in 2013, with the value of foreign tourists each year which is likely to decrease. Apart from the infrastructure problems, marketing and managing also form of obstacles for tourism growth. An evaluation and self-analysis should be done by the stakeholder to respond toward this problem and capture opportunities that related to tourism satisfaction from tourists review. Recently, one of technology to answer this problem only relying on the subjective of statistical data which collected by voting or grading from user randomly. So the result is still not to be accountable. Thus, we proposed sentiment analysis with probabilistic topic model using Latent Dirichlet Allocation (LDA) method to be applied for reading general tendency from tourist review into certain topics that can be classified toward positive and negative sentiment.

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

PubMed

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

2014-11-01

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

A density-functional study on the electronic and vibrational properties of layered antimony telluride.

PubMed

Stoffel, Ralf P; Deringer, Volker L; Simon, Ronnie E; Hermann, Raphaël P; Dronskowski, Richard

2015-03-04

We present a comprehensive survey of electronic and lattice-dynamical properties of crystalline antimony telluride (Sb2Te3). In a first step, the electronic structure and chemical bonding have been investigated, followed by calculations of the atomic force constants, phonon dispersion relationships and densities of states. Then, (macroscopic) physical properties of Sb2Te3 have been computed, namely, the atomic thermal displacement parameters, the Grüneisen parameter γ, the volume expansion of the lattice, and finally the bulk modulus B. We compare theoretical results from three popular and economic density-functional theory (DFT) approaches: the local density approximation (LDA), the generalized gradient approximation (GGA), and a posteriori dispersion corrections to the latter. Despite its simplicity, the LDA shows excellent performance for all properties investigated-including the Grüneisen parameter, which only the LDA is able to recover with confidence. In the absence of computationally more demanding hybrid DFT methods, the LDA seems to be a good choice for further lattice dynamical studies of Sb2Te3 and related layered telluride materials.

Angle-resolved Photoemission of CeCoIn5: Detailed Comparison to LDA and LDA+DMFT

NASA Astrophysics Data System (ADS)

Allen, J. W.

2008-03-01

Highly-automated photon-dependent angle resolved photoemission spectroscopy (ARPES) in the energy range of 80-200 eV has been used to characterize the three dimensional (3D) Fermi surface (FS) topology and electronic band structure of cleaved single crystals of CeCoIn5. The sample temperature of 26K is well below the lattice coherence onset temperature of 45K found in a recent ``two fluid'' analysis of transport data. Detailed comparison of ARPES FS contours to LDA calculations for the Ce 4f electrons treated as itinerant or confined to the core reveals remarkable agreement to fine topological details of the f-core calculations. Also in agreement to the f-core calculations is the experimental absence of extra electron-like contours predicted in the f-itinerant calculation, originating from α and β bands re-entrant below EF along Z-A. Finally, the areas enclosed by FS contours for the α and β bands are significantly smaller than are found in very low temperature CeCoIn5 de Haas van Alphen data that agrees generally with the f-itinerant calculation. It is concluded that clear signatures of coherence in the transport data can develop at temperatures for which the f-electrons are not yet included in the FS. In this connection, comparison will also be made to recent T-dependent LDA+DMFT calculations for CeIrIn5. This work was done in collaboration with J. D. Denlinger, Feng Wang, R. S. Singh, K. Rossnagel, S. Elgazzar, P. M. Oppeneer, V. S. Zapf and M. B. Maple, and was supported by the U.S. DOE (DE-AC03-76SF00098 at the ALS, DE-FG02-07ER46379 at UM for current work, DE FG02-04ER-46105 at UCSD), by the U.S. NSF (DMR-03-02825 at UM for initial work, DMR-03-35173 at UCSD) and by the Swedish Research Council (VR) and the European Commission (JRC-ITU).

On application of image analysis and natural language processing for music search

NASA Astrophysics Data System (ADS)

Gwardys, Grzegorz

2013-10-01

In this paper, I investigate a problem of finding most similar music tracks using, popular in Natural Language Processing, techniques like: TF-IDF and LDA. I de ned document as music track. Each music track is transformed to spectrogram, thanks that, I can use well known techniques to get words from images. I used SURF operation to detect characteristic points and novel approach for their description. The standard kmeans was used for clusterization. Clusterization is here identical with dictionary making, so after that I can transform spectrograms to text documents and perform TF-IDF and LDA. At the final, I can make a query in an obtained vector space. The research was done on 16 music tracks for training and 336 for testing, that are splitted in four categories: Hiphop, Jazz, Metal and Pop. Although used technique is completely unsupervised, results are satisfactory and encouraging to further research.

Classification of adulterated honeys by multivariate analysis.

PubMed

Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

2017-06-01

In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Near-infrared-excited confocal Raman spectroscopy advances in vivo diagnosis of cervical precancer.

PubMed

Duraipandian, Shiyamala; Zheng, Wei; Ng, Joseph; Low, Jeffrey J H; Ilancheran, Arunachalam; Huang, Zhiwei

2013-06-01

Raman spectroscopy is a unique optical technique that can probe the changes of vibrational modes of biomolecules associated with tissue premalignant transformation. This study evaluates the clinical utility of confocal Raman spectroscopy over near-infrared (NIR) autofluorescence (AF) spectroscopy and composite NIR AF/Raman spectroscopy for improving early diagnosis of cervical precancer in vivo at colposcopy. A rapid NIR Raman system coupled with a ball-lens fiber-optic confocal Raman probe was utilized for in vivo NIR AF/Raman spectral measurements of the cervix. A total of 1240 in vivo Raman spectra [normal (n=993), dysplasia (n=247)] were acquired from 84 cervical patients. Principal components analysis (PCA) and linear discriminant analysis (LDA) together with a leave-one-patient-out, cross-validation method were used to extract the diagnostic information associated with distinctive spectroscopic modalities. The diagnostic ability of confocal Raman spectroscopy was evaluated using the PCA-LDA model developed from the significant principal components (PCs) [i.e., PC4, 0.0023%; PC5, 0.00095%; PC8, 0.00022%, (p<0.05)], representing the primary tissue Raman features (e.g., 854, 937, 1095, 1253, 1311, 1445, and 1654 cm(-1)). Confocal Raman spectroscopy coupled with PCA-LDA modeling yielded the diagnostic accuracy of 84.1% (a sensitivity of 81.0% and a specificity of 87.1%) for in vivo discrimination of dysplastic cervix. The receiver operating characteristic curves further confirmed that the best classification was achieved using confocal Raman spectroscopy compared to the composite NIR AF/Raman spectroscopy or NIR AF spectroscopy alone. This study illustrates that confocal Raman spectroscopy has great potential to improve early diagnosis of cervical precancer in vivo during clinical colposcopy.

Contingency Management interventions for non-prescribed drug use during treatment for opiate addiction: A systematic review and meta-analysis.

PubMed

Ainscough, Tom S; McNeill, Ann; Strang, John; Calder, Robert; Brose, Leonie S

2017-09-01

Use of non-prescribed drugs during treatment for opiate addiction reduces treatment success, creating a need for effective interventions. This review aimed to assess the efficacy of contingency management, a behavioural treatment that uses rewards to encourage desired behaviours, for treating non-prescribed drug use during opiate addiction treatment. A systematic search of the databases Embase, PsychInfo, PsychArticles and Medline from inception to March 2015 was performed. Random effects meta-analysis tested the use of contingency management to treat the use of drugs during opiate addiction treatment, using either longest duration of abstinence (LDA) or percentage of negative samples (PNS). Random effects moderator analyses were performed for six potential moderators: drug targeted for intervention, decade in which the study was carried out, study quality, intervention duration, type of reinforcer, and form of opiate treatment. The search returned 3860 papers; 22 studies met inclusion criteria and were meta-analysed. Follow-up data was only available for three studies, so all analyses used end of treatment data. Contingency management performed significantly better than control in reducing drug use measured using LDA (d=0.57, 95% CI: 0.42-0.72) or PNS (d=0.41) (95% CI: 0.28-0.54). This was true for all drugs other than opiates. The only significant moderator was drug targeted (LDA: Q=10.75, p=0.03). Contingency management appears to be efficacious for treating most drug use during treatment for opiate addiction. Further research is required to ascertain the full effects of moderating variables, and longer term effects. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

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

PubMed

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

2011-02-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-06-01

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

A microcomputer based frequency-domain processor for laser Doppler anemometry

NASA Technical Reports Server (NTRS)

Horne, W. Clifton; Adair, Desmond

1988-01-01

A prototype multi-channel laser Doppler anemometry (LDA) processor was assembled using a wideband transient recorder and a microcomputer with an array processor for fast Fourier transform (FFT) computations. The prototype instrument was used to acquire, process, and record signals from a three-component wind tunnel LDA system subject to various conditions of noise and flow turbulence. The recorded data was used to evaluate the effectiveness of burst acceptance criteria, processing algorithms, and selection of processing parameters such as record length. The recorded signals were also used to obtain comparative estimates of signal-to-noise ratio between time-domain and frequency-domain signal detection schemes. These comparisons show that the FFT processing scheme allows accurate processing of signals for which the signal-to-noise ratio is 10 to 15 dB less than is practical using counter processors.

A Beneficial Effect of Low-Dose Aspirin in a Murine Model of Active Tuberculosis

PubMed Central

Kroesen, Vera Marie; Rodríguez-Martínez, Paula; García, Eric; Rosales, Yaiza; Díaz, Jorge; Martín-Céspedes, Montse; Tapia, Gustavo; Sarrias, Maria Rosa; Cardona, Pere-Joan; Vilaplana, Cristina

2018-01-01

An excessive, non-productive host-immune response is detrimental in active, chronic tuberculosis (TB) disease as it typically leads to tissue damage. Given their anti-inflammatory effect, non-steroidal anti-inflammatory drugs can potentially attenuate excessive inflammation in active TB disease. As such, we investigated the prophylactic and therapeutic effect of low-dose aspirin (LDA) (3 mg/kg/day), either alone or in combination with common anti-TB treatment or BCG vaccination, on disease outcome in an experimental murine model of active TB. Survival rate, bacillary load (BL) in lungs, and lung pathology were measured. The possible mechanism of action of LDA on the host’s immune response was also evaluated by measuring levels of CD5L/AIM, selected cytokines/chemokines and other inflammatory markers in serum and lung tissue. LDA increased survival, had anti-inflammatory effects, reduced lung pathology, and decreased bacillary load in late-stage TB disease. Moreover, in combination with common anti-TB treatment, LDA enhanced survival and reduced lung pathology. Results from the immunological studies suggest the anti-inflammatory action of LDA at both a local and a systemic level. Our results showed a systemic decrease in neutrophilic recruitment, decreased levels of acute-phase reaction cytokines (IL-6, IL-1β, and TNF-α) at late stage and a delay in the decrease in T cell response (in terms of IFN-γ, IL-2, and IL-10 serum levels) that occurs during the course of Mycobacterium tuberculosis infection. An anti-inflammatory milieu was detected in the lung, with less neutrophil recruitment and lower levels of tissue factor. In conclusion, LDA may be beneficial as an adjunct to standard anti-TB treatment in the later stage of active TB by reducing excess, non-productive inflammation, while enhancing Th1-cell responses for elimination of the bacilli. PMID:29740435

MATRIX DISCRIMINANT ANALYSIS WITH APPLICATION TO COLORIMETRIC SENSOR ARRAY DATA

PubMed Central

Suslick, Kenneth S.

2014-01-01

With the rapid development of nano-technology, a “colorimetric sensor array” (CSA) which is referred to as an optical electronic nose has been developed for the identification of toxicants. Unlike traditional sensors which rely on a single chemical interaction, CSA can measure multiple chemical interactions by using chemo-responsive dyes. The color changes of the chemo-responsive dyes are recorded before and after exposure to toxicants and serve as a template for classification. The color changes are digitalized in the form of a matrix with rows representing dye effects and columns representing the spectrum of colors. Thus, matrix-classification methods are highly desirable. In this article, we develop a novel classification method, matrix discriminant analysis (MDA), which is a generalization of linear discriminant analysis (LDA) for the data in matrix form. By incorporating the intrinsic matrix-structure of the data in discriminant analysis, the proposed method can improve CSA’s sensitivity and more importantly, specificity. A penalized MDA method, PMDA, is also introduced to further incorporate sparsity structure in discriminant function. Numerical studies suggest that the proposed MDA and PMDA methods outperform LDA and other competing discriminant methods for matrix predictors. The asymptotic consistency of MDA is also established. R code and data are available online as supplementary material. PMID:26783371

Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems

NASA Astrophysics Data System (ADS)

Chaa, Mourad; Boukezzoula, Naceur-Eddine; Attia, Abdelouahab

2017-01-01

Two types of scores extracted from two-dimensional (2-D) and three-dimensional (3-D) palmprint for personal recognition systems are merged, introducing a local image descriptor for 2-D palmprint-based recognition systems, named bank of binarized statistical image features (B-BSIF). The main idea of B-BSIF is that the extracted histograms from the binarized statistical image features (BSIF) code images (the results of applying the different BSIF descriptor size with the length 12) are concatenated into one to produce a large feature vector. 3-D palmprint contains the depth information of the palm surface. The self-quotient image (SQI) algorithm is applied for reconstructing illumination-invariant 3-D palmprint images. To extract discriminative Gabor features from SQI images, Gabor wavelets are defined and used. Indeed, the dimensionality reduction methods have shown their ability in biometrics systems. Given this, a principal component analysis (PCA)+linear discriminant analysis (LDA) technique is employed. For the matching process, the cosine Mahalanobis distance is applied. Extensive experiments were conducted on a 2-D and 3-D palmprint database with 10,400 range images from 260 individuals. Then, a comparison was made between the proposed algorithm and other existing methods in the literature. Results clearly show that the proposed framework provides a higher correct recognition rate. Furthermore, the best results were obtained by merging the score of B-BSIF descriptor with the score of the SQI+Gabor wavelets+PCA+LDA method, yielding an equal error rate of 0.00% and a recognition rate of rank-1=100.00%.

Electronic structure of LiGaS 2

NASA Astrophysics Data System (ADS)

Atuchin, V. V.; Isaenko, L. I.; Kesler, V. G.; Lobanov, S.; Huang, H.; Lin, Z. S.

2009-04-01

X-ray photoelectron spectroscopy (XPS) measurement has been performed to determine the valence band structure of LiGaS 2 crystals. The experimental measurement is compared with the electronic structure obtained from the density functional calculations. It is found that the Ga 3d states in the XPS spectrum are much higher than the calculated results. In order to eliminate this discrepancy, the LDA+ U method is employed and reasonable agreement is achieved. Further calculations show that the difference of the linear and nonlinear optical coefficients between LDA and LDA+ U calculations is negligibly small, indicating that the Ga 3d states are actually independent of the excited properties of LiGaS 2 crystals since they are located at a very deep position in the valence bands.

DFT calculations of electronic and optical properties of SrS with LDA, GGA and mGGA functionals

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

Sharma, Shatendra, E-mail: shatendra@gmai.com; Sharma, Jyotsna; Sharma, Yogita

2016-05-06

The theoretical investigations of electronic and optical properties of SrS are made using the first principle DFT calculations. The calculations are performed for the local-density approximation (LDA), generalized gradient approximation (GGA) and for an alternative form of GGA i.e. metaGGA for both rock salt type (B1, Fm3m) and cesium chloride (B2, Pm3m) structures. The band structure, density of states and optical spectra are calculated under various available functional. The calculations with LDA and GGA functional underestimate the values of band gaps with all functional, however the values with mGGA show reasonably good agreement with experimental and those calculated by usingmore » other methods.« less

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

Singh, Prashant; Harbola, Manoj K.; Johnson, Duane D.

Here, this work constitutes a comprehensive and improved account of electronic-structure and mechanical properties of silicon-nitride (more » $${\\rm Si}_{3}$$ $${\\rm N}_{4}$$ ) polymorphs via van Leeuwen and Baerends (LB) exchange-corrected local density approximation (LDA) that enforces the exact exchange potential asymptotic behavior. The calculated lattice constant, bulk modulus, and electronic band structure of $${\\rm Si}_{3}$$ $${\\rm N}_{4}$$ polymorphs are in good agreement with experimental results. We also show that, for a single electron in a hydrogen atom, spherical well, or harmonic oscillator, the LB-corrected LDA reduces the (self-interaction) error to exact total energy to ~10%, a factor of three to four lower than standard LDA, due to a dramatically improved representation of the exchange-potential.« less

Communication: Disorder-suppressed vibrational relaxation in vapor-deposited high-density amorphous ice

NASA Astrophysics Data System (ADS)

Shalit, Andrey; Perakis, Fivos; Hamm, Peter

2014-04-01

We apply two-dimensional infrared spectroscopy to differentiate between the two polyamorphous forms of glassy water, low-density (LDA) and high-density (HDA) amorphous ices, that were obtained by slow vapor deposition at 80 and 11 K, respectively. Both the vibrational lifetime and the bandwidth of the 1-2 transition of the isolated OD stretch vibration of HDO in H2O exhibit characteristic differences when comparing hexagonal (Ih), LDA, and HDA ices, which we attribute to the different local structures - in particular the presence of interstitial waters in HDA ice - that cause different delocalization lengths of intermolecular phonon degrees of freedom. Moreover, temperature dependent measurements show that the vibrational lifetime closely follows the structural transition between HDA and LDA phases.

Short, intermediate and long range order in amorphous ices

NASA Astrophysics Data System (ADS)

Martelli, Fausto; Torquato, Salvatore; Giovanbattista, Nicolas; Car, Roberto

Water exhibits polyamorphism, i.e., it exists in more than one amorphous state. The most common forms of glassy water are the low-density amorphous (LDA) and the high-density amorphous (HDA) ices. LDA, the most abundant form of ice in the Universe, transforms into HDA upon isothermal compression. We model the transformation of LDA into HDA under isothermal compression with classical molecular dynamics simulations. We analyze the molecular structures with a recently introduced scalar order metric to measure short and intermediate range order. In addition, we rank the structures by their degree of hyperuniformity, i.e.,the extent to which long range density fluctuations are suppressed. F.M. and R.C. acknowledge support from the Department of Energy (DOE) under Grant No. DE-SC0008626.

Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents.

PubMed

Speck-Planche, Alejandro; Kleandrova, Valeria V; Luan, Feng; Cordeiro, M Natália D S

2012-08-01

The discovery of new and more potent anti-cancer agents constitutes one of the most active fields of research in chemotherapy. Colorectal cancer (CRC) is one of the most studied cancers because of its high prevalence and number of deaths. In the current pharmaceutical design of more efficient anti-CRC drugs, the use of methodologies based on Chemoinformatics has played a decisive role, including Quantitative-Structure-Activity Relationship (QSAR) techniques. However, until now, there is no methodology able to predict anti-CRC activity of compounds against more than one CRC cell line, which should constitute the principal goal. In an attempt to overcome this problem we develop here the first multi-target (mt) approach for the virtual screening and rational in silico discovery of anti-CRC agents against ten cell lines. Here, two mt-QSAR classification models were constructed using a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted from the molecules and their contributions to anti-CRC activity were calculated using mt-QSAR-LDA model. Several fragments were identified as potential substructural features responsible for the anti-CRC activity and new molecules designed from those fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-CRC agents. Copyright © 2012 Elsevier Ltd. All rights reserved.

Computer-aided classification of breast microcalcification clusters: merging of features from image processing and radiologists

NASA Astrophysics Data System (ADS)

Lo, Joseph Y.; Gavrielides, Marios A.; Markey, Mia K.; Jesneck, Jonathan L.

2003-05-01

We developed an ensemble classifier for the task of computer-aided diagnosis of breast microcalcification clusters,which are very challenging to characterize for radiologists and computer models alike. The purpose of this study is to help radiologists identify whether suspicious calcification clusters are benign vs. malignant, such that they may potentially recommend fewer unnecessary biopsies for actually benign lesions. The data consists of mammographic features extracted by automated image processing algorithms as well as manually interpreted by radiologists according to a standardized lexicon. We used 292 cases from a publicly available mammography database. From each cases, we extracted 22 image processing features pertaining to lesion morphology, 5 radiologist features also pertaining to morphology, and the patient age. Linear discriminant analysis (LDA) models were designed using each of the three data types. Each local model performed poorly; the best was one based upon image processing features which yielded ROC area index AZ of 0.59 +/- 0.03 and partial AZ above 90% sensitivity of 0.08 +/- 0.03. We then developed ensemble models using different combinations of those data types, and these models all improved performance compared to the local models. The final ensemble model was based upon 5 features selected by stepwise LDA from all 28 available features. This ensemble performed with AZ of 0.69 +/- 0.03 and partial AZ of 0.21 +/- 0.04, which was statistically significantly better than the model based on the image processing features alone (p<0.001 and p=0.01 for full and partial AZ respectively). This demonstrated the value of the radiologist-extracted features as a source of information for this task. It also suggested there is potential for improved performance using this ensemble classifier approach to combine different sources of currently available data.

Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock.

PubMed

Papaioannou, Vasilios E; Chouvarda, Ioanna G; Maglaveras, Nikos K; Pneumatikos, Ioannis A

2012-12-12

Even though temperature is a continuous quantitative variable, its measurement has been considered a snapshot of a process, indicating whether a patient is febrile or afebrile. Recently, other diagnostic techniques have been proposed for the association between different properties of the temperature curve with severity of illness in the Intensive Care Unit (ICU), based on complexity analysis of continuously monitored body temperature. In this study, we tried to assess temperature complexity in patients with systemic inflammation during a suspected ICU-acquired infection, by using wavelets transformation and multiscale entropy of temperature signals, in a cohort of mixed critically ill patients. Twenty-two patients were enrolled in the study. In five, systemic inflammatory response syndrome (SIRS, group 1) developed, 10 had sepsis (group 2), and seven had septic shock (group 3). All temperature curves were studied during the first 24 hours of an inflammatory state. A wavelet transformation was applied, decomposing the signal in different frequency components (scales) that have been found to reflect neurogenic and metabolic inputs on temperature oscillations. Wavelet energy and entropy per different scales associated with complexity in specific frequency bands and multiscale entropy of the whole signal were calculated. Moreover, a clustering technique and a linear discriminant analysis (LDA) were applied for permitting pattern recognition in data sets and assessing diagnostic accuracy of different wavelet features among the three classes of patients. Statistically significant differences were found in wavelet entropy between patients with SIRS and groups 2 and 3, and in specific ultradian bands between SIRS and group 3, with decreased entropy in sepsis. Cluster analysis using wavelet features in specific bands revealed concrete clusters closely related with the groups in focus. LDA after wrapper-based feature selection was able to classify with an accuracy of more than 80% SIRS from the two sepsis groups, based on multiparametric patterns of entropy values in the very low frequencies and indicating reduced metabolic inputs on local thermoregulation, probably associated with extensive vasodilatation. We suggest that complexity analysis of temperature signals can assess inherent thermoregulatory dynamics during systemic inflammation and has increased discriminating value in patients with infectious versus noninfectious conditions, probably associated with severity of illness.

Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock

PubMed Central

2012-01-01

Background Even though temperature is a continuous quantitative variable, its measurement has been considered a snapshot of a process, indicating whether a patient is febrile or afebrile. Recently, other diagnostic techniques have been proposed for the association between different properties of the temperature curve with severity of illness in the Intensive Care Unit (ICU), based on complexity analysis of continuously monitored body temperature. In this study, we tried to assess temperature complexity in patients with systemic inflammation during a suspected ICU-acquired infection, by using wavelets transformation and multiscale entropy of temperature signals, in a cohort of mixed critically ill patients. Methods Twenty-two patients were enrolled in the study. In five, systemic inflammatory response syndrome (SIRS, group 1) developed, 10 had sepsis (group 2), and seven had septic shock (group 3). All temperature curves were studied during the first 24 hours of an inflammatory state. A wavelet transformation was applied, decomposing the signal in different frequency components (scales) that have been found to reflect neurogenic and metabolic inputs on temperature oscillations. Wavelet energy and entropy per different scales associated with complexity in specific frequency bands and multiscale entropy of the whole signal were calculated. Moreover, a clustering technique and a linear discriminant analysis (LDA) were applied for permitting pattern recognition in data sets and assessing diagnostic accuracy of different wavelet features among the three classes of patients. Results Statistically significant differences were found in wavelet entropy between patients with SIRS and groups 2 and 3, and in specific ultradian bands between SIRS and group 3, with decreased entropy in sepsis. Cluster analysis using wavelet features in specific bands revealed concrete clusters closely related with the groups in focus. LDA after wrapper-based feature selection was able to classify with an accuracy of more than 80% SIRS from the two sepsis groups, based on multiparametric patterns of entropy values in the very low frequencies and indicating reduced metabolic inputs on local thermoregulation, probably associated with extensive vasodilatation. Conclusions We suggest that complexity analysis of temperature signals can assess inherent thermoregulatory dynamics during systemic inflammation and has increased discriminating value in patients with infectious versus noninfectious conditions, probably associated with severity of illness. PMID:22424316

Bimodal spectroscopic evaluation of ultra violet-irradiated mouse skin inflammatory and precancerous stages: instrumentation, spectral feature extraction/selection and classification (k-NN, LDA and SVM)

NASA Astrophysics Data System (ADS)

Díaz-Ayil, G.; Amouroux, M.; Blondel, W. C. P. M.; Bourg-Heckly, G.; Leroux, A.; Guillemin, F.; Granjon, Y.

2009-07-01

This paper deals with the development and application of in vivo spatially-resolved bimodal spectroscopy (AutoFluorescence AF and Diffuse Reflectance DR), to discriminate various stages of skin precancer in a preclinical model (UV-irradiated mouse): Compensatory Hyperplasia CH, Atypical Hyperplasia AH and Dysplasia D. A programmable instrumentation was developed for acquiring AF emission spectra using 7 excitation wavelengths: 360, 368, 390, 400, 410, 420 and 430 nm, and DR spectra in the 390-720 nm wavelength range. After various steps of intensity spectra preprocessing (filtering, spectral correction and intensity normalization), several sets of spectral characteristics were extracted and selected based on their discrimination power statistically tested for every pair-wise comparison of histological classes. Data reduction with Principal Components Analysis (PCA) was performed and 3 classification methods were implemented (k-NN, LDA and SVM), in order to compare diagnostic performance of each method. Diagnostic performance was studied and assessed in terms of sensitivity (Se) and specificity (Sp) as a function of the selected features, of the combinations of 3 different inter-fibers distances and of the numbers of principal components, such that: Se and Sp ≈ 100% when discriminating CH vs. others; Sp ≈ 100% and Se > 95% when discriminating Healthy vs. AH or D; Sp ≈ 74% and Se ≈ 63%for AH vs. D.

Fundamental gaps with approximate density functionals: The derivative discontinuity revealed from ensemble considerations

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

Kraisler, Eli; Kronik, Leeor

2014-05-14

The fundamental gap is a central quantity in the electronic structure of matter. Unfortunately, the fundamental gap is not generally equal to the Kohn-Sham gap of density functional theory (DFT), even in principle. The two gaps differ precisely by the derivative discontinuity, namely, an abrupt change in slope of the exchange-correlation energy as a function of electron number, expected across an integer-electron point. Popular approximate functionals are thought to be devoid of a derivative discontinuity, strongly compromising their performance for prediction of spectroscopic properties. Here we show that, in fact, all exchange-correlation functionals possess a derivative discontinuity, which arises naturallymore » from the application of ensemble considerations within DFT, without any empiricism. This derivative discontinuity can be expressed in closed form using only quantities obtained in the course of a standard DFT calculation of the neutral system. For small, finite systems, addition of this derivative discontinuity indeed results in a greatly improved prediction for the fundamental gap, even when based on the most simple approximate exchange-correlation density functional – the local density approximation (LDA). For solids, the same scheme is exact in principle, but when applied to LDA it results in a vanishing derivative discontinuity correction. This failure is shown to be directly related to the failure of LDA in predicting fundamental gaps from total energy differences in extended systems.« less

High-resolution Compton scattering study of the electron momentum density in Al

NASA Astrophysics Data System (ADS)

Ohata, T.; Itou, M.; Matsumoto, I.; Sakurai, Y.; Kawata, H.; Shiotani, N.; Kaprzyk, S.; Mijnarends, P. E.; Bansil, A.

2000-12-01

We report high-resolution Compton profiles (CP's) of Al along the three principal symmetry directions at a photon energy of 59.38 keV, together with corresponding highly accurate theoretical profiles obtained within the local-density approximation (LDA) based band-theory framework. A good accord between theory and experiment is found with respect to the overall shapes of the CP's and their first and second derivatives, as well as the anisotropies in the CP's defined as differences between pairs of various CP's. There are, however, discrepancies in that, in comparison to the LDA predictions, the measured profiles are lower at low momenta, show a Fermi cutoff that is broader, and display a tail that is higher at momenta above the Fermi momentum. A number of simple model calculations are carried out in order to gain insight into the nature of the underlying 3D momentum density in Al and the role of the Fermi surface in inducing fine structure in the CP's. The present results when compared with those on Li show clearly that the size of discrepancies between theoretical and experimental CP's is markedly smaller in Al than in Li. This indicates that, with increasing electron density, the conventional picture of the electron gas becomes more representative of the momentum density and that shortcomings of the LDA framework in describing the electron correlation effects become less important.

Sentiment topic mining based on comment tags

NASA Astrophysics Data System (ADS)

Zhang, Daohai; Liu, Xue; Li, Juan; Fan, Mingyue

2018-03-01

With the development of e-commerce, various comments based on tags are generated, how to extract valuable information from these comment tags has become an important content of business management decisions. This study takes HUAWEI mobile phone tags as an example using the sentiment analysis and topic LDA mining method. The first step is data preprocessing and classification of comment tag topic mining. And then make the sentiment classification for comment tags. Finally, mine the comments again and analyze the emotional theme distribution under different sentiment classification. The results show that HUAWEI mobile phone has a good user experience in terms of fluency, cost performance, appearance, etc. Meanwhile, it should pay more attention to independent research and development, product design and development. In addition, battery and speed performance should be enhanced.

Continuous detection and decoding of dexterous finger flexions with implantable myoelectric sensors.

PubMed

Baker, Justin J; Scheme, Erik; Englehart, Kevin; Hutchinson, Douglas T; Greger, Bradley

2010-08-01

A rhesus monkey was trained to perform individuated and combined finger flexions of the thumb, index, and middle finger. Nine implantable myoelectric sensors (IMES) were then surgically implanted into the finger muscles of the monkey's forearm, without any adverse effects over two years postimplantation. Using an inductive link, EMG was wirelessly recorded from the IMES as the monkey performed a finger flexion task. The EMG from the different IMES implants showed very little cross correlation. An offline parallel linear discriminant analysis (LDA) based algorithm was used to decode finger activity based on features extracted from continuously presented frames of recorded EMG. The offline parallel LDA was run on intraday sessions as well as on sessions where the algorithm was trained on one day and tested on following days. The performance of the algorithm was evaluated continuously by comparing classification output by the algorithm to the current state of the finger switches. The algorithm detected and classified seven different finger movements, including individual and combined finger flexions, and a no-movement state (chance performance = 12.5%) . When the algorithm was trained and tested on data collected the same day, the average performance was 43.8+/-3.6% n=10. When the training-testing separation period was five months, the average performance of the algorithm was 46.5+/-3.4% n=8. These results demonstrated that using EMG recorded and wirelessly transmitted by IMES offers a promising approach for providing intuitive, dexterous control of artificial limbs where human patients have sufficient, functional residual muscle following amputation.

Titin strain contributes to the Frank–Starling law of the heart by structural rearrangements of both thin- and thick-filament proteins

PubMed Central

Ait-Mou, Younss; Hsu, Karen; Farman, Gerrie P.; Kumar, Mohit; Greaser, Marion L.; Irving, Thomas C.; de Tombe, Pieter P.

2016-01-01

The Frank–Starling mechanism of the heart is due, in part, to modulation of myofilament Ca2+ sensitivity by sarcomere length (SL) [length-dependent activation (LDA)]. The molecular mechanism(s) that underlie LDA are unknown. Recent evidence has implicated the giant protein titin in this cellular process, possibly by positioning the myosin head closer to actin. To clarify the role of titin strain in LDA, we isolated myocardium from either WT or homozygous mutant (HM) rats that express a giant splice isoform of titin, and subjected the muscles to stretch from 2.0 to 2.4 μm of SL. Upon stretch, HM compared with WT muscles displayed reduced passive force, twitch force, and myofilament LDA. Time-resolved small-angle X-ray diffraction measurements of WT twitching muscles during diastole revealed stretch-induced increases in the intensity of myosin (M2 and M6) and troponin (Tn3) reflections, as well as a reduction in cross-bridge radial spacing. Independent fluorescent probe analyses in relaxed permeabilized myocytes corroborated these findings. X-ray electron density reconstruction revealed increased mass/ordering in both thick and thin filaments. The SL-dependent changes in structure observed in WT myocardium were absent in HM myocardium. Overall, our results reveal a correlation between titin strain and the Frank–Starling mechanism. The molecular basis underlying this phenomenon appears not to involve interfilament spacing or movement of myosin toward actin but, rather, sarcomere stretch-induced simultaneous structural rearrangements within both thin and thick filaments that correlate with titin strain and myofilament LDA. PMID:26858417

Thermal and Optical Activation Mechanisms of Nanospring-Based Chemiresistors

PubMed Central

Dobrokhotov, Vladimir; Oakes, Landon; Sowell, Dewayne; Larin, Alexander; Hall, Jessica; Barzilov, Alexander; Kengne, Alex; Bakharev, Pavel; Corti, Giancarlo; Cantrell, Timothy; Prakash, Tej; Williams, Joseph; Bergman, Leah; Huso, Jesse; McIlroy, David

2012-01-01

Chemiresistors (conductometric sensor) were fabricated on the basis of novel nanomaterials—silica nanosprings ALD coated with ZnO. The effects of high temperature and UV illumination on the electronic and gas sensing properties of chemiresistors are reported. For the thermally activated chemiresistors, a discrimination mechanism was developed and an integrated sensor-array for simultaneous real-time resistance scans was built. The integrated sensor response was tested using linear discriminant analysis (LDA). The distinguished electronic signatures of various chemical vapors were obtained at ppm level. It was found that the recovery rate at high temperature drastically increases upon UV illumination. The feasibility study of the activation method by UV illumination at room temperature was conducted. PMID:22778604

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features.

PubMed

Ion-Mărgineanu, Adrian; Kocevar, Gabriel; Stamile, Claudio; Sima, Diana M; Durand-Dubief, Françoise; Van Huffel, Sabine; Sappey-Marinier, Dominique

2017-01-01

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N -acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests. Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71-72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features. Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms.

Latent Dirichlet Allocation (LDA) Model and kNN Algorithm to Classify Research Project Selection

NASA Astrophysics Data System (ADS)

Safi’ie, M. A.; Utami, E.; Fatta, H. A.

2018-03-01

Universitas Sebelas Maret has a teaching staff more than 1500 people, and one of its tasks is to carry out research. In the other side, the funding support for research and service is limited, so there is need to be evaluated to determine the Research proposal submission and devotion on society (P2M). At the selection stage, research proposal documents are collected as unstructured data and the data stored is very large. To extract information contained in the documents therein required text mining technology. This technology applied to gain knowledge to the documents by automating the information extraction. In this articles we use Latent Dirichlet Allocation (LDA) to the documents as a model in feature extraction process, to get terms that represent its documents. Hereafter we use k-Nearest Neighbour (kNN) algorithm to classify the documents based on its terms.

Surface-enhanced Raman spectroscopy for differentiation between benign and malignant thyroid tissues

NASA Astrophysics Data System (ADS)

Li, Zuanfang; Li, Chao; Lin, Duo; Huang, Zufang; Pan, Jianji; Chen, Guannan; Lin, Juqiang; Liu, Nenrong; Yu, Yun; Feng, Shangyuan; Chen, Rong

2014-04-01

The aim of this study was to evaluate the potential of applying silver nano-particle based surface-enhanced Raman scattering (SERS) to discriminate different types of human thyroid tissues. SERS measurements were performed on three groups of tissue samples including thyroid cancers (n = 32), nodular goiters (n = 20) and normal thyroid tissues (n = 25). Tentative assignments of the measured tissue SERS spectra suggest interesting cancer specific biomolecular differences. The principal component analysis (PCA) and linear discriminate analysis (LDA) together with the leave-one-out, cross-validated technique yielded diagnostic sensitivities of 92%, 75% and 87.5%; and specificities of 82.6%, 89.4% and 84.4%, respectively, for differentiation among normal, nodular and malignant thyroid tissue samples. This work demonstrates that tissue SERS spectroscopy associated with multivariate analysis diagnostic algorithms has great potential for detection of thyroid cancer at the molecular level.

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.

Using hyperspectral imaging to determine germination of native Australian plant seeds.

PubMed

Nansen, Christian; Zhao, Genpin; Dakin, Nicole; Zhao, Chunhui; Turner, Shane R

2015-04-01

We investigated the ability to accurately and non-destructively determine the germination of three native Australian tree species, Acacia cowleana Tate (Fabaceae), Banksia prionotes L.F. (Proteaceae), and Corymbia calophylla (Lindl.) K.D. Hill & L.A.S. Johnson (Myrtaceae) based on hyperspectral imaging data. While similar studies have been conducted on agricultural and horticultural seeds, we are unaware of any published studies involving reflectance-based assessments of the germination of tree seeds. Hyperspectral imaging data (110 narrow spectral bands from 423.6nm to 878.9nm) were acquired of individual seeds after 0, 1, 2, 5, 10, 20, 30, and 50days of standardized rapid ageing. At each time point, seeds were subjected to hyperspectral imaging to obtain reflectance profiles from individual seeds. A standard germination test was performed, and we predicted that loss of germination was associated with a significant change in seed coat reflectance profiles. Forward linear discriminant analysis (LDA) was used to select the 10 spectral bands with the highest contribution to classifications of the three species. In all species, germination decreased from over 90% to below 20% in about 10-30days of experimental ageing. P50 values (equal to 50% germination) for each species were 19.3 (A. cowleana), 7.0 (B. prionotes) and 22.9 (C. calophylla) days. Based on independent validation of classifications of hyperspectral imaging data, we found that germination of Acacia and Corymbia seeds could be classified with over 85% accuracy, while it was about 80% for Banksia seeds. The selected spectral bands in each LDA-based classification were located near known pigment peaks involved in photosynthesis and/or near spectral bands used in published indices to predict chlorophyll or nitrogen content in leaves. The results suggested that seed germination may be successfully classified (predicted) based on reflectance in narrow spectral bands associated with the primary metabolism function and performance of plants. Copyright © 2015 Elsevier B.V. All rights reserved.

Analysis of intraspecific seed diversity in Astragalus aquilanus (Fabaceae), an endemic species of Central Apennine.

PubMed

Di Cecco, V; Di Musciano, M; D'Archivio, A A; Frattaroli, A R; Di Martino, L

2018-05-20

This work aims to study seeds of the endemic species Astragalus aquilanus from four different populations of central Italy. We investigated seed morpho-colorimetric features (shape and size) and chemical differences (through infrared spectroscopy) among populations and between dark and light seeds. Seed morpho-colorimetric quantitative variables, describing shape, size and colour traits, were measured using image analysis techniques. Fourier transform infrared (FT-IR) spectroscopy was used to attempt seed chemical characterisation. The measured data were analysed by step-wise linear discriminant analysis (LDA). Moreover, we analysed the correlation between the four most important traits and six climatic variables extracted from WorldClim 2.0. The LDA on seeds traits shows clear differentiation of the four populations, which can be attributed to different chemical composition, as confirmed by Wilk's lambda test (P < 0.001). A strong correlation between morphometric traits and temperature (annual mean temperature, mean temperature of the warmest and coolest quarter), colorimetric traits and precipitation (annual precipitation, precipitation of wettest and driest quarter) was observed. The characterisation of A. aquilanus seeds shows large intraspecific plasticity both in morpho-colorimetric and chemical composition. These results confirm the strong relationship between the type of seed produced and the climatic variables. © 2018 German Society for Plant Sciences and The Royal Botanical Society of the Netherlands.

Identifying biological concepts from a protein-related corpus with a probabilistic topic model

PubMed Central

Zheng, Bin; McLean, David C; Lu, Xinghua

2006-01-01

Background Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE© titles and abstracts by applying a probabilistic topic model. Results The latent Dirichlet allocation (LDA) model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO) terms based on mutual information. Conclusion The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text. PMID:16466569

First-Principles Study on the Ferromagnetism and Curie Temperature of Mn-Doped AlX and InX (X=N, P, As, and Sb)

NASA Astrophysics Data System (ADS)

Sato, Kazunori; Dederichs, Peter H.; Katayama-Yoshida, Hiroshi

2007-02-01

We investigate the electronic structure and magnetic properties of AlN-, AlP-, AlAs-, AlSb-, InN-, InP-, InAs-, and InSb-based dilute magnetic semiconductors (DMS) with Mn impurities from first-principles. The electronic structure of DMS is calculated by using the Korringa-Kohn-Rostoker coherent potential approximation (KKR-CPA) method in connection with the local density approximation (LDA) and the LDA+U method. Describing the magnetic properties by a classical Heisenberg model, effective exchange interactions are calculated by applying magnetic force theorem for two impurities embedded in the CPA medium. With the calculated exchange interactions, TC is estimated by using the mean field approximation, the random phase approximation and the Monte Carlo simulation. It is found that the p-d exchange model [Dietl et al.: Science 287 (2000) 1019] is adequate for a limited class of DMS and insufficient to describe the ferromagnetism in wide gap semiconductor based DMS such as (Ga,Mn)N and the presently investigated (Al,Mn)N and (In,Mn)N.

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

PubMed

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

2016-12-15

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

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

PubMed

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

2018-02-05

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

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

PubMed

Yudthavorasit, Soparat; Wongravee, Kanet; Leepipatpiboon, Natchanun

2014-09-01

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

Application of texture analysis method for classification of benign and malignant thyroid nodules in ultrasound images.

PubMed

Abbasian Ardakani, Ali; Gharbali, Akbar; Mohammadi, Afshin

2015-01-01

The aim of this study was to evaluate computer aided diagnosis (CAD) system with texture analysis (TA) to improve radiologists' accuracy in identification of thyroid nodules as malignant or benign. A total of 70 cases (26 benign and 44 malignant) were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of interests (ROIs) in three normalization schemes (default, 3s and 1%-99%). Then features by the lowest probability of classification error and average correlation coefficients (POE+ACC), and Fisher coefficient (Fisher) eliminated to 10 best and most effective features. These features were analyzed under standard and nonstandard states. For TA of the thyroid nodules, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA) were applied. First Nearest-Neighbour (1-NN) classifier was performed for the features resulting from PCA and LDA. NDA features were classified by artificial neural network (A-NN). Receiver operating characteristic (ROC) curve analysis was used for examining the performance of TA methods. The best results were driven in 1-99% normalization with features extracted by POE+ACC algorithm and analyzed by NDA with the area under the ROC curve ( Az) of 0.9722 which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Our results indicate that TA is a reliable method, can provide useful information help radiologist in detection and classification of benign and malignant thyroid nodules.

Titin strain contributes to the Frank–Starling law of the heart by structural rearrangements of both thin- and thick-filament proteins

DOE PAGES

Ait-Mou, Younss; Hsu, Karen; Farman, Gerrie P.; ...

2016-02-08

The Frank–Starling mechanism of the heart is due, in part, to modulation of myofilament Ca 2+ sensitivity by sarcomere length (SL) [length-dependent activation (LDA)]. The molecular mechanism(s) that underlie LDA are unknown. Recent evidence has implicated the giant protein titin in this cellular process, possibly by positioning the myosin head closer to actin. To clarify the role of titin strain in LDA, we isolated myocardium from either WT or homozygous mutant (HM) rats that express a giant splice isoform of titin, and subjected the muscles to stretch from 2.0 to 2.4 μm of SL. Upon stretch, HM compared with WTmore » muscles displayed reduced passive force, twitch force, and myofilament LDA. Time-resolved small-angle X-ray diffraction measurements of WT twitching muscles during diastole revealed stretch-induced increases in the intensity of myosin (M2 and M6) and troponin (Tn3) reflections, as well as a reduction in cross-bridge radial spacing. Independent fluorescent probe analyses in relaxed permeabilized myocytes corroborated these findings. X-ray electron density reconstruction revealed increased mass/ordering in both thick and thin filaments. The SL-dependent changes in structure observed in WT myocardium were absent in HM myocardium. Overall, our results reveal a correlation between titin strain and the Frank–Starling mechanism. The molecular basis underlying this phenomenon appears not to involve interfilament spacing or movement of myosin toward actin but, rather, sarcomere stretch-induced simultaneous structural rearrangements within both thin and thick filaments that correlate with titin strain and myofilament LDA.« less

First-principles energy band calculation of Ruddlesden–Popper compound Sr{sub 3}Sn{sub 2}O{sub 7} using modified Becke–Johnson exchange potential

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

Kamimura, Sunao, E-mail: kamimura-sunao@che.kyutech.ac.jp; National Institute of Advanced Industrial Science and Technology; Department of Molecular and Material Sciences, Interdisciplinary Graduate School of Engineering Science, Kyushu University, 6-1 Kasuga Kouen, Kasuga, Fukuoka 816-8580 Japan

The electronic structure of Sr{sub 3}Sn{sub 2}O{sub 7} is evaluated by the scalar-relativistic full potential linearized augmented plane wave (FLAPW+lo) method using the modified Becke–Johnson potential (Tran–Blaha potential) combined with the local density approximation correlation (MBJ–LDA). The fundamental gap between the valence band (VB) and conduction band (CB) is estimated to be 3.96 eV, which is close to the experimental value. Sn 5s states and Sr 4d states are predominant in the lower and upper CB, respectively. On the other hand, the lower VB is mainly composed of Sn 5s, 5p, and O 2p states, while the upper VB mainlymore » consists of O 2p states. These features of the DOS are well reflected by the optical transition between the upper VB and lower CB, as seen in the energy dependence of the dielectric function. Furthermore, the absorption coefficient estimated from the MBJ–LDA is similar to the experimental result. - Graphical abstract: Calculated energy band structure along the symmetry lines of the first BZ of Sr{sub 3}Sn{sub 2}O{sub 7} crystal obtained using the MBJ potential. - Highlights: • Electronic structure of Sr{sub 3}Sn{sub 2}O{sub 7} is calculated on the basis of MBJ–LDA method for the first time. • Band gap of Sr{sub 3}Sn{sub 2}O{sub 7} is determined accurately on the basis of MBJ–LDA method. • The experimental absorption spectrum of Sr{sub 3}Sn{sub 2}O{sub 7} produced by MBJ–LDA is more accurate than that obtained by GGA method.« less

Titin strain contributes to the Frank–Starling law of the heart by structural rearrangements of both thin- and thick-filament proteins

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

Ait-Mou, Younss; Hsu, Karen; Farman, Gerrie P.

The Frank–Starling mechanism of the heart is due, in part, to modulation of myofilament Ca 2+ sensitivity by sarcomere length (SL) [length-dependent activation (LDA)]. The molecular mechanism(s) that underlie LDA are unknown. Recent evidence has implicated the giant protein titin in this cellular process, possibly by positioning the myosin head closer to actin. To clarify the role of titin strain in LDA, we isolated myocardium from either WT or homozygous mutant (HM) rats that express a giant splice isoform of titin, and subjected the muscles to stretch from 2.0 to 2.4 μm of SL. Upon stretch, HM compared with WTmore » muscles displayed reduced passive force, twitch force, and myofilament LDA. Time-resolved small-angle X-ray diffraction measurements of WT twitching muscles during diastole revealed stretch-induced increases in the intensity of myosin (M2 and M6) and troponin (Tn3) reflections, as well as a reduction in cross-bridge radial spacing. Independent fluorescent probe analyses in relaxed permeabilized myocytes corroborated these findings. X-ray electron density reconstruction revealed increased mass/ordering in both thick and thin filaments. The SL-dependent changes in structure observed in WT myocardium were absent in HM myocardium. Overall, our results reveal a correlation between titin strain and the Frank–Starling mechanism. The molecular basis underlying this phenomenon appears not to involve interfilament spacing or movement of myosin toward actin but, rather, sarcomere stretch-induced simultaneous structural rearrangements within both thin and thick filaments that correlate with titin strain and myofilament LDA.« less

Effect of Low-Dose Aspirin on Chronic Acid Reflux Esophagitis in Rats.

PubMed

Masuda, Takahiro; Yano, Fumiaki; Omura, Nobuo; Tsuboi, Kazuto; Hoshino, Masato; Yamamoto, Se Ryung; Akimoto, Shunsuke; Kashiwagi, Hideyuki; Yanaga, Katsuhiko

2018-01-01

Clinical role of low-dose aspirin (LDA) in pathogenesis of gastroesophageal reflux disease is by far controversial. This can be attributed to the paucity of basic research detailing the mechanism of LDA-induced esophageal mucosal injury (EI) on underlying chronic acid reflux esophagitis (RE). The aim of this study was to clarify the effect of LDA on chronic RE in rats. Esophagitis was induced in 8-week-old male Wistar rats by ligating the border between forestomach and glandular portion with a 2-0 silk tie and covering the duodenum with a small piece of 18-Fr Nélaton catheter. Seventy-eight chronic RE rat models were divided into five treatment groups, consisting of orally administered vehicle (controls), and aspirin doses of 2, 5, 50 or 100 mg/kg once daily for 28 days. EI was assessed by gross area of macroscopic mucosal injury, severity grade of esophagitis and microscopic depth of infiltration by inflammatory cells. Area of esophagitis in animals with aspirin dose of 100 mg/kg/day showed a 36.5% increase compared with controls, although it failed to achieve statistical significance (p = 0.812). Additionally, the rate of severe EI was increased in animals with aspirin dose of 100 mg/kg/day as compared with controls (p < 0.05). The grade of severity correlated with the depth of inflammation (r s  = 0.492, p < 0.001). Maximal dose aspirin (100 mg/kg/day) contributed in exacerbating preexisting EI. LDA (2 and 5 mg/kg/day), on the other hand, did not affect chronic RE in this model. LDA seems to be safe for use in patients with chronic RE.

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

PubMed

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

2015-08-01

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

WE-E-17A-02: Predictive Modeling of Outcome Following SABR for NSCLC Based On Radiomics of FDG-PET Images

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

Li, R; Aguilera, T; Shultz, D

2014-06-15

Purpose: This study aims to develop predictive models of patient outcome by extracting advanced imaging features (i.e., Radiomics) from FDG-PET images. Methods: We acquired pre-treatment PET scans for 51 stage I NSCLC patients treated with SABR. We calculated 139 quantitative features from each patient PET image, including 5 morphological features, 8 statistical features, 27 texture features, and 100 features from the intensity-volume histogram. Based on the imaging features, we aim to distinguish between 2 risk groups of patients: those with regional failure or distant metastasis versus those without. We investigated 3 pattern classification algorithms: linear discriminant analysis (LDA), naive Bayesmore » (NB), and logistic regression (LR). To avoid the curse of dimensionality, we performed feature selection by first removing redundant features and then applying sequential forward selection using the wrapper approach. To evaluate the predictive performance, we performed 10-fold cross validation with 1000 random splits of the data and calculated the area under the ROC curve (AUC). Results: Feature selection identified 2 texture features (homogeneity and/or wavelet decompositions) for NB and LR, while for LDA SUVmax and one texture feature (correlation) were identified. All 3 classifiers achieved statistically significant improvements over conventional PET imaging metrics such as tumor volume (AUC = 0.668) and SUVmax (AUC = 0.737). Overall, NB achieved the best predictive performance (AUC = 0.806). This also compares favorably with MTV using the best threshold at an SUV of 11.6 (AUC = 0.746). At a sensitivity of 80%, NB achieved 69% specificity, while SUVmax and tumor volume only had 36% and 47% specificity. Conclusion: Through a systematic analysis of advanced PET imaging features, we are able to build models with improved predictive value over conventional imaging metrics. If validated in a large independent cohort, the proposed techniques could potentially aid in identifying patients who might benefit from adjuvant therapy.« less

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

PubMed

Ibrahim, Wisam; Abadeh, Mohammad Saniee

2017-05-21

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Novel Method for Differentiating Histological Types of Gastric Adenocarcinoma by Using Confocal Raman Microspectroscopy

PubMed Central

Hsu, Chih-Wei; Huang, Chia-Chi; Sheu, Jeng-Horng; Lin, Chia-Wen; Lin, Lien-Fu; Jin, Jong-Shiaw; Chau, Lai-Kwan; Chen, Wenlung

2016-01-01

Gastric adenocarcinoma, a single heterogeneous disease with multiple epidemiological and histopathological characteristics, accounts for approximately 10% of cancers worldwide. It is categorized into four histological types: papillary adenocarcinoma (PAC), tubular adenocarcinoma (TAC), mucinous adenocarcinoma (MAC), and signet ring cell adenocarcinoma (SRC). Effective differentiation of the four types of adenocarcinoma will greatly improve the treatment of gastric adenocarcinoma to increase its five-year survival rate. We reported here the differentiation of the four histological types of gastric adenocarcinoma from the molecularly structural viewpoint of confocal Raman microspectroscopy. In total, 79 patients underwent laparoscopic or open radical gastrectomy during 2008–2011: 21 for signet ring cell carcinoma, 21 for tubular adenocarcinoma, 14 for papillary adenocarcinoma, 6 for mucinous carcinoma, and 17 for normal gastric mucosas obtained from patients underwent operation for other benign lesions. Clinical data were retrospectively reviewed from medical charts, and Raman data were processed and analyzed by using principal component analysis (PCA) and linear discriminant analysis (LDA). Two-dimensional plots of PCA and LDA clearly demonstrated that the four histological types of gastric adenocarcinoma could be differentiated, and confocal Raman microspectroscopy provides potentially a rapid and effective method for differentiating SRC and MAC from TAC or PAC. PMID:27472385

Intersubject variability and intrasubject reproducibility of 12-lead ECG metrics: Implications for human verification.

PubMed

Jekova, Irena; Krasteva, Vessela; Leber, Remo; Schmid, Ramun; Twerenbold, Raphael; Müller, Christian; Reichlin, Tobias; Abächerli, Roger

Electrocardiogram (ECG) biometrics is an advanced technology, not yet covered by guidelines on criteria, features and leads for maximal authentication accuracy. This study aims to define the minimal set of morphological metrics in 12-lead ECG by optimization towards high reliability and security, and validation in a person verification model across a large population. A standard 12-lead resting ECG database from 574 non-cardiac patients with two remote recordings (>1year apart) was used. A commercial ECG analysis module (Schiller AG) measured 202 morphological features, including lead-specific amplitudes, durations, ST-metrics, and axes. Coefficient of variation (CV, intersubject variability) and percent-mean-absolute-difference (PMAD, intrasubject reproducibility) defined the optimization (PMAD/CV→min) and restriction (CV<30%) criteria for selection of the most stable and distinctive features. Linear discriminant analysis (LDA) validated the non-redundant feature set for person verification. Maximal LDA verification sensitivity (85.3%) and specificity (86.4%) were validated for 11 optimal features: R-amplitude (I,II,V1,V2,V3,V5), S-amplitude (V1,V2), Tnegative-amplitude (aVR), and R-duration (aVF,V1). Copyright © 2016 Elsevier Inc. All rights reserved.

Constraints on martian lobate debris apron evolution and rheology from numerical modeling of ice flow

NASA Astrophysics Data System (ADS)

Parsons, Reid A.; Nimmo, Francis; Miyamoto, Hideaki

2011-07-01

Radar observations in the Deuteronilus Mensae region by Mars Reconnaissance Orbiter have constrained the thickness and dust concentration found within mid-latitude ice deposits, providing an opportunity to more accurately estimate the rheology of ice responsible for the formation of lobate debris aprons based on their apparent age of ˜100 Myr. We developed a numerical model simulating ice flow under martian conditions using results from ice deformation experiments, theory of ice grain growth based on terrestrial ice cores, and observational constraints from radar profiles and laser altimetry. By varying the ice grain size, the ice temperature, the subsurface slope, and the initial ice volume we determine the combination of parameters that best reproduce the observed LDA lengths and thicknesses over a period of time comparable to the apparent ages of LDA surfaces (90-300 Myr). We find that an ice temperature of 205 K, an ice grain size of 5 mm, and a flat subsurface slope give reasonable ages for many LDAs in the northern mid-latitudes of Mars. Assuming that the ice grain size is limited by the grain boundary pinning effect of incorporated dust, these results limit the dust volume concentration to less than 4%. However, assuming all LDAs were emplaced by a single event, we find that there is no single combination of grain size, temperature, and subsurface slope which can give realistic ages for all LDAs, suggesting that some or all of these variables are spatially heterogeneous. Based on our model we conclude that the majority of northern mid-latitude LDAs are composed of clean (⩽4 vol%), coarse (⩾1 mm) grained ice, but regional differences in either the amount of dust mixed in with the ice, or in the presence of a basal slope below the LDA ice must be invoked. Alternatively, the ice temperature and/or timing of ice deposition may vary significantly between different mid-latitude regions. Either eventuality can be tested with future observations.

Wave-function-based approach to quasiparticle bands: Insight into the electronic structure of c-ZnS

NASA Astrophysics Data System (ADS)

Stoyanova, A.; Hozoi, L.; Fulde, P.; Stoll, H.

2011-05-01

Ab initio wave-function-based methods are employed for the study of quasiparticle energy bands of zinc-blende ZnS, with focus on the Zn 3d “semicore” states. The relative energies of these states with respect to the top of the S 3p valence bands appear to be poorly described as compared to experimental values not only within the local density approximation (LDA), but also when many-body corrections within the GW approximation are applied to the LDA or LDA + U mean-field solutions [T. Miyake, P. Zhang, M. L. Cohen, and S. G. Louie, Phys. Rev. BPRBMDO1098-012110.1103/PhysRevB.74.245213 74, 245213 (2006)]. In the present study, we show that for the accurate description of the Zn 3d states a correlation treatment based on wave-function methods is needed. Our study rests on a local Hamiltonian approach which rigorously describes the short-range polarization and charge redistribution effects around an extra hole or electron placed into the valence respective conduction bands of semiconductors and insulators. The method also facilitates the computation of electron correlation effects beyond relaxation and polarization. The electron correlation treatment is performed on finite clusters cut off the infinite system. The formalism makes use of localized Wannier functions and embedding potentials derived explicitly from prior periodic Hartree-Fock calculations. The on-site and nearest-neighbor charge relaxation lead to corrections of several eV to the Hartree-Fock band energies and gap. Corrections due to long-range polarization are of the order of 1.0 eV. The dispersion of the Hartree-Fock bands is only slightly affected by electron correlations. We find the Zn 3d “semicore” states to lie ~9.0 eV below the top of the S 3p valence bands, in very good agreement with values from valence-band x-ray photoemission.

Ferromagnetic insulating state in tensile-strained LaCoO3 thin films from LDA + U calculations

NASA Astrophysics Data System (ADS)

Hsu, Han; Blaha, Peter; Wentzcovitch, Renata M.

2012-04-01

With local density approximation+Hubbard U (LDA+U) calculations, we show that the ferromagnetic (FM) insulating state observed in tensile-strained LaCoO3 epitaxial thin films is most likely a mixture of low-spin (LS) and high-spin (HS) Co, namely, a HS/LS mixture state. Compared with other FM states, including the intermediate-spin (IS) state (metallic within LDA+U), which consists of IS Co only, and the insulating IS/LS mixture state, the HS/LS state is the most favorable one. The FM order in the HS/LS state is stabilized via the superexchange interactions between adjacent LS and HS Co. We also show that the Co spin state can be identified by measuring the electric field gradient at the Co nucleus via nuclear magnetic resonance spectroscopy.

Better band gaps for wide-gap semiconductors from a locally corrected exchange-correlation potential that nearly eliminates self-interaction errors

DOE PAGES

Singh, Prashant; Harbola, Manoj K.; Johnson, Duane D.

2017-09-08

Here, this work constitutes a comprehensive and improved account of electronic-structure and mechanical properties of silicon-nitride (more » $${\\rm Si}_{3}$$ $${\\rm N}_{4}$$ ) polymorphs via van Leeuwen and Baerends (LB) exchange-corrected local density approximation (LDA) that enforces the exact exchange potential asymptotic behavior. The calculated lattice constant, bulk modulus, and electronic band structure of $${\\rm Si}_{3}$$ $${\\rm N}_{4}$$ polymorphs are in good agreement with experimental results. We also show that, for a single electron in a hydrogen atom, spherical well, or harmonic oscillator, the LB-corrected LDA reduces the (self-interaction) error to exact total energy to ~10%, a factor of three to four lower than standard LDA, due to a dramatically improved representation of the exchange-potential.« less

Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps

NASA Astrophysics Data System (ADS)

Süveges, M.; Barblan, F.; Lecoeur-Taïbi, I.; Prša, A.; Holl, B.; Eyer, L.; Kochoska, A.; Mowlavi, N.; Rimoldini, L.

2017-07-01

Context. Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. Aims: We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We intend to estimate class labels according to two different, well-known classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, and consider their use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. Methods: We have explored the performance of principal component analysis (PCA), linear discriminant analysis (LDA), Random Forest classification and self-organizing maps (SOM) for the above aims. We pre-processed the photometric time series by combining a double Gaussian profile fit and a constrained smoothing spline, in order to de-noise and interpolate the observed light curves. We achieved further denoising, and selected the most important variability elements from the light curves using PCA. Supervised classification was performed using Random Forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimated the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed on randomized training sets. Results: We obtain excellent results (about 5% global error rate) with classification into light curve morphology classes on the Hipparcos data. The classification into system morphology classes using the Catalog and Atlas of Eclipsing binaries (CALEB) has a higher error rate (about 10.5%), most importantly due to the (sometimes strong) similarity of the photometric light curves originating from physically different systems. When trained on CALEB and then applied to Kepler-detected eclipsing binaries subsampled according to Gaia observing times, LDA and SOM provide tractable, easy-to-visualize subspaces of the full (functional) space of light curves that summarize the most important phenomenological elements of the individual light curves. The sequence of light curves ordered by their first linear discriminant coefficient is compared to results obtained using local linear embedding. The SOM method proves able to find a 2-dimensional embedded surface in the space of the light curves which separates the system morphology classes in its different regions, and also identifies a few other phenomena, such as the asymmetry of the light curves due to spots, eccentric systems, and systems with a single eclipse. Furthermore, when data from other surveys are projected to the same SOM surface, the resulting map yields a good overview of the general biases and distortions due to differences in time sampling or population.

What are we ‘tweeting’ about obesity? Mapping tweets with Topic Modeling and Geographic Information System

PubMed Central

Ghosh, Debarchana (Debs); Guha, Rajarshi

2014-01-01

Public health related tweets are difficult to identify in large conversational datasets like Twitter.com. Even more challenging is the visualization and analyses of the spatial patterns encoded in tweets. This study has the following objectives: How can topic modeling be used to identify relevant public health topics such as obesity on Twitter.com? What are the common obesity related themes? What is the spatial pattern of the themes? What are the research challenges of using large conversational datasets from social networking sites? Obesity is chosen as a test theme to demonstrate the effectiveness of topic modeling using Latent Dirichlet Allocation (LDA) and spatial analysis using Geographic Information System (GIS). The dataset is constructed from tweets (originating from the United States) extracted from Twitter.com on obesity-related queries. Examples of such queries are ‘food deserts’, ‘fast food’, and ‘childhood obesity’. The tweets are also georeferenced and time stamped. Three cohesive and meaningful themes such as ‘childhood obesity and schools’, ‘obesity prevention’, and ‘obesity and food habits’ are extracted from the LDA model. The GIS analysis of the extracted themes show distinct spatial pattern between rural and urban areas, northern and southern states, and between coasts and inland states. Further, relating the themes with ancillary datasets such as US census and locations of fast food restaurants based upon the location of the tweets in a GIS environment opened new avenues for spatial analyses and mapping. Therefore the techniques used in this study provide a possible toolset for computational social scientists in general and health researchers in specific to better understand health problems from large conversational datasets. PMID:25126022

What are we 'tweeting' about obesity? Mapping tweets with Topic Modeling and Geographic Information System.

PubMed

Ghosh, Debarchana Debs; Guha, Rajarshi

2013-01-01

Public health related tweets are difficult to identify in large conversational datasets like Twitter.com. Even more challenging is the visualization and analyses of the spatial patterns encoded in tweets. This study has the following objectives: How can topic modeling be used to identify relevant public health topics such as obesity on Twitter.com? What are the common obesity related themes? What is the spatial pattern of the themes? What are the research challenges of using large conversational datasets from social networking sites? Obesity is chosen as a test theme to demonstrate the effectiveness of topic modeling using Latent Dirichlet Allocation (LDA) and spatial analysis using Geographic Information System (GIS). The dataset is constructed from tweets (originating from the United States) extracted from Twitter.com on obesity-related queries. Examples of such queries are 'food deserts', 'fast food', and 'childhood obesity'. The tweets are also georeferenced and time stamped. Three cohesive and meaningful themes such as 'childhood obesity and schools', 'obesity prevention', and 'obesity and food habits' are extracted from the LDA model. The GIS analysis of the extracted themes show distinct spatial pattern between rural and urban areas, northern and southern states, and between coasts and inland states. Further, relating the themes with ancillary datasets such as US census and locations of fast food restaurants based upon the location of the tweets in a GIS environment opened new avenues for spatial analyses and mapping. Therefore the techniques used in this study provide a possible toolset for computational social scientists in general and health researchers in specific to better understand health problems from large conversational datasets.

Determination of the geographic origin of onions between three main production areas in Japan and other countries by mineral composition.

PubMed

Ariyama, Kaoru; Aoyama, Yoshinori; Mochizuki, Akashi; Homura, Yuji; Kadokura, Masashi; Yasui, Akemi

2007-01-24

Onions (Allium cepa L.) are produced in many countries and are one of the most popular vegetables in the world, thus leading to an enormous amount of international trade. It is currently important that a scientific technique be developed for determining geographic origin as a means to detect fraudulent labeling. We have therefore developed a technique based on mineral analysis and linear discriminant analysis (LDA). The onion samples used in this study were from Hokkaido, Hyogo, and Saga, which are the primary onion-growing areas in Japan, and those from countries that export onions to Japan (China, the United States, New Zealand, Thailand, Australia, and Chile). Of 309 samples, 108 were from Hokkaido, 52 were from Saga, 77 were from Hyogo, and 72 were from abroad. Fourteen elements (Na, Mg, P, Mn, Co, Ni, Cu, Zn, Rb, Sr, Mo, Cd, Cs, and Ba) in the samples were determined by frame atomic adsorption spectrometry, inductively coupled plasma optical emission spectrometry, and inductively coupled plasma mass spectrometry. The models established by LDA were used to discriminate the geographic origin between Hokkaido and abroad, Hyogo and abroad, and Saga and abroad. Ten-fold cross-validations were conducted using these models. The discrimination accuracies obtained by cross-validation between Hokkaido and abroad were 100 and 86%, respectively. Those between Hyogo and abroad were 100 and 90%, respectively. Those between Saga and abroad were 98 and 90%, respectively. In addition, it was demonstrated that the fingerprint of an element pattern from a specific production area, which a crop receives, did not easily change by the variations of fertilization, crop year, variety, soil type, and production year if appropriate elements were chosen.

Electronic structure and magnetic properties of dilute U impurities in metals

NASA Astrophysics Data System (ADS)

Mohanta, S. K.; Cottenier, S.; Mishra, S. N.

2016-05-01

The electronic structure and magnetic moment of dilute U impurity in metallic hosts have been calculated from first principles. The calculations have been performed within local density approximation of the density functional theory using Augmented plane wave+local orbital (APW+lo) technique, taking account of spin-orbit coupling and Coulomb correlation through LDA+U approach. We present here our results for the local density of states, magnetic moment and hyperfine field calculated for an isolated U impurity embedded in hosts with sp-, d- and f-type conduction electrons. The results of our systematic study provide a comprehensive insight on the pressure dependence of 5f local magnetism in metallic systems. The unpolarized local density of states (LDOS), analyzed within the frame work of Stoner model suggest the occurrence of local moment for U in sp-elements, noble metals and f-block hosts like La, Ce, Lu and Th. In contrast, U is predicted to be nonmagnetic in most transition metal hosts except in Sc, Ti, Y, Zr, and Hf consistent with the results obtained from spin polarized calculation. The spin and orbital magnetic moments of U computed within the frame of LDA+U formalism show a scaling behavior with lattice compression. We have also computed the spin and orbital hyperfine fields and a detail analysis has been carried out. The host dependent trends for the magnetic moment, hyperfine field and 5f occupation reflect pressure induced change of electronic structure with U valency changing from 3+ to 4+ under lattice compression. In addition, we have made a detailed analysis of the impurity induced host spin polarization suggesting qualitatively different roles of f-band electrons on moment stability. The results presented in this work would be helpful towards understanding magnetism and spin fluctuation in U based alloys.

Early response to therapy predicts 6-month and 1-year disease activity outcomes in psoriatic arthritis patients.

PubMed

Schoels, Monika M; Landesmann, Uriel; Alasti, Farideh; Baker, Daniel; Smolen, Josef S; Aletaha, Daniel

2018-06-01

In PsA management, remission and low disease activity represent preferential treatment targets. We aimed at evaluating the predictive value and clinical use of initial therapeutic response for subsequent achievement of these targets. Based on data of 216 patients enrolled in a randomized controlled trial of golimumab (GO-REVEAL), we performed diagnostic testing analyses using 3- and 6-month disease activity as tests for treatment outcomes to understand the implications of early response. In regression analyses, we estimated the probabilities for achieving at least LDA. Disease activity was measured by the disease activity index for PsA (DAPSA). Three-month DAPSA levels were excellent tests for disease activity at 6 months (and at 1 year), with areas under the receiver operating characteristic curves of 0.92 (and 0.88, respectively). The estimated probability for 6-month LDA could be quantified as <22% if patients did not reach at least moderate disease activity after 3 months on golimumab. Similar data were seen for early DAPSA response: patients achieving a DAPSA 85% at 3 months had an 84% probability for 6-month LDA or REM. All results were validated in an independent trial cohort of patients treated with infliximab (IMPACT 2). Three months after implementation of therapy in PsA, it is already possible to evaluate the potential for accomplishing therapeutic goals. This substantiates the choice of the 3-month assessment as essential for treatment adaptations.

78 FR 14010 - Standard Instrument Approach Procedures, and Takeoff Minimums and Obstacle Departure Procedures...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-04

... by reference in this amendment under 5 U.S.C. 552(a), 1 CFR part 51, and 14 CFR part 97.20. The... 97 continues to read as follows: Authority: 49 U.S.C. 106(g), 40103, 40106, 40113, 40114, 40120... Intl, LDA PRM RWY 6R, Amdt 1C, CANCELED Cleveland, OH, Cleveland-Hopkins Intl, LDA PRM RWY 24L, Orig-C...

A systems approach for analysis of high content screening assay data with topic modeling.

PubMed

Bisgin, Halil; Chen, Minjun; Wang, Yuping; Kelly, Reagan; Fang, Hong; Xu, Xiaowei; Tong, Weida

2013-01-01

High Content Screening (HCS) has become an important tool for toxicity assessment, partly due to its advantage of handling multiple measurements simultaneously. This approach has provided insight and contributed to the understanding of systems biology at cellular level. To fully realize this potential, the simultaneously measured multiple endpoints from a live cell should be considered in a probabilistic relationship to assess the cell's condition to response stress from a treatment, which poses a great challenge to extract hidden knowledge and relationships from these measurements. In this work, we applied a text mining method of Latent Dirichlet Allocation (LDA) to analyze cellular endpoints from in vitro HCS assays and related to the findings to in vivo histopathological observations. We measured multiple HCS assay endpoints for 122 drugs. Since LDA requires the data to be represented in document-term format, we first converted the continuous value of the measurements to the word frequency that can processed by the text mining tool. For each of the drugs, we generated a document for each of the 4 time points. Thus, we ended with 488 documents (drug-hour) each having different values for the 10 endpoints which are treated as words. We extracted three topics using LDA and examined these to identify diagnostic topics for 45 common drugs located in vivo experiments from the Japanese Toxicogenomics Project (TGP) observing their necrosis findings at 6 and 24 hours after treatment. We found that assay endpoints assigned to particular topics were in concordance with the histopathology observed. Drugs showing necrosis at 6 hour were linked to severe damage events such as Steatosis, DNA Fragmentation, Mitochondrial Potential, and Lysosome Mass. DNA Damage and Apoptosis were associated with drugs causing necrosis at 24 hours, suggesting an interplay of the two pathways in these drugs. Drugs with no sign of necrosis we related to the Cell Loss and Nuclear Size assays, which is suggestive of hepatocyte regeneration. The evidence from this study suggests that topic modeling with LDA can enable us to interpret relationships of endpoints of in vitro assays along with an in vivo histological finding, necrosis. Effectiveness of this approach may add substantially to our understanding of systems biology.

Large Band Gap of alpha-RuCl3 Probed by Photoemission and Inverse Photoemission Spectroscopy

NASA Astrophysics Data System (ADS)

Sinn, Soobin; Kim, Choong Hyun; Sandilands, Luke; Lee, Kyungdong; Won, Choongjae; Oh, Ji Seop; Han, Moonsup; Chang, Young Jun; Hur, Namjung; Sato, Hitoshi; Park, Byeong-Gyu; Kim, Changyoung; Kim, Hyeong-Do; Noh, Tae Won

The Kitaev honeycomb lattice model has attracted great attention because of its possibility to stabilize a quantum spin liquid ground state. Recently, it was proposed that alpha-RuCl3 is its material realization and the first 4 d relativistic Mott insulator from an optical spectrum and LDA + U + SO calculations. Here, we present photoemission and inverse photoemission spectra of alpha-RuCl3. The observed band gap is about 1.8 eV, which suggests that the previously assigned optical gap of 0.3 eV is misinterpreted, and that the strong peak at about 1.2 eV in the optical spectrum may be associated with an actual optical gap. Assuming a strong excitonic effect of 0.6 eV in the optical spectrum, all the structures except for the peak at 0.3 eV are consistent with our electronic spectra. When compared with LDA + U + SO calculations, the value of U should be considerably larger than the previous one, which implies that the spin-orbit coupling is not a necessary ingredient for the insulating mechanism of alpha-RuCl3. We also present angle-resolved photoemission spectra to be compared with LDA + U + SO and LDA +DMFT calculations.

Quantum Monte Carlo Simulations of the Quartz to Stishovite Transition in SiO2

NASA Astrophysics Data System (ADS)

Cohen, R. E.; Towler, Mike; Lopez Rios, Pablo; Drummond, Neil; Needs, Richard

2007-03-01

The quartz-stishovite transition has been a long standing problem for density functional theory (DFT). Although conventional DFT computations within the local density approximation (LDA) give reasonably good properties of silica phases individually, they do not give the energy difference between quartz and stishovite accurately. The LDA gives stishovite as a lower energy structure than quartz at zero pressure, which is incorrect. The generalized gradient approximation (GGA) has been shown to give the correct energy difference between quartz and stishovite (about 0.5 eV/formula unit) (Hamann, PRL 76, 660, 1996; Zupan et al., PRB 58, 11266, 1998), and it was generally thought that the GGA was simply a better approximation than the LDA. However, closer inspection shows that other properties are not better for the GGA than the LDA, so there is room for improvement. A new density functional that is an improvement for most materials unfortunately does not improve the quartz-stishovite transition (Wu and Cohen, PRB 73, 235116, 2006). We are performing QMC computations using the CASINO code to obtain the accurate energy difference between quartz and stishovite to obtain more accurate high pressure properties, and to better understand the errors on DFT and how DFT can be improved.

The Simultaneous Abolition of Three Starch Hydrolases Blocks Transient Starch Breakdown in Arabidopsis*

PubMed Central

Streb, Sebastian; Eicke, Simona; Zeeman, Samuel C.

2012-01-01

In this study, we investigated which enzymes are involved in debranching amylopectin during transient starch degradation. Previous studies identified two debranching enzymes, isoamylase 3 (ISA3) and limit dextrinase (LDA), involved in this process. However, plants lacking both enzymes still degrade substantial amounts of starch. Thus, other enzymes/mechanisms must contribute to starch breakdown. We show that the chloroplastic α-amylase 3 (AMY3) also participates in starch degradation and provide evidence that all three enzymes can act directly at the starch granule surface. The isa3 mutant has a starch excess phenotype, reflecting impaired starch breakdown. In contrast, removal of AMY3, LDA, or both enzymes together has no impact on starch degradation. However, removal of AMY3 or LDA in addition to ISA3 enhances the starch excess phenotype. In plants lacking all three enzymes, starch breakdown is effectively blocked, and starch accumulates to the highest levels observed so far. This provides indirect evidence that the heteromultimeric debranching enzyme ISA1-ISA2 is not involved in starch breakdown. However, we illustrate that ISA1-ISA2 can hydrolyze small soluble branched glucans that accumulate when ISA3 and LDA are missing, albeit at a slow rate. Starch accumulation in the mutants correlates inversely with plant growth. PMID:23019330

Accurate critical pressures for structural phase transitions of group IV, III-V, and II-VI compounds from the SCAN density functional

NASA Astrophysics Data System (ADS)

Shahi, Chandra; Sun, Jianwei; Perdew, John P.

2018-03-01

Most of the group IV, III-V, and II-VI compounds crystallize in semiconductor structures under ambient conditions. Upon application of pressure, they undergo structural phase transitions to more closely packed structures, sometimes metallic phases. We have performed density functional calculations using projector augmented wave (PAW) pseudopotentials to determine the transition pressures for these transitions within the local density approximation (LDA), the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA), and the strongly constrained and appropriately normed (SCAN) meta-GGA. LDA underestimates the transition pressure for most of the studied materials. PBE under- or overestimates in many cases. SCAN typically corrects the errors of LDA and PBE for the transition pressure. The accuracy of SCAN is comparable to that of computationally expensive methods like the hybrid functional HSE06, the random phase approximation (RPA), and quantum Monte Carlo (QMC), in cases where calculations with these methods have been reported, but at a more modest computational cost. The improvement from LDA to PBE to SCAN is especially clearcut and dramatic for covalent semiconductor-metal transitions, as for Si and Ge, where it reflects the increasing relative stabilization of the covalent semiconducting phases under increasing functional sophistication.

The half-metallicity of Co2FeGe full Heusler alloy in (001) thin film: First principles study

NASA Astrophysics Data System (ADS)

Hyun, Jung-Min; Kim, Miyoung

2018-01-01

The electronic and magnetic properties of the Co2FeGe full Heusler alloy in (001) thin film are investigated using the first-principles electronic structure calculations within the density functional theory. We employ various exchange correlation functionals including the local density approximation (LDA), the generalized gradient approximation (GGA), and the additional + U corrections for strong on-site Coulomb interaction of transition metal 3d states, aiming to examine the correlation effect on the electronic structures which determine the spin gap and thus the half-metallicity. Our results reveal that the Co2FeGe thin film is metallic in both LDA and GGA, while the + U correction opens up the spin gap for spin minority channel in GGA+ U but not in LDA+U in contrast to its bulk alloy which is predicted to be half-metallic in both LDA+ U and GGA+ U approaches with total spin magnetic moment of 6 μ B . It is found that the surface states developed around the Fermi level and the enhanced 3d e g - t 2 g band splitting for the spin minority channel due to the correlation effect play critical roles to determine the emergence of the half-metallicity.

Vibrational mode frequencies of H2S and H2O adsorbed on Ge(0 0 1)-(2 × 1) surfaces

NASA Astrophysics Data System (ADS)

Hartnett, M.; Fahy, S.

2015-02-01

The equilibrium geometry and vibrational modes of H2S and H2O-terminated Ge(0 0 1)-(2 × 1) surfaces are calculated in a supercell approach using first-principles density functional theory in the local density (LDA), generalized gradient (GGA) approximations and van der Waals (vdW) interactions. Mode frequencies are found using the frozen phonon method. For the H2S-passivated surface, the calculated frequencies in LDA (GGA) are 2429 cm-1 (2490) for the Hsbnd S stretch mode, 712 cm-1 (706) for the Hsbnd S bond bending mode, 377 cm-1 (36) for the Gesbnd S stretch mode and 328 cm-1 (337) for Hsbnd S wag mode. Frequencies for the H2O passivated surface are 3590 cm-1 (3600) for the Hsbnd O stretch mode, 921 cm-1 (947) for the bending mode, 609 cm-1 (559) for the Gesbnd O stretch, 1995 cm-1 (1991) for the Gesbnd H stretch mode, 498 cm-1 (478) for the Gesbnd H bending mode and 342 cm-1 (336) for the Hsbnd O wag mode. The differences between the functionals including vdW terms and the LDA or GGA are less than the differences between LDA and GGA for the vibrational mode frequencies.

A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images.

PubMed

Gloger, Oliver; Kühn, Jens; Stanski, Adam; Völzke, Henry; Puls, Ralf

2010-07-01

Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties. Copyright 2010 Elsevier Inc. All rights reserved.

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

PubMed

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

2013-07-01

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

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

PubMed Central

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

2017-01-01

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

Identifying College Students at Risk for Learning Disabilities: Evidence for Use of the Learning Difficulties Assessment in Postsecondary Settings

ERIC Educational Resources Information Center

Kane, Steven T.; Roy, Soma; Medina, Steffanie

2013-01-01

This article describes research supporting the use of the Learning Difficulties Assessment (LDA), a normed and no-cost, web-based survey that assesses difficulties with reading, writing, spelling, mathematics, listening, concentration, memory, organizational skills, sense of control, and anxiety in college students. Previous research has supported…

Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees.

PubMed

Anam, Khairul; Al-Jumaily, Adel

2017-01-01

The success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluates a fast classifier, extreme learning machine (ELM), to classify individual and combined finger movements on amputees and non-amputees. ELM is a single hidden layer feed-forward network (SLFN) that avoids iterative learning by determining input weights randomly and output weights analytically. Therefore, it can accelerate the training time of SLFNs. In addition to the classifier evaluation, this paper evaluates various feature combinations to improve the performance of M-PR and investigate some feature projections to improve the class separability of the features. Different from other studies on the implementation of ELM in the myoelectric controller, this paper presents a complete and thorough investigation of various types of ELMs including the node-based and kernel-based ELM. Furthermore, this paper provides comparisons of ELMs and other well-known classifiers such as linear discriminant analysis (LDA), k-nearest neighbour (kNN), support vector machine (SVM) and least-square SVM (LS-SVM). The experimental results show the most accurate ELM classifier is radial basis function ELM (RBF-ELM). The comparison of RBF-ELM and other well-known classifiers shows that RBF-ELM is as accurate as SVM and LS-SVM but faster than the SVM family; it is superior to LDA and kNN. The experimental results also indicate that the accuracy gap of the M-PR on the amputees and non-amputees is not too much with the accuracy of 98.55% on amputees and 99.5% on the non-amputees using six electromyography (EMG) channels. Copyright © 2016 Elsevier Ltd. All rights reserved.

First-principles study of SnS electronic properties using LDA, PBE and HSE06 functionals

NASA Astrophysics Data System (ADS)

Ibragimova, R.; Ganchenkova, M.; Karazhanov, S.; Marstein, E. S.

2018-03-01

Recently, tin sulphide (SnS) has emerged as a promising alternative to conventional CIGS and CZTC for use in solar cells, possessing such properties as non-toxicity, low cost and production stability. SnS has a high theoretically predicted efficiency above 20%, but the experimentally achieved efficiency so far is as low as 4.36%. The reason for the low achieved efficiency is unclear. One of the powerful tools to get deeper insights about the nature of the problem is first-principles calculation approaches. That is why SnS has become an attractive subject for first-principles calculations recently. Previously calculated data, however, show a widespread of such fundamental value as the bandgap varying from 0.26 to 1.26 eV. In order to understand a reason for that, in this work, we concentrate on a systematic study of calculation parameters effects on the resulting electronic structure, with the particular attention paid to the influence of the exchange-correlation functional chosen for calculations. Several exchange-correlation functionals (LDA, PBE and HSE06) were considered. The systematic analysis has shown that the bandgap variation can result from a tensile/compressive hydrostatic pressure introduced by non-equilibrium lattice parameters used for the calculations. The study of the applicability of three functionals has shown that HSE06 gives the best match to both experimentally obtained bandgap and the XPS valence band spectra. LDA underestimates the bandgap but qualitatively reproduces experimentally measured valence DOS similar to that of HSE06 in contrast to PBE. PBE underestimates the bandgap and does not match to the measured XPS spectra.

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

PubMed Central

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

2016-01-01

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

Experience of diffusion tensor imaging and 1H spectroscopy for outcome prediction in severe traumatic brain injury: Preliminary results.

PubMed

Tollard, Eléonore; Galanaud, Damien; Perlbarg, Vincent; Sanchez-Pena, Paola; Le Fur, Yann; Abdennour, Lamine; Cozzone, Patrick; Lehericy, Stéphane; Chiras, Jacques; Puybasset, Louis

2009-04-01

The objective of the study is to test whether multimodal magnetic resonance imaging can provide a reliable outcome prediction of the clinical status, focusing on consciousness at 1 year after severe traumatic brain injury (TBI). Single center prospective cohort with consecutive inclusions. Critical Care Neurosurgical Unit of a university hospital. Forty-three TBI patients not responding to simple orders after sedation cessation and 15 healthy controls. A multimodal magnetic resonance imaging combining morphologic sequences, diffusion tensor imaging (DTI), and H proton magnetic resonance spectroscopy (MRS) was performed 24 +/- 11 days after severe TBI. The ability of DTI and MRS to predict 1-year outcome was assessed by linear discriminant analysis (LDA). Robustness of the classification was tested using a bootstrap procedure. Fractional anisotropy (FA) was computed as the mean of values at discrete brain sites in the infratentorial and supratentorial regions. The N-acetyl aspartate/creatine (NAA/Cr) ratio was measured in the thalamus, lenticular nucleus, insular cortex, occipital periventricular white matter, and pons. After 1 year, 19 (44%) patients had unfavorable outcomes (death, persistent vegetative state, or minimally conscious state) and 24 (56%) favorable outcomes (normal consciousness with or without functional impairments). Analysis of variance was performed to compare FA and NAA/Cr in the two outcome groups and controls. FA and MRS findings showed highly significant differences between the outcome groups, with significant variables by LDA being supratentorial FA, NAA/Cr (pons), NAA/Cr (thalamus), NAA/Cr (insula), and infratentorial FA. LDA of combined FA and MRS data clearly separated the unfavorable outcome, favorable outcome, and control groups, with no overlap. Unfavorable outcome was predicted with up to 86% sensitivity and 97% specificity; these values were better than those obtained with DTI or MRS alone. FA and NAA/Cr hold potential as quantitative outcome-prediction tools at the subacute phase of TBI.

Finding complex biological relationships in recent PubMed articles using Bio-LDA.

PubMed

Wang, Huijun; Ding, Ying; Tang, Jie; Dong, Xiao; He, Bing; Qiu, Judy; Wild, David J

2011-03-23

The overwhelming amount of available scholarly literature in the life sciences poses significant challenges to scientists wishing to keep up with important developments related to their research, but also provides a useful resource for the discovery of recent information concerning genes, diseases, compounds and the interactions between them. In this paper, we describe an algorithm called Bio-LDA that uses extracted biological terminology to automatically identify latent topics, and provides a variety of measures to uncover putative relations among topics and bio-terms. Relationships identified using those approaches are combined with existing data in life science datasets to provide additional insight. Three case studies demonstrate the utility of the Bio-LDA model, including association predication, association search and connectivity map generation. This combined approach offers new opportunities for knowledge discovery in many areas of biology including target identification, lead hopping and drug repurposing.

Finding Complex Biological Relationships in Recent PubMed Articles Using Bio-LDA

PubMed Central

Wang, Huijun; Ding, Ying; Tang, Jie; Dong, Xiao; He, Bing; Qiu, Judy; Wild, David J.

2011-01-01

The overwhelming amount of available scholarly literature in the life sciences poses significant challenges to scientists wishing to keep up with important developments related to their research, but also provides a useful resource for the discovery of recent information concerning genes, diseases, compounds and the interactions between them. In this paper, we describe an algorithm called Bio-LDA that uses extracted biological terminology to automatically identify latent topics, and provides a variety of measures to uncover putative relations among topics and bio-terms. Relationships identified using those approaches are combined with existing data in life science datasets to provide additional insight. Three case studies demonstrate the utility of the Bio-LDA model, including association predication, association search and connectivity map generation. This combined approach offers new opportunities for knowledge discovery in many areas of biology including target identification, lead hopping and drug repurposing. PMID:21448266

Relaxation dynamics and transformation kinetics of deeply supercooled water: Temperature, pressure, doping, and proton/deuteron isotope effects.

PubMed

Lemke, Sonja; Handle, Philip H; Plaga, Lucie J; Stern, Josef N; Seidl, Markus; Fuentes-Landete, Violeta; Amann-Winkel, Katrin; Köster, Karsten W; Gainaru, Catalin; Loerting, Thomas; Böhmer, Roland

2017-07-21

Above its glass transition, the equilibrated high-density amorphous ice (HDA) transforms to the low-density pendant (LDA). The temperature dependence of the transformation is monitored at ambient pressure using dielectric spectroscopy and at elevated pressures using dilatometry. It is found that near the glass transition temperature of deuterated samples, the transformation kinetics is 300 times slower than the structural relaxation, while for protonated samples, the time scale separation is at least 30 000 and insensitive to doping. The kinetics of the HDA to LDA transformation lacks a proton/deuteron isotope effect, revealing that this process is dominated by the restructuring of the oxygen network. The x-ray diffraction experiments performed on samples at intermediate transition stages reflect a linear combination of the LDA and HDA patterns implying a macroscopic phase separation, instead of a local intermixing of the two amorphous states.

Relaxation dynamics and transformation kinetics of deeply supercooled water: Temperature, pressure, doping, and proton/deuteron isotope effects

NASA Astrophysics Data System (ADS)

Lemke, Sonja; Handle, Philip H.; Plaga, Lucie J.; Stern, Josef N.; Seidl, Markus; Fuentes-Landete, Violeta; Amann-Winkel, Katrin; Köster, Karsten W.; Gainaru, Catalin; Loerting, Thomas; Böhmer, Roland

2017-07-01

Above its glass transition, the equilibrated high-density amorphous ice (HDA) transforms to the low-density pendant (LDA). The temperature dependence of the transformation is monitored at ambient pressure using dielectric spectroscopy and at elevated pressures using dilatometry. It is found that near the glass transition temperature of deuterated samples, the transformation kinetics is 300 times slower than the structural relaxation, while for protonated samples, the time scale separation is at least 30 000 and insensitive to doping. The kinetics of the HDA to LDA transformation lacks a proton/deuteron isotope effect, revealing that this process is dominated by the restructuring of the oxygen network. The x-ray diffraction experiments performed on samples at intermediate transition stages reflect a linear combination of the LDA and HDA patterns implying a macroscopic phase separation, instead of a local intermixing of the two amorphous states.

Ultrasensitive HCV RNA Quantification in Antiviral Triple Therapy: New Insight on Viral Clearance Dynamics and Treatment Outcome Predictors

PubMed Central

Garbuglia, Anna Rosa; Visco-Comandini, Ubaldo; Lionetti, Raffaella; Lapa, Daniele; Castiglione, Filippo; D’Offizi, Gianpiero; Taibi, Chiara; Montalbano, Marzia; Capobianchi, Maria Rosaria; Paci, Paola

2016-01-01

Objectives Identifying the predictive factors of Sustained Virological Response (SVR) represents an important challenge in new interferon-based DAA therapies. Here, we analyzed the kinetics of antiviral response associated with a triple drug regimen, and the association between negative residual viral load at different time points during treatment. Methods Twenty-three HCV genotype 1 (GT 1a n = 11; GT1b n = 12) infected patients were included in the study. Linear Discriminant Analysis (LDA) was used to establish possible association between HCV RNA values at days 1 and 4 from start of therapy and SVR. Principal component analysis (PCA) was applied to analyze the correlation between HCV RNA slope and SVR. A ultrasensitive (US) method was established to measure the residual HCV viral load in those samples which resulted “detected <12IU/ml” or undetectable with ABBOTT standard assay, and was retrospectively used on samples collected at different time points to establish its predictive power for SVR. Results According to LDA, there was no association between SVR and viral kinetics neither at time points earlier than 1 week (days 1 and 4) after therapy initiation nor later. The slopes were not relevant for classifying patients as SVR or no-SVR. No significant differences were observed in the median HCV RNA values at T0 among SVR and no-SVR patients. HCV RNA values with US protocol (LOD 1.2 IU/ml) after 1 month of therapy were considered; the area under the ROC curve was 0.70. Overall, PPV and NPV of undetectable HCV RNA with the US method for SVR was 100% and 46.7%, respectively; sensitivity and specificity were 38.4% and 100% respectively. Conclusion HCV RNA “not detected” by the US method after 1 month of treatment is predictive of SVR in first generation Protease inhibitor (PI)-based triple therapy. The US method could have clinical utility for advanced monitoring of virological response in new interferon based DAA combination regimens. PMID:27560794

Elasticity of Pargasite Amphibole: A Hydrous Phase at Mid Lithospheric Discontinuity

NASA Astrophysics Data System (ADS)

Peng, Y.; Mookherjee, M.

2017-12-01

Mid Lithospheric Discontinuity (MLD) is characterized by a low shear wave velocity ( 3 to 10 %). In cratons, the depth of MLD varies between 80 and 100 km. The reduction of the shear wave velocity at MLD is similar to what is observed in the lithosphere-asthenosphere boundary (LAB). Such low velocity at MLD could be caused by partial melting, temperature induced grain boundary sliding, changes in the elastic anisotropy, and/or metasomatism which may lead to the formation of hydrous phases including mica and amphibole. Thus, it is clear that in order to assess the role of metasomatism at MLD, we need better constraints on the elasticity of hydrous phases. However, such elasticity data are scarce. In this study, we explore elasticity of pargasite amphibole [NaCa2(Mg4Al)(Si6Al2)O22(OH)2] using density functional theory (DFT) with local density approximation (LDA) and generalized gradient approximation (GGA). We find that the pressure-volume results can be adequately described by a finite strain equation with the bulk modulus, K0 being 102 and 85 GPa for LDA and GGA respectively. We also determined the full elastic constant tensor (Cij) using the finite difference method. The bulk modulus, K0 determined from the full elastic constant tensor is 104 GPa for LDA and 87 GPa for GGA. The shear modulus, G0 determined from the full elastic constant tensor is 64 GPa for LDA and 58 GPa for GGA. The bulk and shear moduli predicted with LDA are 5 and 1 % stiffer than the recent results [1]. In contrast, the bulk and shear moduli predicted with GGA are 12 and 10 % softer compared to the recent results [1]. The full elastic constant tensor for pargasite shows significant anisotropy. For instance, LDA predicts compressional (AVP) and shear (AVS) wave anisotropy of 22 and 20 % respectively. At higher pressure, elastic moduli stiffen. However, temperature is likely to have an opposite effect on the elasticity and this remains largely unknown for pargasite. Compared to the major mantle minerals, pargasite has softer elastic constants and significant anisotropy and may explain the reduction in shear wave velocity at MLD. Reference: [1] Brown, J. M., Abramson, E. H.,2016, Phys. Earth Planet. Int., 261, 161-171. Acknowledgement: This work is supported by US NSF award EAR 1639552.

Applications of the First-principles LDA+Usc Method to Spin-crossover Minerals: the NAL Phase and (Mg,Fe)CO3 ferromagnesite

NASA Astrophysics Data System (ADS)

Hsu, H.

2016-12-01

Spin crossover (SCO) in iron-bearing minerals has attracted tremendous attention in recent years, as SCO usually leads to anomalous changes of the elastic, conducting, and thermodynamic properties of these minerals. Possible geophysical effects of SCO have been anticipated as well. With the development of the local density approximation + self-consistent Hubbard U (LDA+Usc) method, first-principles calculations have elucidated SCO in many lower-mantle minerals. The success of LDA+Usc lies in its capability to correctly identify the ground state in a wide pressure range and to accurately determine the mechanism of SCO, including the transition pressure PT. In this talk, two recent LDA+Usc studies of SCO minerals are presented: the "new aluminous (NAL) phase" [1] and (Mg,Fe)CO3 ferromagnesite [2]. The former is considered as a main host of aluminum in the subducted basalt and may be related to the seismic heterogeneities, and the latter is believed to be the major carbon carrier in the Earth's lower mantle and play a key role in the deep carbon cycle. For both minerals, the abrupt change of iron quadrupole splitting and the volume/elastic anomalies accompanying the SCO obtained in our calculations are in great agreement with experiments. Our calculations also suggest that the spin transition pressure PT in the NAL phase is not very sensitive to temperature, due to its nearly degenerate low-spin (LS) states, in contrast with (Mg,Fe)O ferropericlase and (Mg,Fe)CO3 systems, in which PT significantly increases with temperature. By examining the overall performance of the LDA+Usc method in the NAL phase and ferromagnesite, along with our previous calculations for ferropericlase and Fe-bearing MgSiO3 bridgmanite [3-5], we have established LDA+Usc a highly reliable method to study iron-bearing minerals and related materials under high pressure. [1] H. Hsu, in preparation. [2] S.-C. Huang and H. Hsu, Phys. Rev. B (Rapid Comm.), in press. [3] H. Hsu and R. M. Wentzcovitch, Phys. Rev. B 90, 195205 (2014). [4] H. Hsu et al., Earth Planet. Sci. Lett. 359-360, 34 (2012). [5] H. Hsu et al., Phys. Rev. Lett. 106, 118501 (2011).

Metal mixtures in urban and rural populations in the US: The Multi-Ethnic Study of Atherosclerosis and the Strong Heart Study.

PubMed

Pang, Yuanjie; Peng, Roger D; Jones, Miranda R; Francesconi, Kevin A; Goessler, Walter; Howard, Barbara V; Umans, Jason G; Best, Lyle G; Guallar, Eliseo; Post, Wendy S; Kaufman, Joel D; Vaidya, Dhananjay; Navas-Acien, Ana

2016-05-01

Natural and anthropogenic sources of metal exposure differ for urban and rural residents. We searched to identify patterns of metal mixtures which could suggest common environmental sources and/or metabolic pathways of different urinary metals, and compared metal-mixtures in two population-based studies from urban/sub-urban and rural/town areas in the US: the Multi-Ethnic Study of Atherosclerosis (MESA) and the Strong Heart Study (SHS). We studied a random sample of 308 White, Black, Chinese-American, and Hispanic participants in MESA (2000-2002) and 277 American Indian participants in SHS (1998-2003). We used principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) to evaluate nine urinary metals (antimony [Sb], arsenic [As], cadmium [Cd], lead [Pb], molybdenum [Mo], selenium [Se], tungsten [W], uranium [U] and zinc [Zn]). For arsenic, we used the sum of inorganic and methylated species (∑As). All nine urinary metals were higher in SHS compared to MESA participants. PCA and CA revealed the same patterns in SHS, suggesting 4 distinct principal components (PC) or clusters (∑As-U-W, Pb-Sb, Cd-Zn, Mo-Se). In MESA, CA showed 2 large clusters (∑As-Mo-Sb-U-W, Cd-Pb-Se-Zn), while PCA showed 4 PCs (Sb-U-W, Pb-Se-Zn, Cd-Mo, ∑As). LDA indicated that ∑As, U, W, and Zn were the most discriminant variables distinguishing MESA and SHS participants. In SHS, the ∑As-U-W cluster and PC might reflect groundwater contamination in rural areas, and the Cd-Zn cluster and PC could reflect common sources from meat products or metabolic interactions. Among the metals assayed, ∑As, U, W and Zn differed the most between MESA and SHS, possibly reflecting disproportionate exposure from drinking water and perhaps food in rural Native communities compared to urban communities around the US. Copyright © 2016 Elsevier Inc. All rights reserved.

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

PubMed Central

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

2016-01-01

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

Development of a biometric method to estimate age on hand radiographs.

PubMed

Remy, Floriane; Hossu, Gabriela; Cendre, Romain; Micard, Emilien; Mainard-Simard, Laurence; Felblinger, Jacques; Martrille, Laurent; Lalys, Loïc

2017-02-01

Age estimation of living individuals aged less than 13, 18 or 21 years, which are some relevant legal ages in most European countries, is currently problematic in the forensic context. Thus, numerous methods are available for legal authorities, although their efficiency can be discussed. For those reasons, we aimed to propose a new method, based on the biometric analysis of hand bones. 451 hand radiographs of French individuals under the age of 21 were retrospectively analyzed. This total sample was divided into three subgroups bounded by the relevant legal ages previously mentioned: 0-13, 13-18 and 18-21 years. On these radiographs, we numerically applied the osteometric board method used in anthropology, by including each metacarpal and proximal phalange of the five hand rays in the smallest rectangle possible. In that we can access their length and width information thanks to a measurement protocol developed precisely for our treatment with the ORS Visual ® software. Then, a statistical analysis was performed from these biometric data: a Linear Discriminant Analysis (LDA) evaluated the probability for an individual to belong to one of the age group (0-13, 13-18 or 18-21); and several multivariate regression models were tested for the establishment of age estimation formulas for each of these age groups. The mean Correlation Coefficient between chronological age and both lengths and widths of hand bones is equal to 0.90 for the total sample. Repeatability and reproducibility were assessed. The LDA could more easily predict the belonging to the 0-13 age group. Age can be estimated with a mean standard error which never exceeds 1 year for the 95% confidence interval. Finally, compared to the literature, we can conclude that estimating an age from the biometric information of metacarpals and proximal phalanges is promising. Copyright © 2016. Published by Elsevier B.V.

Actinide electronic structure and atomic forces

NASA Astrophysics Data System (ADS)

Albers, R. C.; Rudin, Sven P.; Trinkle, Dallas R.; Jones, M. D.

2000-07-01

We have developed a new method[1] of fitting tight-binding parameterizations based on functional forms developed at the Naval Research Laboratory.[2] We have applied these methods to actinide metals and report our success using them (see below). The fitting procedure uses first-principles local-density-approximation (LDA) linear augmented plane-wave (LAPW) band structure techniques[3] to first calculate an electronic-structure band structure and total energy for fcc, bcc, and simple cubic crystal structures for the actinide of interest. The tight-binding parameterization is then chosen to fit the detailed energy eigenvalues of the bands along symmetry directions, and the symmetry of the parameterization is constrained to agree with the correct symmetry of the LDA band structure at each eigenvalue and k-vector that is fit to. By fitting to a range of different volumes and the three different crystal structures, we find that the resulting parameterization is robust and appears to accurately calculate other crystal structures and properties of interest.

Analytical gradients for subsystem density functional theory within the slater-function-based amsterdam density functional program.

PubMed

Schlüns, Danny; Franchini, Mirko; Götz, Andreas W; Neugebauer, Johannes; Jacob, Christoph R; Visscher, Lucas

2017-02-05

We present a new implementation of analytical gradients for subsystem density-functional theory (sDFT) and frozen-density embedding (FDE) into the Amsterdam Density Functional program (ADF). The underlying theory and necessary expressions for the implementation are derived and discussed in detail for various FDE and sDFT setups. The parallel implementation is numerically verified and geometry optimizations with different functional combinations (LDA/TF and PW91/PW91K) are conducted and compared to reference data. Our results confirm that sDFT-LDA/TF yields good equilibrium distances for the systems studied here (mean absolute deviation: 0.09 Å) compared to reference wave-function theory results. However, sDFT-PW91/PW91k quite consistently yields smaller equilibrium distances (mean absolute deviation: 0.23 Å). The flexibility of our new implementation is demonstrated for an HCN-trimer test system, for which several different setups are applied. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Detection and identification of Huo-Xue-Hua-Yu decoction (HXHYD) using surface-enhanced Raman scattering (SERS) spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Chen, Weiwei; Lin, Jia; Chen, Rong; Feng, Shangyuan; Yu, Yun; Lin, Duo; Huang, Meizhen; Shi, Hong; Huang, Hao

2015-04-01

We have evaluated the capabilities of surface-enhanced Raman scattering (SERS) technology for analyzing two Huo-Xue-Hua-Yu decoctions (HXHYDs) prepared according to different prescriptions. The aim of this study was to evaluate the relevance of SERS technology applied to decoction of traditional Chinese medicines (TCM). HXHYD I was prepared according to the original prescription; the same preparation method was used for the HXHYD II, except for the crudeweight ratio described in the original prescription. There was no Raman signal in conventional Raman spectra of HXHYDs. Silver nanoparticles were directly mixed with HXHYDs to enhance the Raman scattering of biochemical constituents, and high quality SERS spectra were obtained. Significant differences in SERS spectra between HXHYD I and II can be observed, which showed special changes in the percentage of biochemical constituents in different decoctions. Principal components analysis (PCA) combined with linear discriminant analysis (LDA) were employed to generate diagnostic algorithms for classification of SERS spectra of two HXHYDs, and showed that a diagnostic accuracy of 100% can be achieved. This work demonstrated that the SERS technique has potential for spectral characteristic detection for decoction of TCM with high sensitivity, and that this technique, combined with PCA-LDA, can be used for quality control of the extracted decoction of TCM and production management of Chinese herbal preparations.

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

NASA Astrophysics Data System (ADS)

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

2010-11-01

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

Etude, par principes premiers, des effets de la correlation entre electrons sur les proprietes electroniques et magnetiques de polymeres pontes et de supraconducteurs a haute temperature critique

NASA Astrophysics Data System (ADS)

Pesant, Simon

Description of complex systems by Density functional theory is treated in this thesis. First, the Density functional theory and a few functionals used to simulate cristals are presented. Specifically, the LDA and GGA functionnals are described and their limits are exposed. Furthermore, the Hubbard model as well as the LDA+U functionnal are addressed in this chapter. These methods enable the study of highly correlated materials. Then, results obtained on polymers are summarized in two articles. The first one treats the band gap variation of ladder-type polymers compared to non ladder type ones. The second article considers small band gap polymers. In this case, it will be shown that an hybrid functional, which contains exact exchange, is required to describe the electronic properties of the polymers under study. Finally, the last chapter address the study of cuprates superconductors. The LDA+U can account for the localization of electrons in copper orbitals. Consequently, a study of the impact of this functionnal on electronic properties of cuprates is conducted. The chapter is ended by an article treating magnetic orders in doped La 2CuO4. Supplementary materials of the second article and a description of the theory of superconductivity of Bardeen, Cooper and Schrieffer are put in annex. Keywords : Electronic correlation, DFT, LDA+U, cuprates, polymers, magnetic orders

Structural vibration-based damage classification of delaminated smart composite laminates

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Random Walk Quantum Clustering Algorithm Based on Space

NASA Astrophysics Data System (ADS)

Xiao, Shufen; Dong, Yumin; Ma, Hongyang

2018-01-01

In the random quantum walk, which is a quantum simulation of the classical walk, data points interacted when selecting the appropriate walk strategy by taking advantage of quantum-entanglement features; thus, the results obtained when the quantum walk is used are different from those when the classical walk is adopted. A new quantum walk clustering algorithm based on space is proposed by applying the quantum walk to clustering analysis. In this algorithm, data points are viewed as walking participants, and similar data points are clustered using the walk function in the pay-off matrix according to a certain rule. The walk process is simplified by implementing a space-combining rule. The proposed algorithm is validated by a simulation test and is proved superior to existing clustering algorithms, namely, Kmeans, PCA + Kmeans, and LDA-Km. The effects of some of the parameters in the proposed algorithm on its performance are also analyzed and discussed. Specific suggestions are provided.

Interplay of the Glass Transition and the Liquid-Liquid Phase Transition in Water

NASA Astrophysics Data System (ADS)

Giovambattista, Nicolas

2013-03-01

Most liquids can form a single glass or amorphous state when cooled sufficiently fast (in order to prevent crystallization). However, there are a few substances that are relevant to scientific and technological applications which can exist in at least two different amorphous states, a property known as polyamorphism. Examples include silicon, silica, and in particular, water. In the case of water, experiments show the existence of a low-density (LDA) and high-density (HDA) amorphous ice that are separated by a dramatic, first-order like phase transition. It has been argued that the LDA-HDA transformation evolves into a first-order liquid-liquid phase transition (LLPT) at temperatures above the glass transition temperature Tg. However, obtaining direct experimental evidence of the LLPT has been challenging since the LLPT occurs at conditions where water rapidly crystallizes. In this talk, I will (i) discuss the general phenomenology of polyamorphism in water and its implications, and (ii) explore the effects of a LLPT on the pressure dependence of Tg(P) for LDA and HDA. Our study is based on computer simulations of two water models - one with a LLPT (ST2 model), and one without (SPC/E model). In the absence of a LLPT, Tg(P) for all glasses nearly coincide. Instead, when there is a LLPT, different glasses exhibit dramatically different Tg(P) loci which are directly linked with the LLPT. Available experimental data for Tg(P) are only consistent with the scenario that includes a LLPT (ST2 model) and hence, our results support the view that a LLPT may exist for the case of water.

Efficacy of ABT-116, an antagonist of transient receptor potential vanilloid type 1, in providing analgesia for dogs with chemically induced synovitis.

PubMed

Cathcart, Curtis J; Johnston, Spencer A; Reynolds, Lisa R; Al-Nadaf, Sami; Budsberg, Steven C

2012-01-01

To investigate the ability of ABT-116 (a proprietary antagonist of transient receptor potential vanilloid type 1) administered at 2 doses to attenuate lameness in dogs with experimentally induced urate synovitis. 8 purpose-bred mixed-breed dogs. In a 4-way crossover study, dogs orally received each of low-dose ABT-116 treatment (LDA; 10 mg/kg), high-dose ABT-116 treatment (HDA; 30 mg/kg), firocoxib (5 mg/kg), and no treatment (nontreatment) once a day for 2 days, in a randomly assigned order. Synovitis was induced on the second day of each treatment period by intra-articular injection of either stifle joint with sodium urate, alternating between joints for each treatment period, beginning with the left stifle joint. Ground reaction forces, clinical lameness scores, and rectal temperature were assessed before the injection (baseline) and at various points afterward. Lameness scores at the 2-, 6-, and 12-hour assessment points were higher than baseline scores for HDA and nontreatment, whereas scores at the 2- and 6-hour points were higher than baseline scores for LDA. For firocoxib, there was no difference from baseline scores in lameness scores at any point. Compared with baseline values, peak vertical force and vertical impulse were lower at 2 and 6 hours for HDA and nontreatment and at 2 hours for LDA. No changes in these values were evident for firocoxib. The HDA or LDA resulted in higher rectal temperatures than did treatment with firocoxib or nothing, but those temperatures did not differ among treatments. HDA had no apparent effect on sodium urate-induced lameness; LDA did attenuate the lameness but not as completely as firocoxib treatment. High rectal temperature is an adverse effect of oral ABT-116 administration that may be of clinical concern.

Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper.

PubMed

Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio

2016-09-01

Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Rapid authentication of edible bird's nest by FTIR spectroscopy combined with chemometrics.

PubMed

Guo, Lili; Wu, Yajun; Liu, Mingchang; Ge, Yiqiang; Chen, Ying

2018-06-01

Edible bird's nests (EBNs) have been traditionally regarded as a kind of medicinal and healthy food in China. For economic reasons, they are frequently subjected to adulteration with some cheaper substitutes, such as Tremella fungus, agar, fried pigskin, and egg white. As a kind of precious and functional product, it is necessary to establish a robust method for the rapid authentication of EBNs with small amounts of samples by simple processes. In this study, the Fourier transform infrared spectroscopy (FTIR) system was utilized and its feasibility for identification of EBNs was verified. FTIR spectra data of authentic and adulterated EBNs were analyzed by chemometrics analyses including principal component analysis, linear discriminant analysis (LDA), support vector machine (SVM) and one-class partial least squares (OCPLS). The results showed that the established LDA and SVM models performed well and had satisfactory classification ability, with the former 94.12% and the latter 100%. The OCPLS model was developed with prediction sensitivity of 0.937 and specificity of 0.886. Further detection of commercial EBN samples confirmed these results. FTIR is applicable in the scene of rapid authentication of EBNs, especially for quality supervision departments, entry-exit inspection and quarantine, and customs administration. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Houttuynia cordata Thunb extract induces cytotoxicity in human nasopharyngeal carcinoma cells: Raman spectroscopic studies

NASA Astrophysics Data System (ADS)

Chen, Weiwei; Li, Zuanfang; Yu, Yun; Lin, Duo; Huang, Hao; Shi, Hong

2016-01-01

The molecular mechanisms of cytotoxicity induced by Houttuynia cordata Thunb (HCT) in nasopharyngeal carcinoma (NPC) cells was investigated by Raman spectroscopy (RS). The average Raman spectra of cell groups treated with HCT (0, 62.5, 125, 250, and 500 μg ml-1) for 24 h were measured separately. Compared to the control group, the intensities of the selected bands (1002, 1338, and 1448 cm-1) related to protein, DNA, and lipid in the treatment groups decreased obviously as the concentration of HCT increased. Both cell groups treated with 250 and 500 μg ml-1 of HCT could be differentiated from the control group by principal component analysis (PCA) combined with linear discriminate analysis (LDA) with a diagnostic accuracy of 100%, suggesting that cytotoxicity occurred and that 250 μg ml-1 was the proper dose for treatment. Simultaneously, the Raman spectra of cells treated with different treatment times with 250 μg ml-1 of HCT were obtained. We can get that treatment with HCT decreased cell viability in a dose and time-dependent fashion. The results indicated that the RS combined with PCA-LDA can be used for pharmacokinetics studies of HCT in NPC cells, which could also provide useful data for clinical dosage optimization for HCT.

Measurement of the human esophageal cancer in an early stage with Raman spectroscopy

NASA Astrophysics Data System (ADS)

Maeda, Yasuhiro; Ishigaki, Mika; Taketani, Akinori; Andriana, Bibin B.; Ishihara, Ryu; Sato, Hidetoshi

2014-02-01

The esophageal cancer has a tendency to transfer to another part of the body and the surgical operation itself sometimes gives high risk in vital function because many delicate organs exist near the esophagus. So the esophageal cancer is a disease with a high mortality. So, in order to lead a higher survival rate five years after the cancer's treatment, the investigation of the diagnosis methods or techniques of the cancer in an early stage and support the therapy are required. In this study, we performed the ex vivo experiments to obtain the Raman spectra from normal and early-stage tumor (stage-0) human esophageal sample by using Raman spectroscopy. The Raman spectra are collected by the homemade Raman spectrometer with the wavelength of 785 nm and Raman probe with 600-um-diameter. The principal component analysis (PCA) is performed after collection of spectra to recognize which materials changed in normal part and cancerous pert. After that, the linear discriminant analysis (LDA) is performed to predict the tissue type. The result of PCA indicates that the tumor tissue is associated with a decrease in tryptophan concentration. Furthermore, we can predict the tissue type with 80% accuracy by LDA which model is made by tryptophan bands.

High wavenumber Raman spectroscopic characterization of normal and oral cancer using blood plasma

NASA Astrophysics Data System (ADS)

Pachaiappan, Rekha; Prakasarao, Aruna; Suresh Kumar, Murugesan; Singaravelu, Ganesan

2017-02-01

Blood plasma possesses the biomolecules released from cells/tissues after metabolism and reflects the pathological conditions of the subjects. The analysis of biofluids for disease diagnosis becomes very attractive in the diagnosis of cancers due to the ease in the collection of samples, easy to transport, multiple sampling for regular screening of the disease and being less invasive to the patients. Hence, the intention of this study was to apply near-infrared (NIR) Raman spectroscopy in the high wavenumber (HW) region (2500-3400 cm-1) for the diagnosis of oral malignancy using blood plasma. From the Raman spectra it is observed that the biomolecules protein and lipid played a major role in the discrimination between groups. The diagnostic algorithms based on principal components analysis coupled with linear discriminant analysis (PCA-LDA) with the leave-one-patient-out cross-validation method on HW Raman spectra yielded a promising results in the identification of oral malignancy. The details of results will be discussed.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Detection of Genetically Modified Sugarcane by Using Terahertz Spectroscopy and Chemometrics

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

The dispersion of particles in a separated backward-facing step flow

NASA Astrophysics Data System (ADS)

Ruck, B.; Makiola, B.

1991-05-01

Flows in technical and natural circuits often involve a particulate phase. To measure the dynamics of suspended, naturally resident or artificially seeded particles in the flow, optical measuring techniques, e.g., laser Doppler anemometry (LDA) can be used advantageously. In this paper the dispersion of particles in a single-sided backward-facing step flow is investigated by LDA. The investigation is of relevance for both, two-phase flow problems in separated flows with the associated particle diameter range of 1-70 μm and the accuracy of LDA with tracer particles of different sizes. The latter is of interest for all LDA applications to measure continuous phase properties, where interest for experimental restraints require tracer diameters in the upper micrometer range, e.g., flame resistant particles for measurements inside reactors, cylinders, etc. For the experiments, a closed-loop wind tunnel with a step expansion was used. Part of this tunnel, the test section, was made of glass. The step had a height H=25 mm (channel height before the step 25 mm, after 50 mm, i.e., an expansion ratio of 2). The width of the channel was 500 mm. The length of the glass test section was chosen as 116 step heights. The wind tunnel, driven by a radial fan, allowed flow velocities up to 50 m/sec which is equivalent to ReH=105. Seeding was performed with particles of well-known size: 1, 15, 30, and 70 μm in diameter. As 1 μm tracers oil droplets were used, whereas for the upper micron range starch particles (density 1.500 kg/m3) were chosen. Starch particles have a spherical shape and are not soluble in cold water. Particle velocities were measured locally using a conventional 1-D LDA system. The measurements deliver the resultant ``flow'' field information stemming from different particle size classes. Thus, the particle behavior in the separated flow field can be resolved. The results show that with increasing particle size, the particle velocity field differs increasingly from the flow field of the continuous phase (inferred from the smallest tracers used). The velocity fluctuations successively decrease with increasing particle diameter. In separation zones, bigger particles have a lower mean velocity than smaller ones. The opposite holds for the streamwise portions of the particle velocity field, where bigger particles show a higher velocity. The measurements give detailed insight into the particle dynamics in separated flow regions. LDA-measured dividing streamlines and lines of zero velocity of different particle classes in the recirculation region have been plotted and compared. In LDA the use of tracer particles in the upper micrometer size range leads to erroneous determinations of continuous phase flow characteristics. It turned out that the dimensions of the measured recirculation zones are reduced with increasing particle diameter. The physical reasons for these findings (relaxation time of particles, Stokes numbers, etc.) are explained in detail.

Effect of Discontinuation or Initiation of Methotrexate or Glucocorticoids on Tofacitinib Efficacy in Patients with Rheumatoid Arthritis: A Post Hoc Analysis.

PubMed

Fleischmann, Roy; Wollenhaupt, Jürgen; Cohen, Stanley; Wang, Lisy; Fan, Haiyun; Bandi, Vara; Andrews, John; Takiya, Liza; Bananis, Eustratios; Weinblatt, Michael E

2018-06-01

Tofacitinib is an oral Janus kinase inhibitor for the treatment of rheumatoid arthritis (RA). We evaluated the effect of concomitant methotrexate (MTX) or glucocorticoid (GC) use on tofacitinib clinical efficacy. Data were pooled from two open-label, long-term extension studies of tofacitinib 5 or 10 mg twice daily in patients with RA. Response according to Clinical Disease Activity Index (CDAI) was assessed separately in patients who discontinued (no MTX/GC use within 30 days prior to year-3 visit; assessment at month 3/year 3) or initiated (on/before year 3; assessment at initiation and year 3) MTX/GC. By year 3, among patients receiving background MTX at baseline, 186/1608 (11.6%) discontinued MTX, and 319/1434 (22.2%) patients receiving GC at baseline discontinued GC. Overall, 70.4/69.1% of patients who discontinued/continued MTX and 72.7/65.9% who discontinued/continued GC achieved CDAI remission or low disease activity (LDA) at year 3. Month 3 remission/LDA rates were maintained at year 3 in the majority of patients, irrespective of MTX/GC discontinuation/continuation. By year 3, 6.2% of patients receiving tofacitinib without MTX at baseline had initiated concomitant MTX, and 25.1% receiving tofacitinib without GC initiated GC; 69.0% and 45.4% initiating MTX or GC, respectively, had a CDAI-defined incomplete response prior to initiation. RA signs/symptoms improved following MTX initiation; only modest improvement was observed with GC initiation. Patients achieving remission/LDA with tofacitinib may discontinue MTX or GC and maintain treatment response. Patients with an incomplete response may benefit from adding concomitant MTX. Pfizer Inc. Study A3921024 [NCT00413699] and Study A3921041 [NCT00661661].

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

Wilson, Andrew T.; Robinson, David Gerald

Most topic modeling algorithms that address the evolution of documents over time use the same number of topics at all times. This obscures the common occurrence in the data where new subjects arise and old ones diminish or disappear entirely. We propose an algorithm to model the birth and death of topics within an LDA-like framework. The user selects an initial number of topics, after which new topics are created and retired without further supervision. Our approach also accommodates many of the acceleration and parallelization schemes developed in recent years for standard LDA. In recent years, topic modeling algorithms suchmore » as latent semantic analysis (LSA)[17], latent Dirichlet allocation (LDA)[10] and their descendants have offered a powerful way to explore and interrogate corpora far too large for any human to grasp without assistance. Using such algorithms we are able to search for similar documents, model and track the volume of topics over time, search for correlated topics or model them with a hierarchy. Most of these algorithms are intended for use with static corpora where the number of documents and the size of the vocabulary are known in advance. Moreover, almost all current topic modeling algorithms fix the number of topics as one of the input parameters and keep it fixed across the entire corpus. While this is appropriate for static corpora, it becomes a serious handicap when analyzing time-varying data sets where topics come and go as a matter of course. This is doubly true for online algorithms that may not have the option of revising earlier results in light of new data. To be sure, these algorithms will account for changing data one way or another, but without the ability to adapt to structural changes such as entirely new topics they may do so in counterintuitive ways.« less

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.

Robust feature extraction for rapid classification of damage in composites

NASA Astrophysics Data System (ADS)

Coelho, Clyde K.; Reynolds, Whitney; Chattopadhyay, Aditi

2009-03-01

The ability to detect anomalies in signals from sensors is imperative for structural health monitoring (SHM) applications. Many of the candidate algorithms for these applications either require a lot of training examples or are very computationally inefficient for large sample sizes. The damage detection framework presented in this paper uses a combination of Linear Discriminant Analysis (LDA) along with Support Vector Machines (SVM) to obtain a computationally efficient classification scheme for rapid damage state determination. LDA was used for feature extraction of damage signals from piezoelectric sensors on a composite plate and these features were used to train the SVM algorithm in parts, reducing the computational intensity associated with the quadratic optimization problem that needs to be solved during training. SVM classifiers were organized into a binary tree structure to speed up classification, which also reduces the total training time required. This framework was validated on composite plates that were impacted at various locations. The results show that the algorithm was able to correctly predict the different impact damage cases in composite laminates using less than 21 percent of the total available training data after data reduction.

Qualitative and quantitative detection of honey adulterated with high-fructose corn syrup and maltose syrup by using near-infrared spectroscopy.

PubMed

Li, Shuifang; Zhang, Xin; Shan, Yang; Su, Donglin; Ma, Qiang; Wen, Ruizhi; Li, Jiaojuan

2017-03-01

Near-infrared spectroscopy (NIR) was used for qualitative and quantitative detection of honey adulterated with high-fructose corn syrup (HFCS) or maltose syrup (MS). Competitive adaptive reweighted sampling (CARS) was employed to select key variables. Partial least squares linear discriminant analysis (PLS-LDA) was adopted to classify the adulterated honey samples. The CARS-PLS-LDA models showed an accuracy of 86.3% (honey vs. adulterated honey with HFCS) and 96.1% (honey vs. adulterated honey with MS), respectively. PLS regression (PLSR) was used to predict the extent of adulteration in the honeys. The results showed that NIR combined with PLSR could not be used to quantify adulteration with HFCS, but could be used to quantify adulteration with MS: coefficient (R p 2 ) and root mean square of prediction (RMSEP) were 0.901 and 4.041 for MS-adulterated samples from different floral origins, and 0.981 and 1.786 for MS-adulterated samples from the same floral origin (Brassica spp.), respectively. Copyright © 2016. Published by Elsevier Ltd.

Electronic Correlation and Magnetism in the Ferromagnetic Metal Fe 3GeTe 2

DOE PAGES

Zhu, Jian-Xin; Janoschek, Marc; Chaves, D. S.; ...

2016-04-05

Motivated by the search for design principles of rare-earth-free strong magnets, we present a study of electronic structure and magnetic properties of the ferromagnetic metal Fe3GeTe2 within local density approximation (LDA) of the density functional theory, and its combination with dynamical mean-field theory (DMFT). For comparison to these calculations, we have measured magnetic and thermodynamic properties as well as X-ray magnetic circular dichroism and the photoemission spectrum of single crystal Fe3GeTe2. We find that the experimentally determined Sommerfeld coefficient is enhanced by an order of magnitude with respect to the LDA value. This enhancement can be partially explained by LDA+DMFT.more » Additionally, the inclusion of dynamical electronic correlation effects provides the experimentally observed magnetic moments, and the spectral density is in better agreement with photoemission data. Lastly, these results establish the importance of electronic correlations in this ferromagnet.« less

Interplay of the Glass Transition and the Liquid-Liquid Phase Transition in Water

PubMed Central

Giovambattista, Nicolas; Loerting, Thomas; Lukanov, Boris R.; Starr, Francis W.

2012-01-01

Water has multiple glassy states, often called amorphous ices. Low-density (LDA) and high-density (HDA) amorphous ice are separated by a dramatic, first-order like phase transition. It has been argued that the LDA-HDA transformation connects to a first-order liquid-liquid phase transition (LLPT) above the glass transition temperature Tg. Direct experimental evidence of the LLPT is challenging to obtain, since the LLPT occurs at conditions where water rapidly crystallizes. In this work, we explore the implications of a LLPT on the pressure dependence of Tg(P) for LDA and HDA by performing computer simulations of two water models – one with a LLPT, and one without. In the absence of a LLPT, Tg(P) for all glasses nearly coincide. When there is a LLPT, different glasses exhibit dramatically different Tg(P) which are directly linked with the LLPT. Available experimental data for Tg(P) are only consistent with the scenario including a LLPT. PMID:22550566

Density-functional expansion methods: evaluation of LDA, GGA, and meta-GGA functionals and different integral approximations.

PubMed

Giese, Timothy J; York, Darrin M

2010-12-28

We extend the Kohn-Sham potential energy expansion (VE) to include variations of the kinetic energy density and use the VE formulation with a 6-31G* basis to perform a "Jacob's ladder" comparison of small molecule properties using density functionals classified as being either LDA, GGA, or meta-GGA. We show that the VE reproduces standard Kohn-Sham DFT results well if all integrals are performed without further approximation, and there is no substantial improvement in using meta-GGA functionals relative to GGA functionals. The advantages of using GGA versus LDA functionals becomes apparent when modeling hydrogen bonds. We furthermore examine the effect of using integral approximations to compute the zeroth-order energy and first-order matrix elements, and the results suggest that the origin of the short-range repulsive potential within self-consistent charge density-functional tight-binding methods mainly arises from the approximations made to the first-order matrix elements.

Band structure calculations of CuAlO2, CuGaO2, CuInO2, and CuCrO2 by screened exchange

NASA Astrophysics Data System (ADS)

Gillen, Roland; Robertson, John

2011-07-01

We report density functional theory band structure calculations on the transparent conducting oxides CuAlO2, CuGaO2, CuInO2, and CuCrO2. The use of the hybrid functional screened-exchange local density approximation (sX-LDA) leads to considerably improved electronic properties compared to standard LDA and generalized gradient approximation (GGA) approaches. We show that the resulting electronic band gaps compare well with experimental values and previous quasiparticle calculations, and show the correct trends with respect to the atomic number of the cation (Al, Ga, In). The resulting energetic depths of Cu d and O p levels and the valence-band widths are considerable improvements compared to LDA and GGA and are in good agreement with available x-ray photoelectron spectroscopy data. Lastly, we show the calculated imaginary part of the dielectric function for all four systems.

Electron interference effects in energetic photoelectrons from C60@C240 probed by the Fourier spectroscopy

NASA Astrophysics Data System (ADS)

McCreary, Meghan; Chakraborty, Himadri

2013-05-01

The ground state structure of the simplest two-fullerene onion system, the C60@C240 molecule, is solved in the Kohn-Sham framework of local density approximation (LDA). Calculations are carried out with delocalized carbon valence electrons after modeling the onion ion-core of sixty C4+ ions from C60 and two hundred and forty of those from C240 in a smeared out jellium-type double-shell structure. Ionization cross sections of all the levels are then calculated in both independent particle LDA and many-particle time dependent LDA approaches at photon energies above the plasmon resonances. These high-energy results exhibit rich structures of energy dependent oscillations from the quantum interference of electron waves produced at the edges of the fullerene layers. A detailed scrutiny of these structures is conducted by Fourier transforming the spectra to the configuration space that relates the oscillations to the onion geometry. Supported by NSF and DOE.

X-Ray Absorption Spectra of Amorphous Ices from GW Quasiparticle Calculation

NASA Astrophysics Data System (ADS)

Kong, Lingzhu; Car, Roberto

2013-03-01

We use a GW approach[2] to compute the x-ray absorption spectra of model low- and high-density amorphous ice structures(LDA and HDA)[3]. We include the structural effects of quantum zero point motion using colored-noise Langevin molecular dynamics[4]. The calculated spectra differences in the main and post edge region between LDA and HDA agree well with experimental observations. We attribute these differences to the presence of interstitial molecules within the first coordination shell range in HDA. This assignment is further supported by a calculation of the spectrum of ice VIII, a high-pressure structure that maximizes the number of interstitial molecules and, accordingly, shows a much weaker post-edge feature. We further rationalize the spectral similarity between HDA and liquid water, and between LDA and ice Ih in terms of the respective similarities in the H-bond network topology and bond angle distributions. Supported by grants DOE-DE-SC0005180, DOE DE-SC0008626 and NSF-CHE-0956500.

Interplay of the Glass Transition and the Liquid-Liquid Phase Transition in Water

NASA Astrophysics Data System (ADS)

Giovambattista, Nicolas; Loerting, Thomas; Lukanov, Boris R.; Starr, Francis W.

2012-05-01

Water has multiple glassy states, often called amorphous ices. Low-density (LDA) and high-density (HDA) amorphous ice are separated by a dramatic, first-order like phase transition. It has been argued that the LDA-HDA transformation connects to a first-order liquid-liquid phase transition (LLPT) above the glass transition temperature Tg. Direct experimental evidence of the LLPT is challenging to obtain, since the LLPT occurs at conditions where water rapidly crystallizes. In this work, we explore the implications of a LLPT on the pressure dependence of Tg(P) for LDA and HDA by performing computer simulations of two water models - one with a LLPT, and one without. In the absence of a LLPT, Tg(P) for all glasses nearly coincide. When there is a LLPT, different glasses exhibit dramatically different Tg(P) which are directly linked with the LLPT. Available experimental data for Tg(P) are only consistent with the scenario including a LLPT.

Mechanisms of anomalous compressibility of vitreous silica

NASA Astrophysics Data System (ADS)

Clark, Alisha N.; Lesher, Charles E.; Jacobsen, Steven D.; Sen, Sabyasachi

2014-11-01

The anomalous compressibility of vitreous silica has been known for nearly a century, but the mechanisms responsible for it remain poorly understood. Using GHz-ultrasonic interferometry, we measured longitudinal and transverse acoustic wave travel times at pressures up to 5 GPa in vitreous silica with fictive temperatures (Tf) ranging between 985 °C and 1500 °C. The maximum in ultrasonic wave travel times-corresponding to a minimum in acoustic velocities-shifts to higher pressure with increasing Tf for both acoustic waves, with complete reversibility below 5 GPa. These relationships reflect polyamorphism in the supercooled liquid, which results in a glassy state possessing different proportions of domains of high- and low-density amorphous phases (HDA and LDA, respectively). The relative proportion of HDA and LDA is set at Tf and remains fixed on compression below the permanent densification pressure. The bulk material exhibits compression behavior systematically dependent on synthesis conditions that arise from the presence of floppy modes in a mixture of HDA and LDA domains.

An LDA+U study of the photoemission spectra of ground state phase of americium and curium

NASA Astrophysics Data System (ADS)

Islam, Md; Ray, Asok

2009-03-01

We have investigated the photoemission spectra and other ground state properties such as equilibrium volume and bulk modulus of dhcp americium and the density of states and magnetic properties of dhcp curium using LDA+U method. Our calculations show that spin polarized americium is energetically favorable but spin degenerate configuration produces experimental quantities much better than that calculated using spin polarized configuration. The DOS calculated using LDA+U with both non-magnetic and spin polarized configurations is compared and the non-magnetic DOS is shown to be in good agreement with experimental photoemission spectra when U=4.5 eV. In spin polarized case, U is observed to increase the splitting between occupied and unoccupied bands by enhancing Stoner parameter. The results are shown to be in good agreement with that calculated using dynamical mean field theory for these two heavy actinides. For curium, exchange interaction appears to play the dominant role in its magnetic stability.

Moiré-pattern interlayer potentials in van der Waals materials in the random-phase approximation

NASA Astrophysics Data System (ADS)

Leconte, Nicolas; Jung, Jeil; Lebègue, Sébastien; Gould, Tim

2017-11-01

Stacking-dependent interlayer interactions are important for understanding the structural and electronic properties in incommensurable two-dimensional material assemblies where long-range moiré patterns arise due to small lattice constant mismatch or twist angles. Here we study the stacking-dependent interlayer coupling energies between graphene (G) and hexagonal boron nitride (BN) homo- and heterostructures using high-level random-phase approximation (RPA) ab initio calculations. Our results show that although total binding energies within LDA and RPA differ substantially by a factor of 200%-400%, the energy differences as a function of stacking configuration yield nearly constant values with variations smaller than 20%, meaning that LDA estimates are quite reliable. We produce phenomenological fits to these energy differences, which allows us to calculate various properties of interest including interlayer spacing, sliding energetics, pressure gradients, and elastic coefficients to high accuracy. The importance of long-range interactions (captured by RPA but not LDA) on various properties is also discussed. Parametrizations for all fits are provided.

Age-specific discrimination of blood plasma samples of healthy and ovarian cancer prone mice using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Melikechi, Noureddine; Markushin, Yuri; Connolly, Denise C.; Lasue, Jeremie; Ewusi-Annan, Ebo; Makrogiannis, Sokratis

2016-09-01

Epithelial ovarian cancer (EOC) mortality rates are strongly correlated with the stage at which it is diagnosed. Detection of EOC prior to its dissemination from the site of origin is known to significantly improve the patient outcome. However, there are currently no effective methods for early detection of the most common and lethal subtype of EOC. We sought to determine whether laser-induced breakdown spectroscopy (LIBS) and classification techniques such as linear discriminant analysis (LDA) and random forest (RF) could classify and differentiate blood plasma specimens from transgenic mice with ovarian carcinoma and wild type control mice. Herein we report results using this approach to distinguish blood plasma samples obtained from serially bled (at 8, 12, and 16 weeks) tumor-bearing TgMISIIR-TAg transgenic and wild type cancer-free littermate control mice. We have calculated the age-specific accuracy of classification using 18,000 laser-induced breakdown spectra of the blood plasma samples from tumor-bearing mice and wild type controls. When the analysis is performed in the spectral range 250 nm to 680 nm using LDA, these are 76.7 (± 2.6)%, 71.2 (± 1.3)%, and 73.1 (± 1.4)%, for the 8, 12 and 16 weeks. When the RF classifier is used, we obtain values of 78.5 (± 2.3)%, 76.9 (± 2.1)% and 75.4 (± 2.0)% in the spectral range of 250 nm to 680 nm, and 81.0 (± 1.8)%, 80.4 (± 2.1)% and 79.6 (± 3.5)% in 220 nm to 850 nm. In addition, we report, the positive and negative predictive values of the classification of the two classes of blood plasma samples. The approach used in this study is rapid, requires only 5 μL of blood plasma, and is based on the use of unsupervised and widely accepted multivariate analysis algorithms. These findings suggest that LIBS and multivariate analysis may be a novel approach for detecting EOC.