A two-stage linear discriminant analysis via QR-decomposition.

PubMed

Ye, Jieping; Li, Qi

2005-06-01

Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as image and text classification. An intrinsic limitation of classical LDA is the so-called singularity problems; that is, it fails when all scatter matrices are singular. Many LDA extensions were proposed in the past to overcome the singularity problems. Among these extensions, PCA+LDA, a two-stage method, received relatively more attention. In PCA+LDA, the LDA stage is preceded by an intermediate dimension reduction stage using Principal Component Analysis (PCA). Most previous LDA extensions are computationally expensive, and not scalable, due to the use of Singular Value Decomposition or Generalized Singular Value Decomposition. In this paper, we propose a two-stage LDA method, namely LDA/QR, which aims to overcome the singularity problems of classical LDA, while achieving efficiency and scalability simultaneously. The key difference between LDA/QR and PCA+LDA lies in the first stage, where LDA/QR applies QR decomposition to a small matrix involving the class centroids, while PCA+LDA applies PCA to the total scatter matrix involving all training data points. We further justify the proposed algorithm by showing the relationship among LDA/QR and previous LDA methods. Extensive experiments on face images and text documents are presented to show the effectiveness of the proposed algorithm.

Breast Shape Analysis With Curvature Estimates and Principal Component Analysis for Cosmetic and Reconstructive Breast Surgery.

PubMed

Catanuto, Giuseppe; Taher, Wafa; Rocco, Nicola; Catalano, Francesca; Allegra, Dario; Milotta, Filippo Luigi Maria; Stanco, Filippo; Gallo, Giovanni; Nava, Maurizio Bruno

2018-03-20

Breast shape is defined utilizing mainly qualitative assessment (full, flat, ptotic) or estimates, such as volume or distances between reference points, that cannot describe it reliably. We will quantitatively describe breast shape with two parameters derived from a statistical methodology denominated principal component analysis (PCA). We created a heterogeneous dataset of breast shapes acquired with a commercial infrared 3-dimensional scanner on which PCA was performed. We plotted on a Cartesian plane the two highest values of PCA for each breast (principal components 1 and 2). Testing of the methodology on a preoperative and postoperative surgical case and test-retest was performed by two operators. The first two principal components derived from PCA are able to characterize the shape of the breast included in the dataset. The test-retest demonstrated that different operators are able to obtain very similar values of PCA. The system is also able to identify major changes in the preoperative and postoperative stages of a two-stage reconstruction. Even minor changes were correctly detected by the system. This methodology can reliably describe the shape of a breast. An expert operator and a newly trained operator can reach similar results in a test/re-testing validation. Once developed and after further validation, this methodology could be employed as a good tool for outcome evaluation, auditing, and benchmarking.

Low-Dimensional Feature Representation for Instrument Identification

NASA Astrophysics Data System (ADS)

Ihara, Mizuki; Maeda, Shin-Ichi; Ikeda, Kazushi; Ishii, Shin

For monophonic music instrument identification, various feature extraction and selection methods have been proposed. One of the issues toward instrument identification is that the same spectrum is not always observed even in the same instrument due to the difference of the recording condition. Therefore, it is important to find non-redundant instrument-specific features that maintain information essential for high-quality instrument identification to apply them to various instrumental music analyses. For such a dimensionality reduction method, the authors propose the utilization of linear projection methods: local Fisher discriminant analysis (LFDA) and LFDA combined with principal component analysis (PCA). After experimentally clarifying that raw power spectra are actually good for instrument classification, the authors reduced the feature dimensionality by LFDA or by PCA followed by LFDA (PCA-LFDA). The reduced features achieved reasonably high identification performance that was comparable or higher than those by the power spectra and those achieved by other existing studies. These results demonstrated that our LFDA and PCA-LFDA can successfully extract low-dimensional instrument features that maintain the characteristic information of the instruments.

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

PubMed

Liu, Chengjun

2004-05-01

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

An extended data mining method for identifying differentially expressed assay-specific signatures in functional genomic studies.

PubMed

Rollins, Derrick K; Teh, Ailing

2010-12-17

Microarray data sets provide relative expression levels for thousands of genes for a small number, in comparison, of different experimental conditions called assays. Data mining techniques are used to extract specific information of genes as they relate to the assays. The multivariate statistical technique of principal component analysis (PCA) has proven useful in providing effective data mining methods. This article extends the PCA approach of Rollins et al. to the development of ranking genes of microarray data sets that express most differently between two biologically different grouping of assays. This method is evaluated on real and simulated data and compared to a current approach on the basis of false discovery rate (FDR) and statistical power (SP) which is the ability to correctly identify important genes. This work developed and evaluated two new test statistics based on PCA and compared them to a popular method that is not PCA based. Both test statistics were found to be effective as evaluated in three case studies: (i) exposing E. coli cells to two different ethanol levels; (ii) application of myostatin to two groups of mice; and (iii) a simulated data study derived from the properties of (ii). The proposed method (PM) effectively identified critical genes in these studies based on comparison with the current method (CM). The simulation study supports higher identification accuracy for PM over CM for both proposed test statistics when the gene variance is constant and for one of the test statistics when the gene variance is non-constant. PM compares quite favorably to CM in terms of lower FDR and much higher SP. Thus, PM can be quite effective in producing accurate signatures from large microarray data sets for differential expression between assays groups identified in a preliminary step of the PCA procedure and is, therefore, recommended for use in these applications.

Model Reduction via Principe Component Analysis and Markov Chain Monte Carlo (MCMC) Methods

NASA Astrophysics Data System (ADS)

Gong, R.; Chen, J.; Hoversten, M. G.; Luo, J.

2011-12-01

Geophysical and hydrogeological inverse problems often include a large number of unknown parameters, ranging from hundreds to millions, depending on parameterization and problems undertaking. This makes inverse estimation and uncertainty quantification very challenging, especially for those problems in two- or three-dimensional spatial domains. Model reduction technique has the potential of mitigating the curse of dimensionality by reducing total numbers of unknowns while describing the complex subsurface systems adequately. In this study, we explore the use of principal component analysis (PCA) and Markov chain Monte Carlo (MCMC) sampling methods for model reduction through the use of synthetic datasets. We compare the performances of three different but closely related model reduction approaches: (1) PCA methods with geometric sampling (referred to as 'Method 1'), (2) PCA methods with MCMC sampling (referred to as 'Method 2'), and (3) PCA methods with MCMC sampling and inclusion of random effects (referred to as 'Method 3'). We consider a simple convolution model with five unknown parameters as our goal is to understand and visualize the advantages and disadvantages of each method by comparing their inversion results with the corresponding analytical solutions. We generated synthetic data with noise added and invert them under two different situations: (1) the noised data and the covariance matrix for PCA analysis are consistent (referred to as the unbiased case), and (2) the noise data and the covariance matrix are inconsistent (referred to as biased case). In the unbiased case, comparison between the analytical solutions and the inversion results show that all three methods provide good estimates of the true values and Method 1 is computationally more efficient. In terms of uncertainty quantification, Method 1 performs poorly because of relatively small number of samples obtained, Method 2 performs best, and Method 3 overestimates uncertainty due to inclusion of random effects. However, in the biased case, only Method 3 correctly estimates all the unknown parameters, and both Methods 1 and 2 provide wrong values for the biased parameters. The synthetic case study demonstrates that if the covariance matrix for PCA analysis is inconsistent with true models, the PCA methods with geometric or MCMC sampling will provide incorrect estimates.

Online dimensionality reduction using competitive learning and Radial Basis Function network.

PubMed

Tomenko, Vladimir

2011-06-01

The general purpose dimensionality reduction method should preserve data interrelations at all scales. Additional desired features include online projection of new data, processing nonlinearly embedded manifolds and large amounts of data. The proposed method, called RBF-NDR, combines these features. RBF-NDR is comprised of two modules. The first module learns manifolds by utilizing modified topology representing networks and geodesic distance in data space and approximates sampled or streaming data with a finite set of reference patterns, thus achieving scalability. Using input from the first module, the dimensionality reduction module constructs mappings between observation and target spaces. Introduction of specific loss function and synthesis of the training algorithm for Radial Basis Function network results in global preservation of data structures and online processing of new patterns. The RBF-NDR was applied for feature extraction and visualization and compared with Principal Component Analysis (PCA), neural network for Sammon's projection (SAMANN) and Isomap. With respect to feature extraction, the method outperformed PCA and yielded increased performance of the model describing wastewater treatment process. As for visualization, RBF-NDR produced superior results compared to PCA and SAMANN and matched Isomap. For the Topic Detection and Tracking corpus, the method successfully separated semantically different topics. Copyright © 2011 Elsevier Ltd. All rights reserved.

Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD.

PubMed

Sidhu, Gagan S; Asgarian, Nasimeh; Greiner, Russell; Brown, Matthew R G

2012-01-01

This study explored various feature extraction methods for use in automated diagnosis of Attention-Deficit Hyperactivity Disorder (ADHD) from functional Magnetic Resonance Image (fMRI) data. Each participant's data consisted of a resting state fMRI scan as well as phenotypic data (age, gender, handedness, IQ, and site of scanning) from the ADHD-200 dataset. We used machine learning techniques to produce support vector machine (SVM) classifiers that attempted to differentiate between (1) all ADHD patients vs. healthy controls and (2) ADHD combined (ADHD-c) type vs. ADHD inattentive (ADHD-i) type vs. controls. In different tests, we used only the phenotypic data, only the imaging data, or else both the phenotypic and imaging data. For feature extraction on fMRI data, we tested the Fast Fourier Transform (FFT), different variants of Principal Component Analysis (PCA), and combinations of FFT and PCA. PCA variants included PCA over time (PCA-t), PCA over space and time (PCA-st), and kernelized PCA (kPCA-st). Baseline chance accuracy was 64.2% produced by guessing healthy control (the majority class) for all participants. Using only phenotypic data produced 72.9% accuracy on two class diagnosis and 66.8% on three class diagnosis. Diagnosis using only imaging data did not perform as well as phenotypic-only approaches. Using both phenotypic and imaging data with combined FFT and kPCA-st feature extraction yielded accuracies of 76.0% on two class diagnosis and 68.6% on three class diagnosis-better than phenotypic-only approaches. Our results demonstrate the potential of using FFT and kPCA-st with resting-state fMRI data as well as phenotypic data for automated diagnosis of ADHD. These results are encouraging given known challenges of learning ADHD diagnostic classifiers using the ADHD-200 dataset (see Brown et al., 2012).

Coarse Point Cloud Registration by Egi Matching of Voxel Clusters

NASA Astrophysics Data System (ADS)

Wang, Jinhu; Lindenbergh, Roderik; Shen, Yueqian; Menenti, Massimo

2016-06-01

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

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

NASA Astrophysics Data System (ADS)

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

2012-01-01

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

Electric Power Engineering Cost Predicting Model Based on the PCA-GA-BP

NASA Astrophysics Data System (ADS)

Wen, Lei; Yu, Jiake; Zhao, Xin

2017-10-01

In this paper a hybrid prediction algorithm: PCA-GA-BP model is proposed. PCA algorithm is established to reduce the correlation between indicators of original data and decrease difficulty of BP neural network in complex dimensional calculation. The BP neural network is established to estimate the cost of power transmission project. The results show that PCA-GA-BP algorithm can improve result of prediction of electric power engineering cost.

Systematic ultrasound-guided saturation and template biopsy of the prostate: indications and advantages of extended sampling.

PubMed

Isbarn, Hendrik; Briganti, Alberto; De Visschere, Pieter J L; Fütterer, Jurgen J; Ghadjar, Pirus; Giannarini, Gianluca; Ost, Piet; Ploussard, Guillaume; Sooriakumaran, Prasanna; Surcel, Christian I; van Oort, Inge M; Yossepowitch, Ofer; van den Bergh, Roderick C N

2015-04-01

Prostate biopsy (PB) is the gold standard for the diagnosis of prostate cancer (PCa). However, the optimal number of biopsy cores remains debatable. We sought to compare contemporary standard (10-12 cores) vs. saturation (=18 cores) schemes on initial as well as repeat PB. A non-systematic review of the literature was performed from 2000 through 2013. Studies of highest evidence (randomized controlled trials, prospective non-randomized studies, and retrospective reports of high quality) comparing standard vs saturation schemes on initial and repeat PB were evaluated. Outcome measures were overall PCa detection rate, detection rate of insignificant PCa, and procedure-associated morbidity. On initial PB, there is growing evidence that a saturation scheme is associated with a higher PCa detection rate compared to a standard one in men with lower PSA levels (<10 ng/ml), larger prostates (>40 cc), or lower PSA density values (<0.25 ng/ml/cc). However, these cut-offs are not uniform and differ among studies. Detection rates of insignificant PCa do not differ in a significant fashion between standard and saturation biopsies. On repeat PB, PCa detection rate is likewise higher with saturation protocols. Estimates of insignificant PCa vary widely due to differing definitions of insignificant disease. However, the rates of insignificant PCa appear to be comparable for the schemes in patients with only one prior negative biopsy, while saturation biopsy seems to detect more cases of insignificant PCa compared to standard biopsy in men with two or more prior negative biopsies. Very extensive sampling is associated with a high rate of acute urinary retention, whereas other severe adverse events, such as sepsis, appear not to occur more frequently with saturation schemes. Current evidence suggests that saturation schemes are associated with a higher PCa detection rate compared to standard ones on initial PB in men with lower PSA levels or larger prostates, and on repeat PB. Since most data are derived from retrospective studies, other endpoints such as detection rate of insignificant disease - especially on repeat PB - show broad variations throughout the literature and must, thus, be interpreted with caution. Future prospective controlled trials should be conducted to compare extended templates with newer techniques, such as image-guided sampling, in order to optimize PCa diagnostic strategy.

Cluster and principal component analysis based on SSR markers of Amomum tsao-ko in Jinping County of Yunnan Province

NASA Astrophysics Data System (ADS)

Ma, Mengli; Lei, En; Meng, Hengling; Wang, Tiantao; Xie, Linyan; Shen, Dong; Xianwang, Zhou; Lu, Bingyue

2017-08-01

Amomum tsao-ko is a commercial plant that used for various purposes in medicinal and food industries. For the present investigation, 44 germplasm samples were collected from Jinping County of Yunnan Province. Clusters analysis and 2-dimensional principal component analysis (PCA) was used to represent the genetic relations among Amomum tsao-ko by using simple sequence repeat (SSR) markers. Clustering analysis clearly distinguished the samples groups. Two major clusters were formed; first (Cluster I) consisted of 34 individuals, the second (Cluster II) consisted of 10 individuals, Cluster I as the main group contained multiple sub-clusters. PCA also showed 2 groups: PCA Group 1 included 29 individuals, PCA Group 2 included 12 individuals, consistent with the results of cluster analysis. The purpose of the present investigation was to provide information on genetic relationship of Amomum tsao-ko germplasm resources in main producing areas, also provide a theoretical basis for the protection and utilization of Amomum tsao-ko resources.

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%.

Contact- and distance-based principal component analysis of protein dynamics.

PubMed

Ernst, Matthias; Sittel, Florian; Stock, Gerhard

2015-12-28

To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between Cα-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

Contact- and distance-based principal component analysis of protein dynamics

NASA Astrophysics Data System (ADS)

Ernst, Matthias; Sittel, Florian; Stock, Gerhard

2015-12-01

To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between Cα-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

Multivariate Analysis of Two-Dimensional 1H, 13C Methyl NMR Spectra of Monoclonal Antibody Therapeutics To Facilitate Assessment of Higher Order Structure.

PubMed

Arbogast, Luke W; Delaglio, Frank; Schiel, John E; Marino, John P

2017-11-07

Two-dimensional (2D) 1 H- 13 C methyl NMR provides a powerful tool to probe the higher order structure (HOS) of monoclonal antibodies (mAbs), since spectra can readily be acquired on intact mAbs at natural isotopic abundance, and small changes in chemical environment and structure give rise to observable changes in corresponding spectra, which can be interpreted at atomic resolution. This makes it possible to apply 2D NMR spectral fingerprinting approaches directly to drug products in order to systematically characterize structure and excipient effects. Systematic collections of NMR spectra are often analyzed in terms of the changes in specifically identified peak positions, as well as changes in peak height and line widths. A complementary approach is to apply principal component analysis (PCA) directly to the matrix of spectral data, correlating spectra according to similarities and differences in their overall shapes, rather than according to parameters of individually identified peaks. This is particularly well-suited for spectra of mAbs, where some of the individual peaks might not be well resolved. Here we demonstrate the performance of the PCA method for discriminating structural variation among systematic sets of 2D NMR fingerprint spectra using the NISTmAb and illustrate how spectral variability identified by PCA may be correlated to structure.

Epileptic seizure detection in EEG signal with GModPCA and support vector machine.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2017-01-01

Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space complexities of GModPCA are less as compared to PCA. This study suggests that GModPCA and SVM could be used for automated epileptic seizure detection in EEG signal.

Statistical Exploration of Electronic Structure of Molecules from Quantum Monte-Carlo Simulations

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

Prabhat, Mr; Zubarev, Dmitry; Lester, Jr., William A.

In this report, we present results from analysis of Quantum Monte Carlo (QMC) simulation data with the goal of determining internal structure of a 3N-dimensional phase space of an N-electron molecule. We are interested in mining the simulation data for patterns that might be indicative of the bond rearrangement as molecules change electronic states. We examined simulation output that tracks the positions of two coupled electrons in the singlet and triplet states of an H2 molecule. The electrons trace out a trajectory, which was analyzed with a number of statistical techniques. This project was intended to address the following scientificmore » questions: (1) Do high-dimensional phase spaces characterizing electronic structure of molecules tend to cluster in any natural way? Do we see a change in clustering patterns as we explore different electronic states of the same molecule? (2) Since it is hard to understand the high-dimensional space of trajectories, can we project these trajectories to a lower dimensional subspace to gain a better understanding of patterns? (3) Do trajectories inherently lie in a lower-dimensional manifold? Can we recover that manifold? After extensive statistical analysis, we are now in a better position to respond to these questions. (1) We definitely see clustering patterns, and differences between the H2 and H2tri datasets. These are revealed by the pamk method in a fairly reliable manner and can potentially be used to distinguish bonded and non-bonded systems and get insight into the nature of bonding. (2) Projecting to a lower dimensional subspace ({approx}4-5) using PCA or Kernel PCA reveals interesting patterns in the distribution of scalar values, which can be related to the existing descriptors of electronic structure of molecules. Also, these results can be immediately used to develop robust tools for analysis of noisy data obtained during QMC simulations (3) All dimensionality reduction and estimation techniques that we tried seem to indicate that one needs 4 or 5 components to account for most of the variance in the data, hence this 5D dataset does not necessarily lie on a well-defined, low dimensional manifold. In terms of specific clustering techniques, K-means was generally useful in exploring the dataset. The partition around medoids (pam) technique produced the most definitive results for our data showing distinctive patterns for both a sample of the complete data and time-series. The gap statistic with tibshirani criteria did not provide any distinction across the 2 dataset. The gap statistic w/DandF criteria, Model based clustering and hierarchical modeling simply failed to run on our datasets. Thankfully, the vanilla PCA technique was successful in handling our entire dataset. PCA revealed some interesting patterns for the scalar value distribution. Kernel PCA techniques (vanilladot, RBF, Polynomial) and MDS failed to run on the entire dataset, or even a significant fraction of the dataset, and we resorted to creating an explicit feature map followed by conventional PCA. Clustering using K-means and PAM in the new basis set seems to produce promising results. Understanding the new basis set in the scientific context of the problem is challenging, and we are currently working to further examine and interpret the results.« less

The Application of Infrared Thermographic Inspection Techniques to the Space Shuttle Thermal Protection System

NASA Technical Reports Server (NTRS)

Cramer, K. E.; Winfree, W. P.

2005-01-01

The Nondestructive Evaluation Sciences Branch at NASA s Langley Research Center has been actively involved in the development of thermographic inspection techniques for more than 15 years. Since the Space Shuttle Columbia accident, NASA has focused on the improvement of advanced NDE techniques for the Reinforced Carbon-Carbon (RCC) panels that comprise the orbiter s wing leading edge. Various nondestructive inspection techniques have been used in the examination of the RCC, but thermography has emerged as an effective inspection alternative to more traditional methods. Thermography is a non-contact inspection method as compared to ultrasonic techniques which typically require the use of a coupling medium between the transducer and material. Like radiographic techniques, thermography can be used to inspect large areas, but has the advantage of minimal safety concerns and the ability for single-sided measurements. Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. A typical implementation of PCA is when the eigenvectors are generated from the data set being analyzed. Although it is a powerful tool for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the "good" material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued where a fixed set of eigenvectors, generated from an analytic model of the thermal response of the material under examination, is used to process the thermal data from the RCC materials. Details of a one-dimensional analytic model and a two-dimensional finite-element model will be presented. An overview of the PCA process as well as a quantitative signal-to-noise comparison of the results of performing both embodiments of PCA on thermographic data from various RCC specimens will be shown. Finally, a number of different applications of this technology to various RCC components will be presented.

TH-CD-207A-07: Prediction of High Dimensional State Subject to Respiratory Motion: A Manifold Learning Approach

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

Liu, W; Sawant, A; Ruan, D

Purpose: The development of high dimensional imaging systems (e.g. volumetric MRI, CBCT, photogrammetry systems) in image-guided radiotherapy provides important pathways to the ultimate goal of real-time volumetric/surface motion monitoring. This study aims to develop a prediction method for the high dimensional state subject to respiratory motion. Compared to conventional linear dimension reduction based approaches, our method utilizes manifold learning to construct a descriptive feature submanifold, where more efficient and accurate prediction can be performed. Methods: We developed a prediction framework for high-dimensional state subject to respiratory motion. The proposed method performs dimension reduction in a nonlinear setting to permit moremore » descriptive features compared to its linear counterparts (e.g., classic PCA). Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where low-dimensional prediction is performed. A fixed-point iterative pre-image estimation method is applied subsequently to recover the predicted value in the original state space. We evaluated and compared the proposed method with PCA-based method on 200 level-set surfaces reconstructed from surface point clouds captured by the VisionRT system. The prediction accuracy was evaluated with respect to root-mean-squared-error (RMSE) for both 200ms and 600ms lookahead lengths. Results: The proposed method outperformed PCA-based approach with statistically higher prediction accuracy. In one-dimensional feature subspace, our method achieved mean prediction accuracy of 0.86mm and 0.89mm for 200ms and 600ms lookahead lengths respectively, compared to 0.95mm and 1.04mm from PCA-based method. The paired t-tests further demonstrated the statistical significance of the superiority of our method, with p-values of 6.33e-3 and 5.78e-5, respectively. Conclusion: The proposed approach benefits from the descriptiveness of a nonlinear manifold and the prediction reliability in such low dimensional manifold. The fixed-point iterative approach turns out to work well practically for the pre-image recovery. Our approach is particularly suitable to facilitate managing respiratory motion in image-guide radiotherapy. This work is supported in part by NIH grant R01 CA169102-02.« less

Gas-Chromatographic Determination Of Water In Freon PCA

NASA Technical Reports Server (NTRS)

Melton, Donald M.

1994-01-01

Gas-chromatographic apparatus measures small concentrations of water in specimens of Freon PCA. Testing by use of apparatus faster and provides greater protection against accidental contamination of specimens by water in testing environment. Automated for unattended operation. Also used to measure water contents of materials, other than Freon PCA. Innovation extended to development of purgeable sampling accessory for gas chromatographs.

Performance analysis of robust road sign identification

NASA Astrophysics Data System (ADS)

Ali, Nursabillilah M.; Mustafah, Y. M.; Rashid, N. K. A. M.

2013-12-01

This study describes performance analysis of a robust system for road sign identification that incorporated two stages of different algorithms. The proposed algorithms consist of HSV color filtering and PCA techniques respectively in detection and recognition stages. The proposed algorithms are able to detect the three standard types of colored images namely Red, Yellow and Blue. The hypothesis of the study is that road sign images can be used to detect and identify signs that are involved with the existence of occlusions and rotational changes. PCA is known as feature extraction technique that reduces dimensional size. The sign image can be easily recognized and identified by the PCA method as is has been used in many application areas. Based on the experimental result, it shows that the HSV is robust in road sign detection with minimum of 88% and 77% successful rate for non-partial and partial occlusions images. For successful recognition rates using PCA can be achieved in the range of 94-98%. The occurrences of all classes are recognized successfully is between 5% and 10% level of occlusions.

Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy.

PubMed

Jesse, Stephen; Kalinin, Sergei V

2009-02-25

An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.

ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap

PubMed Central

Metsalu, Tauno; Vilo, Jaak

2015-01-01

The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely used, the method is lacking an easy-to-use web interface that scientists with little programming skills could use to make plots of their own data. The same applies to creating heatmaps: it is possible to add conditional formatting for Excel cells to show colored heatmaps, but for more advanced features such as clustering and experimental annotations, more sophisticated analysis tools have to be used. We present a web tool called ClustVis that aims to have an intuitive user interface. Users can upload data from a simple delimited text file that can be created in a spreadsheet program. It is possible to modify data processing methods and the final appearance of the PCA and heatmap plots by using drop-down menus, text boxes, sliders etc. Appropriate defaults are given to reduce the time needed by the user to specify input parameters. As an output, users can download PCA plot and heatmap in one of the preferred file formats. This web server is freely available at http://biit.cs.ut.ee/clustvis/. PMID:25969447

Understanding the pattern of the BSE Sensex

NASA Astrophysics Data System (ADS)

Mukherjee, I.; Chatterjee, Soumya; Giri, A.; Barat, P.

2017-09-01

An attempt is made to understand the pattern of behaviour of the BSE Sensex by analysing the tick-by-tick Sensex data for the years 2006 to 2012 on yearly as well as cumulative basis using Principal Component Analysis (PCA) and its nonlinear variant Kernel Principal Component Analysis (KPCA). The latter technique ensures that the nonlinear character of the interactions present in the system gets captured in the analysis. The analysis is carried out by constructing vector spaces of varying dimensions. The size of the data set ranges from a minimum of 360,000 for one year to a maximum of 2,520,000 for seven years. In all cases the prices appear to be highly correlated and restricted to a very low dimensional subspace of the original vector space. An external perturbation is added to the system in the form of noise. It is observed that while standard PCA is unable to distinguish the behaviour of the noise-mixed data from that of the original, KPCA clearly identifies the effect of the noise. The exercise is extended in case of daily data of other stock markets and similar results are obtained.

Inflammation: an important parameter in the search of prostate cancer biomarkers

PubMed Central

2014-01-01

Background A more specific and early diagnostics for prostate cancer (PCa) is highly desirable. In this study, being inflammation the focus of our effort, serum protein profiles were analyzed in order to investigate if this parameter could interfere with the search of discriminating proteins between PCa and benign prostatic hyperplasia (BPH). Methods Patients with clinical suspect of PCa and candidates for trans-rectal ultrasound guided prostate biopsy (TRUS) were enrolled. Histological specimens were examined in order to grade and classify the tumor, identify BPH and detect inflammation. Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry (SELDI-ToF-MS) and two-dimensional gel electrophoresis (2-DE) coupled with Liquid Chromatography-MS/MS (LC-MS/MS) were used to analyze immuno-depleted serum samples from patients with PCa and BPH. Results The comparison between PCa (with and without inflammation) and BPH (with and without inflammation) serum samples by SELDI-ToF-MS analysis did not show differences in protein expression, while changes were only observed when the concomitant presence of inflammation was taken into consideration. In fact, when samples with histological sign of inflammation were excluded, 20 significantly different protein peaks were detected. Subsequent comparisons (PCa with inflammation vs PCa without inflammation, and BPH with inflammation vs BPH without inflammation) showed that 16 proteins appeared to be modified in the presence of inflammation, while 4 protein peaks were not modified. With 2-DE analysis, comparing PCa without inflammation vs PCa with inflammation, and BPH without inflammation vs the same condition in the presence of inflammation, were identified 29 and 25 differentially expressed protein spots, respectively. Excluding samples with inflammation the comparison between PCa vs BPH showed 9 unique PCa proteins, 4 of which overlapped with those previously identified in the presence of inflammation, while other 2 were new proteins, not identified in our previous comparisons. Conclusions The present study indicates that inflammation might be a confounding parameter during the proteomic research of candidate biomarkers of PCa. These results indicate that some possible biomarker-candidate proteins are strongly influenced by the presence of inflammation, hence only a well-selected protein pattern should be considered for potential marker of PCa. PMID:24944525

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

Yamanishi, Junsuke; Iwase, Shigeru; Ishida, Nobuyuki; Fujita, Daisuke

2018-01-01

We applied principal component analysis (PCA) to two-dimensional tunneling spectroscopy (2DTS) data obtained on a Si(111)-(7 × 7) surface to explore the effectiveness of multivariate analysis for interpreting 2DTS data. We demonstrated that several components that originated mainly from specific atoms at the Si(111)-(7 × 7) surface can be extracted by PCA. Furthermore, we showed that hidden components in the tunneling spectra can be decomposed (peak separation), which is difficult to achieve with normal 2DTS analysis without the support of theoretical calculations. Our analysis showed that multivariate analysis can be an additional powerful way to analyze 2DTS data and extract hidden information from a large amount of spectroscopic data.

Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga-Koontz Transform

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Ochilov, Shuhrat; Alam, Mohammad S.; Bal, Abdullah

2017-02-01

Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga-Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.

A Tale of Two Signals: AR and WNT in Development and Tumorigenesis of Prostate and Mammary Gland

PubMed Central

Pakula, Hubert; Xiang, Dongxi; Li, Zhe

2017-01-01

Prostate cancer (PCa) is one of the most common cancers and among the leading causes of cancer deaths for men in industrialized countries. It has long been recognized that the prostate is an androgen-dependent organ and PCa is an androgen-dependent disease. Androgen action is mediated by the androgen receptor (AR). Androgen deprivation therapy (ADT) is the standard treatment for metastatic PCa. However, almost all advanced PCa cases progress to castration-resistant prostate cancer (CRPC) after a period of ADT. A variety of mechanisms of progression from androgen-dependent PCa to CRPC under ADT have been postulated, but it remains largely unclear as to when and how castration resistance arises within prostate tumors. In addition, AR signaling may be modulated by extracellular factors among which are the cysteine-rich glycoproteins WNTs. The WNTs are capable of signaling through several pathways, the best-characterized being the canonical WNT/β-catenin/TCF-mediated canonical pathway. Recent studies from sequencing PCa genomes revealed that CRPC cells frequently harbor mutations in major components of the WNT/β-catenin pathway. Moreover, the finding of an interaction between β-catenin and AR suggests a possible mechanism of cross talk between WNT and androgen/AR signaling pathways. In this review, we discuss the current knowledge of both AR and WNT pathways in prostate development and tumorigenesis, and their interaction during development of CRPC. We also review the possible therapeutic application of drugs that target both AR and WNT/β-catenin pathways. Finally, we extend our review of AR and WNT signaling to the mammary gland system and breast cancer. We highlight that the role of AR signaling and its interaction with WNT signaling in these two hormone-related cancer types are highly context-dependent. PMID:28134791

A Tale of Two Signals: AR and WNT in Development and Tumorigenesis of Prostate and Mammary Gland.

PubMed

Pakula, Hubert; Xiang, Dongxi; Li, Zhe

2017-01-27

Prostate cancer (PCa) is one of the most common cancers and among the leading causes of cancer deaths for men in industrialized countries. It has long been recognized that the prostate is an androgen-dependent organ and PCa is an androgen-dependent disease. Androgen action is mediated by the androgen receptor (AR). Androgen deprivation therapy (ADT) is the standard treatment for metastatic PCa. However, almost all advanced PCa cases progress to castration-resistant prostate cancer (CRPC) after a period of ADT. A variety of mechanisms of progression from androgen-dependent PCa to CRPC under ADT have been postulated, but it remains largely unclear as to when and how castration resistance arises within prostate tumors. In addition, AR signaling may be modulated by extracellular factors among which are the cysteine-rich glycoproteins WNTs. The WNTs are capable of signaling through several pathways, the best-characterized being the canonical WNT/β-catenin/TCF-mediated canonical pathway. Recent studies from sequencing PCa genomes revealed that CRPC cells frequently harbor mutations in major components of the WNT/β-catenin pathway. Moreover, the finding of an interaction between β-catenin and AR suggests a possible mechanism of cross talk between WNT and androgen/AR signaling pathways. In this review, we discuss the current knowledge of both AR and WNT pathways in prostate development and tumorigenesis, and their interaction during development of CRPC. We also review the possible therapeutic application of drugs that target both AR and WNT/β-catenin pathways. Finally, we extend our review of AR and WNT signaling to the mammary gland system and breast cancer. We highlight that the role of AR signaling and its interaction with WNT signaling in these two hormone-related cancer types are highly context-dependent.

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, S.; Bez, H. E.

2007-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

Principle Component Analysis with Incomplete Data: A simulation of R pcaMethods package in Constructing an Environmental Quality Index with Missing Data

EPA Science Inventory

Missing data is a common problem in the application of statistical techniques. In principal component analysis (PCA), a technique for dimensionality reduction, incomplete data points are either discarded or imputed using interpolation methods. Such approaches are less valid when ...

Automatic age and gender classification using supervised appearance model

NASA Astrophysics Data System (ADS)

Bukar, Ali Maina; Ugail, Hassan; Connah, David

2016-11-01

Age and gender classification are two important problems that recently gained popularity in the research community, due to their wide range of applications. Research has shown that both age and gender information are encoded in the face shape and texture, hence the active appearance model (AAM), a statistical model that captures shape and texture variations, has been one of the most widely used feature extraction techniques for the aforementioned problems. However, AAM suffers from some drawbacks, especially when used for classification. This is primarily because principal component analysis (PCA), which is at the core of the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account how the predictor variables relate to the response (class labels). Rather, it explores only the underlying structure of the predictor variables, thus, it is no surprise if PCA discards valuable parts of the data that represent discriminatory features. Toward this end, we propose a supervised appearance model (sAM) that improves on AAM by replacing PCA with partial least-squares regression. This feature extraction technique is then used for the problems of age and gender classification. Our experiments show that sAM has better predictive power than the conventional AAM.

Analyzing brain networks with PCA and conditional Granger causality.

PubMed

Zhou, Zhenyu; Chen, Yonghong; Ding, Mingzhou; Wright, Paul; Lu, Zuhong; Liu, Yijun

2009-07-01

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Copyright 2009 Wiley-Liss, Inc

An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

PubMed

Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

2017-12-07

Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

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.

Discrimination of multilocus sequence typing-based Campylobacter jejuni subgroups by MALDI-TOF mass spectrometry.

PubMed

Zautner, Andreas Erich; Masanta, Wycliffe Omurwa; Tareen, Abdul Malik; Weig, Michael; Lugert, Raimond; Groß, Uwe; Bader, Oliver

2013-11-07

Campylobacter jejuni, the most common bacterial pathogen causing gastroenteritis, shows a wide genetic diversity. Previously, we demonstrated by the combination of multi locus sequence typing (MLST)-based UPGMA-clustering and analysis of 16 genetic markers that twelve different C. jejuni subgroups can be distinguished. Among these are two prominent subgroups. The first subgroup contains the majority of hyperinvasive strains and is characterized by a dimeric form of the chemotaxis-receptor Tlp7(m+c). The second has an extended amino acid metabolism and is characterized by the presence of a periplasmic asparaginase (ansB) and gamma-glutamyl-transpeptidase (ggt). Phyloproteomic principal component analysis (PCA) hierarchical clustering of MALDI-TOF based intact cell mass spectrometry (ICMS) spectra was able to group particular C. jejuni subgroups of phylogenetic related isolates in distinct clusters. Especially the aforementioned Tlp7(m+c)(+) and ansB+/ ggt+ subgroups could be discriminated by PCA. Overlay of ICMS spectra of all isolates led to the identification of characteristic biomarker ions for these specific C. jejuni subgroups. Thus, mass peak shifts can be used to identify the C. jejuni subgroup with an extended amino acid metabolism. Although the PCA hierarchical clustering of ICMS-spectra groups the tested isolates into a different order as compared to MLST-based UPGMA-clustering, the isolates of the indicator-groups form predominantly coherent clusters. These clusters reflect phenotypic aspects better than phylogenetic clustering, indicating that the genes corresponding to the biomarker ions are phylogenetically coupled to the tested marker genes. Thus, PCA clustering could be an additional tool for analyzing the relatedness of bacterial isolates.

Locally linear embedding: dimension reduction of massive protostellar spectra

NASA Astrophysics Data System (ADS)

Ward, J. L.; Lumsden, S. L.

2016-09-01

We present the results of the application of locally linear embedding (LLE) to reduce the dimensionality of dereddened and continuum subtracted near-infrared spectra using a combination of models and real spectra of massive protostars selected from the Red MSX Source survey data base. A brief comparison is also made with two other dimension reduction techniques; principal component analysis (PCA) and Isomap using the same set of spectra as well as a more advanced form of LLE, Hessian locally linear embedding. We find that whilst LLE certainly has its limitations, it significantly outperforms both PCA and Isomap in classification of spectra based on the presence/absence of emission lines and provides a valuable tool for classification and analysis of large spectral data sets.

Individualized statistical learning from medical image databases: application to identification of brain lesions.

PubMed

Erus, Guray; Zacharaki, Evangelia I; Davatzikos, Christos

2014-04-01

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a "target-specific" feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject's images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an "estimability" criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated. Copyright © 2014 Elsevier B.V. All rights reserved.

Individualized Statistical Learning from Medical Image Databases: Application to Identification of Brain Lesions

PubMed Central

Erus, Guray; Zacharaki, Evangelia I.; Davatzikos, Christos

2014-01-01

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a “target-specific” feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject’s images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an “estimability” criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated. PMID:24607564

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

PubMed

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

Comparison of Grain Proteome Profiles of Four Brazilian Common Bean (Phaseolus vulgaris L.) Cultivars.

PubMed

Rossi, Gabriela Barbosa; Valentim-Neto, Pedro Alexandre; Blank, Martina; Faria, Josias Correa de; Arisi, Ana Carolina Maisonnave

2017-08-30

Common bean (Phaseolus vulgaris L.) is a source of proteins for about one billion people worldwide. In Brazil, 'BRS Sublime', 'BRS Vereda', 'BRS Esteio', and 'BRS Estilo' cultivars were developed by Embrapa to offer high yield to farmers and excellent quality to final consumers. In this work, grain proteomes of these common bean cultivars were compared based on two-dimensional gel electrophoresis (2-DE) and tandem mass spectrometry (MS/MS). Principal component analysis (PCA) was applied to compare 349 matched spots in these cultivars proteomes, and all cultivars were clearly separated in PCA plot. Thirty-two differentially accumulated proteins were identified by MS. Storage proteins such as phaseolins, legumins, and lectins were the most abundant, and novel proteins were also identified. We have built a useful platform that could be used to analyze other Brazilian cultivars and genotypes of common beans.

How Adequate are One- and Two-Dimensional Free Energy Landscapes for Protein Folding Dynamics?

NASA Astrophysics Data System (ADS)

Maisuradze, Gia G.; Liwo, Adam; Scheraga, Harold A.

2009-06-01

The molecular dynamics trajectories of protein folding or unfolding, generated with the coarse-grained united-residue force field for the B domain of staphylococcal protein A, were analyzed by principal component analysis (PCA). The folding or unfolding process was examined by using free-energy landscapes (FELs) in PC space. By introducing a novel multidimensional FEL, it was shown that the low-dimensional FELs are not always sufficient for the description of folding or unfolding processes. Similarities between the topographies of FELs along low- and high-indexed principal components were observed.

Dimensionality and summary measures of the SF-36 v1.6: comparison of scale- and item-based approach across ECRHS II adults population.

PubMed

Grassi, Mario; Nucera, Andrea

2010-01-01

The objective of this study was twofold: 1) to confirm the hypothetical eight scales and two-component summaries of the questionnaire Short Form 36 Health Survey (SF-36), and 2) to evaluate the performance of two alternative measures to the original physical component summary (PCS) and mental component summary (MCS). We performed principal component analysis (PCA) based on 35 items, after optimal scaling via multiple correspondence analysis (MCA), and subsequently on eight scales, after standard summative scoring. Item-based summary measures were planned. Data from the European Community Respiratory Health Survey II follow-up of 8854 subjects from 25 centers were analyzed to cross-validate the original and the novel PCS and MCS. Overall, the scale- and item-based comparison indicated that the SF-36 scales and summaries meet the supposed dimensionality. However, vitality, social functioning, and general health items did not fit data optimally. The novel measures, derived a posteriori by unit-rule from an oblique (correlated) MCA/PCA solution, are simple item sums or weighted scale sums where the weights are the raw scale ranges. These item-based scores yielded consistent scale-summary results for outliers profiles, with an expected known-group differences validity. We were able to confirm the hypothesized dimensionality of eight scales and two summaries of the SF-36. The alternative scoring reaches at least the same required standards of the original scoring. In addition, it can reduce the item-scale inconsistencies without loss of predictive validity.

Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM

PubMed Central

Zhao, Zhizhen; Singer, Amit

2014-01-01

We introduce a new rotationally invariant viewing angle classification method for identifying, among a large number of cryo-EM projection images, similar views without prior knowledge of the molecule. Our rotationally invariant features are based on the bispectrum. Each image is denoised and compressed using steerable principal component analysis (PCA) such that rotating an image is equivalent to phase shifting the expansion coefficients. Thus we are able to extend the theory of bispectrum of 1D periodic signals to 2D images. The randomized PCA algorithm is then used to efficiently reduce the dimensionality of the bispectrum coefficients, enabling fast computation of the similarity between any pair of images. The nearest neighbors provide an initial classification of similar viewing angles. In this way, rotational alignment is only performed for images with their nearest neighbors. The initial nearest neighbor classification and alignment are further improved by a new classification method called vector diffusion maps. Our pipeline for viewing angle classification and alignment is experimentally shown to be faster and more accurate than reference-free alignment with rotationally invariant K-means clustering, MSA/MRA 2D classification, and their modern approximations. PMID:24631969

Features of Vocal Fold Adductor Paralysis and the Management of Posterior Muscle in Thyroplasty.

PubMed

Konomi, Ujimoto; Tokashiki, Ryoji; Hiramatsu, Hiroyuki; Motohashi, Ray; Sakurai, Eriko; Toyomura, Fumimasa; Nomoto, Masaki; Kawada, Yuri; Suzuki, Mamoru

2016-03-01

To present the pathologic characteristics of unilateral recurrent nerve adductor branch paralysis (AdBP), and to investigate the management of posterior cricoarytenoid (PCA) muscle on the basis of our experience of surgical treatment for AdBP. This is a retrospective review of clinical records Four cases of AdBP, in which surgical treatment was performed, are presented. AdBP shows disorders of vocal fold adduction because of paralysis of the thyroarytenoid and lateral cricoarytenoid muscles. The PCA muscle, dominated by the recurrent nerve PCA muscle branch, does not show paralysis. Thus, this type of partial recurrent nerve paresis retains the abductive function and is difficult to distinguish from arytenoid cartilage dislocation because of their similar endoscopic findings. The features include acute onset, and all cases were idiopathic etiology. Thyroarytenoid muscle paralysis was determined by electromyography and stroboscopic findings. The adduction and abduction of paralytic arytenoids were evaluated from 3 dimensional computed tomography (3DCT). In all cases, surgical treatments were arytenoid adduction combined with thyroplasty. When we adducted the arytenoid cartilage during inspiration, strong resistance was observed. In the two cases where we could cut the PCA muscle sufficiently, the maximum phonation time was improved to ≥30 seconds after surgery, from 2 to 3 seconds preoperatively, providing good postoperative voices. In contrast, in the two cases of insufficient resection, the surgical outcomes were poorer. Because the preoperative voice in AdBP patients is typically very coarse, surgical treatment is needed, as well as ordinary recurrent nerve paralysis. In our experience, adequate PCA muscle resection might be helpful in surgical treatment of AdBP. Copyright © 2016 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound.

PubMed

Virmani, Jitendra; Kumar, Vinod; Kalra, Naveen; Khandelwal, Niranjan

2014-08-01

A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.

Accelerated echo planar J-resolved spectroscopic imaging in prostate cancer: a pilot validation of non-linear reconstruction using total variation and maximum entropy.

PubMed

Nagarajan, Rajakumar; Iqbal, Zohaib; Burns, Brian; Wilson, Neil E; Sarma, Manoj K; Margolis, Daniel A; Reiter, Robert E; Raman, Steven S; Thomas, M Albert

2015-11-01

The overlap of metabolites is a major limitation in one-dimensional (1D) spectral-based single-voxel MRS and multivoxel-based MRSI. By combining echo planar spectroscopic imaging (EPSI) with a two-dimensional (2D) J-resolved spectroscopic (JPRESS) sequence, 2D spectra can be recorded in multiple locations in a single slice of prostate using four-dimensional (4D) echo planar J-resolved spectroscopic imaging (EP-JRESI). The goal of the present work was to validate two different non-linear reconstruction methods independently using compressed sensing-based 4D EP-JRESI in prostate cancer (PCa): maximum entropy (MaxEnt) and total variation (TV). Twenty-two patients with PCa with a mean age of 63.8 years (range, 46-79 years) were investigated in this study. A 4D non-uniformly undersampled (NUS) EP-JRESI sequence was implemented on a Siemens 3-T MRI scanner. The NUS data were reconstructed using two non-linear reconstruction methods, namely MaxEnt and TV. Using both TV and MaxEnt reconstruction methods, the following observations were made in cancerous compared with non-cancerous locations: (i) higher mean (choline + creatine)/citrate metabolite ratios; (ii) increased levels of (choline + creatine)/spermine and (choline + creatine)/myo-inositol; and (iii) decreased levels of (choline + creatine)/(glutamine + glutamate). We have shown that it is possible to accelerate the 4D EP-JRESI sequence by four times and that the data can be reliably reconstructed using the TV and MaxEnt methods. The total acquisition duration was less than 13 min and we were able to detect and quantify several metabolites. Copyright © 2015 John Wiley & Sons, Ltd.

Sparse PCA with Oracle Property.

PubMed

Gu, Quanquan; Wang, Zhaoran; Liu, Han

In this paper, we study the estimation of the k -dimensional sparse principal subspace of covariance matrix Σ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on the semidefinite relaxation of sparse PCA with novel regularizations. In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank- k , and attains a [Formula: see text] statistical rate of convergence with s being the subspace sparsity level and n the sample size. Compared to existing support recovery results for sparse PCA, our approach does not hinge on the spiked covariance model or the limited correlation condition. As a complement to the first estimator that enjoys the oracle property, we prove that, another estimator within the family achieves a sharper statistical rate of convergence than the standard semidefinite relaxation of sparse PCA, even when the previous assumption on the magnitude of the projection matrix is violated. We validate the theoretical results by numerical experiments on synthetic datasets.

Sparse PCA with Oracle Property

PubMed Central

Gu, Quanquan; Wang, Zhaoran; Liu, Han

2014-01-01

In this paper, we study the estimation of the k-dimensional sparse principal subspace of covariance matrix Σ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on the semidefinite relaxation of sparse PCA with novel regularizations. In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank-k, and attains a s/n statistical rate of convergence with s being the subspace sparsity level and n the sample size. Compared to existing support recovery results for sparse PCA, our approach does not hinge on the spiked covariance model or the limited correlation condition. As a complement to the first estimator that enjoys the oracle property, we prove that, another estimator within the family achieves a sharper statistical rate of convergence than the standard semidefinite relaxation of sparse PCA, even when the previous assumption on the magnitude of the projection matrix is violated. We validate the theoretical results by numerical experiments on synthetic datasets. PMID:25684971

3D non-rigid surface-based MR-TRUS registration for image-guided prostate biopsy

NASA Astrophysics Data System (ADS)

Sun, Yue; Qiu, Wu; Romagnoli, Cesare; Fenster, Aaron

2014-03-01

Two dimensional (2D) transrectal ultrasound (TRUS) guided prostate biopsy is the standard approach for definitive diagnosis of prostate cancer (PCa). However, due to the lack of image contrast of prostate tumors needed to clearly visualize early-stage PCa, prostate biopsy often results in false negatives, requiring repeat biopsies. Magnetic Resonance Imaging (MRI) has been considered to be a promising imaging modality for noninvasive identification of PCa, since it can provide a high sensitivity and specificity for the detection of early stage PCa. Our main objective is to develop and validate a registration method of 3D MR-TRUS images, allowing generation of volumetric 3D maps of targets identified in 3D MR images to be biopsied using 3D TRUS images. Our registration method first makes use of an initial rigid registration of 3D MR images to 3D TRUS images using 6 manually placed approximately corresponding landmarks in each image. Following the manual initialization, two prostate surfaces are segmented from 3D MR and TRUS images and then non-rigidly registered using a thin-plate spline (TPS) algorithm. The registration accuracy was evaluated using 4 patient images by measuring target registration error (TRE) of manually identified corresponding intrinsic fiducials (calcifications and/or cysts) in the prostates. Experimental results show that the proposed method yielded an overall mean TRE of 2.05 mm, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm.

Investigation of probabilistic principal component analysis compared to proper orthogonal decomposition methods for basis extraction and missing data estimation

NASA Astrophysics Data System (ADS)

Lee, Kyunghoon

To evaluate the maximum likelihood estimates (MLEs) of probabilistic principal component analysis (PPCA) parameters such as a factor-loading, PPCA can invoke an expectation-maximization (EM) algorithm, yielding an EM algorithm for PPCA (EM-PCA). In order to examine the benefits of the EM-PCA for aerospace engineering applications, this thesis attempts to qualitatively and quantitatively scrutinize the EM-PCA alongside both POD and gappy POD using high-dimensional simulation data. In pursuing qualitative investigations, the theoretical relationship between POD and PPCA is transparent such that the factor-loading MLE of PPCA, evaluated by the EM-PCA, pertains to an orthogonal basis obtained by POD. By contrast, the analytical connection between gappy POD and the EM-PCA is nebulous because they distinctively approximate missing data due to their antithetical formulation perspectives: gappy POD solves a least-squares problem whereas the EM-PCA relies on the expectation of the observation probability model. To juxtapose both gappy POD and the EM-PCA, this research proposes a unifying least-squares perspective that embraces the two disparate algorithms within a generalized least-squares framework. As a result, the unifying perspective reveals that both methods address similar least-squares problems; however, their formulations contain dissimilar bases and norms. Furthermore, this research delves into the ramifications of the different bases and norms that will eventually characterize the traits of both methods. To this end, two hybrid algorithms of gappy POD and the EM-PCA are devised and compared to the original algorithms for a qualitative illustration of the different basis and norm effects. After all, a norm reflecting a curve-fitting method is found to more significantly affect estimation error reduction than a basis for two example test data sets: one is absent of data only at a single snapshot and the other misses data across all the snapshots. From a numerical performance aspect, the EM-PCA is computationally less efficient than POD for intact data since it suffers from slow convergence inherited from the EM algorithm. For incomplete data, this thesis quantitatively found that the number of data missing snapshots predetermines whether the EM-PCA or gappy POD outperforms the other because of the computational cost of a coefficient evaluation, resulting from a norm selection. For instance, gappy POD demands laborious computational effort in proportion to the number of data-missing snapshots as a consequence of the gappy norm. In contrast, the computational cost of the EM-PCA is invariant to the number of data-missing snapshots thanks to the L2 norm. In general, the higher the number of data-missing snapshots, the wider the gap between the computational cost of gappy POD and the EM-PCA. Based on the numerical experiments reported in this thesis, the following criterion is recommended regarding the selection between gappy POD and the EM-PCA for computational efficiency: gappy POD for an incomplete data set containing a few data-missing snapshots and the EM-PCA for an incomplete data set involving multiple data-missing snapshots. Last, the EM-PCA is applied to two aerospace applications in comparison to gappy POD as a proof of concept: one with an emphasis on basis extraction and the other with a focus on missing data reconstruction for a given incomplete data set with scattered missing data. The first application exploits the EM-PCA to efficiently construct reduced-order models of engine deck responses obtained by the numerical propulsion system simulation (NPSS), some of whose results are absent due to failed analyses caused by numerical instability. Model-prediction tests validate that engine performance metrics estimated by the reduced-order NPSS model exhibit considerably good agreement with those directly obtained by NPSS. Similarly, the second application illustrates that the EM-PCA is significantly more cost effective than gappy POD at repairing spurious PIV measurements obtained from acoustically-excited, bluff-body jet flow experiments. The EM-PCA reduces computational cost on factors 8 ˜ 19 compared to gappy POD while generating the same restoration results as those evaluated by gappy POD. All in all, through comprehensive theoretical and numerical investigation, this research establishes that the EM-PCA is an efficient alternative to gappy POD for an incomplete data set containing missing data over an entire data set. (Abstract shortened by UMI.)

Comparative analysis of prostate-specific antigen by two-dimensional gel electrophoresis and capillary electrophoresis.

PubMed

Barrabés, Sílvia; Farina-Gomez, Noemi; Llop, Esther; Puerta, Angel; Diez-Masa, Jose Carlos; Perry, Antoinette; de Llorens, Rafael; de Frutos, Mercedes; Peracaula, Rosa

2017-02-01

Serum levels of Prostate-Specific Antigen (PSA) are not fully specific for prostate cancer (PCa) diagnosis and several efforts are focused on searching to improve PCa markers through the study of PSA subforms that could be cancer associated. We have previously reported by 2DE a decrease in the sialic acid content of PSA from PCa compared to benign prostatic hyperplasia patients based on the different proportion of the PSA spots. However, faster and more quantitative techniques, easier to automate than 2DE, are desirable. In this study, we examined the potential of CE for resolving PSA subforms in different samples and compared the results with those obtained by 2DE. We first fractionated by OFFGEL the subforms of PSA from seminal plasma according to their pIs and analyzed each separated fraction by 2DE and CE. We also analyzed PSA and high pI PSA, both from seminal plasma, and PSA from urine of a PCa patient. These samples with different PSA spots proportions by 2DE, due to different posttranslational modifications, also presented different CE profiles. This study shows that CE is a useful and complementary technique to 2DE for analyzing samples with different PSA subforms, which is of high clinical interest. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction.

PubMed

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

NASA Astrophysics Data System (ADS)

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Discrimination of Rhizoma Gastrodiae (Tianma) using 3D synchronous fluorescence spectroscopy coupled with principal component analysis

NASA Astrophysics Data System (ADS)

Fan, Qimeng; Chen, Chaoyin; Huang, Zaiqiang; Zhang, Chunmei; Liang, Pengjuan; Zhao, Shenglan

2015-02-01

Rhizoma Gastrodiae (Tianma) of different variants and different geographical origins has vital difference in quality and physiological efficacy. This paper focused on the classification and identification of Tianma of six types (two variants from three different geographical origins) using three dimensional synchronous fluorescence spectroscopy (3D-SFS) coupled with principal component analysis (PCA). 3D-SF spectra of aqueous extracts, which were obtained from Tianma of the six types, were measured by a LS-50B luminescence spectrofluorometer. The experimental results showed that the characteristic fluorescent spectral regions of the 3D-SF spectra were similar, while the intensities of characteristic regions are different significantly. Coupled these differences in peak intensities with PCA, Tianma of six types could be discriminated successfully. In conclusion, 3D-SFS coupled with PCA, which has such advantages as effective, specific, rapid, non-polluting, has an edge for discrimination of the similar Chinese herbal medicine. And the proposed methodology is a useful tool to classify and identify Tianma of different variants and different geographical origins.

Kernel-PCA data integration with enhanced interpretability

PubMed Central

2014-01-01

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge. PMID:25032747

Development and Flight Test of an Augmented Thrust-Only Flight Control System on an MD-11 Transport Airplane

NASA Technical Reports Server (NTRS)

Burcham, Frank W., Jr.; Maine, Trindel A.; Burken, John J.; Pappas, Drew

1996-01-01

An emergency flight control system using only engine thrust, called Propulsion-Controlled Aircraft (PCA), has been developed and flight tested on an MD-11 airplane. In this thrust-only control system, pilot flight path and track commands and aircraft feedback parameters are used to control the throttles. The PCA system was installed on the MD-11 airplane using software modifications to existing computers. Flight test results show that the PCA system can be used to fly to an airport and safely land a transport airplane with an inoperative flight control system. In up-and-away operation, the PCA system served as an acceptable autopilot capable of extended flight over a range of speeds and altitudes. The PCA approaches, go-arounds, and three landings without the use of any non-nal flight controls have been demonstrated, including instrument landing system-coupled hands-off landings. The PCA operation was used to recover from an upset condition. In addition, PCA was tested at altitude with all three hydraulic systems turned off. This paper reviews the principles of throttles-only flight control; describes the MD-11 airplane and systems; and discusses PCA system development, operation, flight testing, and pilot comments.

The use of experimental structures to model protein dynamics.

PubMed

Katebi, Ataur R; Sankar, Kannan; Jia, Kejue; Jernigan, Robert L

2015-01-01

The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high-for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods-Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to accomplish each step and show how to use these programs and tools. We also include computer programs to generate movies based on PCs and ENM modes and describe how to visualize them.

The Use of Experimental Structures to Model Protein Dynamics

PubMed Central

Katebi, Ataur R.; Sankar, Kannan; Jia, Kejue; Jernigan, Robert L.

2014-01-01

Summary The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high – for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods – Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to accomplish each step and show how to use these programs and tools. We also include computer programs to generate movies based on PCs and ENM modes and describe how to visualize them. PMID:25330965

Integrative analysis of gene expression and copy number alterations using canonical correlation analysis.

PubMed

Soneson, Charlotte; Lilljebjörn, Henrik; Fioretos, Thoas; Fontes, Magnus

2010-04-15

With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia. Using the correlation-maximizing methods, regularized dual CCA and PCA+CCA, we show that without pre-selection of known disease-relevant genes, and without using information about clinical class membership, an exploratory analysis singles out two patient groups, corresponding to well-known leukemia subtypes. Furthermore, the variables showing the highest relevance to the extracted features agree with previous biological knowledge concerning copy number alterations and gene expression changes in these subtypes. Finally, the correlation-maximizing methods are shown to yield results which are more biologically interpretable than those resulting from a covariance-maximizing method, and provide different insight compared to when each variable set is studied separately using PCA. We conclude that regularized dual CCA as well as PCA+CCA are useful methods for exploratory analysis of paired genetic data sets, and can be efficiently implemented also when the number of variables is very large.

Approximate probabilistic cellular automata for the dynamics of single-species populations under discrete logisticlike growth with and without weak Allee effects.

PubMed

Mendonça, J Ricardo G; Gevorgyan, Yeva

2017-05-01

We investigate one-dimensional elementary probabilistic cellular automata (PCA) whose dynamics in first-order mean-field approximation yields discrete logisticlike growth models for a single-species unstructured population with nonoverlapping generations. Beginning with a general six-parameter model, we find constraints on the transition probabilities of the PCA that guarantee that the ensuing approximations make sense in terms of population dynamics and classify the valid combinations thereof. Several possible models display a negative cubic term that can be interpreted as a weak Allee factor. We also investigate the conditions under which a one-parameter PCA derived from the more general six-parameter model can generate valid population growth dynamics. Numerical simulations illustrate the behavior of some of the PCA found.

Initial results of 3-dimensional 1H-magnetic resonance spectroscopic imaging in the localization of prostate cancer at 3 Tesla: should we use an endorectal coil?

PubMed

Yakar, Derya; Heijmink, Stijn W T P J; Hulsbergen-van de Kaa, Christina A; Huisman, Henkjan; Barentsz, Jelle O; Fütterer, Jurgen J; Scheenen, Tom W J

2011-05-01

The purpose of this study was to compare the diagnostic performance of 3 Tesla, 3-dimensional (3D) magnetic resonance spectroscopic imaging (MRSI) in the localization of prostate cancer (PCa) with and without the use of an endorectal coil (ERC). Our prospective study was approved by the institutional review board, and written informed consent was obtained from all patients. Between October 2004 and January 2006, 18 patients with histologically proven PCa on biopsy and scheduled for radical prostatectomy were included and underwent 3D-MRSI with and without an ERC. The prostate was divided into 14 regions of interest (ROIs). Four readers independently rated (on a 5-point scale) their confidence that cancer was present in each of these ROIs. These findings were correlated with whole-mount prostatectomy specimens. Areas under the receiver-operating characteristic curve were determined. A difference with a P < 0.05 was considered significant. A total of 504 ROIs were rated for the presence and absence of PCa. Localization of PCa with MRSI with the use of an ERC had a significantly higher areas under the receiver-operating characteristic curve (0.68) than MRSI without the use of an ERC (0.63) (P = 0.015). The use of an ERC in 3D MRSI in localizing PCa at 3 Tesla slightly but significantly increased the localization performance compared with not using an ERC.

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

An improved optimization algorithm and Bayes factor termination criterion for sequential projection pursuit

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

Webb-Robertson, Bobbie-Jo M.; Jarman, Kristin H.; Harvey, Scott D.

2005-05-28

A fundamental problem in analysis of highly multivariate spectral or chromatographic data is reduction of dimensionality. Principal components analysis (PCA), concerned with explaining the variance-covariance structure of the data, is a commonly used approach to dimension reduction. Recently an attractive alternative to PCA, sequential projection pursuit (SPP), has been introduced. Designed to elicit clustering tendencies in the data, SPP may be more appropriate when performing clustering or classification analysis. However, the existing genetic algorithm (GA) implementation of SPP has two shortcomings, computation time and inability to determine the number of factors necessary to explain the majority of the structure inmore » the data. We address both these shortcomings. First, we introduce a new SPP algorithm, a random scan sampling algorithm (RSSA), that significantly reduces computation time. We compare the computational burden of the RSS and GA implementation for SPP on a dataset containing Raman spectra of twelve organic compounds. Second, we propose a Bayes factor criterion, BFC, as an effective measure for selecting the number of factors needed to explain the majority of the structure in the data. We compare SPP to PCA on two datasets varying in type, size, and difficulty; in both cases SPP achieves a higher accuracy with a lower number of latent variables.« less

Understanding deformation mechanisms during powder compaction using principal component analysis of compression data.

PubMed

Roopwani, Rahul; Buckner, Ira S

2011-10-14

Principal component analysis (PCA) was applied to pharmaceutical powder compaction. A solid fraction parameter (SF(c/d)) and a mechanical work parameter (W(c/d)) representing irreversible compression behavior were determined as functions of applied load. Multivariate analysis of the compression data was carried out using PCA. The first principal component (PC1) showed loadings for the solid fraction and work values that agreed with changes in the relative significance of plastic deformation to consolidation at different pressures. The PC1 scores showed the same rank order as the relative plasticity ranking derived from the literature for common pharmaceutical materials. The utility of PC1 in understanding deformation was extended to binary mixtures using a subset of the original materials. Combinations of brittle and plastic materials were characterized using the PCA method. The relationships between PC1 scores and the weight fractions of the mixtures were typically linear showing ideal mixing in their deformation behaviors. The mixture consisting of two plastic materials was the only combination to show a consistent positive deviation from ideality. The application of PCA to solid fraction and mechanical work data appears to be an effective means of predicting deformation behavior during compaction of simple powder mixtures. Copyright © 2011 Elsevier B.V. All rights reserved.

Cohort Profile: the National Prostate Cancer Register of Sweden and Prostate Cancer data Base Sweden 2.0.

PubMed

Van Hemelrijck, Mieke; Wigertz, Annette; Sandin, Fredrik; Garmo, Hans; Hellström, Karin; Fransson, Per; Widmark, Anders; Lambe, Mats; Adolfsson, Jan; Varenhorst, Eberhard; Johansson, Jan-Erik; Stattin, Pär

2013-08-01

In 1987, the first Regional Prostate Cancer Register was set up in the South-East health-care region of Sweden. Other health-care regions joined and since 1998 virtually all prostate cancer (PCa) cases are registered in the National Prostate Cancer Register (NPCR) of Sweden to provide data for quality assurance, bench marking and clinical research. NPCR includes data on tumour stage, Gleason score, serum level of prostate-specific antigen (PSA) and primary treatment. In 2008, the NPCR was linked to a number of other population-based registers by use of the personal identity number. This database named Prostate Cancer data Base Sweden (PCBaSe) has now been extended with more cases, longer follow-up and a selection of two control series of men free of PCa at the time of sampling, as well as information on brothers of men diagnosed with PCa, resulting in PCBaSe 2.0. This extension allows for studies with case-control, cohort or longitudinal case-only design on aetiological factors, pharmaceutical prescriptions and assessment of long-term outcomes. The NPCR covers >96% of all incident PCa cases registered by the Swedish Cancer Register, which has an underreporting of <3.7%. The NPCR is used to assess trends in incidence, treatment and outcome of men with PCa. Since the national registers linked to PCBaSe are complete, studies from PCBaSe 2.0 are truly population based.

The Phenazine 2-Hydroxy-Phenazine-1-Carboxylic Acid Promotes Extracellular DNA Release and Has Broad Transcriptomic Consequences in Pseudomonas chlororaphis 30–84

DOE PAGES

Wang, Dongping; Yu, Jun Myoung; Dorosky, Robert J.; ...

2016-01-26

Enhanced production of 2-hydroxy-phenazine-1-carboxylic acid (2-OH-PCA) by the biological control strain Pseudomonas chlororaphis 30–84 derivative 30-84O* was shown previously to promote cell adhesion and alter the three-dimensional structure of surfaceattached biofilms compared to the wild type. The current study demonstrates that production of 2-OH-PCA promotes the release of extracellular DNA, which is correlated with the production of structured biofilm matrix. Moreover, the essential role of the extracellular DNA in maintaining the mass and structure of the 30–84 biofilm matrix is demonstrated. To better understand the role of different phenazines in biofilm matrix production and gene expression, transcriptomic analyses were conductedmore » comparing gene expression patterns of populations of wild type, 30-84O* and a derivative of 30–84 producing only PCA (30-84PCA) to a phenazine defective mutant (30-84ZN) when grown in static cultures. RNA-Seq analyses identified a group of 802 genes that were differentially expressed by the phenazine producing derivatives compared to 30-84ZN, including 240 genes shared by the two 2-OH-PCA producing derivatives, the wild type and 30-84O*. A gene cluster encoding a bacteriophage- derived pyocin and its lysis cassette was upregulated in 2-OH-PCA producing derivatives. A holin encoded in this gene cluster was found to contribute to the release of eDNA in 30–84 biofilm matrices, demonstrating that the influence of 2-OH-PCA on eDNA production is due in part to cell autolysis as a result of pyocin production and release. The results expand the current understanding of the functions different phenazines play in the survival of bacteria in biofilm-forming communities.« less

Altering the Ratio of Phenazines in Pseudomonas chlororaphis (aureofaciens) Strain 30-84: Effects on Biofilm Formation and Pathogen Inhibition▿

PubMed Central

Maddula, V. S. R. K.; Pierson, E. A.; Pierson, L. S.

2008-01-01

Pseudomonas chlororaphis strain 30-84 is a plant-beneficial bacterium that is able to control take-all disease of wheat caused by the fungal pathogen Gaeumannomyces graminis var. tritici. The production of phenazines (PZs) by strain 30-84 is the primary mechanism of pathogen inhibition and contributes to the persistence of strain 30-84 in the rhizosphere. PZ production is regulated in part by the PhzR/PhzI quorum-sensing (QS) system. Previous flow cell analyses demonstrated that QS and PZs are involved in biofilm formation in P. chlororaphis (V. S. R. K. Maddula, Z. Zhang, E. A. Pierson, and L. S. Pierson III, Microb. Ecol. 52:289-301, 2006). P. chlororaphis produces mainly two PZs, phenazine-1-carboxylic acid (PCA) and 2-hydroxy-PCA (2-OH-PCA). In the present study, we examined the effect of altering the ratio of PZs produced by P. chlororaphis on biofilm formation and pathogen inhibition. As part of this study, we generated derivatives of strain 30-84 that produced only PCA or overproduced 2-OH-PCA. Using flow cell assays, we found that these PZ-altered derivatives of strain 30-84 differed from the wild type in initial attachment, mature biofilm architecture, and dispersal from biofilms. For example, increased 2-OH-PCA production promoted initial attachment and altered the three-dimensional structure of the mature biofilm relative to the wild type. Additionally, both alterations promoted thicker biofilm development and lowered dispersal rates compared to the wild type. The PZ-altered derivatives of strain 30-84 also differed in their ability to inhibit the fungal pathogen G. graminis var. tritici. Loss of 2-OH-PCA resulted in a significant reduction in the inhibition of G. graminis var. tritici. Our findings suggest that alterations in the ratios of antibiotic secondary metabolites synthesized by an organism may have complex and wide-ranging effects on its biology. PMID:18263718

The Phenazine 2-Hydroxy-Phenazine-1-Carboxylic Acid Promotes Extracellular DNA Release and Has Broad Transcriptomic Consequences in Pseudomonas chlororaphis 30–84

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

Wang, Dongping; Yu, Jun Myoung; Dorosky, Robert J.

Enhanced production of 2-hydroxy-phenazine-1-carboxylic acid (2-OH-PCA) by the biological control strain Pseudomonas chlororaphis 30–84 derivative 30-84O* was shown previously to promote cell adhesion and alter the three-dimensional structure of surfaceattached biofilms compared to the wild type. The current study demonstrates that production of 2-OH-PCA promotes the release of extracellular DNA, which is correlated with the production of structured biofilm matrix. Moreover, the essential role of the extracellular DNA in maintaining the mass and structure of the 30–84 biofilm matrix is demonstrated. To better understand the role of different phenazines in biofilm matrix production and gene expression, transcriptomic analyses were conductedmore » comparing gene expression patterns of populations of wild type, 30-84O* and a derivative of 30–84 producing only PCA (30-84PCA) to a phenazine defective mutant (30-84ZN) when grown in static cultures. RNA-Seq analyses identified a group of 802 genes that were differentially expressed by the phenazine producing derivatives compared to 30-84ZN, including 240 genes shared by the two 2-OH-PCA producing derivatives, the wild type and 30-84O*. A gene cluster encoding a bacteriophage- derived pyocin and its lysis cassette was upregulated in 2-OH-PCA producing derivatives. A holin encoded in this gene cluster was found to contribute to the release of eDNA in 30–84 biofilm matrices, demonstrating that the influence of 2-OH-PCA on eDNA production is due in part to cell autolysis as a result of pyocin production and release. The results expand the current understanding of the functions different phenazines play in the survival of bacteria in biofilm-forming communities.« less

The Phenazine 2-Hydroxy-Phenazine-1-Carboxylic Acid Promotes Extracellular DNA Release and Has Broad Transcriptomic Consequences in Pseudomonas chlororaphis 30–84

PubMed Central

Wang, Dongping; Yu, Jun Myoung; Dorosky, Robert J.; Pierson, Leland S.; Pierson, Elizabeth A.

2016-01-01

Enhanced production of 2-hydroxy-phenazine-1-carboxylic acid (2-OH-PCA) by the biological control strain Pseudomonas chlororaphis 30–84 derivative 30-84O* was shown previously to promote cell adhesion and alter the three-dimensional structure of surface-attached biofilms compared to the wild type. The current study demonstrates that production of 2-OH-PCA promotes the release of extracellular DNA, which is correlated with the production of structured biofilm matrix. Moreover, the essential role of the extracellular DNA in maintaining the mass and structure of the 30–84 biofilm matrix is demonstrated. To better understand the role of different phenazines in biofilm matrix production and gene expression, transcriptomic analyses were conducted comparing gene expression patterns of populations of wild type, 30-84O* and a derivative of 30–84 producing only PCA (30-84PCA) to a phenazine defective mutant (30-84ZN) when grown in static cultures. RNA-Seq analyses identified a group of 802 genes that were differentially expressed by the phenazine producing derivatives compared to 30-84ZN, including 240 genes shared by the two 2-OH-PCA producing derivatives, the wild type and 30-84O*. A gene cluster encoding a bacteriophage-derived pyocin and its lysis cassette was upregulated in 2-OH-PCA producing derivatives. A holin encoded in this gene cluster was found to contribute to the release of eDNA in 30–84 biofilm matrices, demonstrating that the influence of 2-OH-PCA on eDNA production is due in part to cell autolysis as a result of pyocin production and release. The results expand the current understanding of the functions different phenazines play in the survival of bacteria in biofilm-forming communities. PMID:26812402

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.

PubMed

Caggiano, Alessandra

2018-03-09

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features ( k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear ( VB max ) was achieved, with predicted values very close to the measured tool wear values.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

PubMed Central

2018-01-01

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values. PMID:29522443

Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty.

PubMed

de Pierrefeu, Amicie; Lofstedt, Tommy; Hadj-Selem, Fouad; Dubois, Mathieu; Jardri, Renaud; Fovet, Thomas; Ciuciu, Philippe; Frouin, Vincent; Duchesnay, Edouard

2018-02-01

Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover the dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA's interpretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful patterns. Furthermore, the fact that the components are usually non-sparse may also impede interpretation, unless arbitrary thresholding is applied. However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population. Recently, some alternatives to the standard PCA approach, such as sparse PCA (SPCA), have been proposed, their aim being to limit the density of the components. Nonetheless, sparsity alone does not entirely solve the interpretability problem in neuroimaging, since it may yield scattered and unstable components. We hypothesized that the incorporation of prior information regarding the structure of the data may lead to improved relevance and interpretability of brain patterns. We therefore present a simple extension of the popular PCA framework that adds structured sparsity penalties on the loading vectors in order to identify the few stable regions in the brain images that capture most of the variability. Such structured sparsity can be obtained by combining, e.g., and total variation (TV) penalties, where the TV regularization encodes information on the underlying structure of the data. This paper presents the structured SPCA (denoted SPCA-TV) optimization framework and its resolution. We demonstrate SPCA-TV's effectiveness and versatility on three different data sets. It can be applied to any kind of structured data, such as, e.g., -dimensional array images or meshes of cortical surfaces. The gains of SPCA-TV over unstructured approaches (such as SPCA and ElasticNet PCA) or structured approach (such as GraphNet PCA) are significant, since SPCA-TV reveals the variability within a data set in the form of intelligible brain patterns that are easier to interpret and more stable across different samples.

Factor structure and dimensionality of the two depression scales in STAR*D using level 1 datasets.

PubMed

Bech, P; Fava, M; Trivedi, M H; Wisniewski, S R; Rush, A J

2011-08-01

The factor structure and dimensionality of the HAM-D(17) and the IDS-C(30) are as yet uncertain, because psychometric analyses of these scales have been performed without a clear separation between factor structure profile and dimensionality (total scores being a sufficient statistic). The first treatment step (Level 1) in the STAR*D study provided a dataset of 4041 outpatients with DSM-IV nonpsychotic major depression. The HAM-D(17) and IDS-C(30) were evaluated by principal component analysis (PCA) without rotation. Mokken analysis tested the unidimensionality of the IDS-C(6), which corresponds to the unidimensional HAM-D(6.) For both the HAM-D(17) and IDS-C(30), PCA identified a bi-directional factor contrasting the depressive symptoms versus the neurovegetative symptoms. The HAM-D(6) and the corresponding IDS-C(6) symptoms all emerged in the depression factor. Both the HAM-D(6) and IDS-C(6) were found to be unidimensional scales, i.e., their total scores are each a sufficient statistic for the measurement of depressive states. STAR*D used only one medication in Level 1. The unidimensional HAM-D(6) and IDS-C(6) should be used when evaluating the pure clinical effect of antidepressive treatment, whereas the multidimensional HAM-D(17) and IDS-C(30) should be considered when selecting antidepressant treatment. Copyright © 2011 Elsevier B.V. All rights reserved.

Three-dimensional prostate tumor model based on a hyaluronic acid-alginate hydrogel for evaluation of anti-cancer drug efficacy.

PubMed

Tang, Yadong; Huang, Boxin; Dong, Yuqin; Wang, Wenlong; Zheng, Xi; Zhou, Wei; Zhang, Kun; Du, Zhiyun

2017-10-01

In vitro cell-based assays are widely applied to evaluate anti-cancer drug efficacy. However, the conventional approaches are mostly based on two-dimensional (2D) culture systems, making it difficult to recapitulate the in vivo tumor scenario because of spatial limitations. Here, we develop an in vitro three-dimensional (3D) prostate tumor model based on a hyaluronic acid (HA)-alginate hybrid hydrogel to bridge the gap between in vitro and in vivo anticancer drug evaluations. In situ encapsulation of PCa cells was achieved by mixing HA and alginate aqueous solutions in the presence of cells and then crosslinking with calcium ions. Unlike in 2D culture, cells were found to aggregate into spheroids in a 3D matrix. The expression of epithelial to mesenchyme transition (EMT) biomarkers was found to be largely enhanced, indicating an increased invasion and metastasis potential in the hydrogel matrix. A significant up-regulation of proangiogenic growth factors (IL-8, VEGF) and matrix metalloproteinases (MMPs) was observed in 3D-cultured PCa cells. The results of anti-cancer drug evaluation suggested a higher drug tolerance within the 3D tumor model compared to conventional 2D-cultured cells. Finally, we found that the drug effect within the in vitro 3D cancer model based on HA-alginate matrix exhibited better predictability for in vivo drug efficacy.

Bilinear identities for an extended B-type Kadomtsev-Petviashvili hierarchy

NASA Astrophysics Data System (ADS)

Lin, Runliang; Cao, Tiancheng; Liu, Xiaojun; Zeng, Yunbo

2016-03-01

We construct bilinear identities for wave functions of an extended B-type Kadomtsev-Petviashvili (BKP) hierarchy containing two types of (2+1)-dimensional Sawada-Kotera equations with a self-consistent source. Introducing an auxiliary variable corresponding to the extended flow for the BKP hierarchy, we find the τ -function and bilinear identities for this extended BKP hierarchy. The bilinear identities generate all the Hirota bilinear equations for the zero-curvature forms of this extended BKP hierarchy. As examples, we obtain the Hirota bilinear equations for the two types of (2+1)-dimensional Sawada-Kotera equations in explicit form.

Cannibalism Affects Core Metabolic Processes in Helicoverpa armigera Larvae-A 2D NMR Metabolomics Study.

PubMed

Vergara, Fredd; Shino, Amiu; Kikuchi, Jun

2016-09-02

Cannibalism is known in many insect species, yet its impact on insect metabolism has not been investigated in detail. This study assessed the effects of cannibalism on the metabolism of fourth-instar larvae of the non-predatory insect Helicoverpa armigera (Lepidotera: Noctuidea). Two groups of larvae were analyzed: one group fed with fourth-instar larvae of H. armigera (cannibal), the other group fed with an artificial plant diet. Water-soluble small organic compounds present in the larvae were analyzed using two-dimensional nuclear magnetic resonance (NMR) and principal component analysis (PCA). Cannibalism negatively affected larval growth. PCA of NMR spectra showed that the metabolic profiles of cannibal and herbivore larvae were statistically different with monomeric sugars, fatty acid- and amino acid-related metabolites as the most variable compounds. Quantitation of ¹H-(13)C HSQC (Heteronuclear Single Quantum Coherence) signals revealed that the concentrations of glucose, glucono-1,5-lactone, glycerol phosphate, glutamine, glycine, leucine, isoleucine, lysine, ornithine, proline, threonine and valine were higher in the herbivore larvae.

New bandwidth selection criterion for Kernel PCA: approach to dimensionality reduction and classification problems.

PubMed

Thomas, Minta; De Brabanter, Kris; De Moor, Bart

2014-05-10

DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history of almost a decade. Disease class predictors can be designed for known disease cases and provide diagnostic confirmation or clarify abnormal cases. The main input to this class predictors are high dimensional data with many variables and few observations. Dimensionality reduction of these features set significantly speeds up the prediction task. Feature selection and feature transformation methods are well known preprocessing steps in the field of bioinformatics. Several prediction tools are available based on these techniques. Studies show that a well tuned Kernel PCA (KPCA) is an efficient preprocessing step for dimensionality reduction, but the available bandwidth selection method for KPCA was computationally expensive. In this paper, we propose a new data-driven bandwidth selection criterion for KPCA, which is related to least squares cross-validation for kernel density estimation. We propose a new prediction model with a well tuned KPCA and Least Squares Support Vector Machine (LS-SVM). We estimate the accuracy of the newly proposed model based on 9 case studies. Then, we compare its performances (in terms of test set Area Under the ROC Curve (AUC) and computational time) with other well known techniques such as whole data set + LS-SVM, PCA + LS-SVM, t-test + LS-SVM, Prediction Analysis of Microarrays (PAM) and Least Absolute Shrinkage and Selection Operator (Lasso). Finally, we assess the performance of the proposed strategy with an existing KPCA parameter tuning algorithm by means of two additional case studies. We propose, evaluate, and compare several mathematical/statistical techniques, which apply feature transformation/selection for subsequent classification, and consider its application in medical diagnostics. Both feature selection and feature transformation perform well on classification tasks. Due to the dynamic selection property of feature selection, it is hard to define significant features for the classifier, which predicts classes of future samples. Moreover, the proposed strategy enjoys a distinctive advantage with its relatively lesser time complexity.

TPSLVM: a dimensionality reduction algorithm based on thin plate splines.

PubMed

Jiang, Xinwei; Gao, Junbin; Wang, Tianjiang; Shi, Daming

2014-10-01

Dimensionality reduction (DR) has been considered as one of the most significant tools for data analysis. One type of DR algorithms is based on latent variable models (LVM). LVM-based models can handle the preimage problem easily. In this paper we propose a new LVM-based DR model, named thin plate spline latent variable model (TPSLVM). Compared to the well-known Gaussian process latent variable model (GPLVM), our proposed TPSLVM is more powerful especially when the dimensionality of the latent space is low. Also, TPSLVM is robust to shift and rotation. This paper investigates two extensions of TPSLVM, i.e., the back-constrained TPSLVM (BC-TPSLVM) and TPSLVM with dynamics (TPSLVM-DM) as well as their combination BC-TPSLVM-DM. Experimental results show that TPSLVM and its extensions provide better data visualization and more efficient dimensionality reduction compared to PCA, GPLVM, ISOMAP, etc.

Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination

NASA Astrophysics Data System (ADS)

Hu, Wenjian; Singh, Rajiv R. P.; Scalettar, Richard T.

2017-06-01

We apply unsupervised machine learning techniques, mainly principal component analysis (PCA), to compare and contrast the phase behavior and phase transitions in several classical spin models—the square- and triangular-lattice Ising models, the Blume-Capel model, a highly degenerate biquadratic-exchange spin-1 Ising (BSI) model, and the two-dimensional X Y model—and we examine critically what machine learning is teaching us. We find that quantified principal components from PCA not only allow the exploration of different phases and symmetry-breaking, but they can distinguish phase-transition types and locate critical points. We show that the corresponding weight vectors have a clear physical interpretation, which is particularly interesting in the frustrated models such as the triangular antiferromagnet, where they can point to incipient orders. Unlike the other well-studied models, the properties of the BSI model are less well known. Using both PCA and conventional Monte Carlo analysis, we demonstrate that the BSI model shows an absence of phase transition and macroscopic ground-state degeneracy. The failure to capture the "charge" correlations (vorticity) in the BSI model (X Y model) from raw spin configurations points to some of the limitations of PCA. Finally, we employ a nonlinear unsupervised machine learning procedure, the "autoencoder method," and we demonstrate that it too can be trained to capture phase transitions and critical points.

Development and Flight Test of an Emergency Flight Control System Using Only Engine Thrust on an MD-11 Transport Airplane

NASA Technical Reports Server (NTRS)

Burcham, Frank W., Jr.; Burken, John J.; Maine, Trindel A.; Fullerton, C. Gordon

1997-01-01

An emergency flight control system that uses only engine thrust, called the propulsion-controlled aircraft (PCA) system, was developed and flight tested on an MD-11 airplane. The PCA system is a thrust-only control system, which augments pilot flightpath and track commands with aircraft feedback parameters to control engine thrust. The PCA system was implemented on the MD-11 airplane using only software modifications to existing computers. Results of a 25-hr flight test show that the PCA system can be used to fly to an airport and safely land a transport airplane with an inoperative flight control system. In up-and-away operation, the PCA system served as an acceptable autopilot capable of extended flight over a range of speeds, altitudes, and configurations. PCA approaches, go-arounds, and three landings without the use of any normal flight controls were demonstrated, including ILS-coupled hands-off landings. PCA operation was used to recover from an upset condition. The PCA system was also tested at altitude with all three hydraulic systems turned off. This paper reviews the principles of throttles-only flight control, a history of accidents or incidents in which some or all flight controls were lost, the MD-11 airplane and its systems, PCA system development, operation, flight testing, and pilot comments.

The Use of the Visualisation of Multidimensional Data Using PCA to Evaluate Possibilities of the Division of Coal Samples Space Due to their Suitability for Fluidised Gasification

NASA Astrophysics Data System (ADS)

Jamróz, Dariusz; Niedoba, Tomasz; Surowiak, Agnieszka; Tumidajski, Tadeusz

2016-09-01

Methods serving to visualise multidimensional data through the transformation of multidimensional space into two-dimensional space, enable to present the multidimensional data on the computer screen. Thanks to this, qualitative analysis of this data can be performed in the most natural way for humans, through the sense of sight. An example of such a method of multidimensional data visualisation is PCA (principal component analysis) method. This method was used in this work to present and analyse a set of seven-dimensional data (selected seven properties) describing coal samples obtained from Janina and Wieczorek coal mines. Coal from these mines was previously subjected to separation by means of a laboratory ring jig, consisting of ten rings. With 5 layers of both types of coal (with 2 rings each) were obtained in this way. It was decided to check if the method of multidimensional data visualisation enables to divide the space of such divided samples into areas with different suitability for the fluidised gasification process. To that end, the card of technological suitability of coal was used (Sobolewski et al., 2012; 2013), in which key, relevant and additional parameters, having effect on the gasification process, were described. As a result of analyses, it was stated that effective determination of coal samples suitability for the on-surface gasification process in a fluidised reactor is possible. The PCA method enables the visualisation of the optimal subspace containing the set requirements concerning the properties of coals intended for this process.

Design of compact and ultra efficient aspherical lenses for extended Lambertian sources in two-dimensional geometry

PubMed Central

Wu, Rengmao; Hua, Hong; Benítez, Pablo; Miñano, Juan C.; Liang, Rongguang

2016-01-01

The energy efficiency and compactness of an illumination system are two main concerns in illumination design for extended sources. In this paper, we present two methods to design compact, ultra efficient aspherical lenses for extended Lambertian sources in two-dimensional geometry. The light rays are directed by using two aspherical surfaces in the first method and one aspherical surface along with an optimized parabola in the second method. The principles and procedures of each design method are introduced in detail. Three examples are presented to demonstrate the effectiveness of these two methods in terms of performance and capacity in designing compact, ultra efficient aspherical lenses. The comparisons made between the two proposed methods indicate that the second method is much simpler and easier to be implemented, and has an excellent extensibility to three-dimensional designs. PMID:29092336

[Experimental study of metabonomics in the diagnosis of allergic rhinitis in mice].

PubMed

Wang, A; Li, Q F; Zhang, G Q; Zhao, C Q

2016-02-01

To investigate the application of metabonomics in the diagnosis of allergic rhinitis. Eighty male Kunming mice were randomly divided into two groups, control group (30 mice) and allergic rhinitis (AR) group (50 mice). After modeling, removal behavior score more than 6 and retain 30 mice behavior score equal to 6.Collect the mice peripheral blood and preparate blood serum, using UPLC-MS chromatographic separation and detection. The data were pretreated by SPSS and Excel, after chromatographic peak matching by MZmine. Firstly , delete interference data in accordance with the 80% rule .Then, the investigate data were analyzed by PLS-DA and PCA-X. Three-dimensional view of the control group (30 mice) and AR group (30 mice) blood serum data was drawn using PCA-X and PLS-DA method. The two groups of samples could be completely separated through views, which showed that there was a significant difference between the two groups of data. There were some differences in the blood metabolites between the control group and AR group . The study showed that it was scientific and feasible to diagnose AR using the metabonomics.

RXTE and BeppoSAX Observations of the Transient X-ray Pulsar XTE J 18591+083

NASA Technical Reports Server (NTRS)

Corbet, R. H. D.; intZand, J. J. M.; Levine, A. M.; Marshall, F. E.

2008-01-01

We present observations of the 9.8 s X-ray pulsar XTE J159+083 made with the All-Sky Monitor (ASM) and Proportional Counter Array (PCA) on board the Rossi X-ray Timing Explorer (RXTE), and the Wide Field Cameras (WFC) on board BeppoSAX. The ASM data cover a 12 year time interval and show that an extended outburst occurred between approximately MJD50, 250, and 50, 460 (1996 June 16 to 1997 January 12). The ASM data excluding this outburst interval suggest a possible 61 day modulation. Eighteen sets of PCA observations were obtained over an approx. one month interval in 1999. The flux variability measured with the PCA appears consistent with the possible period found with the ASM. The PCA measurements of the pulse period showed it to decrease non-monotonically and then to increase significantly. Doppler shifts due to orbital motion rather than accretion torques appear to be better able to explain the pulse period changes. Observations with the WFC during the extended outburst give an error box which is consistent with a previously determined PCA error box but is significantly smaller. The transient nature of XTE J1859+083 and the length of its pulse period are consistent with it being a Be/neutral star binary. The possible 61 day orbital period would be of the expected length for a Be star system with a 9.8 s pulse period.

Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data.

PubMed

Buettner, Florian; Moignard, Victoria; Göttgens, Berthold; Theis, Fabian J

2014-07-01

High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data. We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA. The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm. © The Author 2014. Published by Oxford University Press. All rights reserved.

Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data

PubMed Central

Buettner, Florian; Moignard, Victoria; Göttgens, Berthold; Theis, Fabian J.

2014-01-01

Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data. Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA. Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm. Contact: fbuettner.phys@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24618470

Two-dimensional and three-dimensional dynamic imaging of live biofilms in a microchannel by time-of-flight secondary ion mass spectrometry

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

Hua, Xin; Marshall, Matthew J.; Xiong, Yijia

2015-05-01

A vacuum compatible microfluidic reactor, SALVI (System for Analysis at the Liquid Vacuum Interface) was employed for in situ chemical imaging of live biofilms using time-of-flight secondary ion mass spectrometry (ToF-SIMS). Depth profiling by sputtering materials in sequential layers resulted in live biofilm spatial chemical mapping. 2D images were reconstructed to report the first 3D images of hydrated biofilm elucidating spatial and chemical heterogeneity. 2D image principal component analysis (PCA) was conducted among biofilms at different locations in the microchannel. Our approach directly visualized spatial and chemical heterogeneity within the living biofilm by dynamic liquid ToF-SIMS.

A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.

PubMed

Saâdaoui, Foued; Bertrand, Pierre R; Boudet, Gil; Rouffiac, Karine; Dutheil, Frédéric; Chamoux, Alain

2015-10-01

Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems. A case study is conducted for an occupational medicine problem with the purpose of analyzing patterns in a population of 813 individuals. To reduce the data set dimensionality, we base our approach on the Principal Component Analysis (PCA), which is the statistical tool most commonly used in factorial analysis. However, the problems in nature, especially in medicine, are often based on heterogeneous-type qualitative-quantitative measurements, whereas PCA only processes quantitative ones. Besides, qualitative data are originally unobservable quantitative responses that are usually binary-coded. Hence, we propose a new set of strategies allowing to simultaneously handle quantitative and qualitative data. The principle of this approach is to perform a projection of the qualitative variables on the subspaces spanned by quantitative ones. Subsequently, an optimal model is allocated to the resulting PCA-regressed subspaces.

PCA Based Stress Monitoring of Cylindrical Specimens Using PZTs and Guided Waves

PubMed Central

Mujica, Luis; Ruiz, Magda; Camacho, Johanatan

2017-01-01

Since mechanical stress in structures affects issues such as strength, expected operational life and dimensional stability, a continuous stress monitoring scheme is necessary for a complete integrity assessment. Consequently, this paper proposes a stress monitoring scheme for cylindrical specimens, which are widely used in structures such as pipelines, wind turbines or bridges. The approach consists of tracking guided wave variations due to load changes, by comparing wave statistical patterns via Principal Component Analysis (PCA). Each load scenario is projected to the PCA space by means of a baseline model and represented using the Q-statistical indices. Experimental validation of the proposed methodology is conducted on two specimens: (i) a 12.7 mm (1/2″) diameter, 0.4 m length, AISI 1020 steel rod, and (ii) a 25.4 mm (1″) diameter, 6m length, schedule 40, A-106, hollow cylinder. Specimen 1 was subjected to axial loads, meanwhile specimen 2 to flexion. In both cases, simultaneous longitudinal and flexural guided waves were generated via piezoelectric devices (PZTs) in a pitch-catch configuration. Experimental results show the feasibility of the approach and its potential use as in-situ continuous stress monitoring application. PMID:29194384

Image restoration for three-dimensional fluorescence microscopy using an orthonormal basis for efficient representation of depth-variant point-spread functions

PubMed Central

Patwary, Nurmohammed; Preza, Chrysanthe

2015-01-01

A depth-variant (DV) image restoration algorithm for wide field fluorescence microscopy, using an orthonormal basis decomposition of DV point-spread functions (PSFs), is investigated in this study. The efficient PSF representation is based on a previously developed principal component analysis (PCA), which is computationally intensive. We present an approach developed to reduce the number of DV PSFs required for the PCA computation, thereby making the PCA-based approach computationally tractable for thick samples. Restoration results from both synthetic and experimental images show consistency and that the proposed algorithm addresses efficiently depth-induced aberration using a small number of principal components. Comparison of the PCA-based algorithm with a previously-developed strata-based DV restoration algorithm demonstrates that the proposed method improves performance by 50% in terms of accuracy and simultaneously reduces the processing time by 64% using comparable computational resources. PMID:26504634

EEG channels reduction using PCA to increase XGBoost's accuracy for stroke detection

NASA Astrophysics Data System (ADS)

Fitriah, N.; Wijaya, S. K.; Fanany, M. I.; Badri, C.; Rezal, M.

2017-07-01

In Indonesia, based on the result of Basic Health Research 2013, the number of stroke patients had increased from 8.3 ‰ (2007) to 12.1 ‰ (2013). These days, some researchers are using electroencephalography (EEG) result as another option to detect the stroke disease besides CT Scan image as the gold standard. A previous study on the data of stroke and healthy patients in National Brain Center Hospital (RS PON) used Brain Symmetry Index (BSI), Delta-Alpha Ratio (DAR), and Delta-Theta-Alpha-Beta Ratio (DTABR) as the features for classification by an Extreme Learning Machine (ELM). The study got 85% accuracy with sensitivity above 86 % for acute ischemic stroke detection. Using EEG data means dealing with many data dimensions, and it can reduce the accuracy of classifier (the curse of dimensionality). Principal Component Analysis (PCA) could reduce dimensionality and computation cost without decreasing classification accuracy. XGBoost, as the scalable tree boosting classifier, can solve real-world scale problems (Higgs Boson and Allstate dataset) with using a minimal amount of resources. This paper reuses the same data from RS PON and features from previous research, preprocessed with PCA and classified with XGBoost, to increase the accuracy with fewer electrodes. The specific fewer electrodes improved the accuracy of stroke detection. Our future work will examine the other algorithm besides PCA to get higher accuracy with less number of channels.

PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data.

PubMed

Mejia, Amanda F; Nebel, Mary Beth; Eloyan, Ani; Caffo, Brian; Lindquist, Martin A

2017-07-01

Outlier detection for high-dimensional (HD) data is a popular topic in modern statistical research. However, one source of HD data that has received relatively little attention is functional magnetic resonance images (fMRI), which consists of hundreds of thousands of measurements sampled at hundreds of time points. At a time when the availability of fMRI data is rapidly growing-primarily through large, publicly available grassroots datasets-automated quality control and outlier detection methods are greatly needed. We propose principal components analysis (PCA) leverage and demonstrate how it can be used to identify outlying time points in an fMRI run. Furthermore, PCA leverage is a measure of the influence of each observation on the estimation of principal components, which are often of interest in fMRI data. We also propose an alternative measure, PCA robust distance, which is less sensitive to outliers and has controllable statistical properties. The proposed methods are validated through simulation studies and are shown to be highly accurate. We also conduct a reliability study using resting-state fMRI data from the Autism Brain Imaging Data Exchange and find that removal of outliers using the proposed methods results in more reliable estimation of subject-level resting-state networks using independent components analysis. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Surface-enhanced Raman spectra of hemoglobin for esophageal cancer diagnosis

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

The Application of Deterministic Spectral Domain Method to the Analysis of Planar Circuit Discontinuities on Open Substrates

DTIC Science & Technology

1990-08-01

the spectral domain is extended to include the effects of two-dimensional, two-component current flow in planar transmission line discontinuities 6n...PROFESSOR: Tatsuo Itoh A deterministic formulation of the method of moments carried out in the spectral domain is extended to include the effects of...two-dimensional, two- component current flow in planar transmission line discontinuities on open substrates. The method includes the effects of space

Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease.

PubMed

Taguchi, Y-h; Iwadate, Mitsuo; Umeyama, Hideaki

2015-04-30

Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

Direct design of aspherical lenses for extended non-Lambertian sources in two-dimensional geometry

PubMed Central

Wu, Rengmao; Hua, Hong; Benítez, Pablo; Miñano, Juan C.

2016-01-01

Illumination design for extended sources is very important for practical applications. The existing direct methods that are all developed for extended Lambertian sources are not applicable to extended non-Lambertian sources whose luminance is a function of position and direction. What we present in this Letter is to our knowledge the first direct method for extended non-Lambertian sources. In this method, the edge rays and the interior rays are both used, and the output intensity at a given direction is calculated to be the integral of the luminance function of all the outgoing rays at this direction. No cumbersome iterative illuminance compensation is needed. Two examples are presented to demonstrate the elegance of this method in prescribed intensity design for extended non-Lambertian sources in two-dimensional geometry. PMID:26125361

Evaluation of Deep Learning Representations of Spatial Storm Data

NASA Astrophysics Data System (ADS)

Gagne, D. J., II; Haupt, S. E.; Nychka, D. W.

2017-12-01

The spatial structure of a severe thunderstorm and its surrounding environment provide useful information about the potential for severe weather hazards, including tornadoes, hail, and high winds. Statistics computed over the area of a storm or from the pre-storm environment can provide descriptive information but fail to capture structural information. Because the storm environment is a complex, high-dimensional space, identifying methods to encode important spatial storm information in a low-dimensional form should aid analysis and prediction of storms by statistical and machine learning models. Principal component analysis (PCA), a more traditional approach, transforms high-dimensional data into a set of linearly uncorrelated, orthogonal components ordered by the amount of variance explained by each component. The burgeoning field of deep learning offers two potential approaches to this problem. Convolutional Neural Networks are a supervised learning method for transforming spatial data into a hierarchical set of feature maps that correspond with relevant combinations of spatial structures in the data. Generative Adversarial Networks (GANs) are an unsupervised deep learning model that uses two neural networks trained against each other to produce encoded representations of spatial data. These different spatial encoding methods were evaluated on the prediction of severe hail for a large set of storm patches extracted from the NCAR convection-allowing ensemble. Each storm patch contains information about storm structure and the near-storm environment. Logistic regression and random forest models were trained using the PCA and GAN encodings of the storm data and were compared against the predictions from a convolutional neural network. All methods showed skill over climatology at predicting the probability of severe hail. However, the verification scores among the methods were very similar and the predictions were highly correlated. Further evaluations are being performed to determine how the choice of input variables affects the results.

PCANet: A Simple Deep Learning Baseline for Image Classification?

PubMed

Chan, Tsung-Han; Jia, Kui; Gao, Shenghua; Lu, Jiwen; Zeng, Zinan; Ma, Yi

2015-12-01

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

Free-energy landscape of RNA hairpins constructed via dihedral angle principal component analysis.

PubMed

Riccardi, Laura; Nguyen, Phuong H; Stock, Gerhard

2009-12-31

To systematically construct a low-dimensional free-energy landscape of RNA systems from a classical molecular dynamics simulation, various versions of the principal component analysis (PCA) are compared: the cPCA using the Cartesian coordinates of all atoms, the dPCA using the sine/cosine-transformed six backbone dihedral angles as well as the glycosidic torsional angle chi and the pseudorotational angle P, the aPCA which ignores the circularity of the 6 + 2 dihedral angles of the RNA, and the dPCA(etatheta), which approximates the 6 backbone dihedral angles by 2 pseudotorsional angles eta and theta. As representative examples, a 10-nucleotide UUCG hairpin and the 36-nucleotide segment SL1 of the Psi site of HIV-1 are studied by classical molecular dynamics simulation, using the Amber all-atom force field and explicit solvent. It is shown that the conformational heterogeneity of the RNA hairpins can only be resolved by an angular PCA such as the dPCA but not by the cPCA using Cartesian coordinates. Apart from possible artifacts due to the coupling of overall and internal motion, this is because the details of hydrogen bonding and stacking interactions but also of global structural rearrangements of the RNA are better discriminated by dihedral angles. In line with recent experiments, it is found that the free energy landscape of RNA hairpins is quite rugged and contains various metastable conformational states which may serve as an intermediate for unfolding.

Principal component analysis-based imaging angle determination for 3D motion monitoring using single-slice on-board imaging.

PubMed

Chen, Ting; Zhang, Miao; Jabbour, Salma; Wang, Hesheng; Barbee, David; Das, Indra J; Yue, Ning

2018-04-10

Through-plane motion introduces uncertainty in three-dimensional (3D) motion monitoring when using single-slice on-board imaging (OBI) modalities such as cine MRI. We propose a principal component analysis (PCA)-based framework to determine the optimal imaging plane to minimize the through-plane motion for single-slice imaging-based motion monitoring. Four-dimensional computed tomography (4DCT) images of eight thoracic cancer patients were retrospectively analyzed. The target volumes were manually delineated at different respiratory phases of 4DCT. We performed automated image registration to establish the 4D respiratory target motion trajectories for all patients. PCA was conducted using the motion information to define the three principal components of the respiratory motion trajectories. Two imaging planes were determined perpendicular to the second and third principal component, respectively, to avoid imaging with the primary principal component of the through-plane motion. Single-slice images were reconstructed from 4DCT in the PCA-derived orthogonal imaging planes and were compared against the traditional AP/Lateral image pairs on through-plane motion, residual error in motion monitoring, absolute motion amplitude error and the similarity between target segmentations at different phases. We evaluated the significance of the proposed motion monitoring improvement using paired t test analysis. The PCA-determined imaging planes had overall less through-plane motion compared against the AP/Lateral image pairs. For all patients, the average through-plane motion was 3.6 mm (range: 1.6-5.6 mm) for the AP view and 1.7 mm (range: 0.6-2.7 mm) for the Lateral view. With PCA optimization, the average through-plane motion was 2.5 mm (range: 1.3-3.9 mm) and 0.6 mm (range: 0.2-1.5 mm) for the two imaging planes, respectively. The absolute residual error of the reconstructed max-exhale-to-inhale motion averaged 0.7 mm (range: 0.4-1.3 mm, 95% CI: 0.4-1.1 mm) using optimized imaging planes, averaged 0.5 mm (range: 0.3-1.0 mm, 95% CI: 0.2-0.8 mm) using an imaging plane perpendicular to the minimal motion component only and averaged 1.3 mm (range: 0.4-2.8 mm, 95% CI: 0.4-2.3 mm) in AP/Lateral orthogonal image pairs. The root-mean-square error of reconstructed displacement was 0.8 mm for optimized imaging planes, 0.6 mm for imaging plane perpendicular to the minimal motion component only, and 1.6 mm for AP/Lateral orthogonal image pairs. When using the optimized imaging planes for motion monitoring, there was no significant absolute amplitude error of the reconstructed motion (P = 0.0988), while AP/Lateral images had significant error (P = 0.0097) with a paired t test. The average surface distance (ASD) between overlaid two-dimensional (2D) tumor segmentation at end-of-inhale and end-of-exhale for all eight patients was 0.6 ± 0.2 mm in optimized imaging planes and 1.4 ± 0.8 mm in AP/Lateral images. The Dice similarity coefficient (DSC) between overlaid 2D tumor segmentation at end-of-inhale and end-of-exhale for all eight patients was 0.96 ± 0.03 in optimized imaging planes and 0.89 ± 0.05 in AP/Lateral images. Both ASD (P = 0.034) and DSC (P = 0.022) were significantly improved in the optimized imaging planes. Motion monitoring using imaging planes determined by the proposed PCA-based framework had significantly improved performance. Single-slice image-based motion tracking can be used for clinical implementations such as MR image-guided radiation therapy (MR-IGRT). © 2018 American Association of Physicists in Medicine.

Proteomics analysis of malignant and benign prostate tissue by 2D DIGE/MS reveals new insights into proteins involved in prostate cancer.

PubMed

Davalieva, Katarina; Kostovska, Ivana Maleva; Kiprijanovska, Sanja; Markoska, Katerina; Kubelka-Sabit, Katerina; Filipovski, Vanja; Stavridis, Sotir; Stankov, Oliver; Komina, Selim; Petrusevska, Gordana; Polenakovic, Momir

2015-10-01

The key to a more effective diagnosis, prognosis, and therapeutic management of prostate cancer (PCa) could lie in the direct analysis of cancer tissue. In this study, by comparative proteomics analysis of PCa and benign prostate hyperplasia (BPH) tissues we attempted to elucidate the proteins and regulatory pathways involved in this disease. The samples used in this study were fresh surgical tissues with clinically and histologically confirmed PCa (n = 19) and BPH (n = 33). We used two dimensional difference in gel electrophoresis (2D DIGE) coupled with mass spectrometry (MS) and bioinformatics analysis. Thirty-nine spots with statistically significant 1.8-fold variation or more in abundance, corresponding to 28 proteins were identified. The IPA analysis pointed out to 3 possible networks regulated within MAPK, ERK, TGFB1, and ubiquitin pathways. Thirteen of the identified proteins, namely, constituents of the intermediate filaments (KRT8, KRT18, DES), potential tumor suppressors (ARHGAP1, AZGP1, GSTM2, and MFAP4), transport and membrane organization proteins (FABP5, GC, and EHD2), chaperons (FKBP4 and HSPD1) and known cancer marker (NME1) have been associated with prostate and other cancers by numerous proteomics, genomics or functional studies. We evidenced for the first time the dysregulation of 9 proteins (CSNK1A1, ARID5B, LYPLA1, PSMB6, RABEP1, TALDO1, UBE2N, PPP1CB, and SERPINB1) that may have role in PCa. The UBE2N, PSMB6, and PPP1CB, involved in cell cycle regulation and progression were evaluated by Western blot analysis which confirmed significantly higher abundances of UBE2N and PSMB6 and significantly lower abundance of PPP1CB in PCa. In addition to the identification of substantial number of proteins with known association with PCa, the proteomic approach in this study revealed proteins not previously clearly related to PCa, providing a starting point for further elucidation of their function in disease initiation and progression. © 2015 Wiley Periodicals, Inc.

Scalable Learning for Geostatistics and Speaker Recognition

DTIC Science & Technology

2011-01-01

of prior knowledge of the model or due to improved robustness requirements). Both these methods have their own advantages and disadvantages. The use...application. If the data is well-correlated and low-dimensional, any prior knowledge available on the data can be used to build a parametric model. In the...absence of prior knowledge , non-parametric methods can be used. If the data is high-dimensional, PCA based dimensionality reduction is often the first

Describing temperament in an ungulate: a multidimensional approach.

PubMed

Graunke, Katharina L; Nürnberg, Gerd; Repsilber, Dirk; Puppe, Birger; Langbein, Jan

2013-01-01

Studies on animal temperament have often described temperament using a one-dimensional scale, whereas theoretical framework has recently suggested two or more dimensions using terms like "valence" or "arousal" to describe these dimensions. Yet, the valence or assessment of a situation is highly individual. The aim of this study was to provide support for the multidimensional framework with experimental data originating from an economically important species (Bos taurus). We tested 361 calves at 90 days post natum (dpn) in a novel-object test. Using a principal component analysis (PCA), we condensed numerous behaviours into fewer variables to describe temperament and correlated these variables with simultaneously measured heart rate variability (HRV) data. The PCA resulted in two behavioural dimensions (principal components, PC): novel-object-related (PC 1) and exploration-activity-related (PC 2). These PCs explained 58% of the variability in our data. The animals were distributed evenly within the two behavioural dimensions independent of their sex. Calves with different scores in these PCs differed significantly in HRV, and thus in the autonomous nervous system's activity. Based on these combined behavioural and physiological data we described four distinct temperament types resulting from two behavioural dimensions: "neophobic/fearful--alert", "interested--stressed", "subdued/uninterested--calm", and "neoophilic/outgoing--alert". Additionally, 38 calves were tested at 90 and 197 dpn. Using the same PCA-model, they correlated significantly in PC 1 and tended to correlate in PC 2 between the two test ages. Of these calves, 42% expressed a similar behaviour pattern in both dimensions and 47% in one. No differences in temperament scores were found between sexes or breeds. In conclusion, we described distinct temperament types in calves based on behavioural and physiological measures emphasising the benefits of a multidimensional approach.

Finessing filter scarcity problem in face recognition via multi-fold filter convolution

NASA Astrophysics Data System (ADS)

Low, Cheng-Yaw; Teoh, Andrew Beng-Jin

2017-06-01

The deep convolutional neural networks for face recognition, from DeepFace to the recent FaceNet, demand a sufficiently large volume of filters for feature extraction, in addition to being deep. The shallow filter-bank approaches, e.g., principal component analysis network (PCANet), binarized statistical image features (BSIF), and other analogous variants, endure the filter scarcity problem that not all PCA and ICA filters available are discriminative to abstract noise-free features. This paper extends our previous work on multi-fold filter convolution (ℳ-FFC), where the pre-learned PCA and ICA filter sets are exponentially diversified by ℳ folds to instantiate PCA, ICA, and PCA-ICA offspring. The experimental results unveil that the 2-FFC operation solves the filter scarcity state. The 2-FFC descriptors are also evidenced to be superior to that of PCANet, BSIF, and other face descriptors, in terms of rank-1 identification rate (%).

From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction

PubMed Central

Cocco, Simona; Monasson, Remi; Weigt, Martin

2013-01-01

Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analysis (DCA), a global inference method based on the maximum entropy principle, which aims at predicting residue-residue contacts. In this paper, inspired by the statistical physics of disordered systems, we introduce the Hopfield-Potts model to naturally interpolate between these two approaches. The Hopfield-Potts model allows us to identify relevant ‘patterns’ of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix. We show how the computation of such statistical patterns makes it possible to accurately predict residue-residue contacts with a much smaller number of parameters than DCA. This dimensional reduction allows us to avoid overfitting and to extract contact information from multiple-sequence alignments of reduced size. In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact in the three-dimensional protein fold. PMID:23990764

Targeting monoamine oxidase A in advanced prostate cancer.

PubMed

Flamand, Vincent; Zhao, Hongjuan; Peehl, Donna M

2010-11-01

Inhibitors of monoamine oxidase A (MAOA), a mitochondrial enzyme that degrades neurotransmitters including serotonin and norepinephrine, are commonly used to treat neurological conditions including depression. Recently, we and others identified high expression of MAOA in normal basal prostatic epithelium and high-grade primary prostate cancer (PCa). In contrast, MAOA is low in normal secretory prostatic epithelium and low-grade PCa. An irreversible inhibitor of MAOA, clorgyline, induced secretory differentiation in primary cultures of normal basal epithelial cells and high-grade PCa. Furthermore, clorgyline inhibited several oncogenic pathways in PCa cells, suggesting clinical value of MAOA inhibitors as a pro-differentiation and anti-oncogenic therapy for high-risk PCa. Here, we extended our studies to a model of advanced PCa, VCaP cells, which were derived from castration-resistant metastatic PCa and express a high level of MAOA. Growth of VCaP cells in the presence or absence of clorgyline was evaluated in vitro and in vivo. Gene expression changes in response to clorgyline were determined by microarray and validated by quantitative real-time polymerase chain reaction. Treatment with clorgyline in vitro inhibited growth and altered the transcriptional pattern of VCaP cells in a manner consistent with the pro-differentiation and anti-oncogenic effects seen in treated primary PCa cells. Src, beta-catenin, and MAPK oncogenic pathways, implicated in androgen-independent growth and metastasis, were significantly downregulated. Clorgyline treatment of mice bearing VCaP xenografts slowed tumor growth and induced transcriptome changes similar to those noted in vitro. Our results support the possibility that anti-depressant drugs that target MAOA might find a new application in treating PCa.

Cannibalism Affects Core Metabolic Processes in Helicoverpa armigera Larvae—A 2D NMR Metabolomics Study

PubMed Central

Vergara, Fredd; Shino, Amiu; Kikuchi, Jun

2016-01-01

Cannibalism is known in many insect species, yet its impact on insect metabolism has not been investigated in detail. This study assessed the effects of cannibalism on the metabolism of fourth-instar larvae of the non-predatory insect Helicoverpa armigera (Lepidotera: Noctuidea). Two groups of larvae were analyzed: one group fed with fourth-instar larvae of H. armigera (cannibal), the other group fed with an artificial plant diet. Water-soluble small organic compounds present in the larvae were analyzed using two-dimensional nuclear magnetic resonance (NMR) and principal component analysis (PCA). Cannibalism negatively affected larval growth. PCA of NMR spectra showed that the metabolic profiles of cannibal and herbivore larvae were statistically different with monomeric sugars, fatty acid- and amino acid-related metabolites as the most variable compounds. Quantitation of 1H-13C HSQC (Heteronuclear Single Quantum Coherence) signals revealed that the concentrations of glucose, glucono-1,5-lactone, glycerol phosphate, glutamine, glycine, leucine, isoleucine, lysine, ornithine, proline, threonine and valine were higher in the herbivore larvae. PMID:27598144

Improving 3d Spatial Queries Search: Newfangled Technique of Space Filling Curves in 3d City Modeling

NASA Astrophysics Data System (ADS)

Uznir, U.; Anton, F.; Suhaibah, A.; Rahman, A. A.; Mioc, D.

2013-09-01

The advantages of three dimensional (3D) city models can be seen in various applications including photogrammetry, urban and regional planning, computer games, etc.. They expand the visualization and analysis capabilities of Geographic Information Systems on cities, and they can be developed using web standards. However, these 3D city models consume much more storage compared to two dimensional (2D) spatial data. They involve extra geometrical and topological information together with semantic data. Without a proper spatial data clustering method and its corresponding spatial data access method, retrieving portions of and especially searching these 3D city models, will not be done optimally. Even though current developments are based on an open data model allotted by the Open Geospatial Consortium (OGC) called CityGML, its XML-based structure makes it challenging to cluster the 3D urban objects. In this research, we propose an opponent data constellation technique of space-filling curves (3D Hilbert curves) for 3D city model data representation. Unlike previous methods, that try to project 3D or n-dimensional data down to 2D or 3D using Principal Component Analysis (PCA) or Hilbert mappings, in this research, we extend the Hilbert space-filling curve to one higher dimension for 3D city model data implementations. The query performance was tested using a CityGML dataset of 1,000 building blocks and the results are presented in this paper. The advantages of implementing space-filling curves in 3D city modeling will improve data retrieval time by means of optimized 3D adjacency, nearest neighbor information and 3D indexing. The Hilbert mapping, which maps a subinterval of the [0, 1] interval to the corresponding portion of the d-dimensional Hilbert's curve, preserves the Lebesgue measure and is Lipschitz continuous. Depending on the applications, several alternatives are possible in order to cluster spatial data together in the third dimension compared to its clustering in 2D.

A DATA-DRIVEN MODEL FOR SPECTRA: FINDING DOUBLE REDSHIFTS IN THE SLOAN DIGITAL SKY SURVEY

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

Tsalmantza, P.; Hogg, David W., E-mail: vivitsal@mpia.de

2012-07-10

We present a data-driven method-heteroscedastic matrix factorization, a kind of probabilistic factor analysis-for modeling or performing dimensionality reduction on observed spectra or other high-dimensional data with known but non-uniform observational uncertainties. The method uses an iterative inverse-variance-weighted least-squares minimization procedure to generate a best set of basis functions. The method is similar to principal components analysis (PCA), but with the substantial advantage that it uses measurement uncertainties in a responsible way and accounts naturally for poorly measured and missing data; it models the variance in the noise-deconvolved data space. A regularization can be applied, in the form of a smoothnessmore » prior (inspired by Gaussian processes) or a non-negative constraint, without making the method prohibitively slow. Because the method optimizes a justified scalar (related to the likelihood), the basis provides a better fit to the data in a probabilistic sense than any PCA basis. We test the method on Sloan Digital Sky Survey (SDSS) spectra, concentrating on spectra known to contain two redshift components: these are spectra of gravitational lens candidates and massive black hole binaries. We apply a hypothesis test to compare one-redshift and two-redshift models for these spectra, utilizing the data-driven model trained on a random subset of all SDSS spectra. This test confirms 129 of the 131 lens candidates in our sample and all of the known binary candidates, and turns up very few false positives.« less

Fluorescence Intrinsic Characterization of Excitation-Emission Matrix Using Multi-Dimensional Ensemble Empirical Mode Decomposition

PubMed Central

Chang, Chi-Ying; Chang, Chia-Chi; Hsiao, Tzu-Chien

2013-01-01

Excitation-emission matrix (EEM) fluorescence spectroscopy is a noninvasive method for tissue diagnosis and has become important in clinical use. However, the intrinsic characterization of EEM fluorescence remains unclear. Photobleaching and the complexity of the chemical compounds make it difficult to distinguish individual compounds due to overlapping features. Conventional studies use principal component analysis (PCA) for EEM fluorescence analysis, and the relationship between the EEM features extracted by PCA and diseases has been examined. The spectral features of different tissue constituents are not fully separable or clearly defined. Recently, a non-stationary method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to propose a fluorescence spectroscopy system for EEM measurements and to describe a method for extracting the intrinsic characteristics of EEM by MEEMD. The results indicate that, although PCA provides the principal factor for the spectral features associated with chemical compounds, MEEMD can provide additional intrinsic features with more reliable mapping of the chemical compounds. MEEMD has the potential to extract intrinsic fluorescence features and improve the detection of biochemical changes. PMID:24240806

Fast, Exact Bootstrap Principal Component Analysis for p > 1 million

PubMed Central

Fisher, Aaron; Caffo, Brian; Schwartz, Brian; Zipunnikov, Vadim

2015-01-01

Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same n-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same n-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace. Several uncertainty metrics can be computed solely based on the bootstrap distribution of these low dimensional coordinates, without calculating or storing the p-dimensional bootstrap components. Fast bootstrap PCA is applied to a dataset of sleep electroencephalogram recordings (p = 900, n = 392), and to a dataset of brain magnetic resonance images (MRIs) (p ≈ 3 million, n = 352). For the MRI dataset, our method allows for standard errors for the first 3 principal components based on 1000 bootstrap samples to be calculated on a standard laptop in 47 minutes, as opposed to approximately 4 days with standard methods. PMID:27616801

PCA as a practical indicator of OPLS-DA model reliability.

PubMed

Worley, Bradley; Powers, Robert

Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation. A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models. With increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scores-space remained basically unaffected. Supported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.

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

Bednarz, Natalia; Eltze, Elke; Semjonow, Axel

A recent study concluded that serum prostate specific antigen (PSA)-based screening is beneficial for reducing the lethality of PCa, but was also associated with a high risk of 'overdiagnosis'. Nevertheless, also PCa patients who suffered from organ confined tumors and had negative bone scans succumb to distant metastases after complete tumor resection. It is reasonable to assume that those tumors spread to other organs long before the overt manifestation of metastases. Our current results confirm that prostate tumors are highly heterogeneous. Even a small subpopulation of cells bearing BRCA1 losses can initiate PCa cell regional and distant dissemination indicating thosemore » patients which might be at high risk of metastasis. A preliminary study performed on a small cohort of multifocal prostate cancer (PCa) detected BRCA1 allelic imbalances (AI) among circulating tumor cells (CTCs). The present analysis was aimed to elucidate the biological and clinical role of BRCA1 losses on metastatic spread and tumor progression in prostate cancer patients. Experimental Design: To map molecular progression in PCa outgrowth we used FISH analysis of tissue microarrays (TMA), lymph node sections and CTC from peripheral blood. We found that 14% of 133 tested patients carried monoallelic BRCA1 loss in at least one tumor focus. Extended molecular analysis of chr17q revealed that this aberration was often a part of larger cytogenetic rearrangement involving chr17q21 accompanied by AI of the tumor suppressor gene PTEN and lack of the BRCA1 promoter methylation. The BRCA1 losses correlated with advanced T stage (p < 0.05), invasion to pelvic lymph nodes (LN, p < 0.05) as well as BR (p < 0.01). Their prevalence was twice as high within 62 LN metastases (LNMs) as in primary tumors (27%, p < 0.01). The analysis of 11 matched primary PCa-LNM pairs confirmed the suspected transmission of genetic abnormalities between those two sites. In 4 of 7 patients with metastatic disease, BRCA1 losses appeared in a minute fraction of cytokeratin- and vimentin-positive CTCs. Small subpopulations of PCa cells bearing BRCA1 losses might be one confounding factor initiating tumor dissemination and might provide an early indicator of shortened disease-free survival.« less

EM in high-dimensional spaces.

PubMed

Draper, Bruce A; Elliott, Daniel L; Hayes, Jeremy; Baek, Kyungim

2005-06-01

This paper considers fitting a mixture of Gaussians model to high-dimensional data in scenarios where there are fewer data samples than feature dimensions. Issues that arise when using principal component analysis (PCA) to represent Gaussian distributions inside Expectation-Maximization (EM) are addressed, and a practical algorithm results. Unlike other algorithms that have been proposed, this algorithm does not try to compress the data to fit low-dimensional models. Instead, it models Gaussian distributions in the (N - 1)-dimensional space spanned by the N data samples. We are able to show that this algorithm converges on data sets where low-dimensional techniques do not.

New vanadium tellurites: Syntheses, structures, optical properties of noncentrosymmetric VTeO{sub 4}(OH), centrosymmetric Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})

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

Liang, Ming-Li; State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002; Marsh, Matthew

Two new vanadium tellurites, VTeO{sub 4}(OH) (1) and Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2), have been synthesized successfully with the use of hydrothermal reactions. The crystal structures of the two compounds were determined by single-crystal X-ray diffraction. Compound 1 crystallizes in the polar space group Pca2{sub 1} (No. 29) while compound 2 crystallizes in the centrosymmetric space group C2/c (No. 15). The topography of compound 1 reveals a two-dimensional, layered structure comprised of VO{sub 6} octahedral chains and TeO{sub 3}(OH) zig-zag chains. Compound 2, on the contrary, features a three-dimensional [V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})]{sup 4-} anionic framework withmore » Ba{sup 2+} ions filled into the 10-member ring helical tunnels. The [V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})]{sup 4-} anionic network is the first 3D vanadium tellurite framework to be discovered in the alkaline-earth vanadium tellurite system. Powder second harmonic generation (SHG) measurements indicate that compound 1 shows a weak SHG response of about 0.3×KDP (KH{sub 2}PO{sub 4}) under 1064 nm laser radiation. Infrared spectroscopy, elemental analysis, thermal analysis, and dipole moment calculations have also been carried out. - Graphical abstract: VTeO{sub 4}(OH) (1) crystallizes in the noncentrosymmetric space group Pca2{sub 1} (No. 29) while Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2) crystallizes in the centrosymmetric space group C2/c (No. 15). - Highlights: • VTeO{sub 4}(OH) (1) and Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2) have been synthesized successfully with the use of hydrothermal reactions. • VTeO{sub 4}(OH) (1) crystallizes in the noncentrosymmetric space group Pca2{sub 1} and displays a weak SHG response. • VTeO{sub 4}(OH) (1) represents only the fourth SHG-active material found in vanadium tellurite systems. • Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2) exhibits a novel three-dimensional [V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})]{sup 4-} anionic framework.« less

Total Electron Content forecast model over Australia

NASA Astrophysics Data System (ADS)

Bouya, Zahra; Terkildsen, Michael; Francis, Matthew

Ionospheric perturbations can cause serious propagation errors in modern radio systems such as Global Navigation Satellite Systems (GNSS). Forecasting ionospheric parameters is helpful to estimate potential degradation of the performance of these systems. Our purpose is to establish an Australian Regional Total Electron Content (TEC) forecast model at IPS. In this work we present an approach based on the combined use of the Principal Component Analysis (PCA) and Artificial Neural Network (ANN) to predict future TEC values. PCA is used to reduce the dimensionality of the original TEC data by mapping it into its eigen-space. In this process the top- 5 eigenvectors are chosen to reflect the directions of the maximum variability. An ANN approach was then used for the multicomponent prediction. We outline the design of the ANN model with its parameters. A number of activation functions along with different spectral ranges and different numbers of Principal Components (PCs) were tested to find the PCA-ANN models reaching the best results. Keywords: GNSS, Space Weather, Regional, Forecast, PCA, ANN.

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

Lu, Bo, E-mail: luboufl@gmail.com; Park, Justin C.; Fan, Qiyong

Purpose: Accurately localizing lung tumor localization is essential for high-precision radiation therapy techniques such as stereotactic body radiation therapy (SBRT). Since direct monitoring of tumor motion is not always achievable due to the limitation of imaging modalities for treatment guidance, placement of fiducial markers on the patient’s body surface to act as a surrogate for tumor position prediction is a practical alternative for tracking lung tumor motion during SBRT treatments. In this work, the authors propose an innovative and robust model to solve the multimarker position optimization problem. The model is able to overcome the major drawbacks of the sparsemore » optimization approach (SOA) model. Methods: The principle-component-analysis (PCA) method was employed as the framework to build the authors’ statistical prediction model. The method can be divided into two stages. The first stage is to build the surrogate tumor matrix and calculate its eigenvalues and associated eigenvectors. The second stage is to determine the “best represented” columns of the eigenvector matrix obtained from stage one and subsequently acquire the optimal marker positions as well as numbers. Using 4-dimensional CT (4DCT) and breath hold CT imaging data, the PCA method was compared to the SOA method with respect to calculation time, average prediction accuracy, prediction stability, noise resistance, marker position consistency, and marker distribution. Results: The PCA and SOA methods which were both tested were on all 11 patients for a total of 130 cases including 4DCT and breath-hold CT scenarios. The maximum calculation time for the PCA method was less than 1 s with 64 752 surface points, whereas the average calculation time for the SOA method was over 12 min with 400 surface points. Overall, the tumor center position prediction errors were comparable between the two methods, and all were less than 1.5 mm. However, for the extreme scenarios (breath hold), the prediction errors for the PCA method were not only smaller, but were also more stable than for the SOA method. Results obtained by imposing a series of random noises to the surrogates indicated that the PCA method was much more noise resistant than the SOA method. The marker position consistency tests using various combinations of 4DCT phases to construct the surrogates suggested that the marker position predictions of the PCA method were more consistent than those of the SOA method, in spite of surrogate construction. Marker distribution tests indicated that greater than 80% of the calculated marker positions fell into the high cross correlation and high motion magnitude regions for both of the algorithms. Conclusions: The PCA model is an accurate, efficient, robust, and practical model for solving the multimarker position optimization problem to predict lung tumor motion during SBRT treatments. Due to its generality, PCA model can also be applied to other imaging guidance system whichever using surface motion as the surrogates.« less

Chirality Made Simple: A 1 - and 2-Dimensional Introduction to Stereochemistry

ERIC Educational Resources Information Center

Gawley, Robert E.

2005-01-01

The introduction of chirality in one and two dimensions, along with the concepts of internal and external reflection, can be combined with concepts familiar to all students. Once familiar with 1-Dimensional and 2-Dimensional chirality, the same concepts can be extended to 3-Dimensional and by projecting 3-D back to two, it is possible to interpret…

A systematic review of the literature exploring the interplay between prostate cancer and type two diabetes mellitus

PubMed Central

Crawley, Danielle; Chamberlain, Florence; Garmo, Hans; Rudman, Sarah; Zethelius, Björn; Holmberg, Lars; Adolfsson, Jan; Stattin, Par; Carroll, Paul; Van Hemelrijck, Mieke

2018-01-01

Prostate cancer (PCa) and type two diabetes mellitus (T2DM) are both increasing prevalent conditions and often occur concurrently. However, the relationship between the two is more complex than just two prevalent conditions co-existing. This review systematically explores the literature around the interplay between the two conditions. It covers the impact of pre-existing T2DM on PCa incidence, grade and stage, as well as exploring the impact of T2DM on PCa outcomes and mortality and the interaction between T2DM and PCa treatments. PMID:29456619

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.

Representation of Probability Density Functions from Orbit Determination using the Particle Filter

NASA Technical Reports Server (NTRS)

Mashiku, Alinda K.; Garrison, James; Carpenter, J. Russell

2012-01-01

Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty. In order to obtain an accurate representation of the probability density function (PDF) that incorporates higher order statistical information, we propose the use of nonlinear estimation methods such as the Particle Filter. The Particle Filter (PF) is capable of providing a PDF representation of the state estimates whose accuracy is dependent on the number of particles or samples used. For this method to be applicable to real case scenarios, we need a way of accurately representing the PDF in a compressed manner with little information loss. Hence we propose using the Independent Component Analysis (ICA) as a non-Gaussian dimensional reduction method that is capable of maintaining higher order statistical information obtained using the PF. Methods such as the Principal Component Analysis (PCA) are based on utilizing up to second order statistics, hence will not suffice in maintaining maximum information content. Both the PCA and the ICA are applied to two scenarios that involve a highly eccentric orbit with a lower apriori uncertainty covariance and a less eccentric orbit with a higher a priori uncertainty covariance, to illustrate the capability of the ICA in relation to the PCA.

Electrophysiological auditory responses and language development in infants with periventricular leukomalacia.

PubMed

Avecilla-Ramírez, G N; Ruiz-Correa, S; Marroquin, J L; Harmony, T; Alba, A; Mendoza-Montoya, O

2011-12-01

This study presents evidence suggesting that electrophysiological responses to language-related auditory stimuli recorded at 46weeks postconceptional age (PCA) are associated with language development, particularly in infants with periventricular leukomalacia (PVL). In order to investigate this hypothesis, electrophysiological responses to a set of auditory stimuli consisting of series of syllables and tones were recorded from a population of infants with PVL at 46weeks PCA. A communicative development inventory (i.e., parent report) was applied to this population during a follow-up study performed at 14months of age. The results of this later test were analyzed with a statistical clustering procedure, which resulted in two well-defined groups identified as the high-score (HS) and low-score (LS) groups. The event-induced power of the EEG data recorded at 46weeks PCA was analyzed using a dimensionality reduction approach, resulting in a new set of descriptive variables. The LS and HS groups formed well-separated clusters in the space spanned by these descriptive variables, which can therefore be used to predict whether a new subject will belong to either of these groups. A predictive classification rate of 80% was obtained by using a linear classifier that was trained with a leave-one-out cross-validation technique. 2011 Elsevier Inc. All rights reserved.

Touchdown: The Development of Propulsion Controlled Aircraft at NASA Dryden

NASA Technical Reports Server (NTRS)

Tucker, Tom

1999-01-01

This monograph relates the important history of the Propulsion Controlled Aircraft project at NASA's Dryden Flight Research Center. Spurred by a number of airplane crashes caused by the loss of hydraulic flight controls, a NASA-industry team lead by Frank W. Burcham and C. Gordon Fullerton developed a way to land an aircraft safely using only engine thrust to control the airplane. In spite of initial skepticism, the team discovered that, by manually manipulating an airplane's thrust, there was adequate control for extended up-and-away flight. However, there was not adequate control precision for safe runway landings because of the small control forces, slow response, and difficulty in damping the airplane phugoid and Dutch roll oscillations. The team therefore conceived, developed, and tested the first computerized Propulsion Controlled Aircraft (PCA) system. The PCA system takes pilot commands, uses feedback from airplane measurements, and computes commands for the thrust of each engine, yielding much more precise control. Pitch rate and velocity feedback damp the phugoid oscillation, while yaw rate feedback damps the Dutch roll motion. The team tested the PCA system in simulators and conducted flight research in F-15 and MD-11 airplanes. Later, they developed less sophisticated variants of PCA called PCA Lite and PCA Ultralite to make the system cheaper and therefore more attractive to industry. This monograph tells the PCA story in a non- technical way with emphasis on the human aspects of the engineering and flic,ht-research effort. It thereby supplements the extensive technical literature on PCA and makes the development of this technology accessible to a wide audience.

Extended inflation from higher dimensional theories

NASA Technical Reports Server (NTRS)

Holman, Richard; Kolb, Edward W.; Vadas, Sharon L.; Wang, Yun

1990-01-01

The possibility is considered that higher dimensional theories may, upon reduction to four dimensions, allow extended inflation to occur. Two separate models are analayzed. One is a very simple toy model consisting of higher dimensional gravity coupled to a scalar field whose potential allows for a first-order phase transition. The other is a more sophisticated model incorporating the effects of non-trivial field configurations (monopole, Casimir, and fermion bilinear condensate effects) that yield a non-trivial potential for the radius of the internal space. It was found that extended inflation does not occur in these models. It was also found that the bubble nucleation rate in these theories is time dependent unlike the case in the original version of extended inflation.

Examination of polymorphic glutathione S-transferase (GST) genes, tobacco smoking and prostate cancer risk among Men of African Descent: A case-control study

PubMed Central

2009-01-01

Background Polymorphisms in glutathione S-transferase (GST) genes may influence response to oxidative stress and modify prostate cancer (PCA) susceptibility. These enzymes generally detoxify endogenous and exogenous agents, but also participate in the activation and inactivation of oxidative metabolites that may contribute to PCA development. Genetic variations within selected GST genes may influence PCA risk following exposure to carcinogen compounds found in cigarette smoke and decreased the ability to detoxify them. Thus, we evaluated the effects of polymorphic GSTs (M1, T1, and P1) alone and combined with cigarette smoking on PCA susceptibility. Methods In order to evaluate the effects of GST polymorphisms in relation to PCA risk, we used TaqMan allelic discrimination assays along with a multi-faceted statistical strategy involving conventional and advanced statistical methodologies (e.g., Multifactor Dimensionality Reduction and Interaction Graphs). Genetic profiles collected from 873 men of African-descent (208 cases and 665 controls) were utilized to systematically evaluate the single and joint modifying effects of GSTM1 and GSTT1 gene deletions, GSTP1 105 Val and cigarette smoking on PCA risk. Results We observed a moderately significant association between risk among men possessing at least one variant GSTP1 105 Val allele (OR = 1.56; 95%CI = 0.95-2.58; p = 0.049), which was confirmed by MDR permutation testing (p = 0.001). We did not observe any significant single gene effects among GSTM1 (OR = 1.08; 95%CI = 0.65-1.82; p = 0.718) and GSTT1 (OR = 1.15; 95%CI = 0.66-2.02; p = 0.622) on PCA risk among all subjects. Although the GSTM1-GSTP1 pairwise combination was selected as the best two factor LR and MDR models (p = 0.01), assessment of the hierarchical entropy graph suggested that the observed synergistic effect was primarily driven by the GSTP1 Val marker. Notably, the GSTM1-GSTP1 axis did not provide additional information gain when compared to either loci alone based on a hierarchical entropy algorithm and graph. Smoking status did not significantly modify the relationship between the GST SNPs and PCA. Conclusion A moderately significant association was observed between PCA risk and men possessing at least one variant GSTP1 105 Val allele (p = 0.049) among men of African descent. We also observed a 2.1-fold increase in PCA risk associated with men possessing the GSTP1 (Val/Val) and GSTM1 (*1/*1 + *1/*0) alleles. MDR analysis validated these findings; detecting GSTP1 105 Val (p = 0.001) as the best single factor for predicting PCA risk. Our findings emphasize the importance of utilizing a combination of traditional and advanced statistical tools to identify and validate single gene and multi-locus interactions in relation to cancer susceptibility. PMID:19917083

The role of serum neuron-specific enolase in patients with prostate cancer: a systematic review of the recent literature.

PubMed

Muoio, Barbara; Pascale, Mariarosa; Roggero, Enrico

2018-01-01

In this systematic review, we evaluated the value of serum concentrations of neuron-specific enolase (NSE) in patients with prostate cancer (PCa) in order to clarify the possible role of NSE in the diagnosis, management, treatment and monitoring of PCa. A comprehensive search of the recent literature was conducted to find relevant data on the role of NSE in PCa. Two hundred and eighty-two records were revealed, and 19 articles including 1,772 patients with PCa (either confirmed or suspected) were selected. After reviewing the articles, the major result was that elevated serum NSE appears to correlate with prognosis in advanced PCa, particularly in patients with progressive and metastatic castration-resistant PCa. Based on the existing literature, the role of serum NSE in PCa patients should be further evaluated.

Observed light yield of scintillation pixels: Extending the two-ray model

NASA Astrophysics Data System (ADS)

Kantorski, Igor; Jurkowski, Jacek; Drozdowski, Winicjusz

2016-09-01

In this paper we propose an extended, two dimensional model describing the propagation of scintillation photons inside a cuboid crystal until they reach a PMT window. In the simplest approach the model considers two main reasons for light losses: standard absorption obeying the classical Lambert-Beer law and non-ideal reflectivity of the "mummy" covering formed by several layers of Teflon tape wrapping the sample. Results of the model calculations are juxtaposed with experimental data as well as with predictions of an earlier, one dimensional model.

Principal component analysis of physicochemical and sensory characteristics of beef rounds extended with gum arabic from Acacia senegal var. kerensis.

PubMed

Mwove, Johnson K; Gogo, Lilian A; Chikamai, Ben N; Omwamba, Mary; Mahungu, Symon M

2018-03-01

Principal component analysis (PCA) was carried out to study the relationship between 24 meat quality measurements taken from beef round samples that were injected with curing brines containing gum arabic (1%, 1.5%, 2%, 2.5%, and 3%) and soy protein concentrate (SPC) (3.5%) at two injection levels (30% and 35%). The measurements used to describe beef round quality were expressible moisture, moisture content, cook yield, possible injection, achieved gum arabic level in beef round, and protein content, as well as descriptive sensory attributes for flavor, texture, basic tastes, feeling factors, color, and overall acceptability. Several significant correlations were found between beef round quality parameters. The highest significant negative and positive correlations were recorded between color intensity and gray color and between color intensity and brown color, respectively. The first seven principal components (PCs) were extracted explaining over 95% of the total variance. The first PC was characterized by texture attributes (hardness and denseness), feeling factors (chemical taste and chemical burn), and two physicochemical properties (expressible moisture and achieved gum arabic level). Taste attribute (saltiness), physicochemical attributes (cook yield and possible injection), and overall acceptability were useful in defining the second PC, while the third PC was characterized by metallic taste, gray color, brown color, and physicochemical attributes (moisture and protein content). The correlation loading plot showed that the distribution of the samples on the axes of the first two PCs allowed for differentiation of samples injected to 30% injection level which were placed on the upper side of the biplot from those injected to 35% which were placed on the lower side. Similarly, beef samples extended with gum arabic and those containing SPC were also visible when scores for the first and third PCs were plotted. Thus, PCA was efficient in analyzing the quality characteristics of beef rounds extended with gum arabic.

Isolation of candidate genes for apomictic development in buffelgrass (Pennisetum ciliare).

PubMed

Singh, Manjit; Burson, Byron L; Finlayson, Scott A

2007-08-01

Asexual reproduction through seeds, or apomixis, is a process that holds much promise for agricultural advances. However, the molecular mechanisms underlying apomixis are currently poorly understood. To identify genes related to female gametophyte development in apomictic ovaries of buffelgrass (Pennisetum ciliare (L.) Link), Suppression Subtractive Hybridization of ovary cDNA with leaf cDNA was performed. Through macroarray screening of subtracted cDNAs two genes were identified, Pca21 and Pca24, that showed differential expression between apomictic and sexual ovaries. Sequence analysis showed that both Pca21 and Pca24 are novel genes not previously characterized in plants. Pca21 shows homology to two wheat genes that are also expressed during reproductive development. Pca24 has similarity to coiled-coil-helix-coiled-coil-helix (CHCH) domain containing proteins from maize and sugarcane. Northern blot analysis revealed that both of these genes are expressed throughout female gametophyte development in apomictic ovaries. In situ hybridizations localized the transcript of these two genes to the developing embryo sacs in the apomictic ovaries. Based on the expression patterns it was concluded that Pca21 and Pca24 likely play a role during apomictic development in buffelgrass.

Prostate health index and prostate cancer gene 3 score but not percent-free Prostate Specific Antigen have a predictive role in differentiating histological prostatitis from PCa and other nonneoplastic lesions (BPH and HG-PIN) at repeat biopsy.

PubMed

De Luca, Stefano; Passera, Roberto; Fiori, Cristian; Bollito, Enrico; Cappia, Susanna; Mario Scarpa, Roberto; Sottile, Antonino; Franco Randone, Donato; Porpiglia, Francesco

2015-10-01

To determine if prostate health index (PHI), prostate cancer antigen gene 3 (PCA3) score, and percentage of free prostate-specific antigen (%fPSA) may be used to differentiate asymptomatic acute and chronic prostatitis from prostate cancer (PCa), benign prostatic hyperplasia (BPH), and high-grade prostate intraepithelial neoplasia (HG-PIN) in patients with elevated PSA levels and negative findings on digital rectal examination at repeat biopsy (re-Bx). In this prospective study, 252 patients were enrolled, undergoing PHI, PCA3 score, and %fPSA assessments before re-Bx. We used 3 multivariate logistic regression models to test the PHI, PCA3 score, and %fPSA as risk factors for prostatitis vs. PCa, vs. BPH, and vs. HG-PIN. All the analyses were performed for the whole patient cohort and for the "gray zone" of PSA (4-10ng/ml) cohort (171 individuals). Of the 252 patients, 43 (17.1%) had diagnosis of PCa. The median PHI was significantly different between men with a negative biopsy and those with a positive biopsy (34.9 vs. 48.1, P<0.001), as for the PCA3 score (24 vs. 54, P<0.001) and %fPSA (11.8% vs. 15.8%, P = 0.012). The net benefit of using PCA3 and PHI to differentiate prostatitis and PCa was moderate, although it extended to a good range of threshold probabilities (40%-100%), whereas that from using %fPSA was negligible: this pattern was reported for the whole population as for the "gray zone" PSA cohort. In front of a good diagnostic performance of all the 3 biomarkers in distinguishing negative biopsy vs. positive biopsy, the clinical benefit of using the PCA3 score and PHI to estimate prostatitis vs. PCa was comparable. PHI was the only determinant for prostatitis vs. BPH, whereas no biomarkers could differentiate prostate inflammation from HG-PIN. Copyright © 2015 Elsevier Inc. All rights reserved.

Differential resistances to anthracnose in Capsicum baccatum as responding to two Colletotrichum pathotypes and inoculation methods.

PubMed

Mahasuk, Pitchayapa; Chinthaisong, Jittima; Mongkolporn, Orarat

2013-09-01

Chili anthracnose, caused by Colletotrichum spp., is one of the major diseases to chili production in the tropics and subtropics worldwide. Breeding for durable anthracnose resistance requires a good understanding of the resistance mechanisms to different pathotypes and inoculation methods. This study aimed to investigate the inheritances of differential resistances as responding to two different Colletotrichum pathotypes, PCa2 and PCa3 and as by two different inoculation methods, microinjection (MI) and high pressure spray (HP). Detached ripe fruit of Capsicum baccatum 'PBC80' derived F2 and BC1s populations was assessed for anthracnose resistance. Two dominant genes were identified responsible for the differential resistance to anthracnose. One was responsible for the resistance to PCa2 and PCa3 by MI and the other was responsible for the resistance to PCa3 by HP. The two genes were linked with 16.7 cM distance.

Differential resistances to anthracnose in Capsicum baccatum as responding to two Colletotrichum pathotypes and inoculation methods

PubMed Central

Mahasuk, Pitchayapa; Chinthaisong, Jittima; Mongkolporn, Orarat

2013-01-01

Chili anthracnose, caused by Colletotrichum spp., is one of the major diseases to chili production in the tropics and subtropics worldwide. Breeding for durable anthracnose resistance requires a good understanding of the resistance mechanisms to different pathotypes and inoculation methods. This study aimed to investigate the inheritances of differential resistances as responding to two different Colletotrichum pathotypes, PCa2 and PCa3 and as by two different inoculation methods, microinjection (MI) and high pressure spray (HP). Detached ripe fruit of Capsicum baccatum ‘PBC80’ derived F2 and BC1s populations was assessed for anthracnose resistance. Two dominant genes were identified responsible for the differential resistance to anthracnose. One was responsible for the resistance to PCa2 and PCa3 by MI and the other was responsible for the resistance to PCa3 by HP. The two genes were linked with 16.7 cM distance. PMID:24273429

Incidental prostate cancer in radical cystoprostatectomy specimens.

PubMed

Jin, Xiao-Dong; Chen, Zhao-Dian; Wang, Bo; Cai, Song-Liang; Yao, Xiao-Lin; Jin, Bai-Ye

2008-09-01

To investigate the rates of prostate cancer (PCa) in radical cystoprostatectomy (RCP) specimens for bladder cancer in mainland China. To determine the follow-up outcome of patients with two concurrent cancers and identify whether prostate-specific antigen (PSA) is a useful tool for the detection of PCa prior to surgery. From January 2002 to January 2007, 264 male patients with bladder cancer underwent RCP at our center. All patients underwent digital rectal examination (DRE) and B ultrasound. Serum PSA levels were tested in 168 patients. None of the patients had any evidence of PCa before RCP. Entire prostates were embedded and sectioned at 5 mm intervals. Incidental PCa was observed in 37 of 264 (14.0%) RCP specimens. Of these, 12 (32.4%) were clinically significant according to an accepted definition. The PSA levels were not significantly different between patients with PCa and those without PCa, nor between patients with significant PCa and those with insignificant PCa. Thirty-four patients with incidental PCa were followed up. During a mean follow-up period of 26 months, two patients with PSA > 4 ng/mL underwent castration. None of the patients died of PCa. The incidence of PCa in RCP specimens in mainland China is lower than that in most developed countries. PSA cannot identify asymptomatic PCa prior to RCP. In line with published reports, incidental PCa does not impact the prognosis of bladder cancer patients undergoing RCP. (c) 2008, Asian Journal of Andrology, SIMM and SJTU. All rights reserved.

An Exploratory Study on Using Principal-Component Analysis and Confirmatory Factor Analysis to Identify Bolt-On Dimensions: The EQ-5D Case Study.

PubMed

Finch, Aureliano Paolo; Brazier, John Edward; Mukuria, Clara; Bjorner, Jakob Bue

2017-12-01

Generic preference-based measures such as the EuroQol five-dimensional questionnaire (EQ-5D) are used in economic evaluation, but may not be appropriate for all conditions. When this happens, a possible solution is adding bolt-ons to expand their descriptive systems. Using review-based methods, studies published to date claimed the relevance of bolt-ons in the presence of poor psychometric results. This approach does not identify the specific dimensions missing from the Generic preference-based measure core descriptive system, and is inappropriate for identifying dimensions that might improve the measure generically. This study explores the use of principal-component analysis (PCA) and confirmatory factor analysis (CFA) for bolt-on identification in the EQ-5D. Data were drawn from the international Multi-Instrument Comparison study, which is an online survey on health and well-being measures in five countries. Analysis was based on a pool of 92 items from nine instruments. Initial content analysis provided a theoretical framework for PCA results interpretation and CFA model development. PCA was used to investigate the underlining dimensional structure and whether EQ-5D items were represented in the identified constructs. CFA was used to confirm the structure. CFA was cross-validated in random halves of the sample. PCA suggested a nine-component solution, which was confirmed by CFA. This included psychological symptoms, physical functioning, and pain, which were covered by the EQ-5D, and satisfaction, speech/cognition,relationships, hearing, vision, and energy/sleep which were not. These latter factors may represent relevant candidate bolt-ons. PCA and CFA appear useful methods for identifying potential bolt-ons dimensions for an instrument such as the EQ-5D. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Ionospheric Anomalies Related to the (M = 7.3), August 27, 2012, Puerto Earthquake, (M = 6.8), August 30, 2012 Jan Mayen Island Earthquake, and (M = 7.6), August 31, 2012, Philippines Earthquake: Two-Dimensional Principal Component Analysis

PubMed Central

Lin, Jyh-Woei

2013-01-01

Two-dimensional principal component analysis (2DPCA) and principal component analysis (PCA) are used to examine the ionospheric total electron content (TEC) data during the time period from 00:00 on August 21 to 12: 45 on August 31 (UT), which are 10 days before the M = 7.6 Philippines earthquake at 12:47:34 on August 31, 2012 (UT) with the depth at 34.9 km. From the results by using 2DPCA, a TEC precursor of Philippines earthquake is found during the time period from 4:25 to 4:40 on August 28, 2012 (UT) with the duration time of at least 15 minutes. Another earthquake-related TEC anomaly is detectable for the time period from 04:35 to 04:40 on August 27, 2012 (UT) with the duration time of at least 5 minutes during the Puerto earthquake at 04: 37:20 on August 27, 2012 (UT) (M w = 7.3) with the depth at 20.3 km. The precursor of the Puerto earthquake is not detectable. TEC anomaly is not to be found related to the Jan Mayen Island earthquake (M w = 6.8) at 13:43:24 on August 30, 2012 (UT). These earthquake-related TEC anomalies are detectable by using 2DPCA rather than PCA. They are localized nearby the epicenters of the Philippines and Puerto earthquakes. PMID:23844386

Modeling personnel turnover in the parametric organization

NASA Technical Reports Server (NTRS)

Dean, Edwin B.

1991-01-01

A model is developed for simulating the dynamics of a newly formed organization, credible during all phases of organizational development. The model development process is broken down into the activities of determining the tasks required for parametric cost analysis (PCA), determining the skills required for each PCA task, determining the skills available in the applicant marketplace, determining the structure of the model, implementing the model, and testing it. The model, parameterized by the likelihood of job function transition, has demonstrated by the capability to represent the transition of personnel across functional boundaries within a parametric organization using a linear dynamical system, and the ability to predict required staffing profiles to meet functional needs at the desired time. The model can be extended by revisions of the state and transition structure to provide refinements in functional definition for the parametric and extended organization.

Identification of depth information with stereoscopic mammography using different display methods

NASA Astrophysics Data System (ADS)

Morikawa, Takamitsu; Kodera, Yoshie

2013-03-01

Stereoscopy in radiography was widely used in the late 80's because it could be used for capturing complex structures in the human body, thus proving beneficial for diagnosis and screening. When radiologists observed the images stereoscopically, radiologists usually needed the training of their eyes in order to perceive the stereoscopic effect. However, with the development of three-dimensional (3D) monitors and their use in the medical field, only a visual inspection is no longer required in the medical field. The question then arises as to whether there is any difference in recognizing depth information when using conventional methods and that when using a 3D monitor. We constructed a phantom and evaluated the difference in capacity to identify the depth information between the two methods. The phantom consists of acryl steps and 3mm diameter acryl pillars on the top and bottom of each step. Seven observers viewed these images stereoscopically using the two display methods and were asked to judge the direction of the pillar that was on the top. We compared these judged direction with the direction of the real pillar arranged on the top, and calculated the percentage of correct answerers (PCA). The results showed that PCA obtained using the 3D monitor method was higher PCA by about 5% than that obtained using the naked-eye method. This indicated that people could view images stereoscopically more precisely using the 3D monitor method than when using with conventional methods, like the crossed or parallel eye viewing. We were able to estimate the difference in capacity to identify the depth information between the two display methods.

Landslides Identification Using Airborne Laser Scanning Data Derived Topographic Terrain Attributes and Support Vector Machine Classification

NASA Astrophysics Data System (ADS)

Pawłuszek, Kamila; Borkowski, Andrzej

2016-06-01

Since the availability of high-resolution Airborne Laser Scanning (ALS) data, substantial progress in geomorphological research, especially in landslide analysis, has been carried out. First and second order derivatives of Digital Terrain Model (DTM) have become a popular and powerful tool in landslide inventory mapping. Nevertheless, an automatic landslide mapping based on sophisticated classifiers including Support Vector Machine (SVM), Artificial Neural Network or Random Forests is often computationally time consuming. The objective of this research is to deeply explore topographic information provided by ALS data and overcome computational time limitation. For this reason, an extended set of topographic features and the Principal Component Analysis (PCA) were used to reduce redundant information. The proposed novel approach was tested on a susceptible area affected by more than 50 landslides located on Rożnów Lake in Carpathian Mountains, Poland. The initial seven PCA components with 90% of the total variability in the original topographic attributes were used for SVM classification. Comparing results with landslide inventory map, the average user's accuracy (UA), producer's accuracy (PA), and overall accuracy (OA) were calculated for two models according to the classification results. Thereby, for the PCA-feature-reduced model UA, PA, and OA were found to be 72%, 76%, and 72%, respectively. Similarly, UA, PA, and OA in the non-reduced original topographic model, was 74%, 77% and 74%, respectively. Using the initial seven PCA components instead of the twenty original topographic attributes does not significantly change identification accuracy but reduce computational time.

Duke Workshop on High-Dimensional Data Sensing and Analysis

DTIC Science & Technology

2015-05-06

Bayesian sparse factor analysis formulation of Chen et al . ( 2011 ) this work develops multi-label PCA (MLPCA), a generative dimension reduction...version of this problem was recently treated by Banerjee et al . [1], Ravikumar et al . [2], Kolar and Xing [3], and Ho ̈fling and Tibshirani [4]. As...Not applicable. Final Report Duke Workshop on High-Dimensional Data Sensing and Analysis Workshop Dates: July 26-28, 2011

Extending the 2 x 2 Achievement Goal Framework: Development of a Measure of Scientific Achievement Goals

ERIC Educational Resources Information Center

Deemer, Eric D.; Carter, Alice P.; Lobrano, Michael T.

2010-01-01

The current research sought to extend the 2 x 2 achievement goal framework by developing and testing the Achievement Goals for Research Scale (AGRS). Participants (N = 317) consisted of graduate students in the life, physical, and behavioral sciences. A principal components analysis (PCA) extracted five components accounting for 72.59% of the…

A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets.

PubMed

Li, Der-Chiang; Liu, Chiao-Wen; Hu, Susan C

2011-05-01

Medical data sets are usually small and have very high dimensionality. Too many attributes will make the analysis less efficient and will not necessarily increase accuracy, while too few data will decrease the modeling stability. Consequently, the main objective of this study is to extract the optimal subset of features to increase analytical performance when the data set is small. This paper proposes a fuzzy-based non-linear transformation method to extend classification related information from the original data attribute values for a small data set. Based on the new transformed data set, this study applies principal component analysis (PCA) to extract the optimal subset of features. Finally, we use the transformed data with these optimal features as the input data for a learning tool, a support vector machine (SVM). Six medical data sets: Pima Indians' diabetes, Wisconsin diagnostic breast cancer, Parkinson disease, echocardiogram, BUPA liver disorders dataset, and bladder cancer cases in Taiwan, are employed to illustrate the approach presented in this paper. This research uses the t-test to evaluate the classification accuracy for a single data set; and uses the Friedman test to show the proposed method is better than other methods over the multiple data sets. The experiment results indicate that the proposed method has better classification performance than either PCA or kernel principal component analysis (KPCA) when the data set is small, and suggest creating new purpose-related information to improve the analysis performance. This paper has shown that feature extraction is important as a function of feature selection for efficient data analysis. When the data set is small, using the fuzzy-based transformation method presented in this work to increase the information available produces better results than the PCA and KPCA approaches. Copyright © 2011 Elsevier B.V. All rights reserved.

Scalable Robust Principal Component Analysis Using Grassmann Averages.

PubMed

Hauberg, Sren; Feragen, Aasa; Enficiaud, Raffi; Black, Michael J

2016-11-01

In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average ( GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average ( TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

The Hydrodynamic Study of the Swimming Gliding: a Two-Dimensional Computational Fluid Dynamics (CFD) Analysis.

PubMed

Marinho, Daniel A; Barbosa, Tiago M; Rouboa, Abel I; Silva, António J

2011-09-01

Nowadays the underwater gliding after the starts and the turns plays a major role in the overall swimming performance. Hence, minimizing hydrodynamic drag during the underwater phases should be a main aim during swimming. Indeed, there are several postures that swimmers can assume during the underwater gliding, although experimental results were not conclusive concerning the best body position to accomplish this aim. Therefore, the purpose of this study was to analyse the effect in hydrodynamic drag forces of using different body positions during gliding through computational fluid dynamics (CFD) methodology. For this purpose, two-dimensional models of the human body in steady flow conditions were studied. Two-dimensional virtual models had been created: (i) a prone position with the arms extended at the front of the body; (ii) a prone position with the arms placed alongside the trunk; (iii) a lateral position with the arms extended at the front and; (iv) a dorsal position with the arms extended at the front. The drag forces were computed between speeds of 1.6 m/s and 2 m/s in a two-dimensional Fluent(®) analysis. The positions with the arms extended at the front presented lower drag values than the position with the arms aside the trunk. The lateral position was the one in which the drag was lower and seems to be the one that should be adopted during the gliding after starts and turns.

Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods.

PubMed

Balouchestani, Mohammadreza; Krishnan, Sridhar

2014-01-01

Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.

3D Porous Chitosan-Alginate Scaffolds as an In Vitro Model for Evaluating Nanoparticle-Mediated Tumor Targeting and Gene Delivery to Prostate Cancer.

PubMed

Wang, Kui; Kievit, Forrest M; Florczyk, Stephen J; Stephen, Zachary R; Zhang, Miqin

2015-10-12

Cationic nanoparticles (NPs) for targeted gene delivery are conventionally evaluated using 2D in vitro cultures. However, this does not translate well to corresponding in vivo studies because of the marked difference in NP behavior in the presence of the tumor microenvironment. In this study, we investigated whether prostate cancer (PCa) cells cultured in three-dimensional (3D) chitosan-alginate (CA) porous scaffolds could model cationic NP-mediated gene targeted delivery to tumors in vitro. We assessed in vitro tumor cell proliferation, formation of tumor spheroids, and expression of marker genes that promote tumor malignancy in CA scaffolds. The efficacy of NP-targeted gene delivery was evaluated in PCa cells in 2D cultures, PCa tumor spheroids grown in CA scaffolds, and PCa tumors in a mouse TRAMP-C2 flank tumor model. PCa cells cultured in CA scaffolds grew into tumor spheroids and displayed characteristics of higher malignancy as compared to those in 2D cultures. Significantly, targeted gene delivery was only observed in cells cultured in CA scaffolds, whereas cells cultured on 2D plates showed no difference in gene delivery between targeted and nontarget control NPs. In vivo NP evaluation confirmed targeted gene delivery, indicating that only CA scaffolds correctly modeled NP-mediated targeted delivery in vivo. These findings suggest that CA scaffolds serve as a better in vitro platform than 2D cultures for evaluation of NP-mediated targeted gene delivery to PCa.

Development of new two-dimensional spectral/spatial code based on dynamic cyclic shift code for OCDMA system

NASA Astrophysics Data System (ADS)

Jellali, Nabiha; Najjar, Monia; Ferchichi, Moez; Rezig, Houria

2017-07-01

In this paper, a new two-dimensional spectral/spatial codes family, named two dimensional dynamic cyclic shift codes (2D-DCS) is introduced. The 2D-DCS codes are derived from the dynamic cyclic shift code for the spectral and spatial coding. The proposed system can fully eliminate the multiple access interference (MAI) by using the MAI cancellation property. The effect of shot noise, phase-induced intensity noise and thermal noise are used to analyze the code performance. In comparison with existing two dimensional (2D) codes, such as 2D perfect difference (2D-PD), 2D Extended Enhanced Double Weight (2D-Extended-EDW) and 2D hybrid (2D-FCC/MDW) codes, the numerical results show that our proposed codes have the best performance. By keeping the same code length and increasing the spatial code, the performance of our 2D-DCS system is enhanced: it provides higher data rates while using lower transmitted power and a smaller spectral width.

Predictive spectroscopy and chemical imaging based on novel optical systems

NASA Astrophysics Data System (ADS)

Nelson, Matthew Paul

1998-10-01

This thesis describes two futuristic optical systems designed to surpass contemporary spectroscopic methods for predictive spectroscopy and chemical imaging. These systems are advantageous to current techniques in a number of ways including lower cost, enhanced portability, shorter analysis time, and improved S/N. First, a novel optical approach to predicting chemical and physical properties based on principal component analysis (PCA) is proposed and evaluated. A regression vector produced by PCA is designed into the structure of a set of paired optical filters. Light passing through the paired filters produces an analog detector signal directly proportional to the chemical/physical property for which the regression vector was designed. Second, a novel optical system is described which takes a single-shot approach to chemical imaging with high spectroscopic resolution using a dimension-reduction fiber-optic array. Images are focused onto a two- dimensional matrix of optical fibers which are drawn into a linear distal array with specific ordering. The distal end is imaged with a spectrograph equipped with an ICCD camera for spectral analysis. Software is used to extract the spatial/spectral information contained in the ICCD images and deconvolute them into wave length-specific reconstructed images or position-specific spectra which span a multi-wavelength space. This thesis includes a description of the fabrication of two dimension-reduction arrays as well as an evaluation of the system for spatial and spectral resolution, throughput, image brightness, resolving power, depth of focus, and channel cross-talk. PCA is performed on the images by treating rows of the ICCD images as spectra and plotting the scores of each PC as a function of reconstruction position. In addition, iterative target transformation factor analysis (ITTFA) is performed on the spectroscopic images to generate ``true'' chemical maps of samples. Univariate zero-order images, univariate first-order spectroscopic images, bivariate first-order spectroscopic images, and multivariate first-order spectroscopic images of the temporal development of laser-induced plumes are presented and interpreted. Reconstructed chemical images generated using bivariate and trivariate wavelength techniques, bimodal and trimodal PCA methods, and bimodal and trimodal ITTFA approaches are also included.

Columnar organization of orientation domains in V1

NASA Astrophysics Data System (ADS)

Liedtke, Joscha; Wolf, Fred

In the primary visual cortex (V1) of primates and carnivores, the functional architecture of basic stimulus selectivities appears similar across cortical layers (Hubel & Wiesel, 1962) justifying the use of two-dimensional cortical models and disregarding organization in the third dimension. Here we show theoretically that already small deviations from an exact columnar organization lead to non-trivial three-dimensional functional structures. We extend two-dimensional random field models (Schnabel et al., 2007) to a three-dimensional cortex by keeping a typical scale in each layer and introducing a correlation length in the third, columnar dimension. We examine in detail the three-dimensional functional architecture for different cortical geometries with different columnar correlation lengths. We find that (i) topological defect lines are generally curved and (ii) for large cortical curvatures closed loops and reconnecting topological defect lines appear. This theory extends the class of random field models by introducing a columnar dimension and provides a systematic statistical assessment of the three-dimensional functional architecture of V1 (see also (Tanaka et al., 2011)).

Demixed principal component analysis of neural population data.

PubMed

Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K

2016-04-12

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

Fast and Accurate Radiative Transfer Calculations Using Principal Component Analysis for (Exo-)Planetary Retrieval Models

NASA Astrophysics Data System (ADS)

Kopparla, P.; Natraj, V.; Shia, R. L.; Spurr, R. J. D.; Crisp, D.; Yung, Y. L.

2015-12-01

Radiative transfer (RT) computations form the engine of atmospheric retrieval codes. However, full treatment of RT processes is computationally expensive, prompting usage of two-stream approximations in current exoplanetary atmospheric retrieval codes [Line et al., 2013]. Natraj et al. [2005, 2010] and Spurr and Natraj [2013] demonstrated the ability of a technique using principal component analysis (PCA) to speed up RT computations. In the PCA method for RT performance enhancement, empirical orthogonal functions are developed for binned sets of inherent optical properties that possess some redundancy; costly multiple-scattering RT calculations are only done for those few optical states corresponding to the most important principal components, and correction factors are applied to approximate radiation fields. Kopparla et al. [2015, in preparation] extended the PCA method to a broadband spectral region from the ultraviolet to the shortwave infrared (0.3-3 micron), accounting for major gas absorptions in this region. Here, we apply the PCA method to a some typical (exo-)planetary retrieval problems. Comparisons between the new model, called Universal Principal Component Analysis Radiative Transfer (UPCART) model, two-stream models and line-by-line RT models are performed, for spectral radiances, spectral fluxes and broadband fluxes. Each of these are calculated at the top of the atmosphere for several scenarios with varying aerosol types, extinction and scattering optical depth profiles, and stellar and viewing geometries. We demonstrate that very accurate radiance and flux estimates can be obtained, with better than 1% accuracy in all spectral regions and better than 0.1% in most cases, as compared to a numerically exact line-by-line RT model. The accuracy is enhanced when the results are convolved to typical instrument resolutions. The operational speed and accuracy of UPCART can be further improved by optimizing binning schemes and parallelizing the codes, work on which is under way.

Comparison of generated parallel capillary arrays to three-dimensional reconstructed capillary networks in modeling oxygen transport in discrete microvascular volumes.

PubMed

Fraser, Graham M; Goldman, Daniel; Ellis, Christopher G

2013-11-01

We compare RMN to PCA under several simulated physiological conditions to determine how the use of different vascular geometry affects oxygen transport solutions. Three discrete networks were reconstructed from intravital video microscopy of rat skeletal muscle (84 × 168 × 342 μm, 70 × 157 × 268 μm, and 65 × 240 × 571 μm), and hemodynamic measurements were made in individual capillaries. PCAs were created based on statistical measurements from RMNs. Blood flow and O₂ transport models were applied, and the resulting solutions for RMN and PCA models were compared under four conditions (rest, exercise, ischemia, and hypoxia). Predicted tissue PO₂ was consistently lower in all RMN simulations compared to the paired PCA. PO₂ for 3D reconstructions at rest were 28.2 ± 4.8, 28.1 ± 3.5, and 33.0 ± 4.5 mmHg for networks I, II, and III compared to the PCA mean values of 31.2 ± 4.5, 30.6 ± 3.4, and 33.8 ± 4.6 mmHg. Simulated exercise yielded mean tissue PO₂ in the RMN of 10.1 ± 5.4, 12.6 ± 5.7, and 19.7 ± 5.7 mmHg compared to 15.3 ± 7.3, 18.8 ± 5.3, and 21.7 ± 6.0 in PCA. These findings suggest that volume matched PCA yield different results compared to reconstructed microvascular geometries when applied to O₂ transport modeling; the predominant characteristic of this difference being an over estimate of mean tissue PO₂. Despite this limitation, PCA models remain important for theoretical studies as they produce PO₂ distributions with similar shape and parameter dependence as RMN. © 2013 John Wiley & Sons Ltd.

The sulcus line of the trochlear groove is more accurate than Whiteside's Line in determining femoral component rotation.

PubMed

Talbot, Simon; Dimitriou, Pandelis; Radic, Ross; Zordan, Rachel; Bartlett, John

2015-11-01

The sulcus line (SL) is a three-dimensional curve produced from multiple points along the trochlear groove. Whiteside's Line, also known as the anteroposterior axis (APA), is derived from single anterior and posterior points. The purposes of the two studies presented in this paper are to (1) assess the results from the clinical use of the SL in a large clinical series, (2) measure the SL and the APA on three-dimensional CT reconstructions, (3) demonstrate the effect of parallax error on the use of the APA and (4) determine the accuracy of an axis derived by combining the SL and the posterior condylar axis (PCA). In the first study, we assessed the SL using a large, single surgeon series of consecutive patients undergoing primary total knee arthroplasties. The post-operative CT scans of patients (n = 200) were examined to determine the final rotational alignment of the femoral component. In the second study, measurements were taken in a series of 3DCT reconstructions of osteoarthritic knees (n = 44). The mean position of the femoral component in the clinical series was 0.6° externally rotated to the surgical epicondylar axis, with a standard deviation of 2.9° (ranges from -7.2° to 6.7°). On the 3DCT reconstructions, the APA (88.2° ± 4.2°) had significantly higher variance than the SL (90.3° ± 2.7°) (F = 5.82 and p = 0.017). An axis derived by averaging the SL and the PCA+3° produced a significant decrease in both the number of outliers (p = 0.03 vs. PCA and p = 0.007 vs. SL) and the variance (F = 6.15 and p = 0.015 vs. SL). The coronal alignment of the SL varied widely relative to the mechanical axis (0.4° ± 3.8°) and the distal condylar surface (2.6° ± 4.3°). The multiple points used to determine the SL confer anatomical and geometrical advantages, and therefore, it should be considered a separate rotational landmark to the APA. These findings may explain the high degree of variability in the measurement of the APA which is documented in the literature. Combining a geometrically correct SL and the PCA is likely to further improve accuracy.

A variational principle for compressible fluid mechanics: Discussion of the multi-dimensional theory

NASA Technical Reports Server (NTRS)

Prozan, R. J.

1982-01-01

The variational principle for compressible fluid mechanics previously introduced is extended to two dimensional flow. The analysis is stable, exactly conservative, adaptable to coarse or fine grids, and very fast. Solutions for two dimensional problems are included. The excellent behavior and results lend further credence to the variational concept and its applicability to the numerical analysis of complex flow fields.

Pain Levels Within 24 Hours After UFE: A Comparison of Morphine and Fentanyl Patient-Controlled Analgesia

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

Kim, Hyun S., E-mail: sikhkim@jhmi.edu; Czuczman, Gregory J.; Nicholson, Wanda K.

The purpose of this study was to assess the presence and severity of pain levels during 24 h after uterine fibroid embolization (UFE) for symptomatic leiomyomata and compare the effectiveness and adverse effects of morphine patient-controlled analgesia (PCA) versus fentanyl PCA. We carried out a prospective, nonrandomized study of 200 consecutive women who received UFE and morphine or fentanyl PCA after UFE. Pain perception levels were obtained on a 0-10 scale for the 24-h period after UFE. Linear regression methods were used to determine pain trends and differences in pain trends between two groups and the association between pain scoresmore » and patient covariates. One hundred eighty-five patients (92.5%) reported greater-than-baseline pain after UFE, and 198 patients (99%) required IV opioid PCA. One hundred thirty-six patients (68.0%) developed nausea during the 24-h period. Seventy-two patients (36%) received morphine PCA and 128 (64%) received fentanyl PCA, without demographic differences. The mean dose of morphine used was 33.8 {+-} 26.7 mg, while the mean dose of fentanyl was 698.7 {+-} 537.4 {mu}g. Using this regimen, patients who received morphine PCA had significantly lower pain levels than those who received fentanyl PCA (p < 0.0001). We conclude that patients develop pain requiring IV opioid PCA within 24 h after UFE. Morphine PCA is more effective in reducing post-uterine artery embolization pain than fentanyl PCA. Nausea is a significant adverse effect from opioid PCA.« less

An In Vitro Spectroscopic Analysis to Determine Whether para-Chloroaniline is Produced from Mixing Sodium Hypochlorite and Chlorhexidine

PubMed Central

Thomas, John E.; Sem, Daniel S.

2009-01-01

Introduction The purpose of this in vitro study was to determine whether para-chloroaniline (PCA) is formed through the reaction of mixing sodium hypochlorite (NaOCl) and chlorhexidine (CHX). Methods Initially commercially available samples of chlorhexidine acetate (CHXa) and PCA were analyzed with 1H NMR spectroscopy. Two solutions, NaOCl and CHXa, were warmed to 37°C and when mixed they produced a brown precipitate. This precipitate was separated in half and pure PCA was added to one of the samples for comparison before they were each analyzed with 1H NMR spectroscopy. Results The peaks in the 1H NMR spectra of CHXa and PCA were assigned to specific protons of the molecules, and the location of the aromatic peaks in the PCA spectrum defined the PCA doublet region. While the spectrum of the precipitate alone resulted in a complex combination of peaks, upon magnification there were no peaks in the PCA doublet region which were intense enough to be quantified. In the spectrum of the precipitate, to which PCA was added, two peaks do appear in the PCA doublet region. Comparing this spectrum to that of precipitate alone, the peaks in the PCA doublet region are not visible prior to the addition of PCA. Conclusions Based on this in vitro study, the reaction mixture of NaOCl and CHXa does not produce PCA at any measurable quantity and further investigation is needed to determine the chemical composition of the brown precipitate. PMID:20113799

Two-step relaxation mode analysis with multiple evolution times applied to all-atom molecular dynamics protein simulation.

PubMed

Karasawa, N; Mitsutake, A; Takano, H

2017-12-01

Proteins implement their functionalities when folded into specific three-dimensional structures, and their functions are related to the protein structures and dynamics. Previously, we applied a relaxation mode analysis (RMA) method to protein systems; this method approximately estimates the slow relaxation modes and times via simulation and enables investigation of the dynamic properties underlying the protein structural fluctuations. Recently, two-step RMA with multiple evolution times has been proposed and applied to a slightly complex homopolymer system, i.e., a single [n]polycatenane. This method can be applied to more complex heteropolymer systems, i.e., protein systems, to estimate the relaxation modes and times more accurately. In two-step RMA, we first perform RMA and obtain rough estimates of the relaxation modes and times. Then, we apply RMA with multiple evolution times to a small number of the slowest relaxation modes obtained in the previous calculation. Herein, we apply this method to the results of principal component analysis (PCA). First, PCA is applied to a 2-μs molecular dynamics simulation of hen egg-white lysozyme in aqueous solution. Then, the two-step RMA method with multiple evolution times is applied to the obtained principal components. The slow relaxation modes and corresponding relaxation times for the principal components are much improved by the second RMA.

Two-step relaxation mode analysis with multiple evolution times applied to all-atom molecular dynamics protein simulation

NASA Astrophysics Data System (ADS)

Karasawa, N.; Mitsutake, A.; Takano, H.

2017-12-01

Proteins implement their functionalities when folded into specific three-dimensional structures, and their functions are related to the protein structures and dynamics. Previously, we applied a relaxation mode analysis (RMA) method to protein systems; this method approximately estimates the slow relaxation modes and times via simulation and enables investigation of the dynamic properties underlying the protein structural fluctuations. Recently, two-step RMA with multiple evolution times has been proposed and applied to a slightly complex homopolymer system, i.e., a single [n ] polycatenane. This method can be applied to more complex heteropolymer systems, i.e., protein systems, to estimate the relaxation modes and times more accurately. In two-step RMA, we first perform RMA and obtain rough estimates of the relaxation modes and times. Then, we apply RMA with multiple evolution times to a small number of the slowest relaxation modes obtained in the previous calculation. Herein, we apply this method to the results of principal component analysis (PCA). First, PCA is applied to a 2-μ s molecular dynamics simulation of hen egg-white lysozyme in aqueous solution. Then, the two-step RMA method with multiple evolution times is applied to the obtained principal components. The slow relaxation modes and corresponding relaxation times for the principal components are much improved by the second RMA.

Design of a rotational three-dimensional nonimaging device by a compensated two-dimensional design process.

PubMed

Yang, Yi; Qian, Ke-Yuan; Luo, Yi

2006-07-20

A compensation process has been developed to design rotational three-dimensional (3D) nonimaging devices. By compensating the desired light distribution during a two-dimensional (2D) design process for an extended Lambertian source using a compensation coefficient, the meridian plane of a 3D device with good performance can be obtained. This method is suitable in many cases with fast calculation speed. Solutions to two kinds of optical design problems have been proposed, and the limitation of this compensated 2D design method is discussed.

GENERAL: Scattering Phase Correction for Semiclassical Quantization Rules in Multi-Dimensional Quantum Systems

NASA Astrophysics Data System (ADS)

Huang, Wen-Min; Mou, Chung-Yu; Chang, Cheng-Hung

2010-02-01

While the scattering phase for several one-dimensional potentials can be exactly derived, less is known in multi-dimensional quantum systems. This work provides a method to extend the one-dimensional phase knowledge to multi-dimensional quantization rules. The extension is illustrated in the example of Bogomolny's transfer operator method applied in two quantum wells bounded by step potentials of different heights. This generalized semiclassical method accurately determines the energy spectrum of the systems, which indicates the substantial role of the proposed phase correction. Theoretically, the result can be extended to other semiclassical methods, such as Gutzwiller trace formula, dynamical zeta functions, and semiclassical Landauer-Büttiker formula. In practice, this recipe enhances the applicability of semiclassical methods to multi-dimensional quantum systems bounded by general soft potentials.

Extended self-similarity in the two-dimensional metal-insulator transition

NASA Astrophysics Data System (ADS)

Moriconi, L.

2003-09-01

We show that extended self-similarity, a scaling phenomenon first observed in classical turbulent flows, holds for a two-dimensional metal-insulator transition that belongs to the universality class of random Dirac fermions. Deviations from multifractality, which in turbulence are due to the dominance of diffusive processes at small scales, appear in the condensed-matter context as a large-scale, finite-size effect related to the imposition of an infrared cutoff in the field theory formulation. We propose a phenomenological interpretation of extended self-similarity in the metal-insulator transition within the framework of the random β-model description of multifractal sets. As a natural step, our discussion is bridged to the analysis of strange attractors, where crossovers between multifractal and nonmultifractal regimes are found and extended self-similarity turns out to be verified as well.

Primary cutaneous amyloidosis associated with autoimmune hepatitis-primary biliary cirrhosis overlap syndrome and Sjögren syndrome

PubMed Central

Yan, Xin; Jin, Jinglan

2018-01-01

Abstract Rationale: Primary cutaneous amyloidosis (PCA) is a localized skin disorder characterized by the abnormal deposition of amyloid in the extracellular matrix of the dermis. The association between PCA and other diseases, although rare, has been documented for various autoimmune diseases. PCA associated with autoimmune hepatitis-primary biliary cirrhosis (AIH-PBC) overlap syndrome and Sjögren syndrome (SS) has not been previously reported in the literature. Patient concerns: A 50-year-old woman presented with progressive abnormal liver enzyme levels and was referred to our department. Diagnoses: Due to the patient's symptoms, laboratory test results, radiographic findings, and pathologic results, she was diagnosed with PCA associated with AIH-PBC overlap syndrome and SS. Interventions: She was subsequently treated with a combination of ursodeoxycholic acid (UDCA), prednisone, and azathioprine. Outcomes: While this treatment can achieve therapeutic success, it cannot prevent complications from cirrhosis. This patient remains alive but experienced an emergent gastrointestinal hemorrhage. Lessons: While we acknowledge that this is a single case, these findings extend our knowledge of immunological diseases associated with PCA and suggest a common, immune-mediated pathogenic pathway between PCA, AIH-PBC overlap syndrome, and SS. After 12 years of follow up, clinical manifestations have developed, and these autoimmune diseases have progressed. The combination of UDCA, prednisone, and azathioprine can achieve therapeutic success but cannot prevent disease progression. Routine follow up for this patient is necessary to document disease progression. PMID:29465536

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.

Modeling Pair Distribution Functions of Rare-Earth Phosphate Glasses Using Principal Component Analysis.

PubMed

Cole, Jacqueline M; Cheng, Xie; Payne, Michael C

2016-11-07

The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, codoped with two rare-earth ions (R and R') of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of codoped REPGs presents significant challenges relative to their singly doped counterparts; specifically, R and R' are difficult to distinguish in terms of establishing relative material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown codoped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are prevalidated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. While this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials and be exploited in large-scale data-mining efforts that probe many t(r) functions.

PCA meets RG

NASA Astrophysics Data System (ADS)

Bradde, Serena; Bialek, William

A system with many degrees of freedom can be characterized by a covariance matrix; principal components analysis (PCA) focuses on the eigenvalues of this matrix, hoping to find a lower dimensional description. But when the spectrum is nearly continuous, any distinction between components that we keep and those that we ignore becomes arbitrary; it then is natural to ask what happens as we vary this arbitrary cutoff. We argue that this problem is analogous to the momentum shell renormalization group (RG). Following this analogy, we can define relevant and irrelevant operators, where the role of dimensionality is played by properties of the eigenvalue density. These results also suggest an approach to the analysis of real data. As an example, we study neural activity in the vertebrate retina as it responds to naturalistic movies, and find evidence of behavior controlled by a nontrivial fixed point. Applied to financial data, our analysis separates modes dominated by sampling noise from a smaller but still macroscopic number of modes described by a non-Gaussian distribution.

Advanced method optimization for volatile aroma profiling of beer using two-dimensional gas chromatography time-of-flight mass spectrometry.

PubMed

Stefanuto, Pierre-Hugues; Perrault, Katelynn A; Dubois, Lena M; L'Homme, Benjamin; Allen, Catherine; Loughnane, Caitriona; Ochiai, Nobuo; Focant, Jean-François

2017-07-21

The complex mixture of volatile organic compounds (VOCs) present in the headspace of Trappist and craft beers was studied to illustrate the efficiency of thermal desorption (TD) comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) for highlighting subtle differences between highly complex mixtures of VOCs. Headspace solid-phase microextraction (HS-SPME), multiple (and classical) stir bar sorptive extraction (mSBSE), static headspace (SHS), and dynamic headspace (DHS) were compared for the extraction of a set of 21 representative flavor compounds of beer aroma. A Box-Behnken surface response methodology experimental design optimization (DOE) was used for convex hull calculation (Delaunay's triangulation algorithms) of peak dispersion in the chromatographic space. The predicted value of 0.5 for the ratio between the convex hull and the available space was 10% higher than the experimental value, demonstrating the usefulness of the approach to improve optimization of the GC×GC separation. Chemical variations amongst aligned chromatograms were studied by means of Fisher Ratio (FR) determination and F-distribution threshold filtration at different significance levels (α=0.05 and 0.01) and based on z-score normalized area for data reduction. Statistically significant compounds were highlighted following principal component analysis (PCA) and hierarchical cluster analysis (HCA). The dendrogram structure not only provided clear visual information about similarities between products but also permitted direct identification of the chemicals and their relative weight in clustering. The effective coupling of DHS-TD-GC×GC-TOFMS with PCA and HCA was able to highlight the differences and common typical VOC patterns among 24 samples of different Trappist and selected Canadian craft beers. Copyright © 2017 Elsevier B.V. All rights reserved.

Novel Driving Method for Two-Dimensional and Three-Dimensional Switchable Active Matrix Organic Light-Emitting Diode Displays for Emission and Programming Time Extension

NASA Astrophysics Data System (ADS)

In, Hai-Jung; Kwon, Oh-Kyong

2012-03-01

A novel driving method for two-dimensional (2D) and three-dimensional (3D) switchable active matrix organic light-emitting diode (AMOLED) displays is proposed to extend emission time and data programming time during 3D display operation. The proposed pixel consists of six thin-film transistors (TFTs) and two capacitors, and the aperture ratio of the pixel is 45.8% under 40-in. full-high-definition television condition. By increasing emission time and programming time, the flicker problem can be reduced and the lifetime of AMOLED displays can be extended owing to the decrease in emission current density. Simulation results show that the emission current error range from -0.4 to 1.6% is achieved when the threshold voltage variation of driving TFTs is in the range from -1.0 to 1.0 V, and the emission current error is 1.0% when the power line IR-drop is 2.0 V.

A Two-Dimensional Linear Bicharacteristic FDTD Method

NASA Technical Reports Server (NTRS)

Beggs, John H.

2002-01-01

The linear bicharacteristic scheme (LBS) was originally developed to improve unsteady solutions in computational acoustics and aeroacoustics. The LBS has previously been extended to treat lossy materials for one-dimensional problems. It is a classical leapfrog algorithm, but is combined with upwind bias in the spatial derivatives. This approach preserves the time-reversibility of the leapfrog algorithm, which results in no dissipation, and it permits more flexibility by the ability to adopt a characteristic based method. The use of characteristic variables allows the LBS to include the Perfectly Matched Layer boundary condition with no added storage or complexity. The LBS offers a central storage approach with lower dispersion than the Yee algorithm, plus it generalizes much easier to nonuniform grids. It has previously been applied to two and three-dimensional free-space electromagnetic propagation and scattering problems. This paper extends the LBS to the two-dimensional case. Results are presented for point source radiation problems, and the FDTD algorithm is chosen as a convenient reference for comparison.

Finding Imaging Patterns of Structural Covariance via Non-Negative Matrix Factorization

PubMed Central

Sotiras, Aristeidis; Resnick, Susan M.; Davatzikos, Christos

2015-01-01

In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA. PMID:25497684

Behavior of the PCA3 gene in the urine of men with high grade prostatic intraepithelial neoplasia.

PubMed

Morote, Juan; Rigau, Marina; Garcia, Marta; Mir, Carmen; Ballesteros, Carlos; Planas, Jacques; Raventós, Carles X; Placer, José; de Torres, Inés M; Reventós, Jaume; Doll, Andreas

2010-12-01

An ideal marker for the early detection of prostate cancer (PCa) should also differentiate between men with isolated high grade prostatic intraepithelial neoplasia (HGPIN) and those with PCa. Prostate Cancer Gene 3 (PCA3) is a highly specific PCa gene and its score, in relation to the PSA gene in post-prostate massage urine (PMU-PCA3), seems to be useful in ruling out PCa, especially after a negative prostate biopsy. Because PCA3 is also expressed in the HGPIN lesion, the aim of this study was to determine the efficacy of PMU-PCA3 scores for ruling out PCa in men with previous HGPIN. The PMU-PCA3 score was assessed by quantitative PCR (multiplex research assay) in 244 men subjected to prostate biopsy: 64 men with an isolated HGPIN (no cancer detected after two or more repeated biopsies), 83 men with PCa and 97 men with benign pathology findings (BP: no PCa, HGPIN or ASAP). The median PMU-PCA3 score was 1.56 in men with BP, 2.01 in men with HGPIN (p = 0.128) and 9.06 in men with PCa (p = 0.008). The AUC in the ROC analysis was 0.705 in the subset of men with BP and PCa, while it decreased to 0.629 when only men with isolated HGPIN and PCa were included in the analysis. Fixing the sensitivity of the PMU-PCA3 score at 90%, its specificity was 79% in men with BP and 69% in men with isolated HGPIN. The efficacy of the PMU-PCA3 score to rule out PCa in men with HGPIN is lower than in men with BP.

Derivation of Boundary Manikins: A Principal Component Analysis

NASA Technical Reports Server (NTRS)

Young, Karen; Margerum, Sarah; Barr, Abbe; Ferrer, Mike A.; Rajulu, Sudhakar

2008-01-01

When designing any human-system interface, it is critical to provide realistic anthropometry to properly represent how a person fits within a given space. This study aimed to identify a minimum number of boundary manikins or representative models of subjects anthropometry from a target population, which would realistically represent the population. The boundary manikin anthropometry was derived using, Principal Component Analysis (PCA). PCA is a statistical approach to reduce a multi-dimensional dataset using eigenvectors and eigenvalues. The measurements used in the PCA were identified as those measurements critical for suit and cockpit design. The PCA yielded a total of 26 manikins per gender, as well as their anthropometry from the target population. Reduction techniques were implemented to reduce this number further with a final result of 20 female and 22 male subjects. The anthropometry of the boundary manikins was then be used to create 3D digital models (to be discussed in subsequent papers) intended for use by designers to test components of their space suit design, to verify that the requirements specified in the Human Systems Integration Requirements (HSIR) document are met. The end-goal is to allow for designers to generate suits which accommodate the diverse anthropometry of the user population.

Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

PubMed

Zuendorf, Gerhard; Kerrouche, Nacer; Herholz, Karl; Baron, Jean-Claude

2003-01-01

Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74 normal subjects using [(18)F]fluoro-2-deoxy-D-glucose (FDG) as a tracer. Principal components (PCs) and their relation to age effects were investigated. Correlations between the projections of the images on the new axes and the age of the subjects were carried out. The first two PCs could be identified as being the only PCs significantly correlated to age. The first principal component, which explained 10% of the data set variance, was reduced only in subjects of age 55 or older and was related to loss of signal in and adjacent to ventricles and basal cisterns, reflecting expected age-related brain atrophy with enlarging CSF spaces. The second principal component, which accounted for 8% of the total variance, had high loadings from prefrontal, posterior parietal and posterior cingulate cortices and showed the strongest correlation with age (r = -0.56), entirely consistent with previously documented age-related declines in brain glucose utilization. Thus, our method showed that the effect of aging on brain metabolism has at least two independent dimensions. This method should have widespread applications in multivariate analysis of brain functional images. Copyright 2002 Wiley-Liss, Inc.

Fast trimers in a one-dimensional extended Fermi-Hubbard model

NASA Astrophysics Data System (ADS)

Dhar, A.; Törmä, P.; Kinnunen, J. J.

2018-04-01

We consider a one-dimensional two-component extended Fermi-Hubbard model with nearest-neighbor interactions and mass imbalance between the two species. We study the binding energy of trimers, various observables for detecting them, and expansion dynamics. We generalize the definition of the trimer gap to include the formation of different types of clusters originating from nearest-neighbor interactions. Expansion dynamics reveal rapidly propagating trimers, with speeds exceeding doublon propagation in the strongly interacting regime. We present a simple model for understanding this unique feature of the movement of the trimers, and we discuss the potential for experimental realization.

Two-dimensional PCA-based human gait identification

NASA Astrophysics Data System (ADS)

Chen, Jinyan; Wu, Rongteng

2012-11-01

It is very necessary to recognize person through visual surveillance automatically for public security reason. Human gait based identification focus on recognizing human by his walking video automatically using computer vision and image processing approaches. As a potential biometric measure, human gait identification has attracted more and more researchers. Current human gait identification methods can be divided into two categories: model-based methods and motion-based methods. In this paper a two-Dimensional Principal Component Analysis and temporal-space analysis based human gait identification method is proposed. Using background estimation and image subtraction we can get a binary images sequence from the surveillance video. By comparing the difference of two adjacent images in the gait images sequence, we can get a difference binary images sequence. Every binary difference image indicates the body moving mode during a person walking. We use the following steps to extract the temporal-space features from the difference binary images sequence: Projecting one difference image to Y axis or X axis we can get two vectors. Project every difference image in the difference binary images sequence to Y axis or X axis difference binary images sequence we can get two matrixes. These two matrixes indicate the styles of one walking. Then Two-Dimensional Principal Component Analysis(2DPCA) is used to transform these two matrixes to two vectors while at the same time keep the maximum separability. Finally the similarity of two human gait images is calculated by the Euclidean distance of the two vectors. The performance of our methods is illustrated using the CASIA Gait Database.

Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Senthilkumar, T.; Jayas, D. S.; White, N. D. G.; Fields, P. G.; Gräfenhan, T.

2017-03-01

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.

PCA3-based nomogram for predicting prostate cancer and high grade cancer on initial transrectal guided biopsy.

PubMed

Elshafei, Ahmed; Chevli, K Kent; Moussa, Ayman S; Kara, Onder; Chueh, Shih-Chieh; Walter, Peter; Hatem, Asmaa; Gao, Tianming; Jones, J Stephen; Duff, Michael

2015-12-01

To develop a validated prostate cancer antigen 3 (PCA3) based nomogram that predicts likelihood of overall prostate cancer (PCa) and intermediate/high grade prostate cancer (HGPCa) in men pursuing initial transrectal prostate biopsy (TRUS-PBx). Data were collected on 3,675 men with serum prostate specific antigen level (PSA) ≤ 20 ng/ml who underwent initial prostate biopsy with at least 10 cores sampling at time of the biopsy. Two logistic regression models were constructed to predict overall PCa and HGPCa incorporating age, race, family history (FH) of PCa, PSA at diagnosis, PCA3, total prostate volume (TPV), and digital rectal exam (DRE). One thousand six hundred twenty (44%) patients had biopsy confirmed PCa with 701 men (19.1%) showing HGPCa. Statistically significant predictors of overall PCa were age (P < 0.0001, OR. 1.51), PSA at diagnosis (P < 0.0001, OR.1.95), PCA3 (P < 0.0001, OR.3.06), TPV (P < 0.0001, OR.0.47), FH (P = 0.003, OR.1.32), and abnormal DRE (P = 0.001, OR. 1.32). While for HGPCa, predictors were age (P < 0.0001, OR.1.77), PSA (P < 0.0001, OR.2.73), PCA3 (P < 0.0001, OR.2.26), TPV (P < 0.0001, OR.0.4), and DRE (P < 0.0001, OR.1.53). Two nomograms were reconstructed for predicted overall PCa probability at time of initial biopsy with a concordance index of 0.742 (Fig. 1), and HGPCa with a concordance index of 0.768 (Fig. 2). Our internally validated initial biopsy PCA3 based nomogram is reconstructed based on a large dataset. The c-index indicates high predictive accuracy, especially for high grade PCa and improves the ability to predict biopsy outcomes. © 2015 Wiley Periodicals, Inc.

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.

The Use of Multiple Correspondence Analysis to Explore Associations between Categories of Qualitative Variables in Healthy Ageing.

PubMed

Costa, Patrício Soares; Santos, Nadine Correia; Cunha, Pedro; Cotter, Jorge; Sousa, Nuno

2013-01-01

The main focus of this study was to illustrate the applicability of multiple correspondence analysis (MCA) in detecting and representing underlying structures in large datasets used to investigate cognitive ageing. Principal component analysis (PCA) was used to obtain main cognitive dimensions, and MCA was used to detect and explore relationships between cognitive, clinical, physical, and lifestyle variables. Two PCA dimensions were identified (general cognition/executive function and memory), and two MCA dimensions were retained. Poorer cognitive performance was associated with older age, less school years, unhealthier lifestyle indicators, and presence of pathology. The first MCA dimension indicated the clustering of general/executive function and lifestyle indicators and education, while the second association was between memory and clinical parameters and age. The clustering analysis with object scores method was used to identify groups sharing similar characteristics. The weaker cognitive clusters in terms of memory and executive function comprised individuals with characteristics contributing to a higher MCA dimensional mean score (age, less education, and presence of indicators of unhealthier lifestyle habits and/or clinical pathologies). MCA provided a powerful tool to explore complex ageing data, covering multiple and diverse variables, showing if a relationship exists and how variables are related, and offering statistical results that can be seen both analytically and visually.

Extended principle component analysis - a useful tool to understand processes governing water quality at catchment scales

NASA Astrophysics Data System (ADS)

Selle, B.; Schwientek, M.

2012-04-01

Water quality of ground and surface waters in catchments is typically driven by many complex and interacting processes. While small scale processes are often studied in great detail, their relevance and interplay at catchment scales remain often poorly understood. For many catchments, extensive monitoring data on water quality have been collected for different purposes. These heterogeneous data sets contain valuable information on catchment scale processes but are rarely analysed using integrated methods. Principle component analysis (PCA) has previously been applied to this kind of data sets. However, a detailed analysis of scores, which are an important result of a PCA, is often missing. Mathematically, PCA expresses measured variables on water quality, e.g. nitrate concentrations, as linear combination of independent, not directly observable key processes. These computed key processes are represented by principle components. Their scores are interpretable as process intensities which vary in space and time. Subsequently, scores can be correlated with other key variables and catchment characteristics, such as water travel times and land use that were not considered in PCA. This detailed analysis of scores represents an extension of the commonly applied PCA which could considerably improve the understanding of processes governing water quality at catchment scales. In this study, we investigated the 170 km2 Ammer catchment in SW Germany which is characterised by an above average proportion of agricultural (71%) and urban (17%) areas. The Ammer River is mainly fed by karstic springs. For PCA, we separately analysed concentrations from (a) surface waters of the Ammer River and its tributaries, (b) spring waters from the main aquifers and (c) deep groundwater from production wells. This analysis was extended by a detailed analysis of scores. We analysed measured concentrations on major ions and selected organic micropollutants. Additionally, redox-sensitive variables and environmental tracers indicating groundwater age were analysed for deep groundwater from production wells. For deep groundwater, we found that microbial turnover was stronger influenced by local availability of energy sources than by travel times of groundwater to the wells. Groundwater quality primarily reflected the input of pollutants determined by landuse, e.g. agrochemicals. We concluded that for water quality in the Ammer catchment, conservative mixing of waters with different origin is more important than reactive transport processes along the flow path.

Systematic dissection of phenotypic, functional, and tumorigenic heterogeneity of human prostate cancer cells

PubMed Central

Chao, Hsueh-Ping; Deng, Qu; Jeter, Collene; Liu, Can; Honorio, Sofia; Li, Hangwen; Davis, Tammy; Suraneni, Mahipal; Laffin, Brian; Qin, Jichao; Li, Qiuhui; Yang, Tao; Whitney, Pamela; Shen, Jianjun; Huang, Jiaoti; Tang, Dean G.

2015-01-01

Human cancers are heterogeneous containing stem-like cancer cells operationally defined as cancer stem cells (CSCs) that possess great tumor-initiating and long-term tumor-propagating properties. In this study, we systematically dissect the phenotypic, functional and tumorigenic heterogeneity in human prostate cancer (PCa) using xenograft models and >70 patient tumor samples. In the first part, we further investigate the PSA−/lo PCa cell population, which we have recently shown to harbor self-renewing long-term tumor-propagating cells and present several novel findings. We show that discordant AR and PSA expression in both untreated and castration-resistant PCa (CRPC) results in AR+PSA+, AR+PSA−, AR−PSA−, and AR−PSA+ subtypes of PCa cells that manifest differential sensitivities to therapeutics. We further demonstrate that castration leads to a great enrichment of PSA−/lo PCa cells in both xenograft tumors and CRPC samples and systemic androgen levels dynamically regulate the relative abundance of PSA+ versus PSA−/lo PCa cells that impacts the kinetics of tumor growth. We also present evidence that the PSA−/lo PCa cells possess distinct epigenetic profiles. As the PSA−/lo PCa cell population is heterogeneous, in the second part, we employ two PSA− (Du145 and PC3) and two PSA+ (LAPC9 and LAPC4) PCa models as well as patient tumor cells to further dissect the clonogenic and tumorigenic subsets. We report that different PCa models possess distinct tumorigenic subpopulations that both commonly and uniquely express important signaling pathways that could represent therapeutic targets. Our results have important implications in understanding PCa cell heterogeneity, response to clinical therapeutics, and cellular mechanisms underlying CRPC. PMID:26246472

Role of small-norm components in extended random-phase approximation

NASA Astrophysics Data System (ADS)

Tohyama, Mitsuru

2017-09-01

The role of the small-norm amplitudes in extended random-phase approximation (RPA) theories such as the particle-particle and hole-hole components of one-body amplitudes and the two-body amplitudes other than two-particle/two-hole components are investigated for the one-dimensional Hubbard model using an extended RPA derived from the time-dependent density matrix theory. It is found that these amplitudes cannot be neglected in strongly interacting regions where the effects of ground-state correlations are significant.

Predicting prostate biopsy outcome: prostate health index (phi) and prostate cancer antigen 3 (PCA3) are useful biomarkers.

PubMed

Ferro, Matteo; Bruzzese, Dario; Perdonà, Sisto; Mazzarella, Claudia; Marino, Ada; Sorrentino, Alessandra; Di Carlo, Angelina; Autorino, Riccardo; Di Lorenzo, Giuseppe; Buonerba, Carlo; Altieri, Vincenzo; Mariano, Angela; Macchia, Vincenzo; Terracciano, Daniela

2012-08-16

Indication for prostate biopsy is presently mainly based on prostate-specific antigen (PSA) serum levels and digital-rectal examination (DRE). In view of the unsatisfactory accuracy of these two diagnostic exams, research has focused on novel markers to improve pre-biopsy prostate cancer detection, such as phi and PCA3. The purpose of this prospective study was to assess the diagnostic accuracy of phi and PCA3 for prostate cancer using biopsy as gold standard. Phi index (Beckman coulter immunoassay), PCA3 score (Progensa PCA3 assay) and other established biomarkers (tPSA, fPSA and %fPSA) were assessed before a 18-core prostate biopsy in a group of 251 subjects at their first biopsy. Values of %p2PSA and phi were significantly higher in patients with PCa compared with PCa-negative group (p<0.001) and also compared with high grade prostatic intraepithelial neoplasia (HGPIN) (p<0.001). PCA3 score values were significantly higher in PCa compared with PCa-negative subjects (p<0.001) and in HGPIN vs PCa-negative patients (p<0.001). ROC curve analysis showed that %p2PSA, phi and PCA3 are predictive of malignancy. In conclusion, %p2PSA, phi and PCA3 may predict a diagnosis of PCa in men undergoing their first prostate biopsy. PCA3 score is more useful in discriminating between HGPIN and non-cancer. Copyright © 2012 Elsevier B.V. All rights reserved.

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

NASA Astrophysics Data System (ADS)

Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2016-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

PubMed

Du, Qi-Shi; Wang, Shu-Qing; Xie, Neng-Zhong; Wang, Qing-Yan; Huang, Ri-Bo; Chou, Kuo-Chen

2017-09-19

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

Extended resolvent and inverse scattering with an application to KPI

NASA Astrophysics Data System (ADS)

Boiti, M.; Pempinelli, F.; Pogrebkov, A. K.; Prinari, B.

2003-08-01

We present in detail an extended resolvent approach for investigating linear problems associated to 2+1 dimensional integrable equations. Our presentation is based as an example on the nonstationary Schrödinger equation with potential being a perturbation of the one-soliton potential by means of a decaying two-dimensional function. Modification of the inverse scattering theory as well as properties of the Jost solutions and spectral data as follows from the resolvent approach are given.

Psychometric Measurement Models and Artificial Neural Networks

ERIC Educational Resources Information Center

Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

2004-01-01

The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

Applications of an exponential finite difference technique

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

Handschuh, R.F.; Keith, T.G. Jr.

1988-07-01

An exponential finite difference scheme first presented by Bhattacharya for one dimensional unsteady heat conduction problems in Cartesian coordinates was extended. The finite difference algorithm developed was used to solve the unsteady diffusion equation in one dimensional cylindrical coordinates and was applied to two and three dimensional conduction problems in Cartesian coordinates. Heat conduction involving variable thermal conductivity was also investigated. The method was used to solve nonlinear partial differential equations in one and two dimensional Cartesian coordinates. Predicted results are compared to exact solutions where available or to results obtained by other numerical methods.

A pilot study assessing the association between paraoxonase 1 gene polymorphism and prostate cancer.

PubMed

Uluocak, Nihat; Atılgan, Doğan; Parlaktaş, Bekir Süha; Erdemir, Fikret; Ateş, Ömer

2017-09-01

We aimed to show the relationship between paraoxonase 1 (PON1) gene polymorphism and the development of prostate cancer (PCa). We investigated the association of single nuclotide polymorphisms of PON1 enzyme with the development of PCa risk. A total of 147 male patients were divided into PCa, and control groups. The control group was also divided into two subgroups according to serum prostate specific antigen (PSA) levels as non PCa-high PSA (>4 ng/mL) and non PCa-low PSA (≤4 ng/mL) groups. The mean ages of the patients were 64.81 years, 63.27 years and 64.22 years in PCa group, non PCa-low PSA and non PCa -high PSA groups, respectively. The mean PSA levels were 10.9 ng/mL, 1.16 ng/mL and 6.63 ng/mL for PCa group, non PCa -low PSA and non PCa -high PSA groups, respectively. In terms of PON1 polymorphisms and allele frequencies, there were no statistically significant differences between PCa and control groups. There was not a statistically significant difference between PCa and non PCa-high PSA groups as for genotypic and allelic frequencies. As a result of this small sample sized hypothetical study of polymorphism, a relationship could not be detected between PCa development and PON1 gene polymorphism. According to the results of this preliminary study, it is thought that more comprehensive future studies are necessary to clarify the possible role of PON1 gene polymorphism in the etiology of PCa.

A pilot study assessing the association between paraoxonase 1 gene polymorphism and prostate cancer

PubMed Central

Uluocak, Nihat; Atılgan, Doğan; Parlaktaş, Bekir Süha; Erdemir, Fikret; Ateş, Ömer

2017-01-01

Objective We aimed to show the relationship between paraoxonase 1 (PON1) gene polymorphism and the development of prostate cancer (PCa). Material and methods We investigated the association of single nuclotide polymorphisms of PON1 enzyme with the development of PCa risk. A total of 147 male patients were divided into PCa, and control groups. The control group was also divided into two subgroups according to serum prostate specific antigen (PSA) levels as non PCa-high PSA (>4 ng/mL) and non PCa-low PSA (≤4 ng/mL) groups. Results The mean ages of the patients were 64.81 years, 63.27 years and 64.22 years in PCa group, non PCa-low PSA and non PCa –high PSA groups, respectively. The mean PSA levels were 10.9 ng/mL, 1.16 ng/mL and 6.63 ng/mL for PCa group, non PCa –low PSA and non PCa –high PSA groups, respectively. In terms of PON1 polymorphisms and allele frequencies, there were no statistically significant differences between PCa and control groups. There was not a statistically significant difference between PCa and non PCa-high PSA groups as for genotypic and allelic frequencies. As a result of this small sample sized hypothetical study of polymorphism, a relationship could not be detected between PCa development and PON1 gene polymorphism. Conclusion According to the results of this preliminary study, it is thought that more comprehensive future studies are necessary to clarify the possible role of PON1 gene polymorphism in the etiology of PCa. PMID:28861298

Does adding ketamine to morphine patient-controlled analgesia safely improve post-thoracotomy pain?

PubMed

Mathews, Timothy J; Churchhouse, Antonia M D; Housden, Tessa; Dunning, Joel

2012-02-01

A best evidence topic in thoracic surgery was written according to a structured protocol. The question addressed was 'is the addition of ketamine to morphine patient-controlled analgesia (PCA) following thoracic surgery superior to morphine alone'. Altogether 201 papers were found using the reported search, of which nine represented the best evidence to answer the clinical question. The authors, journal, date and country of publication, patient group studied, study type, relevant outcomes and results of these papers are tabulated. This consisted of one systematic review of PCA morphine with ketamine (PCA-MK) trials, one meta-analysis of PCA-MK trials, four randomized controlled trials of PCA-MK, one meta-analysis of trials using a variety of peri-operative ketamine regimes and two cohort studies of PCA-MK. Main outcomes measured included pain score rated on visual analogue scale, morphine consumption and incidence of psychotomimetic side effects/hallucination. Two papers reported the measurements of respiratory function. This evidence shows that adding ketamine to morphine PCA is safe, with a reported incidence of hallucination requiring intervention of 2.9%, and a meta-analysis finding an incidence of all central nervous system side effects of 18% compared with 15% with morphine alone, P = 0.31, RR 1.27 with 95% CI (0.8-2.01). All randomized controlled trials of its use following thoracic surgery found no hallucination or psychological side effect. All five studies in thoracic surgery (n = 243) found reduced morphine requirements with PCA-MK. Pain scores were significantly lower in PCA-MK patients in thoracic surgery papers, with one paper additionally reporting increased patient satisfaction. However, no significant improvement was found in a meta-analysis of five papers studying PCA-MK in a variety of surgical settings. Both papers reporting respiratory outcomes found improved oxygen saturations and PaCO(2) levels in PCA-MK patients following thoracic surgery. We conclude that adding low-dose ketamine to morphine PCA is safe and post-thoracotomy may provide better pain control than PCA with morphine alone (PCA-MO), with reduced morphine consumption and possible improvement in respiratory function. These studies thus support the routine use of PCA-MK instead of PCA-MO to improve post-thoracotomy pain control.

Demixed principal component analysis of neural population data

PubMed Central

Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K

2016-01-01

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure. DOI: http://dx.doi.org/10.7554/eLife.10989.001 PMID:27067378

Regularized Stokeslet representations for the flow around a human sperm

NASA Astrophysics Data System (ADS)

Ishimoto, Kenta; Gadelha, Hermes; Gaffney, Eamonn; Smith, David; Kirkman-Brown, Jackson

2017-11-01

The sperm flagellum does not simply push the sperm. We have established a new theoretical scheme for the dimensional reduction of swimming sperm dynamics, via high-frame-rate digital microscopy of a swimming human sperm cell. This has allowed the reconstruction of the flagellar waveform as a limit cycle in a phase space of PCA modes. With this waveform, boundary element numerical simulation has successfully captured fine-scale sperm swimming trajectories. Further analyses on the flow field around the cell has also demonstrated a pusher-type time-averaged flow, though the instantaneous flow field can temporarily vary in a more complicated manner - even pulling the sperm. Applying PCA to the flow field, we have further found that a small number of PCA modes explain the temporal patterns of the flow, whose core features are well approximated by a few regularized Stokeslets. Such representations provide a methodology for coarse-graining the time-dependent flow around a human sperm and other flagellar microorganisms for use in developing population level models that retain individual cell dynamics.

Prediction With Dimension Reduction of Multiple Molecular Data Sources for Patient Survival.

PubMed

Kaplan, Adam; Lock, Eric F

2017-01-01

Predictive modeling from high-dimensional genomic data is often preceded by a dimension reduction step, such as principal component analysis (PCA). However, the application of PCA is not straightforward for multisource data, wherein multiple sources of 'omics data measure different but related biological components. In this article, we use recent advances in the dimension reduction of multisource data for predictive modeling. In particular, we apply exploratory results from Joint and Individual Variation Explained (JIVE), an extension of PCA for multisource data, for prediction of differing response types. We conduct illustrative simulations to illustrate the practical advantages and interpretability of our approach. As an application example, we consider predicting survival for patients with glioblastoma multiforme from 3 data sources measuring messenger RNA expression, microRNA expression, and DNA methylation. We also introduce a method to estimate JIVE scores for new samples that were not used in the initial dimension reduction and study its theoretical properties; this method is implemented in the R package R.JIVE on CRAN, in the function jive.predict.

Gacs quantum algorithmic entropy in infinite dimensional Hilbert spaces

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

Benatti, Fabio, E-mail: benatti@ts.infn.it; Oskouei, Samad Khabbazi, E-mail: kh.oskuei@ut.ac.ir; Deh Abad, Ahmad Shafiei, E-mail: shafiei@khayam.ut.ac.ir

We extend the notion of Gacs quantum algorithmic entropy, originally formulated for finitely many qubits, to infinite dimensional quantum spin chains and investigate the relation of this extension with two quantum dynamical entropies that have been proposed in recent years.

A new Lagrangian method for three-dimensional steady supersonic flows

NASA Technical Reports Server (NTRS)

Loh, Ching-Yuen; Liou, Meng-Sing

1993-01-01

In this report, the new Lagrangian method introduced by Loh and Hui is extended for three-dimensional, steady supersonic flow computation. The derivation of the conservation form and the solution of the local Riemann solver using the Godunov and the high-resolution TVD (total variation diminished) scheme is presented. This new approach is accurate and robust, capable of handling complicated geometry and interactions between discontinuous waves. Test problems show that the extended Lagrangian method retains all the advantages of the two-dimensional method (e.g., crisp resolution of a slip-surface (contact discontinuity) and automatic grid generation). In this report, we also suggest a novel three dimensional Riemann problem in which interesting and intricate flow features are present.

Euclidean chemical spaces from molecular fingerprints: Hamming distance and Hempel's ravens.

PubMed

Martin, Eric; Cao, Eddie

2015-05-01

Molecules are often characterized by sparse binary fingerprints, where 1s represent the presence of substructures and 0s represent their absence. Fingerprints are especially useful for similarity calculations, such as database searching or clustering, generally measuring similarity as the Tanimoto coefficient. In other cases, such as visualization, design of experiments, or latent variable regression, a low-dimensional Euclidian "chemical space" is more useful, where proximity between points reflects chemical similarity. A temptation is to apply principal components analysis (PCA) directly to these fingerprints to obtain a low dimensional continuous chemical space. However, Gower has shown that distances from PCA on bit vectors are proportional to the square root of Hamming distance. Unlike Tanimoto similarity, Hamming similarity (HS) gives equal weight to shared 0s as to shared 1s, that is, HS gives as much weight to substructures that neither molecule contains, as to substructures which both molecules contain. Illustrative examples show that proximity in the corresponding chemical space reflects mainly similar size and complexity rather than shared chemical substructures. These spaces are ill-suited for visualizing and optimizing coverage of chemical space, or as latent variables for regression. A more suitable alternative is shown to be Multi-dimensional scaling on the Tanimoto distance matrix, which produces a space where proximity does reflect structural similarity.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification.

PubMed

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification

PubMed Central

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database. PMID:26640813

Autonomic specificity of basic emotions: evidence from pattern classification and cluster analysis.

PubMed

Stephens, Chad L; Christie, Israel C; Friedman, Bruce H

2010-07-01

Autonomic nervous system (ANS) specificity of emotion remains controversial in contemporary emotion research, and has received mixed support over decades of investigation. This study was designed to replicate and extend psychophysiological research, which has used multivariate pattern classification analysis (PCA) in support of ANS specificity. Forty-nine undergraduates (27 women) listened to emotion-inducing music and viewed affective films while a montage of ANS variables, including heart rate variability indices, peripheral vascular activity, systolic time intervals, and electrodermal activity, were recorded. Evidence for ANS discrimination of emotion was found via PCA with 44.6% of overall observations correctly classified into the predicted emotion conditions, using ANS variables (z=16.05, p<.001). Cluster analysis of these data indicated a lack of distinct clusters, which suggests that ANS responses to the stimuli were nomothetic and stimulus-specific rather than idiosyncratic and individual-specific. Collectively these results further confirm and extend support for the notion that basic emotions have distinct ANS signatures. Copyright © 2010 Elsevier B.V. All rights reserved.

Generalizing the bms3 and 2D-conformal algebras by expanding the Virasoro algebra

NASA Astrophysics Data System (ADS)

Caroca, Ricardo; Concha, Patrick; Rodríguez, Evelyn; Salgado-Rebolledo, Patricio

2018-03-01

By means of the Lie algebra expansion method, the centrally extended conformal algebra in two dimensions and the bms3 algebra are obtained from the Virasoro algebra. We extend this result to construct new families of expanded Virasoro algebras that turn out to be infinite-dimensional lifts of the so-called Bk, Ck and Dk algebras recently introduced in the literature in the context of (super)gravity. We also show how some of these new infinite-dimensional symmetries can be obtained from expanded Kač-Moody algebras using modified Sugawara constructions. Applications in the context of three-dimensional gravity are briefly discussed.

Variable importance in nonlinear kernels (VINK): classification of digitized histopathology.

PubMed

Ginsburg, Shoshana; Ali, Sahirzeeshan; Lee, George; Basavanhally, Ajay; Madabhushi, Anant

2013-01-01

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of variable importance in nonlinear kernels (VINK). We show how VINK can be implemented in conjunction with the popular Isomap and Laplacian eigenmap algorithms. VINK is evaluated in the contexts of three different problems in digital pathology: (1) predicting five year PSA failure following radical prostatectomy, (2) predicting Oncotype DX recurrence risk scores for ER+ breast cancers, and (3) distinguishing good and poor outcome p16+ oropharyngeal tumors. We demonstrate that subsets of features identified by VINK provide similar or better classification or regression performance compared to the original high dimensional feature sets.

Common factor analysis versus principal component analysis: choice for symptom cluster research.

PubMed

Kim, Hee-Ju

2008-03-01

The purpose of this paper is to examine differences between two factor analytical methods and their relevance for symptom cluster research: common factor analysis (CFA) versus principal component analysis (PCA). Literature was critically reviewed to elucidate the differences between CFA and PCA. A secondary analysis (N = 84) was utilized to show the actual result differences from the two methods. CFA analyzes only the reliable common variance of data, while PCA analyzes all the variance of data. An underlying hypothetical process or construct is involved in CFA but not in PCA. PCA tends to increase factor loadings especially in a study with a small number of variables and/or low estimated communality. Thus, PCA is not appropriate for examining the structure of data. If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research), CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure.

Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm

NASA Astrophysics Data System (ADS)

Gomez Gonzalez, C. A.; Absil, O.; Absil, P.-A.; Van Droogenbroeck, M.; Mawet, D.; Surdej, J.

2016-05-01

Context. Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. It is a widely used statistical tool developed during the first half of the past century. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. Aims: Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. Methods: We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on β Pictoris with VLT/NACO. Results: Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space. This three-term decomposition brings a detectability boost compared to the full-frame standard PCA approach, especially in the small inner working angle region where complex speckle noise prevents PCA from discerning true companions from noise.

A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis.

PubMed

Reese, Sarah E; Archer, Kellie J; Therneau, Terry M; Atkinson, Elizabeth J; Vachon, Celine M; de Andrade, Mariza; Kocher, Jean-Pierre A; Eckel-Passow, Jeanette E

2013-11-15

Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal component analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data. We present an extension of PCA to quantify the existence of batch effects, called guided PCA (gPCA). We describe a test statistic that uses gPCA to test whether a batch effect exists. We apply our proposed test statistic derived using gPCA to simulated data and to two copy number variation case studies: the first study consisted of 614 samples from a breast cancer family study using Illumina Human 660 bead-chip arrays, whereas the second case study consisted of 703 samples from a family blood pressure study that used Affymetrix SNP Array 6.0. We demonstrate that our statistic has good statistical properties and is able to identify significant batch effects in two copy number variation case studies. We developed a new statistic that uses gPCA to identify whether batch effects exist in high-throughput genomic data. Although our examples pertain to copy number data, gPCA is general and can be used on other data types as well. The gPCA R package (Available via CRAN) provides functionality and data to perform the methods in this article. reesese@vcu.edu

Expression of spermidine/spermine N(1) -acetyl transferase (SSAT) in human prostate tissues is related to prostate cancer progression and metastasis.

PubMed

Huang, Wei; Eickhoff, Jens C; Mehraein-Ghomi, Farideh; Church, Dawn R; Wilding, George; Basu, Hirak S

2015-08-01

Prostate cancer (PCa) in many patients remains indolent for the rest of their lives, but in some patients, it progresses to lethal metastatic disease. Gleason score is the current clinical method for PCa prognosis. It cannot reliably identify aggressive PCa, when GS is ≤ 7. It is shown that oxidative stress plays a key role in PCa progression. We have shown that in cultured human PCa cells, an activation of spermidine/spermine N(1) -acetyl transferase (SSAT; EC 2.3.1.57) enzyme initiates a polyamine oxidation pathway and generates copious amounts of reactive oxygen species in polyamine-rich PCa cells. We used RNA in situ hybridization and immunohistochemistry methods to detect SSAT mRNA and protein expression in two tissue microarrays (TMA) created from patient's prostate tissues. We analyzed 423 patient's prostate tissues in the two TMAs. Our data show that there is a significant increase in both SSAT mRNA and the enzyme protein in the PCa cells as compared to their benign counterpart. This increase is even more pronounced in metastatic PCa tissues as compared to the PCa localized in the prostate. In the prostatectomy tissues from early-stage patients, the SSAT protein level is also high in the tissues obtained from the patients who ultimately progress to advanced metastatic disease. Based on these results combined with published data from our and other laboratories, we propose an activation of an autocrine feed-forward loop of PCa cell proliferation in the absence of androgen as a possible mechanism of castrate-resistant prostate cancer growth. © 2015 Wiley Periodicals, Inc.

Classification of alloys using laser induced breakdown spectroscopy with principle component analysis

NASA Astrophysics Data System (ADS)

Syuhada Mangsor, Aneez; Haider Rizvi, Zuhaib; Chaudhary, Kashif; Safwan Aziz, Muhammad

2018-05-01

The study of atomic spectroscopy has contributed to a wide range of scientific applications. In principle, laser induced breakdown spectroscopy (LIBS) method has been used to analyse various types of matter regardless of its physical state, either it is solid, liquid or gas because all elements emit light of characteristic frequencies when it is excited to sufficiently high energy. The aim of this work was to analyse the signature spectrums of each element contained in three different types of samples. Metal alloys of Aluminium, Titanium and Brass with the purities of 75%, 80%, 85%, 90% and 95% were used as the manipulated variable and their LIBS spectra were recorded. The characteristic emission lines of main elements were identified from the spectra as well as its corresponding contents. Principal component analysis (PCA) was carried out using the data from LIBS spectra. Three obvious clusters were observed in 3-dimensional PCA plot which corresponding to the different group of alloys. Findings from this study showed that LIBS technology with the help of principle component analysis could conduct the variety discrimination of alloys demonstrating the capability of LIBS-PCA method in field of spectro-analysis. Thus, LIBS-PCA method is believed to be an effective method for classifying alloys with different percentage of purifications, which was high-cost and time-consuming before.

Forecasting of UV-Vis absorbance time series using artificial neural networks combined with principal component analysis.

PubMed

Plazas-Nossa, Leonardo; Hofer, Thomas; Gruber, Günter; Torres, Andres

2017-02-01

This work proposes a methodology for the forecasting of online water quality data provided by UV-Vis spectrometry. Therefore, a combination of principal component analysis (PCA) to reduce the dimensionality of a data set and artificial neural networks (ANNs) for forecasting purposes was used. The results obtained were compared with those obtained by using discrete Fourier transform (DFT). The proposed methodology was applied to four absorbance time series data sets composed by a total number of 5705 UV-Vis spectra. Absolute percentage errors obtained by applying the proposed PCA/ANN methodology vary between 10% and 13% for all four study sites. In general terms, the results obtained were hardly generalizable, as they appeared to be highly dependent on specific dynamics of the water system; however, some trends can be outlined. PCA/ANN methodology gives better results than PCA/DFT forecasting procedure by using a specific spectra range for the following conditions: (i) for Salitre wastewater treatment plant (WWTP) (first hour) and Graz West R05 (first 18 min), from the last part of UV range to all visible range; (ii) for Gibraltar pumping station (first 6 min) for all UV-Vis absorbance spectra; and (iii) for San Fernando WWTP (first 24 min) for all of UV range to middle part of visible range.

Complex relationship between sex hormones, insulin resistance and leptin in men with and without prostatic disease.

PubMed

Grosman, Halina; Fabre, Bibiana; Lopez, Miguel; Scorticati, Carlos; Lopez Silva, Maximiliano; Mesch, Viviana; Mazza, Osvaldo; Berg, Gabriela

2016-01-01

To assess sex hormones, leptin and insulin-resistance in men with prostate cancer (PCa) and benign prostatic hyperplasia (BPH) and to study associations between androgens and histologic score of prostate tissue in PCa. Two hundred ten men older than 45 years selected from 2906 participants of a population screening for PCa were studied: 70 with PCa, 70 with BPH and 70 controls (CG), matched by body mass index and age. Insulin, IGF-1, PSA, leptin, total, free (fT) and bioavailable testosterone (bT) and estradiol were measured. Each group was subdivided into two subgroups considering the presence of metabolic syndrome (MS); androgens and leptin levels were analyzed in the subgroups. Prostate cancer and BPH patients presented higher total, fT and bT levels than CG. IGF-1, insulin and HOMA index were higher in BPH than in the other two groups. PCa presented higher leptin [median (range) 6.5 (1.3-28.0) versus 4.8 (1.1-12.3) ng/ml; p < 0.01] and estradiol [median (range) 37.0 (20-90) versus 29.0 (20-118) pg/ml; p = 0.025] levels than CG. After dividing men considering the presence of MS, leptin was higher and total testosterone was lower in MS patients in all the groups. It was observed a coexistence of an altered hormone profile with increased sex hormones and leptin in PCa patients, in accordance with the new perspective of PCa pathogenesis.

MAOA-a novel decision maker of apoptosis and autophagy in hormone refractory neuroendocrine prostate cancer cells

PubMed Central

Lin, Yi-Cheng; Chang, Yi-Ting; Campbell, Mel; Lin, Tzu-Ping; Pan, Chin-Chen; Lee, Hsin-Chen; Shih, Jean C.; Chang, Pei-Ching

2017-01-01

Autophagy and apoptosis are two well-controlled mechanisms regulating cell fate. An understanding of decision-making between these two pathways is in its infancy. Monoamine oxidase A (MAOA) is a mitochondrial enzyme that is well-known in psychiatric research. Emerging reports showed that overexpression MAOA is associated with prostate cancer (PCa). Here, we show that MAOA is involved in mediating neuroendocrine differentiation of PCa cells, a feature associated with hormone-refractory PCa (HRPC), a lethal type of disease. Following recent reports showing that NED of PCa requires down-regulation of repressor element-1 silencing transcription factor (REST) and activation of autophagy; we observe that MAOA is a novel direct target gene of REST. Reactive oxygen species (ROS) produced by overexpressed MAOA plays an essential role in inhibiting apoptosis and activating autophagy in NED PCa cells. MAOA inhibitors significantly reduced NED and autophagy activation of PCa cells. Our results here show MAOA as a new decision-maker for activating autophagy and MAOA inhibitors may be useful as a potential therapy for neuroendocrine tumors. PMID:28402333

MAOA-a novel decision maker of apoptosis and autophagy in hormone refractory neuroendocrine prostate cancer cells.

PubMed

Lin, Yi-Cheng; Chang, Yi-Ting; Campbell, Mel; Lin, Tzu-Ping; Pan, Chin-Chen; Lee, Hsin-Chen; Shih, Jean C; Chang, Pei-Ching

2017-04-12

Autophagy and apoptosis are two well-controlled mechanisms regulating cell fate. An understanding of decision-making between these two pathways is in its infancy. Monoamine oxidase A (MAOA) is a mitochondrial enzyme that is well-known in psychiatric research. Emerging reports showed that overexpression MAOA is associated with prostate cancer (PCa). Here, we show that MAOA is involved in mediating neuroendocrine differentiation of PCa cells, a feature associated with hormone-refractory PCa (HRPC), a lethal type of disease. Following recent reports showing that NED of PCa requires down-regulation of repressor element-1 silencing transcription factor (REST) and activation of autophagy; we observe that MAOA is a novel direct target gene of REST. Reactive oxygen species (ROS) produced by overexpressed MAOA plays an essential role in inhibiting apoptosis and activating autophagy in NED PCa cells. MAOA inhibitors significantly reduced NED and autophagy activation of PCa cells. Our results here show MAOA as a new decision-maker for activating autophagy and MAOA inhibitors may be useful as a potential therapy for neuroendocrine tumors.

Prostate-Specific Antigen (PSA) Screening and New Biomarkers for Prostate Cancer (PCa)

PubMed Central

Rittenhouse, Harry; Hu, Xinhai; Cammann, Henning; Jung, Klaus

2014-01-01

Abstract PSA screening reduces PCa-mortality but the disadvantages overdiagnosis and overtreatment require multivariable risk-prediction tools to select appropriate treatment or active surveillance. This review explains the differences between the two largest screening trials and discusses the drawbacks of screening and its meta-analysisxs. The current American and European screening strategies are described. Nonetheless, PSA is one of the most widely used tumor markers and strongly correlates with the risk of harboring PCa. However, while PSA has limitations for PCa detection with its low specificity there are several potential biomarkers presented in this review with utility for PCa currently being studied. There is an urgent need for new biomarkers especially to detect clinically significant and aggressive PCa. From all PSA-based markers, the FDA-approved prostate health index (phi) shows improved specificity over percent free and total PSA. Another kallikrein panel, 4K, which includes KLK2 has recently shown promise in clinical research studies but has not yet undergone formal validation studies. In urine, prostate cancer gene 3 (PCA3) has also been validated and approved by the FDA for its utility to detect PCa. The potential correlation of PCA3 with cancer aggressiveness requires more clinical studies. The detection of the fusion of androgen-regulated genes with genes of the regulatory transcription factors in tissue of ~50% of all PCa-patients is a milestone in PCa research. A combination of the urinary assays for TMPRSS2:ERG gene fusion and PCA3 shows an improved accuracy for PCa detection. Overall, the field of PCa biomarker discovery is very exciting and prospective. PMID:27683457

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization.

PubMed

Sotiras, Aristeidis; Resnick, Susan M; Davatzikos, Christos

2015-03-01

In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA. Copyright © 2014 Elsevier Inc. All rights reserved.

Segmentation, feature extraction, and multiclass brain tumor classification.

PubMed

Sachdeva, Jainy; Kumar, Vinod; Gupta, Indra; Khandelwal, Niranjan; Ahuja, Chirag Kamal

2013-12-01

Multiclass brain tumor classification is performed by using a diversified dataset of 428 post-contrast T1-weighted MR images from 55 patients. These images are of primary brain tumors namely astrocytoma (AS), glioblastoma multiforme (GBM), childhood tumor-medulloblastoma (MED), meningioma (MEN), secondary tumor-metastatic (MET), and normal regions (NR). Eight hundred fifty-six regions of interest (SROIs) are extracted by a content-based active contour model. Two hundred eighteen intensity and texture features are extracted from these SROIs. In this study, principal component analysis (PCA) is used for reduction of dimensionality of the feature space. These six classes are then classified by artificial neural network (ANN). Hence, this approach is named as PCA-ANN approach. Three sets of experiments have been performed. In the first experiment, classification accuracy by ANN approach is performed. In the second experiment, PCA-ANN approach with random sub-sampling has been used in which the SROIs from the same patient may get repeated during testing. It is observed that the classification accuracy has increased from 77 to 91 %. PCA-ANN has delivered high accuracy for each class: AS-90.74 %, GBM-88.46 %, MED-85 %, MEN-90.70 %, MET-96.67 %, and NR-93.78 %. In the third experiment, to remove bias and to test the robustness of the proposed system, data is partitioned in a manner such that the SROIs from the same patient are not common for training and testing sets. In this case also, the proposed system has performed well by delivering an overall accuracy of 85.23 %. The individual class accuracy for each class is: AS-86.15 %, GBM-65.1 %, MED-63.36 %, MEN-91.5 %, MET-65.21 %, and NR-93.3 %. A computer-aided diagnostic system comprising of developed methods for segmentation, feature extraction, and classification of brain tumors can be beneficial to radiologists for precise localization, diagnosis, and interpretation of brain tumors on MR images.

Avalanches and plasticity for colloids in a time dependent optical trap

DOE PAGES

Olson Reichhardt, Cynthia Jane; McDermott, Danielle Marie; Reichhardt, Charles

2015-08-25

Here, with the use of optical traps it is possible to confine assemblies of colloidal particles in two-dimensional and quasi-one-dimensional arrays. Here we examine how colloidal particles rearrange in a quasi-one-dimensional trap with a time dependent confining potential. The particle motion occurs both through slow elastic uniaxial distortions as well as through abrupt large-scale two-dimensional avalanches associated with plastic rearrangements. During the avalanches the particle velocity distributions extend over a broad range and can be fit to a power law consistent with other studies of plastic events mediated by dislocations.

Solitary wave solutions of two-dimensional nonlinear Kadomtsev-Petviashvili dynamic equation in dust-acoustic plasmas

NASA Astrophysics Data System (ADS)

Seadawy, Aly R.

2017-09-01

Nonlinear two-dimensional Kadomtsev-Petviashvili (KP) equation governs the behaviour of nonlinear waves in dusty plasmas with variable dust charge and two temperature ions. By using the reductive perturbation method, the two-dimensional dust-acoustic solitary waves (DASWs) in unmagnetized cold plasma consisting of dust fluid, ions and electrons lead to a KP equation. We derived the solitary travelling wave solutions of the two-dimensional nonlinear KP equation by implementing sech-tanh, sinh-cosh, extended direct algebraic and fraction direct algebraic methods. We found the electrostatic field potential and electric field in the form travelling wave solutions for two-dimensional nonlinear KP equation. The solutions for the KP equation obtained by using these methods can be demonstrated precisely and efficiency. As an illustration, we used the readymade package of Mathematica program 10.1 to solve the original problem. These solutions are in good agreement with the analytical one.

Qualitative analysis of precipitate formation on the surface and in the tubules of dentin irrigated with sodium hypochlorite and a final rinse of chlorhexidine or QMiX.

PubMed

Kolosowski, Kamil P; Sodhi, Rana N S; Kishen, Anil; Basrani, Bettina R

2014-12-01

Interaction of sodium hypochlorite (NaOCl) mixed with chlorhexidine (CHX) produces a brown precipitate containing para-chloroaniline (PCA). When QMiX is mixed with NaOCl, no precipitate forms, but color change occurs. The aim of this study was to qualitatively assess the formation of precipitate and PCA on the surface and in the tubules of dentin irrigated with NaOCl, followed either by EDTA, NaOCl, and CHX or by saline and QMiX by using time-of-flight secondary ion mass spectrometry (TOF-SIMS). Dentin blocks were obtained from human maxillary molars, embedded in resin, and cross-sectioned to expose dentin. Specimens in group 1 were immersed in 2.5% NaOCl, followed by 17% EDTA, 2.5% NaOCl, and 2% CHX. Specimens in group 2 were immersed in 2.5% NaOCl, followed by saline and QMiX. The dentin surfaces were subjected to TOF-SIMS spectra analysis. Longitudinal sections of dentin blocks were then exposed and subjected to TOF-SIMS analysis. All samples and analysis were performed in triplicate for confirmation. TOF-SIMS analysis of group 1 revealed an irregular precipitate, containing PCA and CHX breakdown products, on the dentin surfaces, occluding and extending into the tubules. In TOF-SIMS analysis of group 2, no precipitates, including PCA, were detected on the dentin surface or in the tubules. Within the limitations of this study, precipitate containing PCA was formed in the tubules of dentin irrigated with NaOCl followed by CHX. No precipitates or PCA were detected in the tubules of dentin irrigated with NaOCl followed by saline and QMiX. Copyright © 2014 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Prostate cancer outcomes in France: treatments, adverse effects and two-year mortality

PubMed Central

2014-01-01

Background This very large population-based study investigated outcomes after a diagnosis of prostate cancer (PCa) in terms of mortality rates, treatments and adverse effects. Methods Among the 11 million men aged 40 years and over covered by the general national health insurance scheme, those with newly managed PCa in 2009 were followed for two years based on data from the national health insurance information system (SNIIRAM). Patients were identified using hospitalisation diagnoses and specific refunds related to PCa and PCa treatments. Adverse effects of PCa treatments were identified by using hospital diagnoses, specific procedures and drug refunds. Results The age-standardised two-year all-cause mortality rate among the 43,460 men included in the study was 8.4%, twice that of all men aged 40 years and over. Among the 36,734 two-year survivors, 38% had undergone prostatectomy, 36% had been treated by hormone therapy, 29% by radiotherapy, 3% by brachytherapy and 20% were not treated. The frequency of treatment-related adverse effects varied according to age and type of treatment. Among men between 50 and 69 years of age treated by prostatectomy alone, 61% were treated for erectile dysfunction and 24% were treated for urinary disorders. The frequency of treatment for these disorders decreased during the second year compared to the first year (erectile dysfunction: 41% vs 53%, urinary disorders: 9% vs 20%). The frequencies of these treatments among men treated by external beam radiotherapy alone were 7% and 14%, respectively. Among men between 50 and 69 years with treated PCa, 46% received treatments for erectile dysfunction and 22% for urinary disorders. For controls without PCa but treated surgically for benign prostatic hyperplasia, these frequencies were 1.5% and 6.0%, respectively. Conclusions We report high survival rates two years after a diagnosis of PCa, but a high frequency of PCa treatment-related adverse effects. These frequencies remain underestimated, as they are based on treatments for erectile dysfunction and urinary disorders and do not reflect all functional outcomes. These results should help urologists and general practitioners to inform their patients about outcomes at the time of screening and diagnosis, and especially about potential treatment-related adverse effects. PMID:24927850

Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations.

PubMed

Zhang, Miaomiao; Wells, William M; Golland, Polina

2016-10-01

Using image-based descriptors to investigate clinical hypotheses and therapeutic implications is challenging due to the notorious "curse of dimensionality" coupled with a small sample size. In this paper, we present a low-dimensional analysis of anatomical shape variability in the space of diffeomorphisms and demonstrate its benefits for clinical studies. To combat the high dimensionality of the deformation descriptors, we develop a probabilistic model of principal geodesic analysis in a bandlimited low-dimensional space that still captures the underlying variability of image data. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than models based on the high-dimensional state-of-the-art approaches such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA).

Blind source separation problem in GPS time series

NASA Astrophysics Data System (ADS)

Gualandi, A.; Serpelloni, E.; Belardinelli, M. E.

2016-04-01

A critical point in the analysis of ground displacement time series, as those recorded by space geodetic techniques, is the development of data-driven methods that allow the different sources of deformation to be discerned and characterized in the space and time domains. Multivariate statistic includes several approaches that can be considered as a part of data-driven methods. A widely used technique is the principal component analysis (PCA), which allows us to reduce the dimensionality of the data space while maintaining most of the variance of the dataset explained. However, PCA does not perform well in finding the solution to the so-called blind source separation (BSS) problem, i.e., in recovering and separating the original sources that generate the observed data. This is mainly due to the fact that PCA minimizes the misfit calculated using an L2 norm (χ 2), looking for a new Euclidean space where the projected data are uncorrelated. The independent component analysis (ICA) is a popular technique adopted to approach the BSS problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we test the use of a modified variational Bayesian ICA (vbICA) method to recover the multiple sources of ground deformation even in the presence of missing data. The vbICA method models the probability density function (pdf) of each source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the sources with respect to standard ICA, and giving a more reliable estimate of them. Here we present its application to synthetic global positioning system (GPS) position time series, generated by simulating deformation near an active fault, including inter-seismic, co-seismic, and post-seismic signals, plus seasonal signals and noise, and an additional time-dependent volcanic source. We evaluate the ability of the PCA and ICA decomposition techniques in explaining the data and in recovering the original (known) sources. Using the same number of components, we find that the vbICA method fits the data almost as well as a PCA method, since the χ 2 increase is less than 10 % the value calculated using a PCA decomposition. Unlike PCA, the vbICA algorithm is found to correctly separate the sources if the correlation of the dataset is low (<0.67) and the geodetic network is sufficiently dense (ten continuous GPS stations within a box of side equal to two times the locking depth of a fault where an earthquake of Mw >6 occurred). We also provide a cookbook for the use of the vbICA algorithm in analyses of position time series for tectonic and non-tectonic applications.

Spatial and spectral analysis of corneal epithelium injury using hyperspectral images

NASA Astrophysics Data System (ADS)

Md Noor, Siti Salwa; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

2017-12-01

Eye assessment is essential in preventing blindness. Currently, the existing methods to assess corneal epithelium injury are complex and require expert knowledge. Hence, we have introduced a non-invasive technique using hyperspectral imaging (HSI) and an image analysis algorithm of corneal epithelium injury. Three groups of images were compared and analyzed, namely healthy eyes, injured eyes, and injured eyes with stain. Dimensionality reduction using principal component analysis (PCA) was applied to reduce massive data and redundancies. The first 10 principal components (PCs) were selected for further processing. The mean vector of 10 PCs with 45 pairs of all combinations was computed and sent to two classifiers. A quadratic Bayes normal classifier (QDC) and a support vector classifier (SVC) were used in this study to discriminate the eleven eyes into three groups. As a result, the combined classifier of QDC and SVC showed optimal performance with 2D PCA features (2DPCA-QDSVC) and was utilized to classify normal and abnormal tissues, using color image segmentation. The result was compared with human segmentation. The outcome showed that the proposed algorithm produced extremely promising results to assist the clinician in quantifying a cornea injury.

Efficient l1 -norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method.

PubMed

Kim, Eunwoo; Lee, Minsik; Choi, Chong-Ho; Kwak, Nojun; Oh, Songhwai

2015-02-01

Low-rank matrix approximation plays an important role in the area of computer vision and image processing. Most of the conventional low-rank matrix approximation methods are based on the l2 -norm (Frobenius norm) with principal component analysis (PCA) being the most popular among them. However, this can give a poor approximation for data contaminated by outliers (including missing data), because the l2 -norm exaggerates the negative effect of outliers. Recently, to overcome this problem, various methods based on the l1 -norm, such as robust PCA methods, have been proposed for low-rank matrix approximation. Despite the robustness of the methods, they require heavy computational effort and substantial memory for high-dimensional data, which is impractical for real-world problems. In this paper, we propose two efficient low-rank factorization methods based on the l1 -norm that find proper projection and coefficient matrices using the alternating rectified gradient method. The proposed methods are applied to a number of low-rank matrix approximation problems to demonstrate their efficiency and robustness. The experimental results show that our proposals are efficient in both execution time and reconstruction performance unlike other state-of-the-art methods.

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

Assessment of metal pollution based on multivariate statistical modeling of 'hot spot' sediments from the Black Sea.

PubMed

Simeonov, V; Massart, D L; Andreev, G; Tsakovski, S

2000-11-01

The paper deals with application of different statistical methods like cluster and principal components analysis (PCA), partial least squares (PLSs) modeling. These approaches are an efficient tool in achieving better understanding about the contamination of two gulf regions in Black Sea. As objects of the study, a collection of marine sediment samples from Varna and Bourgas "hot spots" gulf areas are used. In the present case the use of cluster and PCA make it possible to separate three zones of the marine environment with different levels of pollution by interpretation of the sediment analysis (Bourgas gulf, Varna gulf and lake buffer zone). Further, the extraction of four latent factors offers a specific interpretation of the possible pollution sources and separates natural from anthropogenic factors, the latter originating from contamination by chemical, oil refinery and steel-work enterprises. Finally, the PLSs modeling gives a better opportunity in predicting contaminant concentration on tracer (or tracers) element as compared to the one-dimensional approach of the baseline models. The results of the study are important not only in local aspect as they allow quick response in finding solutions and decision making but also in broader sense as a useful environmetrical methodology.

A three-dimensional kinematic model for the dissolution of crystals

NASA Astrophysics Data System (ADS)

Tellier, C. R.

1989-06-01

The two-dimensional kinematic theory developed by Frank is extended into three dimensions. It is shown that the theoretical equations for the propagation vector associated with the displacement of a moving surface element can be directly derived from the polar equation of the slowness surface.

Data on Support Vector Machines (SVM) model to forecast photovoltaic power.

PubMed

Malvoni, M; De Giorgi, M G; Congedo, P M

2016-12-01

The data concern the photovoltaic (PV) power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled "Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data" (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015) [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA) are applied to the Least Squares Support Vector Machines (LS-SVM) to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

Travelling-wave solutions of a weakly nonlinear two-dimensional higher-order Kadomtsev-Petviashvili dynamical equation for dispersive shallow-water waves

NASA Astrophysics Data System (ADS)

Seadawy, Aly R.

2017-01-01

The propagation of three-dimensional nonlinear irrotational flow of an inviscid and incompressible fluid of the long waves in dispersive shallow-water approximation is analyzed. The problem formulation of the long waves in dispersive shallow-water approximation lead to fifth-order Kadomtsev-Petviashvili (KP) dynamical equation by applying the reductive perturbation theory. By using an extended auxiliary equation method, the solitary travelling-wave solutions of the two-dimensional nonlinear fifth-order KP dynamical equation are derived. An analytical as well as a numerical solution of the two-dimensional nonlinear KP equation are obtained and analyzed with the effects of external pressure flow.

A Quantitative Comparison of the Similarity between Genes and Geography in Worldwide Human Populations

PubMed Central

Wang, Chaolong; Zöllner, Sebastian; Rosenberg, Noah A.

2012-01-01

Multivariate statistical techniques such as principal components analysis (PCA) and multidimensional scaling (MDS) have been widely used to summarize the structure of human genetic variation, often in easily visualized two-dimensional maps. Many recent studies have reported similarity between geographic maps of population locations and MDS or PCA maps of genetic variation inferred from single-nucleotide polymorphisms (SNPs). However, this similarity has been evident primarily in a qualitative sense; and, because different multivariate techniques and marker sets have been used in different studies, it has not been possible to formally compare genetic variation datasets in terms of their levels of similarity with geography. In this study, using genome-wide SNP data from 128 populations worldwide, we perform a systematic analysis to quantitatively evaluate the similarity of genes and geography in different geographic regions. For each of a series of regions, we apply a Procrustes analysis approach to find an optimal transformation that maximizes the similarity between PCA maps of genetic variation and geographic maps of population locations. We consider examples in Europe, Sub-Saharan Africa, Asia, East Asia, and Central/South Asia, as well as in a worldwide sample, finding that significant similarity between genes and geography exists in general at different geographic levels. The similarity is highest in our examples for Asia and, once highly distinctive populations have been removed, Sub-Saharan Africa. Our results provide a quantitative assessment of the geographic structure of human genetic variation worldwide, supporting the view that geography plays a strong role in giving rise to human population structure. PMID:22927824

A quantitative comparison of the similarity between genes and geography in worldwide human populations.

PubMed

Wang, Chaolong; Zöllner, Sebastian; Rosenberg, Noah A

2012-08-01

Multivariate statistical techniques such as principal components analysis (PCA) and multidimensional scaling (MDS) have been widely used to summarize the structure of human genetic variation, often in easily visualized two-dimensional maps. Many recent studies have reported similarity between geographic maps of population locations and MDS or PCA maps of genetic variation inferred from single-nucleotide polymorphisms (SNPs). However, this similarity has been evident primarily in a qualitative sense; and, because different multivariate techniques and marker sets have been used in different studies, it has not been possible to formally compare genetic variation datasets in terms of their levels of similarity with geography. In this study, using genome-wide SNP data from 128 populations worldwide, we perform a systematic analysis to quantitatively evaluate the similarity of genes and geography in different geographic regions. For each of a series of regions, we apply a Procrustes analysis approach to find an optimal transformation that maximizes the similarity between PCA maps of genetic variation and geographic maps of population locations. We consider examples in Europe, Sub-Saharan Africa, Asia, East Asia, and Central/South Asia, as well as in a worldwide sample, finding that significant similarity between genes and geography exists in general at different geographic levels. The similarity is highest in our examples for Asia and, once highly distinctive populations have been removed, Sub-Saharan Africa. Our results provide a quantitative assessment of the geographic structure of human genetic variation worldwide, supporting the view that geography plays a strong role in giving rise to human population structure.

Optical stereo video signal processor

NASA Technical Reports Server (NTRS)

Craig, G. D. (Inventor)

1985-01-01

An otpical video signal processor is described which produces a two-dimensional cross-correlation in real time of images received by a stereo camera system. The optical image of each camera is projected on respective liquid crystal light valves. The images on the liquid crystal valves modulate light produced by an extended light source. This modulated light output becomes the two-dimensional cross-correlation when focused onto a video detector and is a function of the range of a target with respect to the stereo camera. Alternate embodiments utilize the two-dimensional cross-correlation to determine target movement and target identification.

Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network

NASA Astrophysics Data System (ADS)

Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona

2016-10-01

The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

An application of principal component analysis to the clavicle and clavicle fixation devices.

PubMed

Daruwalla, Zubin J; Courtis, Patrick; Fitzpatrick, Clare; Fitzpatrick, David; Mullett, Hannan

2010-03-26

Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.

Statistical modeling of interfractional tissue deformation and its application in radiation therapy planning

NASA Astrophysics Data System (ADS)

Vile, Douglas J.

In radiation therapy, interfraction organ motion introduces a level of geometric uncertainty into the planning process. Plans, which are typically based upon a single instance of anatomy, must be robust against daily anatomical variations. For this problem, a model of the magnitude, direction, and likelihood of deformation is useful. In this thesis, principal component analysis (PCA) is used to statistically model the 3D organ motion for 19 prostate cancer patients, each with 8-13 fractional computed tomography (CT) images. Deformable image registration and the resultant displacement vector fields (DVFs) are used to quantify the interfraction systematic and random motion. By applying the PCA technique to the random DVFs, principal modes of random tissue deformation were determined for each patient, and a method for sampling synthetic random DVFs was developed. The PCA model was then extended to describe the principal modes of systematic and random organ motion for the population of patients. A leave-one-out study tested both the systematic and random motion model's ability to represent PCA training set DVFs. The random and systematic DVF PCA models allowed the reconstruction of these data with absolute mean errors between 0.5-0.9 mm and 1-2 mm, respectively. To the best of the author's knowledge, this study is the first successful effort to build a fully 3D statistical PCA model of systematic tissue deformation in a population of patients. By sampling synthetic systematic and random errors, organ occupancy maps were created for bony and prostate-centroid patient setup processes. By thresholding these maps, PCA-based planning target volume (PTV) was created and tested against conventional margin recipes (van Herk for bony alignment and 5 mm fixed [3 mm posterior] margin for centroid alignment) in a virtual clinical trial for low-risk prostate cancer. Deformably accumulated delivered dose served as a surrogate for clinical outcome. For the bony landmark setup subtrial, the PCA PTV significantly (p<0.05) reduced D30, D20, and D5 to bladder and D50 to rectum, while increasing rectal D20 and D5. For the centroid-aligned setup, the PCA PTV significantly reduced all bladder DVH metrics and trended to lower rectal toxicity metrics. All PTVs covered the prostate with the prescription dose.

Optimum aerodynamic design via boundary control

NASA Technical Reports Server (NTRS)

Jameson, Antony

1994-01-01

These lectures describe the implementation of optimization techniques based on control theory for airfoil and wing design. In previous studies it was shown that control theory could be used to devise an effective optimization procedure for two-dimensional profiles in which the shape is determined by a conformal transformation from a unit circle, and the control is the mapping function. Recently the method has been implemented in an alternative formulation which does not depend on conformal mapping, so that it can more easily be extended to treat general configurations. The method has also been extended to treat the Euler equations, and results are presented for both two and three dimensional cases, including the optimization of a swept wing.

Application of the Analogy Between Water Flow with a Free Surface and Two-dimensional Compressible Gas Flow

NASA Technical Reports Server (NTRS)

Orlin, W James; Lindner, Norman J; Bitterly, Jack G

1947-01-01

The theory of hydraulic analogy, that is, the analogy between water flow with a free surface and two-dimensional compressible gas flow and the limitations and conditions of the analogy are discussed. A test run was made using the hydraulic analogy as applied to the flow about circular cylinders at various diameters at subsonic velocities extending to the super critical range. The apparatus and techniques used in this application are described and criticized. Reasonably satisfactory agreement of pressure distributions and flow fields existed between water and airflow about corresponding bodies. This agreement indicated the possibility of extending experimental compressibility research by new methods.

Application of the Analogy Between Water Flow with a Free Surface and Two-Dimensional Compressible Gas Flow

NASA Technical Reports Server (NTRS)

Orlin, W James; Lindner, Norman J; Butterly, Jack G

1947-01-01

The theory of the hydraulic analogy -- that is, the analogy between water flow with a free surface and two-dimensional compressible gas flow -- and the limitations and conditions of the analogy are discussed. A test was run using the hydraulic analogy as applied to the flow about circular cylinders of various diameters at subsonic velocities extending into the supercritical range. The apparatus and techniques used in this application are described and criticized. Reasonably satisfactory agreement of pressure distributions and flow fields existed between water and air flow about corresponding bodies. This agreement indicated the possibility of extending experimental compressibility research by new methods.

Conjugated organic framework with three-dimensionally ordered stable structure and delocalized π clouds

NASA Astrophysics Data System (ADS)

Guo, Jia; Xu, Yanhong; Jin, Shangbin; Chen, Long; Kaji, Toshihiko; Honsho, Yoshihito; Addicoat, Matthew A.; Kim, Jangbae; Saeki, Akinori; Ihee, Hyotcherl; Seki, Shu; Irle, Stephan; Hiramoto, Masahiro; Gao, Jia; Jiang, Donglin

2013-11-01

Covalent organic frameworks are a class of crystalline organic porous materials that can utilize π-π-stacking interactions as a driving force for the crystallization of polygonal sheets to form layered frameworks and ordered pores. However, typical examples are chemically unstable and lack intrasheet π-conjugation, thereby significantly limiting their applications. Here we report a chemically stable, electronically conjugated organic framework with topologically designed wire frameworks and open nanochannels, in which the π conjugation-spans the two-dimensional sheets. Our framework permits inborn periodic ordering of conjugated chains in all three dimensions and exhibits a striking combination of properties: chemical stability, extended π-delocalization, ability to host guest molecules and hole mobility. We show that the π-conjugated organic framework is useful for high on-off ratio photoswitches and photovoltaic cells. Therefore, this strategy may constitute a step towards realizing ordered semiconducting porous materials for innovations based on two-dimensionally extended π systems.

Tracking Equilibrium and Nonequilibrium Shifts in Data with TREND.

PubMed

Xu, Jia; Van Doren, Steven R

2017-01-24

Principal component analysis (PCA) discovers patterns in multivariate data that include spectra, microscopy, and other biophysical measurements. Direct application of PCA to crowded spectra, images, and movies (without selecting peaks or features) was shown recently to identify their equilibrium or temporal changes. To enable the community to utilize these capabilities with a wide range of measurements, we have developed multiplatform software named TREND to Track Equilibrium and Nonequilibrium population shifts among two-dimensional Data frames. TREND can also carry this out by independent component analysis. We highlight a few examples of finding concurrent processes. TREND extracts dual phases of binding to two sites directly from the NMR spectra of the titrations. In a cardiac movie from magnetic resonance imaging, TREND resolves principal components (PCs) representing breathing and the cardiac cycle. TREND can also reconstruct the series of measurements from selected PCs, as illustrated for a biphasic, NMR-detected titration and the cardiac MRI movie. Fidelity of reconstruction of series of NMR spectra or images requires more PCs than needed to plot the largest population shifts. TREND reads spectra from many spectroscopies in the most common formats (JCAMP-DX and NMR) and multiple movie formats. The TREND package thus provides convenient tools to resolve the processes recorded by diverse biophysical methods. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points

NASA Astrophysics Data System (ADS)

Regis, Rommel G.

2014-02-01

This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems.

Investigation of a four-body coupling in the one-dimensional extended Penson-Kolb-Hubbard model

NASA Astrophysics Data System (ADS)

Ding, Hanqin; Ma, Xiaojuan; Zhang, Jun

2017-09-01

The experimental advances in cold fermion gases motivates the investigation of a one-dimensional (1D) correlated electronic system by incorporating a four-body coupling. Using the low-energy field theory scheme and focusing on the weak-coupling regime, we extend the 1D Penson-Kolb-Hubbard (PKH) model at half filling. It is found that the additional four-body interaction may significantly modify the quantum phase diagram, favoring the presence of the superconducting phase even in the case of two-body repulsions.

An extended approach for computing the critical properties in the two-and three-dimensional lattices within the effective-field renormalization group method

NASA Astrophysics Data System (ADS)

de Albuquerque, Douglas F.; Santos-Silva, Edimilson; Moreno, N. O.

2009-10-01

In this letter we employing the effective-field renormalization group (EFRG) to study the Ising model with nearest neighbors to obtain the reduced critical temperature and exponents ν for bi- and three-dimensional lattices by increasing cluster scheme by extending recent works. The technique follows up the same strategy of the mean field renormalization group (MFRG) by introducing an alternative way for constructing classical effective-field equations of state takes on rigorous Ising spin identities.

Can Prostate-Specific Antigen Kinetics before Prostate Biopsy Predict the Malignant Potential of Prostate Cancer?

PubMed

Kim, Sang Jin; Jeong, Tae Yoong; Yoo, Dae Seon; Park, Jinsung; Cho, Seok; Kang, Seok Ho; Lee, Sang Hyub; Jeon, Seung Hyun; Lee, Tchun Yong; Park, Sung Yul

2015-11-01

To predict the malignant potential of prostate cancer (PCa) according to prostate-specific antigen velocity (PSAV), PSA density (PSAD), free/total PSA ratio (%fPSA), and digital rectal examination (DRE). From January 2009 to December 2012, 548 adult male patients were diagnosed with PCa by prostate biopsy at four hospitals in Korea. We retrospectively analyzed 155 adult male patients with an initial PSA level≤10 ng/mL and whose PSA levels had been checked more than two times at least 6 months before they had been diagnosed with PCa, with test intervals of more than 3 months. Patients with a urinary tract infection, and patients who had previously undergone cystoscopy or surgery of the prostate were excluded. We separated patients into two groups according to Gleason sum [Gleason sum≤7 (n=134) or Gleason sum≥8 (n=21)] and the presence of extracapsular invasion [organ confined (n=129) or extracapsular invasion (n=26)]. Differences between the groups were compared. The group with a Gleason sum≥8 or extracapsular invasion of PCa showed high PSAV and significantly lower %fPSA. There were no significant differences in PSAD and the presence of an abnormality on DRE between two groups. In PCa patients treated with other therapies besides prostatectomy, a high PSA velocity and a low %fPSA may predict high grade PCa with a Gleason sum≥8 or the presence of extracapsular invasion.

Modeling pair distribution functions of rare-earth phosphate glasses using principal component analysis

DOE PAGES

Cole, Jacqueline M.; Cheng, Xie; Payne, Michael C.

2016-10-18

The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, co-doped with two rare-earth ions (R and R’) of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of co-doped REPGs presents significant challenges relative to their singly-doped counterparts; specifically, R and R’ are difficult to distinguish in terms of establishing relativemore » material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown co-doped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are pre-validated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly-doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. Furthermore, while this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials, and be exploited in large-scale data-mining efforts that probe many t(r) functions.« less

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

NASA Astrophysics Data System (ADS)

Laloy, Eric; Hérault, Romain; Lee, John; Jacques, Diederik; Linde, Niklas

2017-12-01

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200-500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging.

Integrated analysis of epigenomic and genomic changes by DNA methylation dependent mechanisms provides potential novel biomarkers for prostate cancer.

PubMed

White-Al Habeeb, Nicole M A; Ho, Linh T; Olkhov-Mitsel, Ekaterina; Kron, Ken; Pethe, Vaijayanti; Lehman, Melanie; Jovanovic, Lidija; Fleshner, Neil; van der Kwast, Theodorus; Nelson, Colleen C; Bapat, Bharati

2014-09-15

Epigenetic silencing mediated by CpG methylation is a common feature of many cancers. Characterizing aberrant DNA methylation changes associated with tumor progression may identify potential prognostic markers for prostate cancer (PCa). We treated two PCa cell lines, 22Rv1 and DU-145 with the demethylating agent 5-Aza 2'-deoxycitidine (DAC) and global methylation status was analyzed by performing methylation-sensitive restriction enzyme based differential methylation hybridization strategy followed by genome-wide CpG methylation array profiling. In addition, we examined gene expression changes using a custom microarray. Gene Set Enrichment Analysis (GSEA) identified the most significantly dysregulated pathways. In addition, we assessed methylation status of candidate genes that showed reduced CpG methylation and increased gene expression after DAC treatment, in Gleason score (GS) 8 vs. GS6 patients using three independent cohorts of patients; the publically available The Cancer Genome Atlas (TCGA) dataset, and two separate patient cohorts. Our analysis, by integrating methylation and gene expression in PCa cell lines, combined with patient tumor data, identified novel potential biomarkers for PCa patients. These markers may help elucidate the pathogenesis of PCa and represent potential prognostic markers for PCa patients.

Classification of Hyperspectral Data Based on Guided Filtering and Random Forest

NASA Astrophysics Data System (ADS)

Ma, H.; Feng, W.; Cao, X.; Wang, L.

2017-09-01

Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to "the curse of dimensionality". In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA), guided image filtering and the random forest classifier (RF). In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.

Modeling of the gain distribution for diode pumping of a solid-state laser rod with nonimaging optics.

PubMed

Koshel, R J; Walmsley, I A

1993-03-20

We investigate the absorption distribution in a cylindrical gain medium that is pumped by a source of distributed laser diodes by means of a pump cavity developed from the edge-ray principle of nonimaging optics. The performance of this pumping arrangement is studied by using a nonsequential, numerical, three-dimensional ray-tracing scheme. A figure of merit is defined for the pump cavities that takes into account the coupling efficiency and uniformity of the absorption distribution. It is found that the nonimaging pump cavity maintains a high coupling efficiency with extended two-dimensional diode arrays and obtains a fairly uniform absorption distribution. The nonimaging cavity is compared with two other designs: a close-coupled side-pumped cavity and an imaging design in the form of a elliptical cavity. The nonimaging cavity has a better figure of merit per diode than these two designs. It also permits the use of an extended, sparse, two-dimensional diode array, which reduces thermal loading of the source and eliminates all cavity optics other than the main reflector.

A numerical study of the 2- and 3-dimensional unsteady Navier-Stokes equations in velocity-vorticity variables using compact difference schemes

NASA Technical Reports Server (NTRS)

Gatski, T. B.; Grosch, C. E.

1984-01-01

A compact finite-difference approximation to the unsteady Navier-Stokes equations in velocity-vorticity variables is used to numerically simulate a number of flows. These include two-dimensional laminar flow of a vortex evolving over a flat plate with an embedded cavity, the unsteady flow over an elliptic cylinder, and aspects of the transient dynamics of the flow over a rearward facing step. The methodology required to extend the two-dimensional formulation to three-dimensions is presented.

Physiological and Molecular Genetic Effects of Time-Varying Electromagnetic Fields on Human Neuronal Cells

NASA Technical Reports Server (NTRS)

Goodwin, Thomas J.

2003-01-01

The present investigation details the development of model systems for growing two- and three-dimensional human neural progenitor cells within a culture medium facilitated by a time-varying electromagnetic field (TVEMF). The cells and culture medium are contained within a two- or three-dimensional culture vessel, and the electromagnetic field is emitted from an electrode or coil. These studies further provide methods to promote neural tissue regeneration by means of culturing the neural cells in either configuration. Grown in two dimensions, neuronal cells extended longitudinally, forming tissue strands extending axially along and within electrodes comprising electrically conductive channels or guides through which a time-varying electrical current was conducted. In the three-dimensional aspect, exposure to TVEMF resulted in the development of three-dimensional aggregates, which emulated organized neural tissues. In both experimental configurations, the proliferation rate of the TVEMF cells was 2.5 to 4.0 times the rate of the non-waveform cells. Each of the experimental embodiments resulted in similar molecular genetic changes regarding the growth potential of the tissues as measured by gene chip analyses, which measured more than 10,000 human genes simultaneously.

Personal authentication through dorsal hand vein patterns

NASA Astrophysics Data System (ADS)

Hsu, Chih-Bin; Hao, Shu-Sheng; Lee, Jen-Chun

2011-08-01

Biometric identification is an emerging technology that can solve security problems in our networked society. A reliable and robust personal verification approach using dorsal hand vein patterns is proposed in this paper. The characteristic of the approach needs less computational and memory requirements and has a higher recognition accuracy. In our work, the near-infrared charge-coupled device (CCD) camera is adopted as an input device for capturing dorsal hand vein images, it has the advantages of the low-cost and noncontact imaging. In the proposed approach, two finger-peaks are automatically selected as the datum points to define the region of interest (ROI) in the dorsal hand vein images. The modified two-directional two-dimensional principal component analysis, which performs an alternate two-dimensional PCA (2DPCA) in the column direction of images in the 2DPCA subspace, is proposed to exploit the correlation of vein features inside the ROI between images. The major advantage of the proposed method is that it requires fewer coefficients for efficient dorsal hand vein image representation and recognition. The experimental results on our large dorsal hand vein database show that the presented schema achieves promising performance (false reject rate: 0.97% and false acceptance rate: 0.05%) and is feasible for dorsal hand vein recognition.

Improvement of Prostate Cancer Diagnosis by Detecting PSA Glycosylation-Specific Changes.

PubMed

Llop, Esther; Ferrer-Batallé, Montserrat; Barrabés, Sílvia; Guerrero, Pedro Enrique; Ramírez, Manel; Saldova, Radka; Rudd, Pauline M; Aleixandre, Rosa N; Comet, Josep; de Llorens, Rafael; Peracaula, Rosa

2016-01-01

New markers based on PSA isoforms have recently been developed to improve prostate cancer (PCa) diagnosis. However, novel approaches are still required to differentiate aggressive from non-aggressive PCa to improve decision making for patients. PSA glycoforms have been shown to be differentially expressed in PCa. In particular, changes in the extent of core fucosylation and sialylation of PSA N-glycans in PCa patients compared to healthy controls or BPH patients have been reported. The objective of this study was to determine these specific glycan structures in serum PSA to analyze their potential value as markers for discriminating between BPH and PCa of different aggressiveness. In the present work, we have established two methodologies to analyze the core fucosylation and the sialic acid linkage of PSA N-glycans in serum samples from BPH (29) and PCa (44) patients with different degrees of aggressiveness. We detected a significant decrease in the core fucose and an increase in the α2,3-sialic acid percentage of PSA in high-risk PCa that differentiated BPH and low-risk PCa from high-risk PCa patients. In particular, a cut-off value of 0.86 of the PSA core fucose ratio, could distinguish high-risk PCa patients from BPH with 90% sensitivity and 95% specificity, with an AUC of 0.94. In the case of the α2,3-sialic acid percentage of PSA, the cut-off value of 30% discriminated between high-risk PCa and the group of BPH, low-, and intermediate-risk PCa with a sensitivity and specificity of 85.7% and 95.5%, respectively, with an AUC of 0.97. The latter marker exhibited high performance in differentiating between aggressive and non-aggressive PCa and has the potential for translational application in the clinic.

Improvement of Prostate Cancer Diagnosis by Detecting PSA Glycosylation-Specific Changes

PubMed Central

Llop, Esther; Ferrer-Batallé, Montserrat; Barrabés, Sílvia; Guerrero, Pedro Enrique; Ramírez, Manel; Saldova, Radka; Rudd, Pauline M.; Aleixandre, Rosa N.; Comet, Josep; de Llorens, Rafael; Peracaula, Rosa

2016-01-01

New markers based on PSA isoforms have recently been developed to improve prostate cancer (PCa) diagnosis. However, novel approaches are still required to differentiate aggressive from non-aggressive PCa to improve decision making for patients. PSA glycoforms have been shown to be differentially expressed in PCa. In particular, changes in the extent of core fucosylation and sialylation of PSA N-glycans in PCa patients compared to healthy controls or BPH patients have been reported. The objective of this study was to determine these specific glycan structures in serum PSA to analyze their potential value as markers for discriminating between BPH and PCa of different aggressiveness. In the present work, we have established two methodologies to analyze the core fucosylation and the sialic acid linkage of PSA N-glycans in serum samples from BPH (29) and PCa (44) patients with different degrees of aggressiveness. We detected a significant decrease in the core fucose and an increase in the α2,3-sialic acid percentage of PSA in high-risk PCa that differentiated BPH and low-risk PCa from high-risk PCa patients. In particular, a cut-off value of 0.86 of the PSA core fucose ratio, could distinguish high-risk PCa patients from BPH with 90% sensitivity and 95% specificity, with an AUC of 0.94. In the case of the α2,3-sialic acid percentage of PSA, the cut-off value of 30% discriminated between high-risk PCa and the group of BPH, low-, and intermediate-risk PCa with a sensitivity and specificity of 85.7% and 95.5%, respectively, with an AUC of 0.97. The latter marker exhibited high performance in differentiating between aggressive and non-aggressive PCa and has the potential for translational application in the clinic. PMID:27279911

Vegetation characteristics important to common songbirds in east Texas

USGS Publications Warehouse

Conner, Richard N.; Dickson, James G.; Locke, Brian A.; Segelquist, Charles A.

1983-01-01

Multivariate studies of breeding bird communities have used principal component analysis (PCA) or several-group (three or more groups) discriminant function analysis (DFA) to ordinate bird species on vegetational continua (Cody 1968, James 1971, Whitmore 1975). In community studies, high resolution of habitat requirements for individual species is not always possible with either PCA or several-group DFA. When habitat characteristics of several species are examined with a DFA the resultant axes optimally discriminate among all species simultaneously. Hence, the characteristics assigned to a particular species reflect in part the presence of other species in the analyses. A better resolution of each species' habitat requirements may be obtained from a two-group DFA, wherein habitats selected by a species are discriminated from all other available habitats. Analyses using two-group DFAs to compare habitat used by a species with habitat unused by the same species have the potential to provide an optimal frame of reference from which to examine habitat variables (Martinka 1972, Conner and Adkisson 1976, Whitmore 1981). Mathematically (DFA) it is possible to maximally separate two groups of multivariate observations with a single axis (Harner and whitmore 1977). A line drawn in three or n-dimensional space can easily be positioned to intersect two multivariate means (centroids). If three or more centroids for species are analyzed simultaneously, a single line can no longer intersect all centroids unless a perfectly linear relationship exists for the species being examined. The probability of such an occurrence is extremely low. Thus, a high degree of resolution can be realized when a two-group DFA is used to determine habitat parameters important to individual species. We have used two-group DFA to identify vegetation variable important to 12 common species of songbirds in East Texas.

Performance assessment of automated tissue characterization for prostate H and E stained histopathology

NASA Astrophysics Data System (ADS)

DiFranco, Matthew D.; Reynolds, Hayley M.; Mitchell, Catherine; Williams, Scott; Allan, Prue; Haworth, Annette

2015-03-01

Reliable automated prostate tumor detection and characterization in whole-mount histology images is sought in many applications, including post-resection tumor staging and as ground-truth data for multi-parametric MRI interpretation. In this study, an ensemble-based supervised classification algorithm for high-resolution histology images was trained on tile-based image features including histogram and gray-level co-occurrence statistics. The algorithm was assessed using different combinations of H and E prostate slides from two separate medical centers and at two different magnifications (400x and 200x), with the aim of applying tumor classification models to new data. Slides from both datasets were annotated by expert pathologists in order to identify homogeneous cancerous and non-cancerous tissue regions of interest, which were then categorized as (1) low-grade tumor (LG-PCa), including Gleason 3 and high-grade prostatic intraepithelial neoplasia (HG-PIN), (2) high-grade tumor (HG-PCa), including various Gleason 4 and 5 patterns, or (3) non-cancerous, including benign stroma and benign prostatic hyperplasia (BPH). Classification models for both LG-PCa and HG-PCa were separately trained using a support vector machine (SVM) approach, and per-tile tumor prediction maps were generated from the resulting ensembles. Results showed high sensitivity for predicting HG-PCa with an AUC up to 0.822 using training data from both medical centres, while LG-PCa showed a lower sensitivity of 0.763 with the same training data. Visual inspection of cancer probability heatmaps from 9 patients showed that 17/19 tumors were detected, and HG-PCa generally reported less false positives than LG-PCa.

Differentiation of neuropsychological features between posterior cortical atrophy and early onset Alzheimer's disease.

PubMed

Li, Jieying; Wu, Liyong; Tang, Yi; Zhou, Aihong; Wang, Fen; Xing, Yi; Jia, Jianping

2018-05-10

Posterior cortical atrophy (PCA) is a group of clinical syndromes characterized by visuospatial and visuoperceptual impairment, with memory relatively preserved. Although PCA is pathologically almost identical to Alzheimer's disease (AD), they have different cognitive features. Those differences have only rarely been reported in any Chinese population. The purpose of the study is to establish neuropsychological tests that distinguish the clinical features of PCA from early onset AD (EOAD). Twenty-one PCA patients, 20 EOAD patients, and 20 healthy controls participated in this study. Patients had disease duration of ≤4 years. All participants completed a series of neuropsychological tests to evaluate their visuospatial, visuoperceptual, visuo-constructive, language, executive function, memory, calculation, writing, and reading abilities. The cognitive features of PCA and EOAD were compared. All the neuropsychological test scores showed that both the PCA and EOAD patients were significantly more impaired than people in the control group. However, PCA patients were significantly more impaired than EOAD patients in visuospatial, visuoperceptual, and visuo-constructive function, as well as in handwriting, and reading Chinese characters. The profile of neuropsychological test results highlights cognitive features that differ between PCA and EOAD. One surprising result is that the two syndromes could be distinguished by patients' ability to read and write Chinese characters. Tests based on these characteristics could therefore form a brief PCA neuropsychological examination that would improve the diagnosis of PCA.

Detection and Classification of Whale Acoustic Signals

NASA Astrophysics Data System (ADS)

Xian, Yin

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification. In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information. In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data. Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear. We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

The Present and Future of Prostate Cancer Urine Biomarkers

PubMed Central

Rigau, Marina; Olivan, Mireia; Garcia, Marta; Sequeiros, Tamara; Montes, Melania; Colás, Eva; Llauradó, Marta; Planas, Jacques; de Torres, Inés; Morote, Juan; Cooper, Colin; Reventós, Jaume; Clark, Jeremy; Doll, Andreas

2013-01-01

In order to successfully cure patients with prostate cancer (PCa), it is important to detect the disease at an early stage. The existing clinical biomarkers for PCa are not ideal, since they cannot specifically differentiate between those patients who should be treated immediately and those who should avoid over-treatment. Current screening techniques lack specificity, and a decisive diagnosis of PCa is based on prostate biopsy. Although PCa screening is widely utilized nowadays, two thirds of the biopsies performed are still unnecessary. Thus the discovery of non-invasive PCa biomarkers remains urgent. In recent years, the utilization of urine has emerged as an attractive option for the non-invasive detection of PCa. Moreover, a great improvement in high-throughput “omic” techniques has presented considerable opportunities for the identification of new biomarkers. Herein, we will review the most significant urine biomarkers described in recent years, as well as some future prospects in that field. PMID:23774836

Pressure distribution under flexible polishing tools. II - Cylindrical (conical) optics

NASA Astrophysics Data System (ADS)

Mehta, Pravin K.

1990-10-01

A previously developed eigenvalue model is extended to determine polishing pressure distribution by rectangular tools with unequal stiffness in two directions on cylindrical optics. Tool misfit is divided into two simplified one-dimensional problems and one simplified two-dimensional problem. Tools with nonuniform cross-sections are treated with a new one-dimensional eigenvalue algorithm, permitting evaluation of tool designs where the edge is more flexible than the interior. This maintains edge pressure variations within acceptable parameters. Finite element modeling is employed to resolve upper bounds, which handle pressure changes in the two-dimensional misfit element. Paraboloids and hyperboloids from the NASA AXAF system are treated with the AXAFPOD software for this method, and are verified with NASTRAN finite element analyses. The maximum deviation from the one-dimensional azimuthal pressure variation is predicted to be 10 percent and 20 percent for paraboloids and hyperboloids, respectively.

Unsteady transonic flow calculations for two-dimensional canard-wing configurations with aeroelastic applications

NASA Technical Reports Server (NTRS)

Batina, J. T.

1985-01-01

Unsteady transonic flow calculations for aerodynamically interfering airfoil configurations are performed as a first step toward solving the three dimensional canard wing interaction problem. These calculations are performed by extending the XTRAN2L two dimensional unsteady transonic small disturbance code to include an additional airfoil. Unsteady transonic forces due to plunge and pitch motions of a two dimensional canard and wing are presented. Results for a variety of canard wing separation distances reveal the effects of aerodynamic interference on unsteady transonic airloads. Aeroelastic analyses employing these unsteady airloads demonstrate the effects of aerodynamic interference on aeroelastic stability and flutter. For the configurations studied, increases in wing flutter speed result with the inclusion of the aerodynamically interfering canard.

Graphanes: Sheets and stacking under pressure

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

Wen, Xiao-Dong; Hand, Louis; Labet, Vanessa

2011-04-26

Eight isomeric two-dimensional graphane sheets are found in a theoretical study. Four of these nets—two built on chair cyclohexanes, two on boat—are more stable thermodynamically than the isomeric benzene, or polyacetylene. Three-dimensional crystals are built up from the two-dimensional sheets, and their hypothetical behavior under pressure (up to 300 GPa) is explored. While the three-dimensional graphanes remain, as expected, insulating or semiconducting in this pressure range, there is a remarkable inversion in stability of the five crystals studied. Two stacking polytypes that are not the most stable at ambient pressure (one based on an unusual chair cyclohexane net, the othermore » on a boat) are significantly stabilized with increasing pressure relative to stackings of simple chair sheets. The explanation may lie in the balance on intra and intersheet contacts in the extended arrays.« less

Comparing Networks from a Data Analysis Perspective

NASA Astrophysics Data System (ADS)

Li, Wei; Yang, Jing-Yu

To probe network characteristics, two predominant ways of network comparison are global property statistics and subgraph enumeration. However, they suffer from limited information and exhaustible computing. Here, we present an approach to compare networks from the perspective of data analysis. Initially, the approach projects each node of original network as a high-dimensional data point, and the network is seen as clouds of data points. Then the dispersion information of the principal component analysis (PCA) projection of the generated data clouds can be used to distinguish networks. We applied this node projection method to the yeast protein-protein interaction networks and the Internet Autonomous System networks, two types of networks with several similar higher properties. The method can efficiently distinguish one from the other. The identical result of different datasets from independent sources also indicated that the method is a robust and universal framework.

Relationships between NIR spectra and sensory attributes of Thai commercial fish sauces.

PubMed

Ritthiruangdej, Pitiporn; Suwonsichon, Thongchai

2007-07-01

Twenty Thai commercial fish sauces were characterized by sensory descriptive analysis and near-infrared (NIR) spectroscopy. The main objectives were i) to investigate the relationships between sensory attributes and NIR spectra of samples and ii) to characterize the sensory characteristics of fish sauces based on NIR data. A generic descriptive analysis with 12 trained panels was used to characterize the sensory attributes. These attributes consisted of 15 descriptors: brown color, 5 aromatics (sweet, caramelized, fermented, fishy, and musty), 4 tastes (sweet, salty, bitter, and umami), 3 aftertastes (sweet, salty and bitter) and 2 flavors (caramelized and fishy). The results showed that Thai fish sauce samples exhibited significant differences in all of sensory attribute values (p < 0.05). NIR transflectance spectra were obtained from 1100 to 2500 nm. Prior to investigation of the relationships between sensory attributes and NIR spectra, principal component analysis (PCA) was applied to reduce the dimensionality of the spectral data from 622 wavelengths to two uncorrelated components (NIR1 and NIR2) which explained 92 and 7% of the total variation, respectively. NIR1 was highly correlated with the wavelength regions of 1100 - 1544, 1774 - 2062, 2092 - 2308, and 2358 - 2440 nm, while NIR2 was highly correlated with the wavelength regions of 1742 - 1764, 2066 - 2088, and 2312 - 2354 nm. Subsequently, the relationships among these two components and all sensory attributes were also investigated by PCA. The results showed that the first three principal components (PCs) named as fishy flavor component (PC1), sweet component (PC2) and bitterness component (PC3), respectively, explained a total of 66.86% of the variation. NIR1 was mainly correlated to the sensory attributes of fishy aromatic, fishy flavor and sweet aftertaste on PC1. In addition, the PCA using only the factor loadings of NIR1 and NIR2 could be used to classify samples into three groups which showed high, medium and low degrees of fishy aromatic, fishy flavor and sweet aftertaste.

Surface Detail Reproduction and Dimensional Stability of Contemporary Irreversible Hydrocolloid Alternatives after Immediate and Delayed Pouring.

PubMed

Kusugal, Preethi; Chourasiya, Ritu Sunil; Ruttonji, Zarir; Astagi, Preeti; Nayak, Ajay Kumar; Patil, Abhishekha

2018-01-01

To overcome the poor dimensional stability of irreversible hydrocolloids, alternative materials were introduced. The dimensional changes of these alternatives after delayed pouring are not well studied and documented in the literature. The purpose of the study is to evaluate and compare the surface detail reproduction and dimensional stability of two irreversible hydrocolloid alternatives with an extended-pour irreversible hydrocolloid at different time intervals. All testing were performed according to the ANSI/ADA specification number 18 for surface detail reproduction and specification number 19 for dimensional change. The test materials used in this study were newer irreversible hydrocolloid alternatives such as AlgiNot FS, Algin-X Ultra FS, and Kromopan 100 which is an extended pour irreversible hydrocolloid as control. The surface detail reproduction was evaluated using stereomicroscope. The dimensional change after storage period of 1 h, 24 h, and 120 h was assessed and compared between the test materials and control. The data were analyzed using one-way ANOVA and post hoc Bonferroni test. Statistically significant results ( P < 0.001) were seen when mean scores of the tested materials were compared with respect to reproduction of 22 μm line from the metal block. Kromopan 100 showed statistically significant differences between different time intervals ( P < 0.001) and exhibited more dimensional change. Algin-X Ultra FS proved to be more accurate and dimensionally stable. Newer irreversible hydrocolloid alternative impression materials were more accurate in surface detail reproduction and exhibited minimal dimensional change after storage period of 1 h, 24 h, and 120 h than extended-pour irreversible hydrocolloid impression material.

Alterations in expressed prostate secretion-urine PSA N-glycosylation discriminate prostate cancer from benign prostate hyperplasia

PubMed Central

Sun, Chenxia; Wen, Fuping; Wang, Haifeng; Guo, Huaizu; Gao, Xu; Xu, Chuanliang; Xu, Chuanliang; Yang, Chenghua; Sun, Yinghao

2017-01-01

The prostate specific antigen (PSA) test is widely used for early diagnosis of prostate cancer (PCa). However, its limited sensitivity has led to over-diagnosis and over-treatment of PCa. Glycosylation alteration is a common phenomenon in cancer development. Different PSA glycan subforms have been proposed as diagnostic markers to better differentiate PCa from benign prostate hyperplasia (BPH). In this study, we purified PSA from expressed prostate secretions (EPS)-urine samples from 32 BPH and 30 PCa patients and provided detailed PSA glycan profiles in Chinese population. We found that most of the PSA glycans from EPS-urine were complex type biantennary glycans. We observed two major patterns in PSA glycan profiles. Overall there was no distinct separation of PSA glycan profiles between BPH and PCa patients. However, we detected a significant increase of glycan FA2 and FM5A2G2S1 in PCa when compared with BPH patients. Furthermore, we observed that the composition of FA2 glycan increased significantly in advanced PCa with Gleason score ≥8, which potentially could be translated to clinic as a marker for aggressive PCa. PMID:29100363

Alterations in expressed prostate secretion-urine PSA N-glycosylation discriminate prostate cancer from benign prostate hyperplasia.

PubMed

Jia, Gaozhen; Dong, Zhenyang; Sun, Chenxia; Wen, Fuping; Wang, Haifeng; Guo, Huaizu; Gao, Xu; Xu, Chuanliang; Xu, Chuanliang; Yang, Chenghua; Sun, Yinghao

2017-09-29

The prostate specific antigen (PSA) test is widely used for early diagnosis of prostate cancer (PCa). However, its limited sensitivity has led to over-diagnosis and over-treatment of PCa. Glycosylation alteration is a common phenomenon in cancer development. Different PSA glycan subforms have been proposed as diagnostic markers to better differentiate PCa from benign prostate hyperplasia (BPH). In this study, we purified PSA from expressed prostate secretions (EPS)-urine samples from 32 BPH and 30 PCa patients and provided detailed PSA glycan profiles in Chinese population. We found that most of the PSA glycans from EPS-urine were complex type biantennary glycans. We observed two major patterns in PSA glycan profiles. Overall there was no distinct separation of PSA glycan profiles between BPH and PCa patients. However, we detected a significant increase of glycan FA2 and FM5A2G2S1 in PCa when compared with BPH patients. Furthermore, we observed that the composition of FA2 glycan increased significantly in advanced PCa with Gleason score ≥8, which potentially could be translated to clinic as a marker for aggressive PCa.

A comparison of clinicopathological features and prognosis in prostate cancer between atomic bomb survivors and control patients

PubMed Central

Shoji, Koichi; Teishima, Jun; Hayashi, Tetsutaro; Shinmei, Shunsuke; Akita, Tomoyuki; Sentani, Kazuhiro; Takeshima, Yukio; Arihiro, Koji; Tanaka, Junko; Yasui, Wataru; Matsubara, Akio

2017-01-01

An atomic bomb (A-bomb) was dropped on Hiroshima on 6th August 1945. Although numerous studies have investigated cancer incidence and mortality among A-bomb survivors, only a small number have addressed urological cancer in these survivors. The aim of the present study was to investigate the clinicopathological features of prostate cancer (PCa) in A-bomb survivors. The clinicopathological features and prognosis of PCa were retrospectively reviewed in 212 survivors and 595 control patients between November 1996 and December 2010. The histopathological and clinical outcomes of surgical treatment of PCa were also evaluated in 69 survivors and 162 control patients. Despite the higher age at diagnosis compared with the control group (P=0.0031), survivors were more likely to have been diagnosed with PCa from a health check compared with the control group (P<0.0001). As a consequence, the survivors were found to exhibit metastasis significantly less frequently (199/212, 93.9%) compared with the control patients (521/595, 87.6%; P=0.0076). Prognosis in the two groups was examined, subsequent to a mean length of follow-up of 44 months. Overall survival (OS) and PCa-specific survival (CS) were similar between the two groups (OS, P=0.2196; CS, P=0.1017). A-bomb exposure was not found to be an independent predictor for prognosis by multivariate analysis (OS, P=0.7800; CS, P=0.8688). The clinicopathological features of patients who underwent a prostatectomy were similar except for the diagnosis opportunity between the two groups. Progression-free survival rates were similar between the two groups (P=0.5630). A-bomb exposure was not a significant and independent predictor for worsening of progression-free prognosis by multivariate analysis (P=0.3763). A-bomb exposure does not appear to exert deleterious effects on the biological aggressiveness of PCa and the prognosis of patients with PCa. PMID:28693168

A comparison of clinicopathological features and prognosis in prostate cancer between atomic bomb survivors and control patients.

PubMed

Shoji, Koichi; Teishima, Jun; Hayashi, Tetsutaro; Shinmei, Shunsuke; Akita, Tomoyuki; Sentani, Kazuhiro; Takeshima, Yukio; Arihiro, Koji; Tanaka, Junko; Yasui, Wataru; Matsubara, Akio

2017-07-01

An atomic bomb (A-bomb) was dropped on Hiroshima on 6th August 1945. Although numerous studies have investigated cancer incidence and mortality among A-bomb survivors, only a small number have addressed urological cancer in these survivors. The aim of the present study was to investigate the clinicopathological features of prostate cancer (PCa) in A-bomb survivors. The clinicopathological features and prognosis of PCa were retrospectively reviewed in 212 survivors and 595 control patients between November 1996 and December 2010. The histopathological and clinical outcomes of surgical treatment of PCa were also evaluated in 69 survivors and 162 control patients. Despite the higher age at diagnosis compared with the control group (P=0.0031), survivors were more likely to have been diagnosed with PCa from a health check compared with the control group (P<0.0001). As a consequence, the survivors were found to exhibit metastasis significantly less frequently (199/212, 93.9%) compared with the control patients (521/595, 87.6%; P=0.0076). Prognosis in the two groups was examined, subsequent to a mean length of follow-up of 44 months. Overall survival (OS) and PCa-specific survival (CS) were similar between the two groups (OS, P=0.2196; CS, P=0.1017). A-bomb exposure was not found to be an independent predictor for prognosis by multivariate analysis (OS, P=0.7800; CS, P=0.8688). The clinicopathological features of patients who underwent a prostatectomy were similar except for the diagnosis opportunity between the two groups. Progression-free survival rates were similar between the two groups (P=0.5630). A-bomb exposure was not a significant and independent predictor for worsening of progression-free prognosis by multivariate analysis (P=0.3763). A-bomb exposure does not appear to exert deleterious effects on the biological aggressiveness of PCa and the prognosis of patients with PCa.

Analysis of absorption and reflection mechanisms in a three-dimensional plate silencer

NASA Astrophysics Data System (ADS)

Wang, Chunqi; Huang, Lixi

2008-06-01

When a segment of a rigid duct is replaced by a plate backed by a hard-walled cavity, grazing incident sound waves induce plate vibration, hence sound reflection. Based on this mechanism, a broadband plate silencer, which works effectively from low-to-medium frequencies have been developed recently. A typical plate silencer consists of an expansion chamber with two side-branch cavities covered by light but extremely stiff plates. Such a configuration is two-dimensional in nature. In this paper, numerical study is extended to three-dimensional configurations to investigate the potential improvement in sound reflection. Finite element simulation shows that the three-dimensional configurations perform better than the corresponding two-dimensional design, especially in the relatively high frequency region. Further analysis shows that the three-dimensional design gives better plate response at higher axial modes than the simple two-dimensional design. Sound absorption mechanism is also introduced to the plate silencer by adding two dissipative chambers on the two lateral sides of a two-cavity wave reflector, hence a hybrid silencer. Numerical simulation shows that the proposed hybrid silencer is able to achieve a good moderate bandwidth with much reduced total length in comparison with pure absorption design.

Prostate Health Index (Phi) and Prostate Cancer Antigen 3 (PCA3) Significantly Improve Prostate Cancer Detection at Initial Biopsy in a Total PSA Range of 2–10 ng/ml

PubMed Central

Perdonà, Sisto; Marino, Ada; Mazzarella, Claudia; Perruolo, Giuseppe; D’Esposito, Vittoria; Cosimato, Vincenzo; Buonerba, Carlo; Di Lorenzo, Giuseppe; Musi, Gennaro; De Cobelli, Ottavio; Chun, Felix K.; Terracciano, Daniela

2013-01-01

Many efforts to reduce prostate specific antigen (PSA) overdiagnosis and overtreatment have been made. To this aim, Prostate Health Index (Phi) and Prostate Cancer Antigen 3 (PCA3) have been proposed as new more specific biomarkers. We evaluated the ability of phi and PCA3 to identify prostate cancer (PCa) at initial prostate biopsy in men with total PSA range of 2–10 ng/ml. The performance of phi and PCA3 were evaluated in 300 patients undergoing first prostate biopsy. ROC curve analyses tested the accuracy (AUC) of phi and PCA3 in predicting PCa. Decision curve analyses (DCA) were used to compare the clinical benefit of the two biomarkers. We found that the AUC value of phi (0.77) was comparable to those of %p2PSA (0.76) and PCA3 (0.73) with no significant differences in pairwise comparison (%p2PSA vs phi p = 0.673, %p2PSA vs. PCA3 p = 0.417 and phi vs. PCA3 p = 0.247). These three biomarkers significantly outperformed fPSA (AUC = 0.60), % fPSA (AUC = 0.62) and p2PSA (AUC = 0.63). At DCA, phi and PCA3 exhibited a very close net benefit profile until the threshold probability of 25%, then phi index showed higher net benefit than PCA3. Multivariable analysis showed that the addition of phi and PCA3 to the base multivariable model (age, PSA, %fPSA, DRE, prostate volume) increased predictive accuracy, whereas no model improved single biomarker performance. Finally we showed that subjects with active surveillance (AS) compatible cancer had significantly lower phi and PCA3 values (p<0.001 and p = 0.01, respectively). In conclusion, both phi and PCA3 comparably increase the accuracy in predicting the presence of PCa in total PSA range 2–10 ng/ml at initial biopsy, outperforming currently used %fPSA. PMID:23861782

Prostate Health Index (Phi) and Prostate Cancer Antigen 3 (PCA3) significantly improve prostate cancer detection at initial biopsy in a total PSA range of 2-10 ng/ml.

PubMed

Ferro, Matteo; Bruzzese, Dario; Perdonà, Sisto; Marino, Ada; Mazzarella, Claudia; Perruolo, Giuseppe; D'Esposito, Vittoria; Cosimato, Vincenzo; Buonerba, Carlo; Di Lorenzo, Giuseppe; Musi, Gennaro; De Cobelli, Ottavio; Chun, Felix K; Terracciano, Daniela

2013-01-01

Many efforts to reduce prostate specific antigen (PSA) overdiagnosis and overtreatment have been made. To this aim, Prostate Health Index (Phi) and Prostate Cancer Antigen 3 (PCA3) have been proposed as new more specific biomarkers. We evaluated the ability of phi and PCA3 to identify prostate cancer (PCa) at initial prostate biopsy in men with total PSA range of 2-10 ng/ml. The performance of phi and PCA3 were evaluated in 300 patients undergoing first prostate biopsy. ROC curve analyses tested the accuracy (AUC) of phi and PCA3 in predicting PCa. Decision curve analyses (DCA) were used to compare the clinical benefit of the two biomarkers. We found that the AUC value of phi (0.77) was comparable to those of %p2PSA (0.76) and PCA3 (0.73) with no significant differences in pairwise comparison (%p2PSA vs phi p = 0.673, %p2PSA vs. PCA3 p = 0.417 and phi vs. PCA3 p = 0.247). These three biomarkers significantly outperformed fPSA (AUC = 0.60), % fPSA (AUC = 0.62) and p2PSA (AUC = 0.63). At DCA, phi and PCA3 exhibited a very close net benefit profile until the threshold probability of 25%, then phi index showed higher net benefit than PCA3. Multivariable analysis showed that the addition of phi and PCA3 to the base multivariable model (age, PSA, %fPSA, DRE, prostate volume) increased predictive accuracy, whereas no model improved single biomarker performance. Finally we showed that subjects with active surveillance (AS) compatible cancer had significantly lower phi and PCA3 values (p<0.001 and p = 0.01, respectively). In conclusion, both phi and PCA3 comparably increase the accuracy in predicting the presence of PCa in total PSA range 2-10 ng/ml at initial biopsy, outperforming currently used %fPSA.

Functional Analysis of Genes for Biosynthesis of Pyocyanin and Phenazine-1-Carboxamide from Pseudomonas aeruginosa PAO1

PubMed Central

Mavrodi, Dmitri V.; Bonsall, Robert F.; Delaney, Shannon M.; Soule, Marilyn J.; Phillips, Greg; Thomashow, Linda S.

2001-01-01

Two seven-gene phenazine biosynthetic loci were cloned from Pseudomonas aeruginosa PAO1. The operons, designated phzA1B1C1D1E1F1G1 and phzA2B2C2D2E2F2G2, are homologous to previously studied phenazine biosynthetic operons from Pseudomonas fluorescens and Pseudomonas aureofaciens. Functional studies of phenazine-nonproducing strains of fluorescent pseudomonads indicated that each of the biosynthetic operons from P. aeruginosa is sufficient for production of a single compound, phenazine-1-carboxylic acid (PCA). Subsequent conversion of PCA to pyocyanin is mediated in P. aeruginosa by two novel phenazine-modifying genes, phzM and phzS, which encode putative phenazine-specific methyltransferase and flavin-containing monooxygenase, respectively. Expression of phzS alone in Escherichia coli or in enzymes, pyocyanin-nonproducing P. fluorescens resulted in conversion of PCA to 1-hydroxyphenazine. P. aeruginosa with insertionally inactivated phzM or phzS developed pyocyanin-deficient phenotypes. A third phenazine-modifying gene, phzH, which has a homologue in Pseudomonas chlororaphis, also was identified and was shown to control synthesis of phenazine-1-carboxamide from PCA in P. aeruginosa PAO1. Our results suggest that there is a complex pyocyanin biosynthetic pathway in P. aeruginosa consisting of two core loci responsible for synthesis of PCA and three additional genes encoding unique enzymes involved in the conversion of PCA to pyocyanin, 1-hydroxyphenazine, and phenazine-1-carboxamide. PMID:11591691

Association between age-related reductions in testosterone and risk of prostate cancer-An analysis of patients' data with prostatic diseases.

PubMed

Wang, Kai; Chen, Xinguang; Bird, Victoria Y; Gerke, Travis A; Manini, Todd M; Prosperi, Mattia

2017-11-01

The relationship between serum total testosterone and prostate cancer (PCa) risk is controversial. The hypothesis that faster age-related reduction in testosterone is linked with increased PCa risk remains untested. We conducted our study at a tertiary-level hospital in southeast of the USA, and derived data from the Medical Registry Database of individuals that were diagnosed of any prostate-related disease from 2001 to 2015. Cases were those diagnosed of PCa and had one or more measurements of testosterone prior to PCa diagnosis. Controls were those without PCa and had one or more testosterone measurements. Multivariable logistic regression models for PCa risk of absolute levels (one-time measure and 5-year average) and annual change in testosterone were respectively constructed. Among a total of 1,559 patients, 217 were PCa cases, and neither one-time measure nor 5-year average of testosterone was found to be significantly associated with PCa risk. Among the 379 patients with two or more testosterone measurements, 27 were PCa cases. For every 10 ng/dL increment in annual reduction of testosterone, the risk of PCa would increase by 14% [adjusted odds ratio, 1.14; 95% confidence interval (CI), 1.03-1.25]. Compared to patients with a relatively stable testosterone, patients with an annual testosterone reduction of more than 30 ng/dL had 5.03 [95% CI: 1.53, 16.55] fold increase in PCa risk. This implies a faster age-related reduction in, but not absolute level of serum total testosterone as a risk factor for PCa. Further longitudinal studies are needed to confirm this finding. © 2017 UICC.

[Cu(I)(bpp)]BF4: the first extended coordination network prepared solvothermally in an ionic liquid solvent.

PubMed

Jin, Kun; Huang, Xiaoying; Pan, Long; Li, Jing; Appel, Aaron; Wherland, Scot; Pang, Long

2002-12-07

Use of an ionic liquid [bmim][BF4] (bmim = 1-butyl-3-methylimidazolium) as solvent has resulted in the first extended coordination structure, the two-dimensional network [Cu(bpp)]BF4 [bpp = 1,3-bis(4-pyridyl)propane], produced via a solvothermal route.

Comparison between a disposable and an electronic PCA device for labor epidural analgesia.

PubMed

Sumikura, Hiroyuki; van de Velde, Marc; Tateda, Takeshi

2004-01-01

The aims of the present study were (1) to investigate if a disposable patient-controlled analgesia (PCA) device can be used for labor analgesia and (2) to evaluate the device by midwives and parturients. Forty healthy parturients were divided into two groups and received combined spinal epidural analgesia for labor pain relief. Following intrathecal administration of 3 mg ropivacaine and 1.5 microg sufentanil, either a disposable PCA device (Coopdech Syrinjector; Daiken Medical, Osaka, Japan) or an electronic PCA device (IVAC PCAM PCA Syringe Pump; Alaris, Basingstoke, UK) was connected to the epidural catheter, and 0.15% ropivacaine with sufentanil 0.75 microg/ml was used for continuous infusion and PCA. For an electronic PCA device, continuous infusion rate, bolus dose, lockout time, and hourly limit were set at 4 ml/h, 3 ml, 15 min, and 16 ml, respectively. For a disposable PCA device, continuous infusion rate, bolus dose, and an hourly limit were set at 4 ml/h, 3 ml, and 16 ml, respectively, but lockout function was not available. No differences were observed between the groups concerning demographic data, obstetric data, and outcome of labor. Anesthetic requirements (disposable, 9.7 +/- 4.7 ml/h; electronic, 8.2 +/- 4.0 ml/h) and VAS score during the delivery (disposable, 26 +/- 25; electronic, 21 +/- 22) were similar between the groups. Midwives praised the disposable PCA device as well as the electronic one. The present results imply that the disposable PCA device can be an alternative to the electronic PCA device for labor analgesia.

The Association Between Calcium Channel Blocker Use and Prostate Cancer Outcome

PubMed Central

Poch, Michael A.; Mehedint, Diana; Green, Dawn J.; Payne-Ondracek, Rochelle; Fontham, Elizabeth T.H.; Bensen, Jeannette T.; Attwood, Kristopher; Wilding, Gregory E.; Guru, Khurshid A.; Underwood, Willie; Mohler, James L.; Heemers, Hannelore V.

2018-01-01

BACKGROUND Epidemiological studies indicate that calcium channel blocker (CCB) use is inversely related to prostate cancer (PCa) incidence. The association between CCB use and PCa aggressiveness at the time of radical prostatectomy (RP) and outcome after RP was examined. METHODS Medication use, PCa aggressiveness and post-RP outcome were retrieved from a prospectively populated database that contains clinical and outcome for RP patients at Roswell Park Cancer Institute (RPCI) from 1993 to 2010. The database was queried for anti-hypertensive medication use at diagnosis for patients with ≥1 year follow-up. Recurrence was defined using NCCN guidelines. Chi-Square tests assessed the relationship between CCB use and PCa aggressiveness. Cox regression models compared the distribution of progression-free survival (PFS) and overall survival (OS) with adjustment for covariates. Results for association between CCB usage and PCa aggressiveness were validated using data from the population-based North Carolina-Louisiana Prostate Cancer Project (PCaP). RESULTS 48%, 37%, and 15% of RPCI’s RP patients (n = 875) had low, intermediate, and high aggressive PCa, respectively. 104 (11%) had a history of CCB use. Patients taking CCBs were more likely to be older, have a higher BMI and use additional anti-hypertensive medications. Diagnostic PSA levels, PCa aggressiveness, and margin status were similar for CCB users and non-users. PFS and OS did not differ between the two groups. Tumor aggressiveness was associated with PFS. CCB use in the PCaP study population was not associated with PCa aggressiveness. CONCLUSIONS CCB use is not associated with PCa aggressiveness at diagnosis, PFS or OS. PMID:23280547

Angiopreventive efficacy of pure flavonolignans from milk thistle extract against prostate cancer: targeting VEGF-VEGFR signaling.

PubMed

Deep, Gagan; Gangar, Subhash Chander; Rajamanickam, Subapriya; Raina, Komal; Gu, Mallikarjuna; Agarwal, Chapla; Oberlies, Nicholas H; Agarwal, Rajesh

2012-01-01

The role of neo-angiogenesis in prostate cancer (PCA) growth and metastasis is well established, but the development of effective and non-toxic pharmacological inhibitors of angiogenesis remains an unaccomplished goal. In this regard, targeting aberrant angiogenesis through non-toxic phytochemicals could be an attractive angiopreventive strategy against PCA. The rationale of the present study was to compare the anti-angiogenic potential of four pure diastereoisomeric flavonolignans, namely silybin A, silybin B, isosilybin A and isosilybin B, which we established previously as biologically active constituents in Milk Thistle extract. Results showed that oral feeding of these flavonolignans (50 and 100 mg/kg body weight) effectively inhibit the growth of advanced human PCA DU145 xenografts. Immunohistochemical analyses revealed that these flavonolignans inhibit tumor angiogenesis biomarkers (CD31 and nestin) and signaling molecules regulating angiogenesis (VEGF, VEGFR1, VEGFR2, phospho-Akt and HIF-1α) without adversely affecting the vessel-count in normal tissues (liver, lung, and kidney) of tumor bearing mice. These flavonolignans also inhibited the microvessel sprouting from mouse dorsal aortas ex vivo, and the VEGF-induced cell proliferation, capillary-like tube formation and invasiveness of human umbilical vein endothelial cells (HUVEC) in vitro. Further studies in HUVEC showed that these diastereoisomers target cell cycle, apoptosis and VEGF-induced signaling cascade. Three dimensional growth assay as well as co-culture invasion and in vitro angiogenesis studies (with HUVEC and DU145 cells) suggested the differential effectiveness of the diastereoisomers toward PCA and endothelial cells. Overall, these studies elucidated the comparative anti-angiogenic efficacy of pure flavonolignans from Milk Thistle and suggest their usefulness in PCA angioprevention.

Three-dimensional motor schema based navigation

NASA Technical Reports Server (NTRS)

Arkin, Ronald C.

1989-01-01

Reactive schema-based navigation is possible in space domains by extending the methods developed for ground-based navigation found within the Autonomous Robot Architecture (AuRA). Reformulation of two dimensional motor schemas for three dimensional applications is a straightforward process. The manifold advantages of schema-based control persist, including modular development, amenability to distributed processing, and responsiveness to environmental sensing. Simulation results show the feasibility of this methodology for space docking operations in a cluttered work area.

High-Order Central WENO Schemes for Multi-Dimensional Hamilton-Jacobi Equations

NASA Technical Reports Server (NTRS)

Bryson, Steve; Levy, Doron; Biegel, Bryan (Technical Monitor)

2002-01-01

We present new third- and fifth-order Godunov-type central schemes for approximating solutions of the Hamilton-Jacobi (HJ) equation in an arbitrary number of space dimensions. These are the first central schemes for approximating solutions of the HJ equations with an order of accuracy that is greater than two. In two space dimensions we present two versions for the third-order scheme: one scheme that is based on a genuinely two-dimensional Central WENO reconstruction, and another scheme that is based on a simpler dimension-by-dimension reconstruction. The simpler dimension-by-dimension variant is then extended to a multi-dimensional fifth-order scheme. Our numerical examples in one, two and three space dimensions verify the expected order of accuracy of the schemes.

The monoamine oxidase A gene promoter repeat and prostate cancer risk.

PubMed

White, Thomas A; Kwon, Erika M; Fu, Rong; Lucas, Jared M; Ostrander, Elaine A; Stanford, Janet L; Nelson, Peter S

2012-11-01

Amine catabolism by monoamine oxidase A (MAOA) contributes to oxidative stress, which plays a role in prostate cancer (PCa) development and progression. An upstream variable-number tandem repeat (uVNTR) in the MAOA promoter influences gene expression and activity, and may thereby affect PCa susceptibility. Caucasian (n = 2,572) men from two population-based case-control studies of PCa were genotyped for the MAOA-VNTR. Logistic regression was used to assess PCa risk in relation to genotype. Common alleles of the MAOA-VNTR were not associated with the relative risk of PCa, nor did the relationship differ by clinical features of the disease. The rare 5-copy variant (frequency: 0.5% in cases; 1.8% in controls), however, was associated with a reduced PCa risk (odds ratio, OR = 0.30, 95% CI 0.13-0.71). A rare polymorphism of the MAOA promoter previously shown to confer low expression was associated with a reduced risk of developing PCa. This novel finding awaits confirmation in other study populations. Copyright © 2012 Wiley Periodicals, Inc.

The PSA−/lo prostate cancer cell population harbors self-renewing long-term tumor-propagating cells that resist castration

PubMed Central

Qin, Jichao; Liu, Xin; Laffin, Brian; Chen, Xin; Choy, Grace; Jeter, Collene; Calhoun-Davis, Tammy; Li, Hangwen; Palapattu, Ganesh S.; Pang, Shen; Lin, Kevin; Huang, Jiaoti; Ivanov, Ivan; Li, Wei; Suraneni, Mahipal V.; Tang, Dean G.

2012-01-01

SUMMARY Prostate cancer (PCa) is heterogeneous and contains both differentiated and undifferentiated tumor cells, but the relative functional contribution of these two cell populations remains unclear. Here we report distinct molecular, cellular, and tumor-propagating properties of PCa cells that express high (PSA+) and low (PSA−/lo) levels of the differentiation marker PSA. PSA−/lo PCa cells are quiescent and refractory to stresses including androgen deprivation, exhibit high clonogenic potential, and possess long-term tumor-propagating capacity. They preferentially express stem cell genes and can undergo asymmetric cell division generating PSA+ cells. Importantly, PSA−/lo PCa cells can initiate robust tumor development and resist androgen ablation in castrated hosts, and harbor highly tumorigenic castration-resistant PCa cells that can be prospectively enriched using ALDH+CD44+α2β1+ phenotype. In contrast, PSA+ PCa cells possess more limited tumor-propagating capacity, undergo symmetric division and are sensitive to castration. Together, our study suggests PSA−/lo cells may represent a critical source of castration-resistant PCa cells. PMID:22560078

STAMP2 increases oxidative stress and is critical for prostate cancer

PubMed Central

Jin, Yang; Wang, Ling; Qu, Su; Sheng, Xia; Kristian, Alexandr; Mælandsmo, Gunhild M; Pällmann, Nora; Yuca, Erkan; Tekedereli, Ibrahim; Gorgulu, Kivanc; Alpay, Neslihan; Sood, Anil; Lopez-Berestein, Gabriel; Fazli, Ladan; Rennie, Paul; Risberg, Bjørn; Wæhre, Håkon; Danielsen, Håvard E; Ozpolat, Bulent; Saatcioglu, Fahri

2015-01-01

The six transmembrane protein of prostate 2 (STAMP2) is an androgen-regulated gene whose mRNA expression is increased in prostate cancer (PCa). Here, we show that STAMP2 protein expression is increased in human PCa compared with benign prostate that is also correlated with tumor grade and treatment response. We also show that STAMP2 significantly increased reactive oxygen species (ROS) in PCa cells through its iron reductase activity which also depleted NADPH levels. Knockdown of STAMP2 expression in PCa cells inhibited proliferation, colony formation, and anchorage-independent growth, and significantly increased apoptosis. Furthermore, STAMP2 effects were, at least in part, mediated by activating transcription factor 4 (ATF4), whose expression is regulated by ROS. Consistent with in vitro findings, silencing STAMP2 significantly inhibited PCa xenograft growth in mice. Finally, therapeutic silencing of STAMP2 by systemically administered nanoliposomal siRNA profoundly inhibited tumor growth in two established preclinical PCa models in mice. These data suggest that STAMP2 is required for PCa progression and thus may serve as a novel therapeutic target. PMID:25680860

Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study

NASA Astrophysics Data System (ADS)

Ghavidel, Sahar; Imani, Farhad; Khallaghi, Siavash; Gibson, Eli; Khojaste, Amir; Gaed, Mena; Moussa, Madeleine; Gomez, Jose A.; Siemens, D. Robert; Leveridge, Michael; Chang, Silvia; Fenster, Aaron; Ward, Aaron D.; Abolmaesumi, Purang; Mousavi, Parvin

2016-03-01

Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out cross-validation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound.

Facilitating text reading in posterior cortical atrophy.

PubMed

Yong, Keir X X; Rajdev, Kishan; Shakespeare, Timothy J; Leff, Alexander P; Crutch, Sebastian J

2015-07-28

We report (1) the quantitative investigation of text reading in posterior cortical atrophy (PCA), and (2) the effects of 2 novel software-based reading aids that result in dramatic improvements in the reading ability of patients with PCA. Reading performance, eye movements, and fixations were assessed in patients with PCA and typical Alzheimer disease and in healthy controls (experiment 1). Two reading aids (single- and double-word) were evaluated based on the notion that reducing the spatial and oculomotor demands of text reading might support reading in PCA (experiment 2). Mean reading accuracy in patients with PCA was significantly worse (57%) compared with both patients with typical Alzheimer disease (98%) and healthy controls (99%); spatial aspects of passages were the primary determinants of text reading ability in PCA. Both aids led to considerable gains in reading accuracy (PCA mean reading accuracy: single-word reading aid = 96%; individual patient improvement range: 6%-270%) and self-rated measures of reading. Data suggest a greater efficiency of fixations and eye movements under the single-word reading aid in patients with PCA. These findings demonstrate how neurologic characterization of a neurodegenerative syndrome (PCA) and detailed cognitive analysis of an important everyday skill (reading) can combine to yield aids capable of supporting important everyday functional abilities. This study provides Class III evidence that for patients with PCA, 2 software-based reading aids (single-word and double-word) improve reading accuracy. © 2015 American Academy of Neurology.

Facilitating text reading in posterior cortical atrophy

PubMed Central

Rajdev, Kishan; Shakespeare, Timothy J.; Leff, Alexander P.; Crutch, Sebastian J.

2015-01-01

Objective: We report (1) the quantitative investigation of text reading in posterior cortical atrophy (PCA), and (2) the effects of 2 novel software-based reading aids that result in dramatic improvements in the reading ability of patients with PCA. Methods: Reading performance, eye movements, and fixations were assessed in patients with PCA and typical Alzheimer disease and in healthy controls (experiment 1). Two reading aids (single- and double-word) were evaluated based on the notion that reducing the spatial and oculomotor demands of text reading might support reading in PCA (experiment 2). Results: Mean reading accuracy in patients with PCA was significantly worse (57%) compared with both patients with typical Alzheimer disease (98%) and healthy controls (99%); spatial aspects of passages were the primary determinants of text reading ability in PCA. Both aids led to considerable gains in reading accuracy (PCA mean reading accuracy: single-word reading aid = 96%; individual patient improvement range: 6%–270%) and self-rated measures of reading. Data suggest a greater efficiency of fixations and eye movements under the single-word reading aid in patients with PCA. Conclusions: These findings demonstrate how neurologic characterization of a neurodegenerative syndrome (PCA) and detailed cognitive analysis of an important everyday skill (reading) can combine to yield aids capable of supporting important everyday functional abilities. Classification of evidence: This study provides Class III evidence that for patients with PCA, 2 software-based reading aids (single-word and double-word) improve reading accuracy. PMID:26138948

Performance characteristics of prostate-specific antigen density and biopsy core details to predict oncological outcome in patients with intermediate to high-risk prostate cancer underwent robot-assisted radical prostatectomy.

PubMed

Yashi, Masahiro; Nukui, Akinori; Tokura, Yuumi; Takei, Kohei; Suzuki, Issei; Sakamoto, Kazumasa; Yuki, Hideo; Kambara, Tsunehito; Betsunoh, Hironori; Abe, Hideyuki; Fukabori, Yoshitatsu; Nakazato, Yoshimasa; Kaji, Yasushi; Kamai, Takao

2017-06-23

Many urologic surgeons refer to biopsy core details for decision making in cases of localized prostate cancer (PCa) to determine whether an extended resection and/or lymph node dissection should be performed. Furthermore, recent reports emphasize the predictive value of prostate-specific antigen density (PSAD) for further risk stratification, not only for low-risk PCa, but also for intermediate- and high-risk PCa. This study focused on these parameters and compared respective predictive impact on oncologic outcomes in Japanese PCa patients. Two-hundred and fifty patients with intermediate- and high-risk PCa according to the National Comprehensive Cancer Network (NCCN) classification, that underwent robot-assisted radical prostatectomy at a single institution, and with observation periods of longer than 6 months were enrolled. None of the patients received hormonal treatments including antiandrogens, luteinizing hormone-releasing hormone analogues, or 5-alpha reductase inhibitors preoperatively. PSAD and biopsy core details, including the percentage of positive cores and the maximum percentage of cancer extent in each positive core, were analyzed in association with unfavorable pathologic results of prostatectomy specimens, and further with biochemical recurrence. The cut-off values of potential predictive factors were set through receiver-operating characteristic curve analyses. In the entire cohort, a higher PSAD, the percentage of positive cores, and maximum percentage of cancer extent in each positive core were independently associated with advanced tumor stage ≥ pT3 and an increased index tumor volume > 0.718 ml. NCCN classification showed an association with a tumor stage ≥ pT3 and a Gleason score ≥8, and the attribution of biochemical recurrence was also sustained. In each NCCN risk group, these preoperative factors showed various associations with unfavorable pathological results. In the intermediate-risk group, the percentage of positive cores showed an independent predictive value for biochemical recurrence. In the high-risk group, PSAD showed an independent predictive value. PSAD and biopsy core details have different performance characteristics for the prediction of oncologic outcomes in each NCCN risk group. Despite the need for further confirmation of the results with a larger cohort and longer observation, these factors are important as preoperative predictors in addition to the NCCN classification for a urologic surgeon to choose a surgical strategy.

Differential distribution of sperm subpopulations and incidence of pleiomorphisms in ejaculates of captive howling monkeys ( Alouatta caraya)

NASA Astrophysics Data System (ADS)

Valle, R. R.; Carvalho, F. M.; Muniz, J. A. P. C.; Leal, C. L. V.; García-Herreros, M.

2013-10-01

The aim of this study was to develop an objective method to determine the incidence of pleiomorphisms and its influence on the distribution of sperm morphometric subpopulations in ejaculates of howling monkeys ( Alouatta caraya) by using a combination of computerized analysis system (ASMA) and principal component analysis (PCA) methods. Ejaculates were collected by electroejaculation methods on a regular basis from five individuals maintained under identical captive environmental, nutritional, and management conditions. Each sperm head was measured for dimensional parameters (Area [ A, (square micrometers)], Perimeter [ P, (micrometers)], Length [ L, (micrometers)], and Width [ W, (micrometers)]) and shape-derived parameters (Ellipticity [( L/ W)], Elongation [( L - W)/( L + W)], and Rugosity [(4л A/ P 2)]). PCA revealed two principal components explaining more than the 96 % of the variance. Clustering methods and discriminant analyzes were performed and seven separate subpopulations were identified. There were differences ( P < 0.001) in the distribution of the seven subpopulations as well as in the incidence of abnormal pleiomorphisms (58.6 %, 49.8 %, 35.1 %, 66.4 %, and 55.1 %, P < 0.05) among the five donors tested. Our results indicated that differences among individuals related to the incidence of pleiomorphisms, and sperm subpopulational structure was not related to the captivity conditions or the sperm collection method, since all individuals were studied under identical conditions. In conclusion, the combination of ASMA and PCA is a useful clinical diagnostic resource for detecting deficiencies in sperm morphology and sperm subpopulations in A. caraya ejaculates that could be used in ex situ conservation programs of threatened species in Alouatta genus or even other endangered neotropical primate species.

Stochastic solution to quantum dynamics

NASA Technical Reports Server (NTRS)

John, Sarah; Wilson, John W.

1994-01-01

The quantum Liouville equation in the Wigner representation is solved numerically by using Monte Carlo methods. For incremental time steps, the propagation is implemented as a classical evolution in phase space modified by a quantum correction. The correction, which is a momentum jump function, is simulated in the quasi-classical approximation via a stochastic process. The technique, which is developed and validated in two- and three- dimensional momentum space, extends an earlier one-dimensional work. Also, by developing a new algorithm, the application to bound state motion in an anharmonic quartic potential shows better agreement with exact solutions in two-dimensional phase space.

A discontinuous Galerkin method for two-dimensional PDE models of Asian options

NASA Astrophysics Data System (ADS)

Hozman, J.; Tichý, T.; Cvejnová, D.

2016-06-01

In our previous research we have focused on the problem of plain vanilla option valuation using discontinuous Galerkin method for numerical PDE solution. Here we extend a simple one-dimensional problem into two-dimensional one and design a scheme for valuation of Asian options, i.e. options with payoff depending on the average of prices collected over prespecified horizon. The algorithm is based on the approach combining the advantages of the finite element methods together with the piecewise polynomial generally discontinuous approximations. Finally, an illustrative example using DAX option market data is provided.

Geocenter motion estimated from GRACE orbits: The impact of F10.7 solar flux

NASA Astrophysics Data System (ADS)

Tseng, Tzu-Pang; Hwang, Cheinway; Sośnica, Krzysztof; Kuo, Chung-Yen; Liu, Ya-Chi; Yeh, Wen-Hao

2017-06-01

We assess the impact of orbit modeling on the origin offsets between GRACE kinematic and reduced-dynamic orbits. The origin of the kinematic orbit is the center of IGS network (CN), whereas the origin of the reduced-dynamic orbit is assumed to be the center of mass of the Earth (CM). Theoretically, the origin offset between these two orbits is associated with the geocenter motion. However, the dynamic property of the reduced-dynamic orbit is highly related to orbit parameterizations. The assessment of the F10.7 impact on the geocenter motion is implemented by using different orbit parameterization setups in the reduced-dynamic method. We generate two types of reduced-dynamic orbits using 15 and 240 empirical parameters per day from 2005 to 2012. The empirical parameter used in Bernese GNSS Software is called piece-wise constant empirical acceleration (PCA) and is mainly to absorb the non-gravitational forces mostly related to the atmospheric drag and solar radiation pressure. The differences between kinematic and dynamic orbits can serve as a measurement for geocenter. The RMS value of the geocenter measurement in the 15-PCA case is approximately 3.5 cm and approximately 2 cm in the 240-PCA case. The correlation between the orbit difference and F10.7 is about 0.90 in the 15-PCA case and -0.10 to 0 in the 240-PCA case. This implies that the reduced-dynamic orbit modeled with 240 PCAs absorbs the F10.7 variation, which aliases to the 15-PCA orbit solution. The annual amplitudes of the geocenter motion are 3.1, 3.1 and 2.5 mm in the 15-PCA case, compared to 0.9, 2.0 and 1.3 mm in the 240-PCA case in the X, Y and Z components, respectively. The 15-PCA solution is thus closer to the geocenter motions derived from other space-geodetic techniques. The proposed method is limited to the parameterizations in the reduced-dynamic approach.

Screening of oil sources by using comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry and multivariate statistical analysis.

PubMed

Zhang, Wanfeng; Zhu, Shukui; He, Sheng; Wang, Yanxin

2015-02-06

Using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOFMS), volatile and semi-volatile organic compounds in crude oil samples from different reservoirs or regions were analyzed for the development of a molecular fingerprint database. Based on the GC×GC/TOFMS fingerprints of crude oils, principal component analysis (PCA) and cluster analysis were used to distinguish the oil sources and find biomarkers. As a supervised technique, the geological characteristics of crude oils, including thermal maturity, sedimentary environment etc., are assigned to the principal components. The results show that tri-aromatic steroid (TAS) series are the suitable marker compounds in crude oils for the oil screening, and the relative abundances of individual TAS compounds have excellent correlation with oil sources. In order to correct the effects of some other external factors except oil sources, the variables were defined as the content ratio of some target compounds and 13 parameters were proposed for the screening of oil sources. With the developed model, the crude oils were easily discriminated, and the result is in good agreement with the practical geological setting. Copyright © 2014 Elsevier B.V. All rights reserved.

Deep Adaptive Log-Demons: Diffeomorphic Image Registration with Very Large Deformations

PubMed Central

Jia, Kebin

2015-01-01

This paper proposes a new framework for capturing large and complex deformation in image registration. Traditionally, this challenging problem relies firstly on a preregistration, usually an affine matrix containing rotation, scale, and translation and afterwards on a nonrigid transformation. According to preregistration, the directly calculated affine matrix, which is obtained by limited pixel information, may misregistrate when large biases exist, thus misleading following registration subversively. To address this problem, for two-dimensional (2D) images, the two-layer deep adaptive registration framework proposed in this paper firstly accurately classifies the rotation parameter through multilayer convolutional neural networks (CNNs) and then identifies scale and translation parameters separately. For three-dimensional (3D) images, affine matrix is located through feature correspondences by a triplanar 2D CNNs. Then deformation removal is done iteratively through preregistration and demons registration. By comparison with the state-of-the-art registration framework, our method gains more accurate registration results on both synthetic and real datasets. Besides, principal component analysis (PCA) is combined with correlation like Pearson and Spearman to form new similarity standards in 2D and 3D registration. Experiment results also show faster convergence speed. PMID:26120356

Deep Adaptive Log-Demons: Diffeomorphic Image Registration with Very Large Deformations.

PubMed

Zhao, Liya; Jia, Kebin

2015-01-01

This paper proposes a new framework for capturing large and complex deformation in image registration. Traditionally, this challenging problem relies firstly on a preregistration, usually an affine matrix containing rotation, scale, and translation and afterwards on a nonrigid transformation. According to preregistration, the directly calculated affine matrix, which is obtained by limited pixel information, may misregistrate when large biases exist, thus misleading following registration subversively. To address this problem, for two-dimensional (2D) images, the two-layer deep adaptive registration framework proposed in this paper firstly accurately classifies the rotation parameter through multilayer convolutional neural networks (CNNs) and then identifies scale and translation parameters separately. For three-dimensional (3D) images, affine matrix is located through feature correspondences by a triplanar 2D CNNs. Then deformation removal is done iteratively through preregistration and demons registration. By comparison with the state-of-the-art registration framework, our method gains more accurate registration results on both synthetic and real datasets. Besides, principal component analysis (PCA) is combined with correlation like Pearson and Spearman to form new similarity standards in 2D and 3D registration. Experiment results also show faster convergence speed.

Visual Exploration of Semantic Relationships in Neural Word Embeddings

DOE PAGES

Liu, Shusen; Bremer, Peer-Timo; Thiagarajan, Jayaraman J.; ...

2017-08-29

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). But, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. Particularly, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or evenmore » misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. We introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.« less

Visual Exploration of Semantic Relationships in Neural Word Embeddings

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

Liu, Shusen; Bremer, Peer-Timo; Thiagarajan, Jayaraman J.

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). But, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. Particularly, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or evenmore » misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. We introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.« less

Opioid Patient Controlled Analgesia (PCA) use during the Initial Experience with the IMPROVE PCA Trial: A Phase III Analgesic Trial for Hospitalized Sickle Cell Patients with Painful Episodes

PubMed Central

Dampier, Carlton D.; Smith, Wally R.; Kim, Hae-Young; Wager, Carrie Greene; Bell, Margaret C.; Minniti, Caterina P.; Keefer, Jeffrey; Hsu, Lewis; Krishnamurti, Lakshmanan; Mack, A. Kyle; McClish, Donna; McKinlay, Sonja M.; Miller, Scott T.; Osunkwo, Ifeyinwa; Seaman, Phillip; Telen, Marilyn J.; Weiner, Debra L.

2015-01-01

Opioid analgesics administered by patient-controlled analgesia (PCA) are frequently used for pain relief in children and adults with sickle cell disease (SCD) hospitalized for persistent vaso-occlusive pain, but optimum opioid dosing is not known. To better define PCA dosing recommendations, a multi-center phase III clinical trial was conducted comparing two alternative opioid PCA dosing strategies (HDLI-higher demand dose with low constant infusion or LDHI- lower demand dose and higher constant infusion) in 38 subjects who completed randomization prior to trial closure. Total opioid utilization (morphine equivalents, mg/kg) in 22 adults was 11.6 ± 2.6 and 4.7 ± 0.9 in the HDLI and in the LDHI arms, respectively, and in 12 children it was 3.7 ± 1.0 and 5.8 ± 2.2, respectively. Opioid-related symptoms were mild and similar in both PCA arms (mean daily opioid symptom intensity score: HDLI 0.9 ± 0.1, LDHI 0.9 ± 0.2). The slow enrollment and early study termination limited conclusions regarding superiority of either treatment regimen. This study adds to our understanding of opioid PCA usage in SCD. Future clinical trial protocol designs for opioid PCA may need to consider potential differences between adults and children in PCA usage. PMID:21953763

Application of metabonomics on an experimental model of fibrosis and cirrhosis induced by thioacetamide in rats

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

Constantinou, Maria A.; Department of Forensic Medicine and Toxicology, Medical School, University of Athens, 75, Mikras Asias str., 11527 Athens; Theocharis, Stamatios E.

2007-01-01

Metabonomics has already been used to discriminate different pathological states in biological fields. The metabolic profiles of chronic experimental fibrosis and cirrhosis induction in rats were investigated using {sup 1}H NMR spectroscopy of liver extracts and serum combined with pattern recognition techniques. Rats were continuously administered with thioacetamide (TAA) in the drinking water (300 mg TAA/L), and sacrificed on 1st, 2nd, and 3rd month of treatment. {sup 1}H NMR spectra of aqueous and lipid liver extracts, together with serum were subjected to Principal Component Analysis (PCA). Liver portions were also subjected to histopathological examination and biochemical determination of malondialdehyde (MDA).more » Liver fibrosis and cirrhosis were progressively induced in TAA-treated rats, verified by the histopathological examination and the alterations of MDA levels. TAA administration revealed a number of changes in the {sup 1}H NMR spectra compared to control samples. The performance of PCA in liver extracts and serum, discriminated the control samples from the fibrotic and cirrhotic ones. Metabolic alterations revealed in NMR spectra during experimental liver fibrosis and cirrhosis induction, characterize the stage of fibrosis and could be illustrated by subsequent PCA of the spectra. Additionally, the PCA plots of the serum samples presented marked clustering during fibrosis progression and could be extended in clinical diagnosis for the management of cirrhotic patients.« less

An Integrated Approach to Parameter Learning in Infinite-Dimensional Space

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

Boyd, Zachary M.; Wendelberger, Joanne Roth

The availability of sophisticated modern physics codes has greatly extended the ability of domain scientists to understand the processes underlying their observations of complicated processes, but it has also introduced the curse of dimensionality via the many user-set parameters available to tune. Many of these parameters are naturally expressed as functional data, such as initial temperature distributions, equations of state, and controls. Thus, when attempting to find parameters that match observed data, being able to navigate parameter-space becomes highly non-trivial, especially considering that accurate simulations can be expensive both in terms of time and money. Existing solutions include batch-parallel simulations,more » high-dimensional, derivative-free optimization, and expert guessing, all of which make some contribution to solving the problem but do not completely resolve the issue. In this work, we explore the possibility of coupling together all three of the techniques just described by designing user-guided, batch-parallel optimization schemes. Our motivating example is a neutron diffusion partial differential equation where the time-varying multiplication factor serves as the unknown control parameter to be learned. We find that a simple, batch-parallelizable, random-walk scheme is able to make some progress on the problem but does not by itself produce satisfactory results. After reducing the dimensionality of the problem using functional principal component analysis (fPCA), we are able to track the progress of the solver in a visually simple way as well as viewing the associated principle components. This allows a human to make reasonable guesses about which points in the state space the random walker should try next. Thus, by combining the random walker's ability to find descent directions with the human's understanding of the underlying physics, it is possible to use expensive simulations more efficiently and more quickly arrive at the desired parameter set.« less

Resolution extension by image summing in serial femtosecond crystallography of two-dimensional membrane-protein crystals

DOE PAGES

Casadei, Cecilia M.; Tsai, Ching-Ju; Barty, Anton; ...

2018-01-01

Previous proof-of-concept measurements on single-layer two-dimensional membrane-protein crystals performed at X-ray free-electron lasers (FELs) have demonstrated that the collection of meaningful diffraction patterns, which is not possible at synchrotrons because of radiation-damage issues, is feasible. Here, the results obtained from the analysis of a thousand single-shot, room-temperature X-ray FEL diffraction images from two-dimensional crystals of a bacteriorhodopsin mutant are reported in detail. The high redundancy in the measurements boosts the intensity signal-to-noise ratio, so that the values of the diffracted intensities can be reliably determined down to the detector-edge resolution of 4 Å. The results show that two-dimensional serial crystallography atmore » X-ray FELs is a suitable method to study membrane proteins to near-atomic length scales at ambient temperature. The method presented here can be extended to pump–probe studies of optically triggered structural changes on submillisecond timescales in two-dimensional crystals, which allow functionally relevant large-scale motions that may be quenched in three-dimensional crystals.« less

Resolution extension by image summing in serial femtosecond crystallography of two-dimensional membrane-protein crystals

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

Casadei, Cecilia M.; Tsai, Ching-Ju; Barty, Anton

Previous proof-of-concept measurements on single-layer two-dimensional membrane-protein crystals performed at X-ray free-electron lasers (FELs) have demonstrated that the collection of meaningful diffraction patterns, which is not possible at synchrotrons because of radiation-damage issues, is feasible. Here, the results obtained from the analysis of a thousand single-shot, room-temperature X-ray FEL diffraction images from two-dimensional crystals of a bacteriorhodopsin mutant are reported in detail. The high redundancy in the measurements boosts the intensity signal-to-noise ratio, so that the values of the diffracted intensities can be reliably determined down to the detector-edge resolution of 4 Å. The results show that two-dimensional serial crystallography atmore » X-ray FELs is a suitable method to study membrane proteins to near-atomic length scales at ambient temperature. The method presented here can be extended to pump–probe studies of optically triggered structural changes on submillisecond timescales in two-dimensional crystals, which allow functionally relevant large-scale motions that may be quenched in three-dimensional crystals.« less

Identification and classification of upper limb motions using PCA.

PubMed

Veer, Karan; Vig, Renu

2018-03-28

This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.

Identification of the traditional Tibetan medicine "Shaji" and their different extracts through tri-step infrared spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Yue; Li, Jingyi; Fan, Gang; Sun, Suqin; Zhang, Yuxin; Zhang, Yi; Tu, Ya

2016-11-01

Hippophae rhamnoides subsp. sinensis Rousi, Hippophae gyantsensis (Rousi) Y. S. Lian, Hippophae neurocarpa S. W. Liu & T. N. He and Hippophae tibetana Schlechtendal are typically used under one name "Shaji", to treat cardiovascular diseases and lung disorders in Tibetan medicine (TM). A complete set of infrared (IR) macro-fingerprints of these four Hippophae species should be characterized and compared simply, accurately, and in detail for identification. In the present study, tri-step IR spectroscopy, which included Fourier transform IR (FT-IR) spectroscopy, second derivative IR (SD-IR) spectroscopy and two-dimensional correlation IR (2D-IR) spectroscopy, was employed to discriminate the four Hippophae species and their corresponding extracts using different solvents. The relevant spectra exhibited the holistic chemical compositions and variations. Flavonoids, fatty acids and sugars were found to be the main chemical components. Characteristic peak positions, intensities and shapes derived from FT-IR, SD-IR and 2D-IR spectra provided valuable information for sample discrimination. Principal component analysis (PCA) of spectral differences was performed to illustrate the objective identification. Results showed that the species and their extracts can be clearly distinguished. Thus, a quick, precise and effective tri-step IR spectroscopy combined with PCA can be applied to identify and discriminate medicinal materials and their extracts in TM research.

A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer.

PubMed

Wu, Jiang; Ji, Yanju; Zhao, Ling; Ji, Mengying; Ye, Zhuang; Li, Suyi

2016-01-01

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

Conjugated organic framework with three-dimensionally ordered stable structure and delocalized π clouds

PubMed Central

Guo, Jia; Xu, Yanhong; Jin, Shangbin; Chen, Long; Kaji, Toshihiko; Honsho, Yoshihito; Addicoat, Matthew A.; Kim, Jangbae; Saeki, Akinori; Ihee, Hyotcherl; Seki, Shu; Irle, Stephan; Hiramoto, Masahiro; Gao, Jia; Jiang, Donglin

2013-01-01

Covalent organic frameworks are a class of crystalline organic porous materials that can utilize π–π-stacking interactions as a driving force for the crystallization of polygonal sheets to form layered frameworks and ordered pores. However, typical examples are chemically unstable and lack intrasheet π-conjugation, thereby significantly limiting their applications. Here we report a chemically stable, electronically conjugated organic framework with topologically designed wire frameworks and open nanochannels, in which the π conjugation-spans the two-dimensional sheets. Our framework permits inborn periodic ordering of conjugated chains in all three dimensions and exhibits a striking combination of properties: chemical stability, extended π-delocalization, ability to host guest molecules and hole mobility. We show that the π-conjugated organic framework is useful for high on-off ratio photoswitches and photovoltaic cells. Therefore, this strategy may constitute a step towards realizing ordered semiconducting porous materials for innovations based on two-dimensionally extended π systems. PMID:24220603

AMACR polymorphisms, dietary intake of red meat and dairy and prostate cancer risk.

PubMed

Wright, Jonathan L; Neuhouser, Marian L; Lin, Daniel W; Kwon, Erika M; Feng, Ziding; Ostrander, Elaine A; Stanford, Janet L

2011-04-01

Alpha-methylacyl CoA racemase (AMACR) is an enzyme involved in fatty acids metabolism. One of AMACRs primary substrates, phytanic acid, is principally obtained from dietary red meat/dairy, which are associated with prostate cancer (PCa) risk. AMACR is also a tumor tissue biomarker over-expressed in PCa. In this study, we explored the potential relationship between AMACR polymorphisms, red meat/dairy intake, and PCa risk. Caucasian participants from two population-based PCa case-control studies were included. AMACR single nucleotide polymorphisms (SNPs) were selected to capture variation across the gene and regulatory regions. Red meat and dairy intake was determined from food frequency questionnaires. The odds ratio (OR) of PCa (overall and by disease aggressiveness) was estimated by logistic and polytomous regression. Potential interactions between genotypes and dietary exposures were evaluated. Data from 1,309 cases and 1,267 controls were analyzed. Carriers of the variant T allele (rs2287939) had an OR of 0.81 (95% CI 0.68-0.97) for less aggressive PCa, but no alteration in risk for more aggressive PCa. Red meat consumption was positively associated with PCa risk, and the association was stronger for more aggressive disease (lowest vs. highest tertile OR=1.55, 95% CI 1.10-2.20). No effect modification of AMACR polymorphisms by either dietary red meat or dairy intake on PCa risk was observed. PCa risk varied by level of red meat intake and by one AMACR SNP, but there was no evidence for gene-environment interaction. These findings suggest that the effects of AMACR polymorphisms and red meat and dairy on PCa risk are independent. Copyright © 2010 Wiley-Liss, Inc.

DWI-associated entire-tumor histogram analysis for the differentiation of low-grade prostate cancer from intermediate-high-grade prostate cancer.

PubMed

Wu, Chen-Jiang; Wang, Qing; Li, Hai; Wang, Xiao-Ning; Liu, Xi-Sheng; Shi, Hai-Bin; Zhang, Yu-Dong

2015-10-01

To investigate diagnostic efficiency of DWI using entire-tumor histogram analysis in differentiating the low-grade (LG) prostate cancer (PCa) from intermediate-high-grade (HG) PCa in comparison with conventional ROI-based measurement. DW images (b of 0-1400 s/mm(2)) from 126 pathology-confirmed PCa (diameter >0.5 cm) in 110 patients were retrospectively collected and processed by mono-exponential model. The measurement of tumor apparent diffusion coefficients (ADCs) was performed with using histogram-based and ROI-based approach, respectively. The diagnostic ability of ADCs from two methods for differentiating LG-PCa (Gleason score, GS ≤ 6) from HG-PCa (GS > 6) was determined by ROC regression, and compared by McNemar's test. There were 49 LG-tumor and 77 HG-tumor at pathologic findings. Histogram-based ADCs (mean, median, 10th and 90th) and ROI-based ADCs (mean) showed dominant relationships with ordinal GS of Pca (ρ = -0.225 to -0.406, p < 0.05). All above imaging indices reflected significant difference between LG-PCa and HG-PCa (all p values <0.01). Histogram 10th ADCs had dominantly high Az (0.738), Youden index (0.415), and positive likelihood ratio (LR+, 2.45) in stratifying tumor GS against mean, median and 90th ADCs, and ROI-based ADCs. Histogram mean, median, and 10th ADCs showed higher specificity (65.3%-74.1% vs. 44.9%, p < 0.01), but lower sensitivity (57.1%-71.3% vs. 84.4%, p < 0.05) than ROI-based ADCs in differentiating LG-PCa from HG-PCa. DWI-associated histogram analysis had higher specificity, Az, Youden index, and LR+ for differentiation of PCa Gleason grade than ROI-based approach.

Increasing patient knowledge on the proper usage of a PCA machine with the use of a post-operative instructional card.

PubMed

Shovel, Louisa; Max, Bryan; Correll, Darin J

2016-01-01

The purpose of this study was to see if an instructional card, attached to the PCA machine following total hip arthroplasty describing proper use of the device, would positively affect subjects' understanding of device usage, pain scores, pain medication consumption and satisfaction. Eighty adults undergoing total hip replacements who had been prescribed PCA were randomized into two study groups. Forty participants received the standard post-operative instruction on PCA device usage at our institution. The other 40 participants received the standard of care in addition to being given a typed instructional card immediately post-operatively, describing proper PCA device use. This card was attached to the PCA device during their recovery period. On post-operative day one, each patient completed a questionnaire on PCA usage, pain scores and satisfaction scores. The pain scores in the Instructional Card group were significantly lower than the Control group (p = 0.024). Subjects' understanding of PCA usage was also improved in the Instructional Card group for six of the seven questions asked. The findings from this study strongly support that postoperative patient information on proper PCA use by means of an instructional card improves pain control and hence the overall recovery for patients undergoing surgery. In addition, through improved understanding it adds an important safety feature in that patients and potentially their family members and/or friends may refrain from PCA-by-proxy. This article demonstrates that the simple intervention of adding an instructional card to a PCA machine is an effective method to improve patients' knowledge as well as pain control and potentially increase the safety of the device use.

Nonlinear Analysis of Cavitating Propellers in Nonuniform Flow

DTIC Science & Technology

1992-10-16

Helmholtz more than a century ago [4]. The method was later extended to treat curved bodies at zero cavitation number by Levi - Civita [4]. The theory was...122, 1895. [63] M.P. Tulin. Steady two -dimensional cavity flows about slender bodies . Technical Report 834, DTMB, May 1953. [64] M.P. Tulin...iterative solution for two -dimensional flows is remarkably fast and that the accuracy of the first iteration solution is sufficient for a wide range of

Solution algorithms for the two-dimensional Euler equations on unstructured meshes

NASA Technical Reports Server (NTRS)

Whitaker, D. L.; Slack, David C.; Walters, Robert W.

1990-01-01

The objective of the study was to analyze implicit techniques employed in structured grid algorithms for solving two-dimensional Euler equations and extend them to unstructured solvers in order to accelerate convergence rates. A comparison is made between nine different algorithms for both first-order and second-order accurate solutions. Higher-order accuracy is achieved by using multidimensional monotone linear reconstruction procedures. The discussion is illustrated by results for flow over a transonic circular arc.

Expanding the limits of endoscopic intraorbital tumor resection using 3-dimensional reconstruction.

PubMed

Gregorio, Luciano Lobato; Busaba, Nicolas Y; Miyake, Marcel M; Freitag, Suzanne K; Bleier, Benjamin S

2017-12-26

Endoscopic orbital surgery is a nascent field and new tools are required to assist with surgical planning and to ascertain the limits of the tumor resectability. We purpose to utilize three-dimensional radiographic reconstruction to define the theoretical lateral limit of endoscopic resectability of primary orbital tumors and to apply these boundary conditions to surgical cases. A three-dimensional orbital model was rendered in 4 representative patients presenting with primary orbital tumors using OsiriX open source imaging software. A 2-Dimensional plane was propagated between the contralateral nare and a line tangential to the long axis of the optic nerve reflecting the trajectory of a trans-septal approach. Any tumor volume falling medial to the optic nerve and/or within the space inferior to this plane of resectability was considered theoretically resectable regardless of how far it extended lateral to the optic nerve as nerve retraction would be unnecessary. Actual tumor volumes were then superimposed over this plan and correlated with surgical outcomes. Among the 4 lesions analyzed, two were fully medial to the optic nerve, one extended lateral to the optic nerve but remained inferior to the plane of resectability, and one extended both lateral to the optic nerve and superior to the plane of resectability. As predicted by the three-dimensional modeling, a complete resection was achieved in all lesions except one that transgressed the plane of resectability. No new diplopia or vision loss was observed in any patient. Three-dimensional reconstruction enhances preoperative planning for endoscopic orbital surgery. Tumors that extend lateral to the optic nerve may still be candidates for a purely endoscopic resection as long as they do not extend above the plane of resectability described herein. Copyright © 2017 Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial. Published by Elsevier Editora Ltda. All rights reserved.

Energetics of short hydrogen bonds in photoactive yellow protein.

PubMed

Saito, Keisuke; Ishikita, Hiroshi

2012-01-03

Recent neutron diffraction studies of photoactive yellow protein (PYP) proposed that the H bond between protonated Glu46 and the chromophore [ionized p-coumaric acid (pCA)] was a low-barrier H bond (LBHB). Using the atomic coordinates of the high-resolution crystal structure, we analyzed the energetics of the short H bond by two independent methods: electrostatic pK(a) calculations and a quantum mechanical/molecular mechanical (QM/MM) approach. (i) In the QM/MM optimized geometry, we reproduced the two short H-bond distances of the crystal structure: Tyr42-pCA (2.50 Å) and Glu46-pCA (2.57 Å). However, the H atoms obviously belonged to the Tyr or Glu moieties, and were not near the midpoint of the donor and acceptor atoms. (ii) The potential-energy curves of the two H bonds resembled those of standard asymmetric double-well potentials, which differ from those of LBHB. (iii) The calculated pK(a) values for Glu46 and pCA were 8.6 and 5.4, respectively. The pK(a) difference was unlikely to satisfy the prerequisite for LBHB. (iv) The LBHB in PYP was originally proposed to stabilize the ionized pCA because deprotonated Arg52 cannot stabilize it. However, the calculated pK(a) of Arg52 and QM/MM optimized geometry suggested that Arg52 was protonated on the protein surface. The short H bond between Glu46 and ionized pCA in the PYP ground state could be simply explained by electrostatic stabilization without invoking LBHB.

Surface Detail Reproduction and Dimensional Stability of Contemporary Irreversible Hydrocolloid Alternatives after Immediate and Delayed Pouring

PubMed Central

Kusugal, Preethi; Chourasiya, Ritu Sunil; Ruttonji, Zarir; Astagi, Preeti; Nayak, Ajay Kumar; Patil, Abhishekha

2018-01-01

Purpose: To overcome the poor dimensional stability of irreversible hydrocolloids, alternative materials were introduced. The dimensional changes of these alternatives after delayed pouring are not well studied and documented in the literature. The purpose of the study is to evaluate and compare the surface detail reproduction and dimensional stability of two irreversible hydrocolloid alternatives with an extended-pour irreversible hydrocolloid at different time intervals. Materials and Methods: All testing were performed according to the ANSI/ADA specification number 18 for surface detail reproduction and specification number 19 for dimensional change. The test materials used in this study were newer irreversible hydrocolloid alternatives such as AlgiNot FS, Algin-X Ultra FS, and Kromopan 100 which is an extended pour irreversible hydrocolloid as control. The surface detail reproduction was evaluated using stereomicroscope. The dimensional change after storage period of 1 h, 24 h, and 120 h was assessed and compared between the test materials and control. The data were analyzed using one-way ANOVA and post hoc Bonferroni test. Results: Statistically significant results (P < 0.001) were seen when mean scores of the tested materials were compared with respect to reproduction of 22 μm line from the metal block. Kromopan 100 showed statistically significant differences between different time intervals (P < 0.001) and exhibited more dimensional change. Algin-X Ultra FS proved to be more accurate and dimensionally stable. Conclusions: Newer irreversible hydrocolloid alternative impression materials were more accurate in surface detail reproduction and exhibited minimal dimensional change after storage period of 1 h, 24 h, and 120 h than extended-pour irreversible hydrocolloid impression material. PMID:29599578

Laguerre-Gaussian, Hermite-Gaussian, Bessel-Gaussian, and Finite-Energy Airy Beams Carrying Orbital Angular Momentum in Strongly Nonlocal Nonlinear Media

NASA Astrophysics Data System (ADS)

Wu, Zhenkun; Gu, Yuzong

2016-12-01

The propagation of two-dimensional beams is analytically and numerically investigated in strongly nonlocal nonlinear media (SNNM) based on the ABCD matrix. The two-dimensional beams reported in this paper are described by the product of the superposition of generalized Laguerre-Gaussian (LG), Hermite-Gaussian (HG), Bessel-Gaussian (BG), and circular Airy (CA) beams, carrying an orbital angular momentum (OAM). Owing to OAM and the modulation of SNNM, we find that the propagation of these two-dimensional beams exhibits complete rotation and periodic inversion: the spatial intensity profile first extends and then diminishes, and during the propagation the process repeats to form a breath-like phenomenon.

Combined data mining/NIR spectroscopy for purity assessment of lime juice

NASA Astrophysics Data System (ADS)

Shafiee, Sahameh; Minaei, Saeid

2018-06-01

This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.

Reconstructing the free-energy landscape of Met-enkephalin using dihedral principal component analysis and well-tempered metadynamics

NASA Astrophysics Data System (ADS)

Sicard, François; Senet, Patrick

2013-06-01

Well-Tempered Metadynamics (WTmetaD) is an efficient method to enhance the reconstruction of the free-energy surface of proteins. WTmetaD guarantees a faster convergence in the long time limit in comparison with the standard metadynamics. It still suffers, however, from the same limitation, i.e., the non-trivial choice of pertinent collective variables (CVs). To circumvent this problem, we couple WTmetaD with a set of CVs generated from a dihedral Principal Component Analysis (dPCA) on the Ramachandran dihedral angles describing the backbone structure of the protein. The dPCA provides a generic method to extract relevant CVs built from internal coordinates, and does not depend on the alignment to an arbitrarily chosen reference structure as usual in Cartesian PCA. We illustrate the robustness of this method in the case of a reference model protein, the small and very diffusive Met-enkephalin pentapeptide. We propose a justification a posteriori of the considered number of CVs necessary to bias the metadynamics simulation in terms of the one-dimensional free-energy profiles associated with Ramachandran dihedral angles along the amino-acid sequence.

Reconstructing the free-energy landscape of Met-enkephalin using dihedral principal component analysis and well-tempered metadynamics.

PubMed

Sicard, François; Senet, Patrick

2013-06-21

Well-Tempered Metadynamics (WTmetaD) is an efficient method to enhance the reconstruction of the free-energy surface of proteins. WTmetaD guarantees a faster convergence in the long time limit in comparison with the standard metadynamics. It still suffers, however, from the same limitation, i.e., the non-trivial choice of pertinent collective variables (CVs). To circumvent this problem, we couple WTmetaD with a set of CVs generated from a dihedral Principal Component Analysis (dPCA) on the Ramachandran dihedral angles describing the backbone structure of the protein. The dPCA provides a generic method to extract relevant CVs built from internal coordinates, and does not depend on the alignment to an arbitrarily chosen reference structure as usual in Cartesian PCA. We illustrate the robustness of this method in the case of a reference model protein, the small and very diffusive Met-enkephalin pentapeptide. We propose a justification a posteriori of the considered number of CVs necessary to bias the metadynamics simulation in terms of the one-dimensional free-energy profiles associated with Ramachandran dihedral angles along the amino-acid sequence.

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS).

PubMed

Latifoğlu, Fatma; Polat, Kemal; Kara, Sadik; Güneş, Salih

2008-02-01

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

Transforming Graph Data for Statistical Relational Learning

DTIC Science & Technology

2012-10-01

Jordan, 2003), PLSA (Hofmann, 1999), ? Classification via RMN (Taskar et al., 2003) or SVM (Hasan, Chaoji, Salem , & Zaki, 2006) ? Hierarchical...dimensionality reduction methods such as Principal 407 Rossi, McDowell, Aha, & Neville Component Analysis (PCA), Principal Factor Analysis ( PFA ), and...clustering algorithm. Journal of the Royal Statistical Society. Series C, Applied statistics, 28, 100–108. Hasan, M. A., Chaoji, V., Salem , S., & Zaki, M

Leukocyte Recognition Using EM-Algorithm

NASA Astrophysics Data System (ADS)

Colunga, Mario Chirinos; Siordia, Oscar Sánchez; Maybank, Stephen J.

This document describes a method for classifying images of blood cells. Three different classes of cells are used: Band Neutrophils, Eosinophils and Lymphocytes. The image pattern is projected down to a lower dimensional sub space using PCA; the probability density function for each class is modeled with a Gaussian mixture using the EM-Algorithm. A new cell image is classified using the maximum a posteriori decision rule.

Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

PubMed Central

CHEN, CHEN; SHEN, HONG; ZHANG, LI-GUO; LIU, JIAN; CAO, XIAO-GE; YAO, AN-LIANG; KANG, SHAO-SAN; GAO, WEI-XING; HAN, HUI; CAO, FENG-HONG; LI, ZHI-GUO

2016-01-01

Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

Conjugate-gradient optimization method for orbital-free density functional calculations.

PubMed

Jiang, Hong; Yang, Weitao

2004-08-01

Orbital-free density functional theory as an extension of traditional Thomas-Fermi theory has attracted a lot of interest in the past decade because of developments in both more accurate kinetic energy functionals and highly efficient numerical methodology. In this paper, we developed a conjugate-gradient method for the numerical solution of spin-dependent extended Thomas-Fermi equation by incorporating techniques previously used in Kohn-Sham calculations. The key ingredient of the method is an approximate line-search scheme and a collective treatment of two spin densities in the case of spin-dependent extended Thomas-Fermi problem. Test calculations for a quartic two-dimensional quantum dot system and a three-dimensional sodium cluster Na216 with a local pseudopotential demonstrate that the method is accurate and efficient. (c) 2004 American Institute of Physics.

Identification of beta-2 as a key cell adhesion molecule in PCa cell neurotropic behavior: a novel ex vivo and biophysical approach.

PubMed

Jansson, Keith H; Castillo, Deborah G; Morris, Joseph W; Boggs, Mary E; Czymmek, Kirk J; Adams, Elizabeth L; Schramm, Lawrence P; Sikes, Robert A

2014-01-01

Prostate cancer (PCa) is believed to metastasize through the blood/lymphatics systems; however, PCa may utilize the extensive innervation of the prostate for glandular egress. The interaction of PCa and its nerve fibers is observed in 80% of PCa and is termed perineural invasion (PNI). PCa cells have been observed traveling through the endoneurium of nerves, although the underlying mechanisms have not been elucidated. Voltage sensitive sodium channels (VSSC) are multimeric transmembrane protein complexes comprised of a pore-forming α subunit and one or two auxiliary beta (β) subunits with inherent cell adhesion molecule (CAM) functions. The beta-2 isoform (gene SCN2B) interacts with several neural CAMs, while interacting putatively with other prominent neural CAMs. Furthermore, beta-2 exhibits elevated mRNA and protein levels in highly metastatic and castrate-resistant PCa. When overexpressed in weakly aggressive LNCaP cells (2BECFP), beta-2 alters LNCaP cell morphology and enhances LNCaP cell metastasis associated behavior in vitro. We hypothesize that PCa cells use beta-2 as a CAM during PNI and subsequent PCa metastasis. The objective of this study was to determine the effect of beta-2 expression on PCa cell neurotropic metastasis associated behavior. We overexpressed beta-2 as a fusion protein with enhanced cyan fluorescence protein (ECFP) in weakly aggressive LNCaP cells and observed neurotropic effects utilizing our novel ex vivo organotypic spinal cord co-culture model, and performed functional assays with neural matrices and atomic force microscopy. With increased beta-2 expression, PCa cells display a trend of enhanced association with nerve axons. On laminin, a neural CAM, overexpression of beta-2 enhances PCa cell migration, invasion, and growth. 2BECFP cells exhibit marked binding affinity to laminin relative to LNECFP controls, and recombinant beta-2 ectodomain elicits more binding events to laminin than BSA control. Functional overexpression of VSSC beta subunits in PCa may mediate PCa metastatic behavior through association with neural matrices.

Permeability Estimation of Rock Reservoir Based on PCA and Elman Neural Networks

NASA Astrophysics Data System (ADS)

Shi, Ying; Jian, Shaoyong

2018-03-01

an intelligent method which based on fuzzy neural networks with PCA algorithm, is proposed to estimate the permeability of rock reservoir. First, the dimensionality reduction process is utilized for these parameters by principal component analysis method. Further, the mapping relationship between rock slice characteristic parameters and permeability had been found through fuzzy neural networks. The estimation validity and reliability for this method were tested with practical data from Yan’an region in Ordos Basin. The result showed that the average relative errors of permeability estimation for this method is 6.25%, and this method had the better convergence speed and more accuracy than other. Therefore, by using the cheap rock slice related information, the permeability of rock reservoir can be estimated efficiently and accurately, and it is of high reliability, practicability and application prospect.

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

PubMed Central

Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2017-01-01

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

Comparison of experiment with calculations using curvature-corrected zero and two equation turbulence models for a two-dimensional U-duct

NASA Astrophysics Data System (ADS)

Monson, D. J.; Seegmiller, H. L.; McConnaughey, P. K.

1990-06-01

In this paper experimental measurements are compared with Navier-Stokes calculations using seven different turbulence models for the internal flow in a two-dimensional U-duct. The configuration is representative of many internal flows of engineering interst that experience strong curvature. In an effort to improve agreement, this paper tests several versions of the two-equation k-epsilon turbulence model including the standard version, an extended version with a production range time scale, and a version that includes curvature time scales. Each is tested in its high and low Reynolds number formulations. Calculations using these new models and the original mixing length model are compared here with measurements of mean and turbulence velocities, static pressure and skin friction in the U-duct at two Reynolds numbers. The comparisons show that only the low Reynolds number version of the extended k-epsilon model does a reasonable job of predicting the important features of this flow at both Reynolds numbers tested.

Multi-robot task allocation based on two dimensional artificial fish swarm algorithm

NASA Astrophysics Data System (ADS)

Zheng, Taixiong; Li, Xueqin; Yang, Liangyi

2007-12-01

The problem of task allocation for multiple robots is to allocate more relative-tasks to less relative-robots so as to minimize the processing time of these tasks. In order to get optimal multi-robot task allocation scheme, a twodimensional artificial swarm algorithm based approach is proposed in this paper. In this approach, the normal artificial fish is extended to be two dimension artificial fish. In the two dimension artificial fish, each vector of primary artificial fish is extended to be an m-dimensional vector. Thus, each vector can express a group of tasks. By redefining the distance between artificial fish and the center of artificial fish, the behavior of two dimension fish is designed and the task allocation algorithm based on two dimension artificial swarm algorithm is put forward. At last, the proposed algorithm is applied to the problem of multi-robot task allocation and comparer with GA and SA based algorithm is done. Simulation and compare result shows the proposed algorithm is effective.

Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit.

PubMed

Quintián, Héctor; Corchado, Emilio

2017-09-01

In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback. The derived learning rules can be linked to an adaptive form of Exploratory Projection Pursuit and with artificial distributions, the networks perform as the theory suggests they should: the use of different learning rules derived from different PDFs allows the identification of "interesting" dimensions (as far from the Gaussian distribution as possible) in high-dimensional datasets. This novel algorithm, BHL, has been tested over seven artificial datasets to study the behavior of BHL parameters, and was later applied successfully over four real datasets, comparing its results, in terms of performance, with other well-known Exploratory and projection models such as Maximum Likelihood Hebbian Learning (MLHL), Locally-Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Isomap and Neural Principal Component Analysis (Neural PCA).

EMPCA and Cluster Analysis of Quasar Spectra: Construction and Application to Simulated Spectra

NASA Astrophysics Data System (ADS)

Marrs, Adam; Leighly, Karen; Wagner, Cassidy; Macinnis, Francis

2017-01-01

Quasars have complex spectra with emission lines influenced by many factors. Therefore, to fully describe the spectrum requires specification of a large number of parameters, such as line equivalent width, blueshift, and ratios. Principal Component Analysis (PCA) aims to construct eigenvectors-or principal components-from the data with the goal of finding a few key parameters that can be used to predict the rest of the spectrum fairly well. Analysis of simulated quasar spectra was used to verify and justify our modified application of PCA.We used a variant of PCA called Weighted Expectation Maximization PCA (EMPCA; Bailey 2012) along with k-means cluster analysis to analyze simulated quasar spectra. Our approach combines both analytical methods to address two known problems with classical PCA. EMPCA uses weights to account for uncertainty and missing points in the spectra. K-means groups similar spectra together to address the nonlinearity of quasar spectra, specifically variance in blueshifts and widths of the emission lines.In producing and analyzing simulations, we first tested the effects of varying equivalent widths and blueshifts on the derived principal components, and explored the differences between standard PCA and EMPCA. We also tested the effects of varying signal-to-noise ratio. Next we used the results of fits to composite quasar spectra (see accompanying poster by Wagner et al.) to construct a set of realistic simulated spectra, and subjected those spectra to the EMPCA /k-means analysis. We concluded that our approach was validated when we found that the mean spectra from our k-means clusters derived from PCA projection coefficients reproduced the trends observed in the composite spectra.Furthermore, our method needed only two eigenvectors to identify both sets of correlations used to construct the simulations, as well as indicating the linear and nonlinear segments. Comparing this to regular PCA, which can require a dozen or more components, or to direct spectral analysis that may need measurement of 20 fit parameters, shows why the dual application of these two techniques is such a powerful tool.

Identification of regional activation by factorization of high-density surface EMG signals: A comparison of Principal Component Analysis and Non-negative Matrix factorization.

PubMed

Gallina, Alessio; Garland, S Jayne; Wakeling, James M

2018-05-22

In this study, we investigated whether principal component analysis (PCA) and non-negative matrix factorization (NMF) perform similarly for the identification of regional activation within the human vastus medialis. EMG signals from 64 locations over the VM were collected from twelve participants while performing a low-force isometric knee extension. The envelope of the EMG signal of each channel was calculated by low-pass filtering (8 Hz) the monopolar EMG signal after rectification. The data matrix was factorized using PCA and NMF, and up to 5 factors were considered for each algorithm. Association between explained variance, spatial weights and temporal scores between the two algorithms were compared using Pearson correlation. For both PCA and NMF, a single factor explained approximately 70% of the variance of the signal, while two and three factors explained just over 85% or 90%. The variance explained by PCA and NMF was highly comparable (R > 0.99). Spatial weights and temporal scores extracted with non-negative reconstruction of PCA and NMF were highly associated (all p < 0.001, mean R > 0.97). Regional VM activation can be identified using high-density surface EMG and factorization algorithms. Regional activation explains up to 30% of the variance of the signal, as identified through both PCA and NMF. Copyright © 2018 Elsevier Ltd. All rights reserved.

A fast image matching algorithm based on key points

NASA Astrophysics Data System (ADS)

Wang, Huilin; Wang, Ying; An, Ru; Yan, Peng

2014-05-01

Image matching is a very important technique in image processing. It has been widely used for object recognition and tracking, image retrieval, three-dimensional vision, change detection, aircraft position estimation, and multi-image registration. Based on the requirements of matching algorithm for craft navigation, such as speed, accuracy and adaptability, a fast key point image matching method is investigated and developed. The main research tasks includes: (1) Developing an improved celerity key point detection approach using self-adapting threshold of Features from Accelerated Segment Test (FAST). A method of calculating self-adapting threshold was introduced for images with different contrast. Hessian matrix was adopted to eliminate insecure edge points in order to obtain key points with higher stability. This approach in detecting key points has characteristics of small amount of computation, high positioning accuracy and strong anti-noise ability; (2) PCA-SIFT is utilized to describe key point. 128 dimensional vector are formed based on the SIFT method for the key points extracted. A low dimensional feature space was established by eigenvectors of all the key points, and each eigenvector was projected onto the feature space to form a low dimensional eigenvector. These key points were re-described by dimension-reduced eigenvectors. After reducing the dimension by the PCA, the descriptor was reduced to 20 dimensions from the original 128. This method can reduce dimensions of searching approximately near neighbors thereby increasing overall speed; (3) Distance ratio between the nearest neighbour and second nearest neighbour searching is regarded as the measurement criterion for initial matching points from which the original point pairs matched are obtained. Based on the analysis of the common methods (e.g. RANSAC (random sample consensus) and Hough transform cluster) used for elimination false matching point pairs, a heuristic local geometric restriction strategy is adopted to discard false matched point pairs further; and (4) Affine transformation model is introduced to correct coordinate difference between real-time image and reference image. This resulted in the matching of the two images. SPOT5 Remote sensing images captured at different date and airborne images captured with different flight attitude were used to test the performance of the method from matching accuracy, operation time and ability to overcome rotation. Results show the effectiveness of the approach.

Process for combining multiple passes of interferometric SAR data

DOEpatents

Bickel, Douglas L.; Yocky, David A.; Hensley, Jr., William H.

2000-11-21

Interferometric synthetic aperture radar (IFSAR) is a promising technology for a wide variety of military and civilian elevation modeling requirements. IFSAR extends traditional two dimensional SAR processing to three dimensions by utilizing the phase difference between two SAR images taken from different elevation positions to determine an angle of arrival for each pixel in the scene. This angle, together with the two-dimensional location information in the traditional SAR image, can be transformed into geographic coordinates if the position and motion parameters of the antennas are known accurately.

Multiple periodic-soliton solutions of the (3+1)-dimensional generalised shallow water equation

NASA Astrophysics Data System (ADS)

Li, Ye-Zhou; Liu, Jian-Guo

2018-06-01

Based on the extended variable-coefficient homogeneous balance method and two new ansätz functions, we construct auto-Bäcklund transformation and multiple periodic-soliton solutions of (3 {+} 1)-dimensional generalised shallow water equations. Completely new periodic-soliton solutions including periodic cross-kink wave, periodic two-solitary wave and breather type of two-solitary wave are obtained. In addition, cross-kink three-soliton and cross-kink four-soliton solutions are derived. Furthermore, propagation characteristics and interactions of the obtained solutions are discussed and illustrated in figures.

A Fast Estimation Algorithm for Two-Dimensional Gravity Data (GEOFAST),

DTIC Science & Technology

1979-11-15

to a wide class of problems (Refs. 9 and 17). The major inhibitor to the widespread appli- ( cation of optimal gravity data processing is the severe...extends directly to two dimensions. Define the nln 2xn1 n2 diagonal window matrix W as the Kronecker product of two one-dimensional windows W = W1 0 W2 (B...Inversion of Separable Matrices Consider the linear system y = T x (B.3-1) where T is block Toeplitz of dimension nln 2xnIn 2 . Its fre- quency domain

Exploring the CAESAR database using dimensionality reduction techniques

NASA Astrophysics Data System (ADS)

Mendoza-Schrock, Olga; Raymer, Michael L.

2012-06-01

The Civilian American and European Surface Anthropometry Resource (CAESAR) database containing over 40 anthropometric measurements on over 4000 humans has been extensively explored for pattern recognition and classification purposes using the raw, original data [1-4]. However, some of the anthropometric variables would be impossible to collect in an uncontrolled environment. Here, we explore the use of dimensionality reduction methods in concert with a variety of classification algorithms for gender classification using only those variables that are readily observable in an uncontrolled environment. Several dimensionality reduction techniques are employed to learn the underlining structure of the data. These techniques include linear projections such as the classical Principal Components Analysis (PCA) and non-linear (manifold learning) techniques, such as Diffusion Maps and the Isomap technique. This paper briefly describes all three techniques, and compares three different classifiers, Naïve Bayes, Adaboost, and Support Vector Machines (SVM), for gender classification in conjunction with each of these three dimensionality reduction approaches.

On a PCA-based lung motion model

NASA Astrophysics Data System (ADS)

Li, Ruijiang; Lewis, John H.; Jia, Xun; Zhao, Tianyu; Liu, Weifeng; Wuenschel, Sara; Lamb, James; Yang, Deshan; Low, Daniel A.; Jiang, Steve B.

2011-09-01

Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772-81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921-9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1 mm (0.7 ± 0.1 mm). When a single artificial internal marker was used to derive the lung motion, the average 3D error was found to be within 2 mm (1.8 ± 0.3 mm) through comprehensive statistical analysis. The optimal number of PCA coefficients needs to be determined on a patient-by-patient basis and two PCA coefficients seem to be sufficient for accurate modeling of the lung motion for most patients. In conclusion, we have presented thorough theoretical analysis and clinical validation of the PCA lung motion model. The feasibility of deriving the entire lung motion using a single marker has also been demonstrated on clinical data using a simulation approach.

On the Decay of Correlations in Non-Analytic SO(n)-Symmetric Models

NASA Astrophysics Data System (ADS)

Naddaf, Ali

We extend the method of complex translations which was originally employed by McBryan-Spencer [2] to obtain a decay rate for the two point function in two-dimensional SO(n)-symmetric models with non-analytic Hamiltonians for $.

PARTIAL RESTRAINING FORCE INTRODUCTION METHOD FOR DESIGNING CONSTRUCTION COUNTERMESURE ON ΔB METHOD

NASA Astrophysics Data System (ADS)

Nishiyama, Taku; Imanishi, Hajime; Chiba, Noriyuki; Ito, Takao

Landslide or slope failure is a three-dimensional movement phenomenon, thus a three-dimensional treatment makes it easier to understand stability. The ΔB method (simplified three-dimensional slope stability analysis method) is based on the limit equilibrium method and equals to an approximate three-dimensional slope stability analysis that extends two-dimensional cross-section stability analysis results to assess stability. This analysis can be conducted using conventional spreadsheets or two-dimensional slope stability computational software. This paper describes the concept of the partial restraining force in-troduction method for designing construction countermeasures using the distribution of the restraining force found along survey lines, which is based on the distribution of survey line safety factors derived from the above-stated analysis. This paper also presents the transverse distributive method of restraining force used for planning ground stabilizing on the basis of the example analysis.

Using artificial intelligence to improve identification of nanofluid gas-liquid two-phase flow pattern in mini-channel

NASA Astrophysics Data System (ADS)

Xiao, Jian; Luo, Xiaoping; Feng, Zhenfei; Zhang, Jinxin

2018-01-01

This work combines fuzzy logic and a support vector machine (SVM) with a principal component analysis (PCA) to create an artificial-intelligence system that identifies nanofluid gas-liquid two-phase flow states in a vertical mini-channel. Flow-pattern recognition requires finding the operational details of the process and doing computer simulations and image processing can be used to automate the description of flow patterns in nanofluid gas-liquid two-phase flow. This work uses fuzzy logic and a SVM with PCA to improve the accuracy with which the flow pattern of a nanofluid gas-liquid two-phase flow is identified. To acquire images of nanofluid gas-liquid two-phase flow patterns of flow boiling, a high-speed digital camera was used to record four different types of flow-pattern images, namely annular flow, bubbly flow, churn flow, and slug flow. The textural features extracted by processing the images of nanofluid gas-liquid two-phase flow patterns are used as inputs to various identification schemes such as fuzzy logic, SVM, and SVM with PCA to identify the type of flow pattern. The results indicate that the SVM with reduced characteristics of PCA provides the best identification accuracy and requires less calculation time than the other two schemes. The data reported herein should be very useful for the design and operation of industrial applications.

Characterizing the molecular features of ERG-positive tumors in primary and castration resistant prostate cancer

PubMed Central

Roudier, Martine P; Winters, Brian R; Coleman, Ilsa; Lam, Hung-Ming; Zhang, Xiaotun; Coleman, Roger; Chéry, Lisly; True, Lawrence D.; Higano, Celestia S.; Montgomery, Bruce; Lange, Paul H.; Snyder, Linda A.; Srivistava, Shiv; Corey, Eva; Vessella, Robert L.; Nelson, Peter S.; Üren, Aykut; Morrissey, Colm

2017-01-01

Background The TMPRSS2-ERG gene fusion is detected in approximately half of primary prostate cancers (PCa) yet the prognostic significance remains unclear. We hypothesized that ERG promotes the expression of common genes in primary PCa and metastatic castration-resistant PCa (CRPC), with the objective of identifying ERG-associated pathways, which may promote the transition from primary PCa to CRPC. Methods We constructed tissue microarrays (TMA) from 127 radical prostatectomy specimens, 20 LuCaP patient-derived xenografts (PDX), and 152 CRPC metastases obtained immediately at time of death. Nuclear ERG was assessed by immunohistochemistry (IHC). To characterize the molecular features of ERG-expressing PCa, a subset of IHC confirmed ERG+ or ERG-specimens including 11 radical prostatectomies, 20 LuCaP PDXs, and 45 CRPC metastases underwent gene expression analysis. Genes were ranked based on expression in primary PCa and CRPC. Common genes of interest were targeted for IHC analysis and expression compared with biochemical recurrence (BCR) status. Results IHC revealed that 43% of primary PCa, 35% of the LuCaP PDXs, and 18% of the CRPC metastases were ERG+ (12 of 48 patients [25%] had at least 1 ERG+ metastasis). Based on gene expression data and previous literature, two proteins involved in calcium signaling (NCALD, CACNA1D), a protein involved in inflammation (HLA-DMB), CD3 positive immune cells, and a novel ERG-associated protein, DCLK1 were evaluated in primary PCa and CRPC metastases. In ERG+ primary PCa, a weak association was seen with NCALD and CACNA1D protein expression. HLA-DMB expression and the presence of CD3 positive immune cells were decreased in CRPC metastases compared to primary PCa. DCLK1 was upregulated at the protein level in unpaired ERG+ primary PCa and CRPC metastases (p=0.0013 and p<0.0001, respectively). In primary PCa, ERG status or expression of targeted proteins was not associated with BCR-free survival. However for primary PCa, ERG+DCLK1+ patients exhibited shorter time to BCR (p=0.06) compared with ERG+DCLK1- patients. Conclusions This study examined ERG expression in primary PCa and CRPC. We have identified altered levels of inflammatory mediators associated with ERG expression. We determined expression of DCLK1 correlates with ERG expression and may play a role in primary PCa progression to metastatic CPRC. PMID:26990456

Life's Still Lifes

NASA Astrophysics Data System (ADS)

McIntosh, Harold V.

The de Bruijn diagram describing those decompositions of the neighborhoods of a one dimensional cellular automaton which conform to predetermined requirements of periodicity and translational symmetry shows how to construct extended configurations satisfying the same requirements. Similar diagrams, formed by stages, describe higher dimensional automata, although they become more laborious to compute with increasing neighborhood size. The procedure is illustrated by computing some still lifes for Conway's game of Life, a widely known two dimensional cellular automaton. This paper is written in September 10, 1988.

Tight-Binding Study of Polarons in Two-Dimensional Systems: Implications for Organic Field-Effect Transistor Materials

NASA Astrophysics Data System (ADS)

Lei, Jie

2011-03-01

In order to understand the electronic and transport properties of organic field-effect transistor (FET) materials, we theoretically studied the polarons in two-dimensional systems using a tight-binding model with the Holstein type and Su--Schrieffer--Heeger type electron--lattice couplings. By numerical calculations, it was found that a carrier accepts four kinds of localization, which are named the point polaron, two-dimensional polaron, one-dimensional polaron, and the extended state. The degree of localization is sensitive to the following parameters in the model: the strength and type of electron--lattice couplings, and the signs and relative magnitudes of transfer integrals. When a parameter set for a single-crystal phase of pentacene is applied within the Holstein model, a considerably delocalized hole polaron is found, consistent with the bandlike transport mechanism.

Phase retrieval from local measurements in two dimensions

NASA Astrophysics Data System (ADS)

Iwen, Mark; Preskitt, Brian; Saab, Rayan; Viswanathan, Aditya

2017-08-01

The phase retrieval problem has appeared in a multitude of applications for decades. While ad hoc solutions have existed since the early 1970s, recent developments have provided algorithms that offer promising theoretical guarantees under increasingly realistic assumptions. Motivated by ptychographic imaging, we generalize a recent result on phase retrieval of a one dimensional objective vector x ∈ ℂd to recover a two dimensional sample Q ∈ ℂd x d from phaseless measurements, using a tensor product formulation to extend the previous work.

Regularity of Solutions of the Nonlinear Sigma Model with Gravitino

NASA Astrophysics Data System (ADS)

Jost, Jürgen; Keßler, Enno; Tolksdorf, Jürgen; Wu, Ruijun; Zhu, Miaomiao

2018-02-01

We propose a geometric setup to study analytic aspects of a variant of the super symmetric two-dimensional nonlinear sigma model. This functional extends the functional of Dirac-harmonic maps by gravitino fields. The system of Euler-Lagrange equations of the two-dimensional nonlinear sigma model with gravitino is calculated explicitly. The gravitino terms pose additional analytic difficulties to show smoothness of its weak solutions which are overcome using Rivière's regularity theory and Riesz potential theory.

Simple motion correction strategy reduces respiratory-induced motion artifacts for k-t accelerated and compressed-sensing cardiovascular magnetic resonance perfusion imaging.

PubMed

Zhou, Ruixi; Huang, Wei; Yang, Yang; Chen, Xiao; Weller, Daniel S; Kramer, Christopher M; Kozerke, Sebastian; Salerno, Michael

2018-02-01

Cardiovascular magnetic resonance (CMR) stress perfusion imaging provides important diagnostic and prognostic information in coronary artery disease (CAD). Current clinical sequences have limited temporal and/or spatial resolution, and incomplete heart coverage. Techniques such as k-t principal component analysis (PCA) or k-t sparcity and low rank structure (SLR), which rely on the high degree of spatiotemporal correlation in first-pass perfusion data, can significantly accelerate image acquisition mitigating these problems. However, in the presence of respiratory motion, these techniques can suffer from significant degradation of image quality. A number of techniques based on non-rigid registration have been developed. However, to first approximation, breathing motion predominantly results in rigid motion of the heart. To this end, a simple robust motion correction strategy is proposed for k-t accelerated and compressed sensing (CS) perfusion imaging. A simple respiratory motion compensation (MC) strategy for k-t accelerated and compressed-sensing CMR perfusion imaging to selectively correct respiratory motion of the heart was implemented based on linear k-space phase shifts derived from rigid motion registration of a region-of-interest (ROI) encompassing the heart. A variable density Poisson disk acquisition strategy was used to minimize coherent aliasing in the presence of respiratory motion, and images were reconstructed using k-t PCA and k-t SLR with or without motion correction. The strategy was evaluated in a CMR-extended cardiac torso digital (XCAT) phantom and in prospectively acquired first-pass perfusion studies in 12 subjects undergoing clinically ordered CMR studies. Phantom studies were assessed using the Structural Similarity Index (SSIM) and Root Mean Square Error (RMSE). In patient studies, image quality was scored in a blinded fashion by two experienced cardiologists. In the phantom experiments, images reconstructed with the MC strategy had higher SSIM (p < 0.01) and lower RMSE (p < 0.01) in the presence of respiratory motion. For patient studies, the MC strategy improved k-t PCA and k-t SLR reconstruction image quality (p < 0.01). The performance of k-t SLR without motion correction demonstrated improved image quality as compared to k-t PCA in the setting of respiratory motion (p < 0.01), while with motion correction there is a trend of better performance in k-t SLR as compared with motion corrected k-t PCA. Our simple and robust rigid motion compensation strategy greatly reduces motion artifacts and improves image quality for standard k-t PCA and k-t SLR techniques in setting of respiratory motion due to imperfect breath-holding.

Free energy landscape of a biomolecule in dihedral principal component space: sampling convergence and correspondence between structures and minima.

PubMed

Maisuradze, Gia G; Leitner, David M

2007-05-15

Dihedral principal component analysis (dPCA) has recently been developed and shown to display complex features of the free energy landscape of a biomolecule that may be absent in the free energy landscape plotted in principal component space due to mixing of internal and overall rotational motion that can occur in principal component analysis (PCA) [Mu et al., Proteins: Struct Funct Bioinfo 2005;58:45-52]. Another difficulty in the implementation of PCA is sampling convergence, which we address here for both dPCA and PCA using a tetrapeptide as an example. We find that for both methods the sampling convergence can be reached over a similar time. Minima in the free energy landscape in the space of the two largest dihedral principal components often correspond to unique structures, though we also find some distinct minima to correspond to the same structure. 2007 Wiley-Liss, Inc.

Human Prostatic Acid Phosphatase: Structure, Function and Regulation

PubMed Central

Muniyan, Sakthivel; Chaturvedi, Nagendra K.; Dwyer, Jennifer G.; LaGrange, Chad A.; Chaney, William G.; Lin, Ming-Fong

2013-01-01

Human prostatic acid phosphatase (PAcP) is a 100 kDa glycoprotein composed of two subunits. Recent advances demonstrate that cellular PAcP (cPAcP) functions as a protein tyrosine phosphatase by dephosphorylating ErbB-2/Neu/HER-2 at the phosphotyrosine residues in prostate cancer (PCa) cells, which results in reduced tumorigenicity. Further, the interaction of cPAcP and ErbB-2 regulates androgen sensitivity of PCa cells. Knockdown of cPAcP expression allows androgen-sensitive PCa cells to develop the castration-resistant phenotype, where cells proliferate under an androgen-reduced condition. Thus, cPAcP has a significant influence on PCa cell growth. Interestingly, promoter analysis suggests that PAcP expression can be regulated by NF-κB, via a novel binding sequence in an androgen-independent manner. Further understanding of PAcP function and regulation of expression will have a significant impact on understanding PCa progression and therapy. PMID:23698773

Opioid patient controlled analgesia use during the initial experience with the IMPROVE PCA trial: a phase III analgesic trial for hospitalized sickle cell patients with painful episodes.

PubMed

Dampier, Carlton D; Smith, Wally R; Kim, Hae-Young; Wager, Carrie Greene; Bell, Margaret C; Minniti, Caterina P; Keefer, Jeffrey; Hsu, Lewis; Krishnamurti, Lakshmanan; Mack, A Kyle; McClish, Donna; McKinlay, Sonja M; Miller, Scott T; Osunkwo, Ifeyinwa; Seaman, Phillip; Telen, Marilyn J; Weiner, Debra L

2011-12-01

Opioid analgesics administered by patient-controlled analgesia (PCA)are frequently used for pain relief in children and adults with sickle cell disease (SCD) hospitalized for persistent vaso-occlusive pain, but optimum opioid dosing is not known. To better define PCA dosing recommendations,a multi-center phase III clinical trial was conducted comparing two alternative opioid PCA dosing strategies (HDLI—higher demand dose with low constant infusion or LDHI—lower demand dose and higher constant infusion) in 38 subjects who completed randomization prior to trial closure. Total opioid utilization (morphine equivalents,mg/kg) in 22 adults was 11.6 ± 2.6 and 4.7 ± 0.9 in the HDLI andin the LDHI arms, respectively, and in 12 children it was 3.7 ± 1.0 and 5.8 ± 2.2, respectively. Opioid-related symptoms were mild and similar in both PCA arms (mean daily opioid symptom intensity score: HDLI0.9 ± 0.1, LDHI 0.9 ± 0.2). The slow enrollment and early study termination limited conclusions regarding superiority of either treatment regimen. This study adds to our understanding of opioid PCA usage in SCD. Future clinical trial protocol designs for opioid PCA may need to consider potential differences between adults and children in PCA usage.

Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging

NASA Astrophysics Data System (ADS)

Schubert, Till; Wenzel, Susanne; Roscher, Ribana; Stachniss, Cyrill

2016-06-01

The detection of traces is a main task of forensics. Hyperspectral imaging is a potential method from which we expect to capture more fluorescence effects than with common forensic light sources. This paper shows that the use of hyperspectral imaging is suited for the analysis of latent traces and extends the classical concept to the conservation of the crime scene for retrospective laboratory analysis. We examine specimen of blood, semen and saliva traces in several dilution steps, prepared on cardboard substrate. As our key result we successfully make latent traces visible up to dilution factor of 1:8000. We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the shortwave infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood classifier, assuming normally distributed data. Further, we use Random Forest as a competitive approach. The classifiers retrieve the exact positions of labelled trace preparation up to highest dilution and determine posterior probabilities. By modelling the classification task with a Markov Random Field we are able to integrate prior information about the spatial relation of neighboured pixel labels.

Racial Differences in Prediction of Time to Prostate Cancer Diagnosis in a Prospective Screening Cohort of High-Risk Men: Effect of TMPRSS2 Met160Val

PubMed Central

Giri, Veda N.; Ruth, Karen; Hughes, Lucinda; Uzzo, Robert G.; Chen, David Y.T.; Boorjian, Stephen A.; Viterbo, Rosalia; Rebbeck, Timothy R.

2011-01-01

Introduction The TMPRSS2-ERG gene fusion occurs in >50% of prostate tumors and has been associated with poor outcomes. The T-allele (Valine) of the Met160Val (rs12329760) in TMPRSS2 has been associated with this fusion. We evaluated this polymorphism with respect to self-identified race or ethnicity (SIRE), time to prostate cancer (PCA) diagnosis, and screening parameters in the Prostate Cancer Risk Assessment Program, a prospective screening program for high-risk men. Patients and Methods 631 men ages 35-69 years were studied. “High-risk” was defined as ≥ one first degree or two second degree relatives with PCA, any African American (AA) man regardless of familial PCA, and men with BRCA1/2 mutations. Men with elevated PSA or other indications for PCA underwent biopsy. Men were followed from time of study entry to PCA diagnosis. Cox models were used to evaluate time to PCA diagnosis by genotype. Results Genotype distribution differed significantly by SIRE (CT/TT vs. CC, p<0.0001). Among 183 Caucasian men with at least one follow-up visit, PCA was more than doubled in men carrying CT/TT vs CC genotypes (HR= 2.55, 95% CI=1.14-5.70) after controlling for age and PSA. No association was seen among AA men by TMPRSS2 genotype. Conclusions The T-allele of the Met160Val variant in TMPRSS2, which has been associated with the TMPRSS2-ERG fusion, may be informative of time to PCA diagnosis for a subset of high-risk Caucasian men who are undergoing regular PCA screening. This variant along with other genetic markers warrant further study for personalizing PCA screening. PMID:20735386

Prediagnostic circulating sex hormones are not associated with mortality for men with prostate cancer.

PubMed

Gershman, Boris; Shui, Irene M; Stampfer, Meir; Platz, Elizabeth A; Gann, Peter H; Sesso, Howard L; DuPre, Natalie; Giovannucci, Edward; Mucci, Lorelei A

2014-04-01

Sex hormones play an important role in the growth and development of the prostate, and low androgen levels have been suggested to carry an adverse prognosis for men with prostate cancer (PCa). To examine the association between prediagnostic circulating sex hormones and lethal PCa in two prospective cohort studies, the Physicians' Health Study (PHS) and the Health Professionals Follow-up Study (HPFS). We included 963 PCa cases (700 HPFS; 263 PHS) that provided prediagnostic blood samples, in 1982 for PHS and in 1993-1995 for HPFS, in which circulating sex hormone levels were assayed. The primary end point was lethal PCa (defined as cancer-specific mortality or development of metastases), and we also assessed total mortality through March 2011. We used Cox proportional hazards models to evaluate the association of prediagnostic sex hormone levels with time from diagnosis to development of lethal PCa or total mortality. PCa cases were followed for a mean of 12.0±4.9 yr after diagnosis. We confirmed 148 cases of lethal PCa and 421 deaths overall. Using Cox proportional hazard models, we found no significant association between quartile of total testosterone, sex hormone binding globulin (SHBG), SHBG-adjusted testosterone, free testosterone, dihydrotestosterone, androstanediol glucuronide, or estradiol and lethal PCa or total mortality. In subset analyses stratified by Gleason score, TNM stage, age, and interval between blood draw and diagnosis, there was also no consistent association between lethal PCa and sex hormone quartile. We found no overall association between prediagnostic circulating sex hormones and lethal PCa or total mortality. Our null results suggest that reverse causation may be responsible in prior studies that noted adverse outcomes for men with low circulating androgens. Copyright © 2013 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Patient-controlled analgesia versus intramuscular analgesic therapy.

PubMed

Smythe, M; Loughlin, K; Schad, R F; Lucarroti, R L

1994-06-01

The pharmacy and nursing time requirements, quality of postoperative pain control, and cost of patient-controlled analgesia (PCA) and intramuscular (i.m.) analgesic therapy were studied. All timings were conducted with a stopwatch on a single nursing unit that primarily receives gynecologic surgery patients. The various work elements involved in each type of therapy were timed individually. Both quality of analgesia and cost were evaluated in a prospective, randomized study in hysterectomy patients. I.M. patients received meperidine hydrochloride 75-100 mg every three to four hours as needed. PCA patients had access to morphine sulfate 1 mg or meperidine hydrochloride 10 mg, with a six-minute lockout period. The patients scored their pain every four hours. Direct costs for PCA were calculated as drug cost plus tubing cost plus form cost plus maintenance cost plus depreciation cost. Direct costs for i.m. therapy consisted of the cost of drugs. The total mean nursing time per patient was 16.9 minutes for PCA and 10.7 minutes for i.m. therapy. Pharmacy time per patient was 5.1 minutes longer for PCA than for i.m. therapy. Thirty-six hysterectomy patients (17 i.m. and 19 PCA) were enrolled in the study of pain control and cost. Among i.m. patients, 64% of the pain scores were mild or worse, compared with 40% for PCA patients. The median pain scores were moderate for i.m. patients and mild for PCA patients. Scores tended to be lower for PCA patients at 16 and 20 hours. Although equal numbers of patients in the two groups experienced nausea, i.m. patients needed more doses of antiemetics than PCA patients.(ABSTRACT TRUNCATED AT 250 WORDS)

Risk of prostate cancer in African-American men: Evidence of mixed effects of dietary quercetin by serum vitamin D status.

PubMed

Paller, C J; Kanaan, Y M; Beyene, D A; Naab, T J; Copeland, R L; Tsai, H L; Kanarek, N F; Hudson, T S

2015-09-01

African-American (AA) men experience higher rates of prostate cancer (PCa) and vitamin D (vitD) deficiency than white men. VitD is promoted for PCa prevention, but there is conflicting data on the association between vitD and PCa. We examined the association between serum vitD and dietary quercetin and their interaction with PCa risk in AA men. Participants included 90 AA men with PCa undergoing treatment at Howard University Hospital (HUH) and 62 controls participating in HUH's free PCa screening program. We measured serum 25-hydroxy vitD [25(OH)D] and used the 98.2 item Block Brief 2000 Food Frequency Questionnaires to measure dietary intake of quercetin and other nutrients. Case and control groups were compared using a two-sample t-test for continuous risk factors and a Fisher exact test for categorical factors. Associations between risk factors and PCa risk were examined via age-adjusted logistic regression models. Interaction effects of dietary quercetin and serum vitD on PCa status were observed. AA men (age 40-70) with normal levels of serum vitD (>30 ng/ml) had a 71% lower risk of PCa compared to AA men with vitD deficiency (OR = 0.29, 95%CI: 0.08-1.03; P = 0.055). In individuals with vitD deficiency, increased dietary quercetin showed a tendency toward lower risk of PCa (OR = 0.91, 95%CI: 0.82-1.00; P = 0.054, age-adjusted) while men with normal vitD were at elevated risk (OR = 1.23, 95%CI: 1.04-1.45). These findings suggest that AA men who are at a higher risk of PCa may benefit more from vitD intake, and supplementation with dietary quercetin may increase the risk of PCa in AA men with normal vitD levels. Further studies with larger populations are needed to better understand the impact of the interaction between sera vitD levels and supplementation with quercetin on PCa in AA men. © 2015 Wiley Periodicals, Inc.

A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI

PubMed Central

Chang, Hing-Chiu; Bilgin, Ali; Bernstein, Adam; Trouard, Theodore P.

2018-01-01

Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses. PMID:29694400

Germline Missense Variants in the BTNL2 Gene Are Associated with Prostate Cancer Susceptibility

PubMed Central

FitzGerald, Liesel M.; Kumar, Akash; Boyle, Evan A.; Zhang, Yuzheng; McIntosh, Laura M.; Kolb, Suzanne; Stott-Miller, Marni; Smith, Tiffany; Karyadi, Danielle M.; Ostrander, Elaine A.; Hsu, Li; Shendure, Jay; Stanford, Janet L.

2013-01-01

Background Rare, inherited mutations account for 5%–10% of all prostate cancer (PCa) cases. However, to date, few causative mutations have been identified. Methods To identify rare mutations for PCa, we performed whole-exome sequencing (WES) in multiple kindreds (n = 91) from 19 hereditary prostate cancer (HPC) families characterized by aggressive or early onset phenotypes. Candidate variants (n = 130) identified through family- and bioinformatics-based filtering of WES data were then genotyped in an independent set of 270 HPC families (n = 819 PCa cases; n = 496 unaffected relatives) for replication. Two variants with supportive evidence were subsequently genotyped in a population-based case-control study (n = 1,155 incident PCa cases; n = 1,060 age-matched controls) for further confirmation. All participants were men of European ancestry. Results The strongest evidence was for two germline missense variants in the butyrophilin-like 2 (BTNL2) gene (rs41441651, p.Asp336Asn and rs28362675, p.Gly454Cys) that segregated with affection status in two of the WES families. In the independent set of 270 HPC families, 1.5% (rs41441651; P = 0.0032) and 1.2% (rs28362675; P = 0.0070) of affected men, but no unaffected men, carried a variant. Both variants were associated with elevated PCa risk in the population-based study (rs41441651: OR = 2.7; 95% CI, 1.27–5.87; P = 0.010; rs28362675: OR = 2.5; 95% CI, 1.16–5.46; P = 0.019). Conclusions Results indicate that rare BTNL2 variants play a role in susceptibility to both familial and sporadic prostate cancer. Impact Results implicate BTNL2 as a novel PCa susceptibility gene. PMID:23833122

De novo assembly and phasing of dikaryotic genomes from two isolates of Puccinia coronata f. sp. avenae, the causal agent of oat crown rust

USDA-ARS?s Scientific Manuscript database

Oat crown rust, caused by the fungus Puccinia coronata f. sp. avenae (Pca), is a devastating disease that impacts worldwide oat production. For much of its life cycle Pca is dikaryotic with two separate haploid nuclei that may vary in virulence genotypes, which highlights the importance of understan...

[Analyzing and modeling methods of near infrared spectroscopy for in-situ prediction of oil yield from oil shale].

PubMed

Liu, Jie; Zhang, Fu-Dong; Teng, Fei; Li, Jun; Wang, Zhi-Hong

2014-10-01

In order to in-situ detect the oil yield of oil shale, based on portable near infrared spectroscopy analytical technology, with 66 rock core samples from No. 2 well drilling of Fuyu oil shale base in Jilin, the modeling and analyzing methods for in-situ detection were researched. By the developed portable spectrometer, 3 data formats (reflectance, absorbance and K-M function) spectra were acquired. With 4 different modeling data optimization methods: principal component-mahalanobis distance (PCA-MD) for eliminating abnormal samples, uninformative variables elimination (UVE) for wavelength selection and their combina- tions: PCA-MD + UVE and UVE + PCA-MD, 2 modeling methods: partial least square (PLS) and back propagation artificial neural network (BPANN), and the same data pre-processing, the modeling and analyzing experiment were performed to determine the optimum analysis model and method. The results show that the data format, modeling data optimization method and modeling method all affect the analysis precision of model. Results show that whether or not using the optimization method, reflectance or K-M function is the proper spectrum format of the modeling database for two modeling methods. Using two different modeling methods and four different data optimization methods, the model precisions of the same modeling database are different. For PLS modeling method, the PCA-MD and UVE + PCA-MD data optimization methods can improve the modeling precision of database using K-M function spectrum data format. For BPANN modeling method, UVE, UVE + PCA-MD and PCA- MD + UVE data optimization methods can improve the modeling precision of database using any of the 3 spectrum data formats. In addition to using the reflectance spectra and PCA-MD data optimization method, modeling precision by BPANN method is better than that by PLS method. And modeling with reflectance spectra, UVE optimization method and BPANN modeling method, the model gets the highest analysis precision, its correlation coefficient (Rp) is 0.92, and its standard error of prediction (SEP) is 0.69%.

Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection

NASA Astrophysics Data System (ADS)

Li, Shao-Xin; Zeng, Qiu-Yao; Li, Lin-Fang; Zhang, Yan-Jiao; Wan, Ming-Ming; Liu, Zhi-Ming; Xiong, Hong-Lian; Guo, Zhou-Yi; Liu, Song-Hao

2013-02-01

The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30) and the other group from healthy volunteers (n=31). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.

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

den Hartog, S.; Kapteyn, C.; van Wees, B.

We have investigated transport in a cross-shaped two-dimensional electron gas with superconducting electrodes coupled to two opposite arms. Multiterminal resistances, measured as a function of the superconducting phase difference and the magnetic flux, are analyzed in terms of an extended Landauer-B{umlt u}ttiker transport formalism. We show that extended reciprocity relations hold. Correlations between transport coefficients are obtained from, e.g., (negative) three-terminal and nonlocal resistances. Energy spectroscopy reveals a reentrant behavior of the transport coefficients around the Thouless energy. {copyright} {ital 1996 The American Physical Society.}

Correlating folding and signaling in a photoreceptor by single molecule measurements and energy landscape calculations

NASA Astrophysics Data System (ADS)

Hoff, Wouter

2007-03-01

Receptor activation is a fundamental process in biological signaling. We study the structural changes during activation of photoactive yellow protein (PYP). This is triggered by photoisomerization of the p-coumaric acid (pCA) chromophore of PYP, which converts the initial pG state into the activated pB state. Mechanical unfolding of Cys-linked PYP multimers probed by atomic force microscopy (AFM) in the presence and absence of illumination reveals that the core of the protein is extended by 3 nm and destabilized by 30 percent in pB. These results establish a generally applicable single molecule approach for mapping functional conformational changes to selected regions of a protein and indicate that stimulus-induced partial protein unfolding can be employed as a signaling mechanism. Comparative measurements, Jarzynski-Hummer-Szabo analysis of the data, and steered MD simulations of two double-Cys PYP mutants reveal strong anisotropy in the unfolding mechanism along the two axes defined by the Cys residues. Unfolding along one axis exhibits a transition-state-like feature where six hydrogen bonds break simultaneously. The other axis displays an unpeaked force profile reflecting a non-cooperative transition, challenging the notion that cooperative unfolding is a universal feature in protein stability. MD simulations with a coarse-grained protein model show that the folding of pG is two-state, consistent with experimental observations. In contrast, the folding free energy surface of a coarse-grained model of pB involves an on-pathway partially unfolded intermediate that closely matches experimental data. The results reveal that interactions between the pCA and its binding pocket can switch the energy landscape for PYP from two- to three-state folding, and show how this can be exploited to trigger large functionally important protein conformational changes.

Functional neural substrates of posterior cortical atrophy patients.

PubMed

Shames, H; Raz, N; Levin, Netta

2015-07-01

Posterior cortical atrophy (PCA) is a neurodegenerative syndrome in which the most pronounced pathologic involvement is in the occipito-parietal visual regions. Herein, we aimed to better define the cortical reflection of this unique syndrome using a thorough battery of behavioral and functional MRI (fMRI) tests. Eight PCA patients underwent extensive testing to map their visual deficits. Assessments included visual functions associated with lower and higher components of the cortical hierarchy, as well as dorsal- and ventral-related cortical functions. fMRI was performed on five patients to examine the neuronal substrate of their visual functions. The PCA patient cohort exhibited stereopsis, saccadic eye movements and higher dorsal stream-related functional impairments, including simultant perception, image orientation, figure-from-ground segregation, closure and spatial orientation. In accordance with the behavioral findings, fMRI revealed intact activation in the ventral visual regions of face and object perception while more dorsal aspects of perception, including motion and gestalt perception, revealed impaired patterns of activity. In most of the patients, there was a lack of activity in the word form area, which is known to be linked to reading disorders. Finally, there was evidence of reduced cortical representation of the peripheral visual field, corresponding to the behaviorally assessed peripheral visual deficit. The findings are discussed in the context of networks extending from parietal regions, which mediate navigationally related processing, visually guided actions, eye movement control and working memory, suggesting that damage to these networks might explain the wide range of deficits in PCA patients.

PCA based feature reduction to improve the accuracy of decision tree c4.5 classification

NASA Astrophysics Data System (ADS)

Nasution, M. Z. F.; Sitompul, O. S.; Ramli, M.

2018-03-01

Splitting attribute is a major process in Decision Tree C4.5 classification. However, this process does not give a significant impact on the establishment of the decision tree in terms of removing irrelevant features. It is a major problem in decision tree classification process called over-fitting resulting from noisy data and irrelevant features. In turns, over-fitting creates misclassification and data imbalance. Many algorithms have been proposed to overcome misclassification and overfitting on classifications Decision Tree C4.5. Feature reduction is one of important issues in classification model which is intended to remove irrelevant data in order to improve accuracy. The feature reduction framework is used to simplify high dimensional data to low dimensional data with non-correlated attributes. In this research, we proposed a framework for selecting relevant and non-correlated feature subsets. We consider principal component analysis (PCA) for feature reduction to perform non-correlated feature selection and Decision Tree C4.5 algorithm for the classification. From the experiments conducted using available data sets from UCI Cervical cancer data set repository with 858 instances and 36 attributes, we evaluated the performance of our framework based on accuracy, specificity and precision. Experimental results show that our proposed framework is robust to enhance classification accuracy with 90.70% accuracy rates.

A Novel Hybrid Dimension Reduction Technique for Undersized High Dimensional Gene Expression Data Sets Using Information Complexity Criterion for Cancer Classification

PubMed Central

Pamukçu, Esra; Bozdogan, Hamparsum; Çalık, Sinan

2015-01-01

Gene expression data typically are large, complex, and highly noisy. Their dimension is high with several thousand genes (i.e., features) but with only a limited number of observations (i.e., samples). Although the classical principal component analysis (PCA) method is widely used as a first standard step in dimension reduction and in supervised and unsupervised classification, it suffers from several shortcomings in the case of data sets involving undersized samples, since the sample covariance matrix degenerates and becomes singular. In this paper we address these limitations within the context of probabilistic PCA (PPCA) by introducing and developing a new and novel approach using maximum entropy covariance matrix and its hybridized smoothed covariance estimators. To reduce the dimensionality of the data and to choose the number of probabilistic PCs (PPCs) to be retained, we further introduce and develop celebrated Akaike's information criterion (AIC), consistent Akaike's information criterion (CAIC), and the information theoretic measure of complexity (ICOMP) criterion of Bozdogan. Six publicly available undersized benchmark data sets were analyzed to show the utility, flexibility, and versatility of our approach with hybridized smoothed covariance matrix estimators, which do not degenerate to perform the PPCA to reduce the dimension and to carry out supervised classification of cancer groups in high dimensions. PMID:25838836

Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test.

PubMed

Palmerini, Luca; Mellone, Sabato; Rocchi, Laura; Chiari, Lorenzo

2011-01-01

The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's disease. Lately instrumented versions of the test are being considered, where inertial sensors assess motion. To improve the pervasiveness, ease of use, and cost, we consider a smartphone's accelerometer as the measurement system. Several parameters (usually highly correlated) can be computed from the signals recorded during the test. To avoid redundancy and obtain the features that are most sensitive to the locomotor performance, a dimensionality reduction was performed through principal component analysis (PCA). Forty-nine healthy subjects of different ages were tested. PCA was performed to extract new features (principal components) which are not redundant combinations of the original parameters and account for most of the data variability. They can be useful for exploratory analysis and outlier detection. Then, a reduced set of the original parameters was selected through correlation analysis with the principal components. This set could be recommended for studies based on healthy adults. The proposed procedure could be used as a first-level feature selection in classification studies (i.e. healthy-Parkinson's disease, fallers-non fallers) and could allow, in the future, a complete system for movement analysis to be incorporated in a smartphone.

Conformal mapping in optical biosensor applications.

PubMed

Zumbrum, Matthew E; Edwards, David A

2015-09-01

Optical biosensors are devices used to investigate surface-volume reaction kinetics. Current mathematical models for reaction dynamics rely on the assumption of unidirectional flow within these devices. However, new devices, such as the Flexchip, include a geometry that introduces two-dimensional flow, complicating the depletion of the volume reactant. To account for this, a previous mathematical model is extended to include two-dimensional flow, and the Schwarz-Christoffel mapping is used to relate the physical device geometry to that for a device with unidirectional flow. Mappings for several Flexchip dimensions are considered, and the ligand depletion effect is investigated for one of these mappings. Estimated rate constants are produced for simulated data to quantify the inclusion of two-dimensional flow in the mathematical model.

PNS calculations for 3-D hypersonic corner flow with two turbulence models

NASA Technical Reports Server (NTRS)

Smith, Gregory E.; Liou, May-Fun; Benson, Thomas J.

1988-01-01

A three-dimensional parabolized Navier-Stokes code has been used as a testbed to investigate two turbulence models, the McDonald Camarata and Bushnell Beckwith model, in the hypersonic regime. The Bushnell Beckwith form factor correction to the McDonald Camarata mixing length model has been extended to three-dimensional flow by use of an inverse averaging of the resultant length scale contributions from each wall. Two-dimensional calculations are compared with experiment for Mach 18 helium flow over a 4-deg wedge. Corner flow calculations have been performed at Mach 11.8 for a Reynolds number of .67 x 10 to the 6th, based on the duct half-width, and a freestream stagnation temperature of 1750-deg Rankine.

Decomposing the Apoptosis Pathway Into Biologically Interpretable Principal Components

PubMed Central

Wang, Min; Kornblau, Steven M; Coombes, Kevin R

2018-01-01

Principal component analysis (PCA) is one of the most common techniques in the analysis of biological data sets, but applying PCA raises 2 challenges. First, one must determine the number of significant principal components (PCs). Second, because each PC is a linear combination of genes, it rarely has a biological interpretation. Existing methods to determine the number of PCs are either subjective or computationally extensive. We review several methods and describe a new R package, PCDimension, that implements additional methods, the most important being an algorithm that extends and automates a graphical Bayesian method. Using simulations, we compared the methods. Our newly automated procedure is competitive with the best methods when considering both accuracy and speed and is the most accurate when the number of objects is small compared with the number of attributes. We applied the method to a proteomics data set from patients with acute myeloid leukemia. Proteins in the apoptosis pathway could be explained using 6 PCs. By clustering the proteins in PC space, we were able to replace the PCs by 6 “biological components,” 3 of which could be immediately interpreted from the current literature. We expect this approach combining PCA with clustering to be widely applicable. PMID:29881252

Direct design of aspherical lenses for extended non-Lambertian sources in three-dimensional rotational geometry

PubMed Central

Wu, Rengmao; Hua, Hong

2016-01-01

Illumination design used to redistribute the spatial energy distribution of light source is a key technique in lighting applications. However, there is still no effective illumination design method for extended sources, especially for extended non-Lambertian sources. What we present here is to our knowledge the first direct method for extended non-Lambertian sources in three-dimensional (3D) rotational geometry. In this method, both meridional rays and skew rays of the extended source are taken into account to tailor the lens profile in the meridional plane. A set of edge rays and interior rays emitted from the extended source which will take a given direction after the refraction of the aspherical lens are found by the Snell’s law, and the output intensity at this direction is then calculated to be the integral of the luminance function of the outgoing rays at this direction. This direct method is effective for both extended non-Lambertian sources and extended Lambertian sources in 3D rotational symmetry, and can directly find a solution to the prescribed design problem without cumbersome iterative illuminance compensation. Two examples are presented to demonstrate the effectiveness of the proposed method in terms of performance and capacity for tackling complex designs. PMID:26832484

Individual and cumulative effect of prostate cancer risk-associated variants on clinicopathologic variables in 5,895 prostate cancer patients.

PubMed

Kader, A Karim; Sun, Jielin; Isaacs, Sarah D; Wiley, Kathleen E; Yan, Guifang; Kim, Seong-Tae; Fedor, Helen; DeMarzo, Angelo M; Epstein, Jonathan I; Walsh, Patrick C; Partin, Alan W; Trock, Bruce; Zheng, S Lilly; Xu, Jianfeng; Isaacs, William

2009-08-01

More than a dozen single nucleotide polymorphisms (SNPs) have been associated with prostate cancer (PCa) risk from genome-wide association studies (GWAS). Their association with PCa aggressiveness and clinicopathologic variables is inconclusive. Twenty PCa risk SNPs implicated in GWAS and fine mapping studies were evaluated in 5,895 PCa cases treated by radical prostatectomy at Johns Hopkins Hospital, where each tumor was uniformly graded and staged using the same protocol. For 18 of the 20 SNPs examined, no statistically significant differences (P > 0.05) were observed in risk allele frequencies between patients with more aggressive (Gleason scores > or =4 + 3, or stage > or =T3b, or N+) or less aggressive disease (Gleason scores < or =3 + 4, and stage < or =T2, and N0). For the two SNPs that had significant differences between more and less aggressive disease rs2735839 in KLK3 (P = 8.4 x 10(-7)) and rs10993994 in MSMB (P = 0.046), the alleles that are associated with increased risk for PCa were more frequent in patients with less aggressive disease. Since these SNPs are known to be associated with PSA levels in men without PCa diagnoses, these latter associations may reflect the enrichment of low grade, low stage cases diagnosed by contemporary disease screening with PSA. The vast majority of PCa risk-associated SNPs are not associated with aggressiveness and clinicopathologic variables of PCa. Correspondingly, they have minimal utility in predicting the risk for developing more or less aggressive forms of PCa.

Individual and cumulative effect of prostate cancer risk-associated variants on clinicopathologic variables in 5,895 prostate cancer patients

PubMed Central

Kader, A. Karim; Sun, Jielin; Isaacs, Sarah D.; Wiley, Kathleen E.; Yan, Guifang; Kim, Seong-Tae; Fedor, Helen; DeMarzo, Angelo M.; Epstein, Jonathan I.; Walsh, Patrick C.; Partin, Alan W.; Trock, Bruce; Zheng, S. Lilly; Xu, Jianfeng; Isaacs, William

2009-01-01

Background More than a dozen single nucleotide polymorphisms (SNPs) have been associated with prostate cancer (PCa) risk from genome-wide association studies (GWAS). Their association with PCa aggressiveness and clinicopathologic variables is inconclusive. Methods Twenty PCa risk SNPs implicated in GWAS and fine mapping studies were evaluated in 5,895 PCa cases treated by radical prostatectomy at Johns Hopkins Hospital, where each tumor was uniformly graded and staged using the same protocol. Results For 18 of the 20 SNPs examined, no statistically significant differences (P > 0.05) were observed in risk allele frequencies between patients with more aggressive (Gleason Scores ≥ 4+3, or stage ≥ T3b, or N+) or less aggressive disease (Gleason Scores ≤ 3+4, and stage ≤ T2, and N0). For the two SNPs that had significant differences between more and less aggressive disease (rs2735839 in KLK3 (P = 8.4 × 10−7) and rs10993994 in MSMB (P = 0.046), the alleles that are associated with increased risk for PCa were more frequent in patients with less aggressive disease. Since these SNPs are known to be associated with PSA levels in men without PCa diagnoses, these latter associations may reflect the enrichment of low grade, low stage cases diagnosed by contemporary disease screening with PSA. Conclusions The vast majority of PCa risk-associated SNPs are not associated with aggressiveness and clinicopathologic variables of PCa. Correspondingly, they have minimal utility in predicting the risk for developing more or less aggressive forms of PCa. PMID:19434657

Transrectal real-time tissue elastography targeted biopsy coupled with peak strain index improves the detection of clinically important prostate cancer.

PubMed

Ma, Qi; Yang, Dong-Rong; Xue, Bo-Xin; Wang, Cheng; Chen, Han-Bin; Dong, Yun; Wang, Cai-Shan; Shan, Yu-Xi

2017-07-01

The focus of the present study was to evaluate transrectal real-time tissue elastography (RTE)-targeted two-core biopsy coupled with peak strain index for the detection of prostate cancer (PCa) and to compare this method with 10-core systematic biopsy. A total of 141 patients were enrolled for evaluation. The diagnostic value of peak strain index was assessed using a receiver operating characteristic curve. The cancer detection rates of the two approaches and corresponding positive cores and Gleason score were compared. The cancer detection rate per core in the RTE-targeted biopsy (44%) was higher compared with that in systematic biopsy (30%). The peak strain index value of PCa was higher compared with that of the benign lesion. PCa was detected with the highest sensitivity (87.5%) and specificity (85.5%) using the threshold value of a peak strain index of ≥5.97 with an area under the curve value of 0.95. When the Gleason score was ≥7, RTE-targeted biopsy coupled with peak strain index detected 95.6% of PCa cases, but 84.4% were detected using systematic biopsy. Peak strain index as a quantitative parameter may improve the differentiation of PCa from benign lesions in the prostate peripheral zone. Transrectal RTE-targeted biopsy coupled with peak strain index may enhance the detection of clinically significant PCa, particularly when combined with systematic biopsy.

National economic and development indicators and international variation in prostate cancer incidence and mortality: an ecological analysis.

PubMed

Neupane, Subas; Bray, Freddie; Auvinen, Anssi

2017-06-01

Macroeconomic indicators are likely associated with prostate cancer (PCa) incidence and mortality globally, but have rarely been assessed. Data on PCa incidence in 2003-2007 for 49 countries with either nationwide cancer registry or at least two regional registries were obtained from Cancer Incidence in Five Continents Vol X and national PCa mortality for 2012 from GLOBOCAN 2012. We compared PCa incidence and mortality rates with various population-level indicators of health, economy and development in 2000. Poisson and linear regression methods were used to quantify the associations. PCa incidence varied more than 15-fold, being highest in high-income countries. PCa mortality exhibited less variation, with higher rates in many low- and middle-income countries. Healthcare expenditure (rate ratio, RR 1.46, 95 % CI 1.45-1.47) and population growth (RR 1.15, 95 % CI 1.14-1.16), as well as computer and mobile phone density, were associated with a higher PCa incidence, while gross domestic product, GDP (RR 0.94, 95 % CI 0.93-0.95) and overall mortality (RR 0.72, 95 % CI 0.71-0.73) were associated with a low incidence. GDP (RR 0.55, 95 % CI 0.46-0.66) was also associated with a low PCa mortality, while life expectancy (RR 3.93, 95 % CI 3.22-4.79) and healthcare expenditure (RR 1.20, 95 % CI 1.09-1.32) were associated with an elevated mortality. Our results show that healthcare expenditure and, thus, the availability of medical resources are an important contributor to the patterns of international variation in PCa incidence. This suggests that there is an iatrogenic component in the current global epidemic of PCa. On the other hand, higher healthcare expenditure is associated with lower PCa death rates.

Quorum sensing systems differentially regulate the production of phenazine-1-carboxylic acid in the rhizobacterium Pseudomonas aeruginosa PA1201

PubMed Central

Sun, Shuang; Zhou, Lian; Jin, Kaiming; Jiang, Haixia; He, Ya-Wen

2016-01-01

Pseudomonas aeruginosa strain PA1201 is a newly identified rhizobacterium that produces high levels of the secondary metabolite phenazine-1-carboxylic acid (PCA), the newly registered biopesticide Shenqinmycin. PCA production in liquid batch cultures utilizing a specialized PCA-promoting medium (PPM) typically occurs after the period of most rapid growth, and production is regulated in a quorum sensing (QS)-dependent manner. PA1201 contains two PCA biosynthetic gene clusters phz1 and phz2; both clusters contribute to PCA production, with phz2 making a greater contribution. PA1201 also contains a complete set of genes for four QS systems (LasI/LasR, RhlI/RhlR, PQS/MvfR, and IQS). By using several methods including gene deletion, the construction of promoter-lacZ fusion reporter strains, and RNA-Seq analysis, this study investigated the effects of the four QS systems on bacterial growth, QS signal production, the expression of phz1 and phz2, and PCA production. The possible mechanisms for the strain- and condition-dependent expression of phz1 and phz2 were discussed, and a schematic model was proposed. These findings provide a basis for further genetic engineering of the QS systems to improve PCA production. PMID:27456813

Automated X-Ray Diffraction of Irradiated Materials

DOE PAGES

Rodman, John; Lin, Yuewei; Sprouster, David; ...

2017-10-26

Synchrotron-based X-ray diffraction (XRD) and small-angle Xray scattering (SAXS) characterization techniques used on unirradiated and irradiated reactor pressure vessel steels yield large amounts of data. Machine learning techniques, including PCA, offer a novel method of analyzing and visualizing these large data sets in order to determine the effects of chemistry and irradiation conditions on the formation of radiation induced precipitates. In order to run analysis on these data sets, preprocessing must be carried out to convert the data to a usable format and mask the 2-D detector images to account for experimental variations. Once the data has been preprocessed, itmore » can be organized and visualized using principal component analysis (PCA), multi-dimensional scaling, and k-means clustering. In conclusion, from these techniques, it is shown that sample chemistry has a notable effect on the formation of the radiation induced precipitates in reactor pressure vessel steels.« less

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

PubMed

Yang, Xin; Liu, Chaoyue; Wang, Zhiwei; Yang, Jun; Min, Hung Le; Wang, Liang; Cheng, Kwang-Ting Tim

2017-12-01

Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions' locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive thresholding are applied to the corresponding cancer response maps for PCa foci localization. Evaluation based on 160 patient data with 12-core systematic TRUS-guided prostate biopsy as the reference standard demonstrates that our system achieves a sensitivity of 0.46, 0.92 and 0.97 at 0.1, 1 and 10 false positives per normal/benign patient which is significantly superior to two state-of-the-art CNN-based methods (Oquab et al., 2015; Zhou et al., 2015) and 6-core systematic prostate biopsies. Copyright © 2017 Elsevier B.V. All rights reserved.

On a PCA-based lung motion model

PubMed Central

Li, Ruijiang; Lewis, John H; Jia, Xun; Zhao, Tianyu; Liu, Weifeng; Wuenschel, Sara; Lamb, James; Yang, Deshan; Low, Daniel A; Jiang, Steve B

2014-01-01

Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772–81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921–9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1 mm (0.7 ± 0.1 mm). When a single artificial internal marker was used to derive the lung motion, the average 3D error was found to be within 2 mm (1.8 ± 0.3 mm) through comprehensive statistical analysis. The optimal number of PCA coefficients needs to be determined on a patient-by-patient basis and two PCA coefficients seem to be sufficient for accurate modeling of the lung motion for most patients. In conclusion, we have presented thorough theoretical analysis and clinical validation of the PCA lung motion model. The feasibility of deriving the entire lung motion using a single marker has also been demonstrated on clinical data using a simulation approach. PMID:21865624

A finite area scheme for shallow granular flows on three-dimensional surfaces

NASA Astrophysics Data System (ADS)

Rauter, Matthias

2017-04-01

Shallow granular flow models have become a popular tool for the estimation of natural hazards, such as landslides, debris flows and avalanches. The shallowness of the flow allows to reduce the three-dimensional governing equations to a quasi two-dimensional system. Three-dimensional flow fields are replaced by their depth-integrated two-dimensional counterparts, which yields a robust and fast method [1]. A solution for a simple shallow granular flow model, based on the so-called finite area method [3] is presented. The finite area method is an adaption of the finite volume method [4] to two-dimensional curved surfaces in three-dimensional space. This method handles the three dimensional basal topography in a simple way, making the model suitable for arbitrary (but mildly curved) topography, such as natural terrain. Furthermore, the implementation into the open source software OpenFOAM [4] is shown. OpenFOAM is a popular computational fluid dynamics application, designed so that the top-level code mimics the mathematical governing equations. This makes the code easy to read and extendable to more sophisticated models. Finally, some hints on how to get started with the code and how to extend the basic model will be given. I gratefully acknowledge the financial support by the OEAW project "beyond dense flow avalanches". Savage, S. B. & Hutter, K. 1989 The motion of a finite mass of granular material down a rough incline. Journal of Fluid Mechanics 199, 177-215. Ferziger, J. & Peric, M. 2002 Computational methods for fluid dynamics, 3rd edn. Springer. Tukovic, Z. & Jasak, H. 2012 A moving mesh finite volume interface tracking method for surface tension dominated interfacial fluid flow. Computers & fluids 55, 70-84. Weller, H. G., Tabor, G., Jasak, H. & Fureby, C. 1998 A tensorial approach to computational continuum mechanics using object-oriented techniques. Computers in physics 12(6), 620-631.

Individual differences in voluntary alcohol intake in rats: relationship with impulsivity, decision making and Pavlovian conditioned approach.

PubMed

Spoelder, Marcia; Flores Dourojeanni, Jacques P; de Git, Kathy C G; Baars, Annemarie M; Lesscher, Heidi M B; Vanderschuren, Louk J M J

2017-07-01

Alcohol use disorder (AUD) has been associated with suboptimal decision making, exaggerated impulsivity, and aberrant responses to reward-paired cues, but the relationship between AUD and these behaviors is incompletely understood. This study aims to assess decision making, impulsivity, and Pavlovian-conditioned approach in rats that voluntarily consume low (LD) or high (HD) amounts of alcohol. LD and HD were tested in the rat gambling task (rGT) or the delayed reward task (DRT). Next, the effect of alcohol (0-1.0 g/kg) was tested in these tasks. Pavlovian-conditioned approach (PCA) was assessed both prior to and after intermittent alcohol access (IAA). Principal component analyses were performed to identify relationships between the most important behavioral parameters. HD showed more optimal decision making in the rGT. In the DRT, HD transiently showed reduced impulsive choice. In both LD and HD, alcohol treatment increased optimal decision making in the rGT and increased impulsive choice in the DRT. PCA prior to and after IAA was comparable for LD and HD. When PCA was tested after IAA only, HD showed a more sign-tracking behavior. The principal component analyses indicated dimensional relationships between alcohol intake, impulsivity, and sign-tracking behavior in the PCA task after IAA. HD showed a more efficient performance in the rGT and DRT. Moreover, alcohol consumption enhanced approach behavior to reward-predictive cues, but sign-tracking did not predict the level of alcohol consumption. Taken together, these findings suggest that high levels of voluntary alcohol intake are associated with enhanced cue- and reward-driven behavior.

Predicting timing of foot strike during running, independent of striking technique, using principal component analysis of joint angles.

PubMed

Osis, Sean T; Hettinga, Blayne A; Leitch, Jessica; Ferber, Reed

2014-08-22

As 3-dimensional (3D) motion-capture for clinical gait analysis continues to evolve, new methods must be developed to improve the detection of gait cycle events based on kinematic data. Recently, the application of principal component analysis (PCA) to gait data has shown promise in detecting important biomechanical features. Therefore, the purpose of this study was to define a new foot strike detection method for a continuum of striking techniques, by applying PCA to joint angle waveforms. In accordance with Newtonian mechanics, it was hypothesized that transient features in the sagittal-plane accelerations of the lower extremity would be linked with the impulsive application of force to the foot at foot strike. Kinematic and kinetic data from treadmill running were selected for 154 subjects, from a database of gait biomechanics. Ankle, knee and hip sagittal plane angular acceleration kinematic curves were chained together to form a row input to a PCA matrix. A linear polynomial was calculated based on PCA scores, and a 10-fold cross-validation was performed to evaluate prediction accuracy against gold-standard foot strike as determined by a 10 N rise in the vertical ground reaction force. Results show 89-94% of all predicted foot strikes were within 4 frames (20 ms) of the gold standard with the largest error being 28 ms. It is concluded that this new foot strike detection is an improvement on existing methods and can be applied regardless of whether the runner exhibits a rearfoot, midfoot, or forefoot strike pattern. Copyright © 2014 Elsevier Ltd. All rights reserved.

Network intrusion detection based on a general regression neural network optimized by an improved artificial immune algorithm.

PubMed

Wu, Jianfa; Peng, Dahao; Li, Zhuping; Zhao, Li; Ling, Huanzhang

2015-01-01

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data.

Analytic topologically nontrivial solutions of the (3 +1 )-dimensional U (1 ) gauged Skyrme model and extended duality

NASA Astrophysics Data System (ADS)

Avilés, L.; Canfora, F.; Dimakis, N.; Hidalgo, D.

2017-12-01

We construct the first analytic examples of topologically nontrivial solutions of the (3 +1 )-dimensional U (1 ) gauged Skyrme model within a finite box in (3 +1 )-dimensional flat space-time. There are two types of gauged solitons. The first type corresponds to gauged Skyrmions living within a finite volume. The second corresponds to gauged time crystals (smooth solutions of the U (1 ) gauged Skyrme model whose periodic time dependence is protected by a winding number). The notion of electromagnetic duality can be extended for these two types of configurations in the sense that the electric and one of the magnetic components can be interchanged. These analytic solutions show very explicitly the Callan-Witten mechanism (according to which magnetic monopoles may "swallow" part of the topological charge of the Skyrmion) since the electromagnetic field contributes directly to the conserved topological charge of the gauged Skyrmions. As it happens in superconductors, the magnetic field is suppressed in the core of the gauged Skyrmions. On the other hand, the electric field is strongly suppresed in the core of gauged time crystals.

Synthesis, Structure and Thermal Behavior of Oxalato-Bridged Rb+ and H3O+ Extended Frameworks with Different Dimensionalities

PubMed Central

Kherfi, Hamza; Hamadène, Malika; Guehria-Laïdoudi, Achoura; Dahaoui, Slimane; Lecomte, Claude

2010-01-01

Correlative studies of three oxalato-bridged polymers, obtained under hydrothermal conditions for the two isostructural compounds {Rb(HC2O4)(H2C2O4)(H2O)2}∞1, 1, {H3O(HC2O4)(H2C2O4).2H2O}∞1, 2, and by conventional synthetic method for {Rb(HC2O4)}∞3, 3, allowed the identification of H-bond patterns and structural dimensionality. Ferroïc domain structures are confirmed by electric measurements performed on 3. Although 2 resembles one oxalic acid sesquihydrate, its structure determination doesn’t display any kind of disorder and leads to recognition of a supramolecular network identical to hybrid s-block series, where moreover, unusual H3O+ and NH4+ similarity is brought out. Thermal behaviors show that 1D frameworks with extended H-bonds, whether with or without a metal center, have the same stability. Inversely, despite the dimensionalities, the same metallic intermediate and final compounds are obtained for the two Rb+ ferroïc materials.

Rational tuning of high-energy visible light absorption for panchromatic small molecules by a two-dimensional conjugation approach

DOE PAGES

He, B.; Zherebetskyy, D.; Wang, H.; ...

2016-02-29

We have demonstrated a rational two-dimensional (2D) conjugation approach towards achieving panchromatic absorption of small molecules. Furthermore, by extending the conjugation on two orthogonal axes of an electron acceptor, namely, bay-annulated indigo (BAI), the optical absorptions could be tuned independently in both high- and low-energy regions. The unconventional modulation of the high-energy absorption is rationalized by density functional theory (DFT) calculations. Finally, we determine that a 2D tuning strategy provides novel guidelines for the design of molecular materials with tailored optoelectronic properties.

Characterizing the molecular features of ERG-positive tumors in primary and castration resistant prostate cancer.

PubMed

Roudier, Martine P; Winters, Brian R; Coleman, Ilsa; Lam, Hung-Ming; Zhang, Xiaotun; Coleman, Roger; Chéry, Lisly; True, Lawrence D; Higano, Celestia S; Montgomery, Bruce; Lange, Paul H; Snyder, Linda A; Srivastava, Shiv; Corey, Eva; Vessella, Robert L; Nelson, Peter S; Üren, Aykut; Morrissey, Colm

2016-06-01

The TMPRSS2-ERG gene fusion is detected in approximately half of primary prostate cancers (PCa) yet the prognostic significance remains unclear. We hypothesized that ERG promotes the expression of common genes in primary PCa and metastatic castration-resistant PCa (CRPC), with the objective of identifying ERG-associated pathways, which may promote the transition from primary PCa to CRPC. We constructed tissue microarrays (TMA) from 127 radical prostatectomy specimens, 20 LuCaP patient-derived xenografts (PDX), and 152 CRPC metastases obtained immediately at time of death. Nuclear ERG was assessed by immunohistochemistry (IHC). To characterize the molecular features of ERG-expressing PCa, a subset of IHC confirmed ERG+ or ERG- specimens including 11 radical prostatectomies, 20 LuCaP PDXs, and 45 CRPC metastases underwent gene expression analysis. Genes were ranked based on expression in primary PCa and CRPC. Common genes of interest were targeted for IHC analysis and expression compared with biochemical recurrence (BCR) status. IHC revealed that 43% of primary PCa, 35% of the LuCaP PDXs, and 18% of the CRPC metastases were ERG+ (12 of 48 patients [25%] had at least one ERG+ metastasis). Based on gene expression data and previous literature, two proteins involved in calcium signaling (NCALD, CACNA1D), a protein involved in inflammation (HLA-DMB), CD3 positive immune cells, and a novel ERG-associated protein, DCLK1 were evaluated in primary PCa and CRPC metastases. In ERG+ primary PCa, a weak association was seen with NCALD and CACNA1D protein expression. HLA-DMB association with ERG was decreased and CD3 cell number association with ERG was changed from positive to negative in CRPC metastases compared to primary PCa. DCLK1 was upregulated at the protein level in unpaired ERG+ primary PCa and CRPC metastases (P = 0.0013 and P < 0.0001, respectively). In primary PCa, ERG status or expression of targeted proteins was not associated with BCR-free survival. However, for primary PCa, ERG+DCLK1+ patients exhibited shorter time to BCR (P = 0.06) compared with ERG+DCLK1- patients. This study examined ERG expression in primary PCa and CRPC. We have identified altered levels of inflammatory mediators associated with ERG expression. We determined expression of DCLK1 correlates with ERG expression and may play a role in primary PCa progression to metastatic CPRC. Prostate 76:810-822, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A dual yet opposite growth-regulating function of miR-204 and its target XRN1 in prostate adenocarcinoma cells and neuroendocrine-like prostate cancer cells

PubMed Central

Ding, Miao; Lin, Biaoyang; Li, Tao; Liu, Yuanyuan; Li, Yuhua; Zhou, Xiaoyu; Miao, Maohua; Gu, Jinfa; Pan, Hongjie; Yang, Fen; Li, Tianqi; Liu, Xin Yuan; Li, Runsheng

2015-01-01

Androgen deprivation therapy in prostate cancer (PCa) causes neuroendocrine differentiation (NED) of prostatic adenocarcinomas (PAC) cells, leading to recurrence of PCa. Androgen-responsive genes involved in PCa progression including NED remain largely unknown. Here we demonstrated the importance of androgen receptor (AR)-microRNA-204 (miR-204)-XRN1 axis in PCa cell lines and the rat ventral prostate. Androgens downregulate miR-204, resulting in induction of XRN1 (5′-3′ exoribonuclease 1), which we identified as a miR-204 target. miR-204 acts as a tumor suppressor in two PAC cell lines (LNCaP and 22Rv1) and as an oncomiR in two neuroendocrine-like prostate cancer (NEPC) cell lines (PC-3 and CL1). Importantly, overexpression of miR-204 and knockdown of XRN1 inhibited AR expression in PCa cells. Repression of miR-34a, a known AR-targeting miRNA, contributes AR expression by XRN1. Thus we revealed the AR-miR-204-XRN1-miR-34a positive feedback loop and a dual function of miR-204/XRN1 axis in prostate cancer. PMID:25797256

Interpreting comprehensive two-dimensional gas chromatography using peak topography maps with application to petroleum forensics.

PubMed

Ghasemi Damavandi, Hamidreza; Sen Gupta, Ananya; Nelson, Robert K; Reddy, Christopher M

2016-01-01

Comprehensive two-dimensional gas chromatography [Formula: see text] provides high-resolution separations across hundreds of compounds in a complex mixture, thus unlocking unprecedented information for intricate quantitative interpretation. We exploit this compound diversity across the [Formula: see text] topography to provide quantitative compound-cognizant interpretation beyond target compound analysis with petroleum forensics as a practical application. We focus on the [Formula: see text] topography of biomarker hydrocarbons, hopanes and steranes, as they are generally recalcitrant to weathering. We introduce peak topography maps (PTM) and topography partitioning techniques that consider a notably broader and more diverse range of target and non-target biomarker compounds compared to traditional approaches that consider approximately 20 biomarker ratios. Specifically, we consider a range of 33-154 target and non-target biomarkers with highest-to-lowest peak ratio within an injection ranging from 4.86 to 19.6 (precise numbers depend on biomarker diversity of individual injections). We also provide a robust quantitative measure for directly determining "match" between samples, without necessitating training data sets. We validate our methods across 34 [Formula: see text] injections from a diverse portfolio of petroleum sources, and provide quantitative comparison of performance against established statistical methods such as principal components analysis (PCA). Our data set includes a wide range of samples collected following the 2010 Deepwater Horizon disaster that released approximately 160 million gallons of crude oil from the Macondo well (MW). Samples that were clearly collected following this disaster exhibit statistically significant match [Formula: see text] using PTM-based interpretation against other closely related sources. PTM-based interpretation also provides higher differentiation between closely correlated but distinct sources than obtained using PCA-based statistical comparisons. In addition to results based on this experimental field data, we also provide extentive perturbation analysis of the PTM method over numerical simulations that introduce random variability of peak locations over the [Formula: see text] biomarker ROI image of the MW pre-spill sample (sample [Formula: see text] in Additional file 4: Table S1). We compare the robustness of the cross-PTM score against peak location variability in both dimensions and compare the results against PCA analysis over the same set of simulated images. Detailed description of the simulation experiment and discussion of results are provided in Additional file 1: Section S8. We provide a peak-cognizant informational framework for quantitative interpretation of [Formula: see text] topography. Proposed topographic analysis enables [Formula: see text] forensic interpretation across target petroleum biomarkers, while including the nuances of lesser-known non-target biomarkers clustered around the target peaks. This allows potential discovery of hitherto unknown connections between target and non-target biomarkers.

Aerodynamic Design of Axial-flow Compressors. Volume III

NASA Technical Reports Server (NTRS)

Johnson, Irving A; Bullock, Robert O; Graham, Robert W; Costilow, Eleanor L; Huppert, Merle C; Benser, William A; Herzig, Howard Z; Hansen, Arthur G; Jackson, Robert J; Yohner, Peggy L;

Hyaluronan (HA) interacting proteins RHAMM and hyaluronidase impact prostate cancer cell behavior and invadopodia formation in 3D HA-based hydrogels.

PubMed

Gurski, Lisa A; Xu, Xian; Labrada, Lyana N; Nguyen, Ngoc T; Xiao, Longxi; van Golen, Kenneth L; Jia, Xinqiao; Farach-Carson, Mary C

2012-01-01

To study the individual functions of hyaluronan interacting proteins in prostate cancer (PCa) motility through connective tissues, we developed a novel three-dimensional (3D) hyaluronic acid (HA) hydrogel assay that provides a flexible, quantifiable, and physiologically relevant alternative to current methods. Invasion in this system reflects the prevalence of HA in connective tissues and its role in the promotion of cancer cell motility and tissue invasion, making the system ideal to study invasion through bone marrow or other HA-rich connective tissues. The bio-compatible cross-linking process we used allows for direct encapsulation of cancer cells within the gel where they adopt a distinct, cluster-like morphology. Metastatic PCa cells in these hydrogels develop fingerlike structures, "invadopodia", consistent with their invasive properties. The number of invadopodia, as well as cluster size, shape, and convergence, can provide a quantifiable measure of invasive potential. Among candidate hyaluronan interacting proteins that could be responsible for the behavior we observed, we found that culture in the HA hydrogel triggers invasive PCa cells to differentially express and localize receptor for hyaluronan mediated motility (RHAMM)/CD168 which, in the absence of CD44, appears to contribute to PCa motility and invasion by interacting with the HA hydrogel components. PCa cell invasion through the HA hydrogel also was found to depend on the activity of hyaluronidases. Studies shown here reveal that while hyaluronidase activity is necessary for invadopodia and inter-connecting cluster formation, activity alone is not sufficient for acquisition of invasiveness to occur. We therefore suggest that development of invasive behavior in 3D HA-based systems requires development of additional cellular features, such as activation of motility associated pathways that regulate formation of invadopodia. Thus, we report development of a 3D system amenable to dissection of biological processes associated with cancer cell motility through HA-rich connective tissues.

An adaptive three-stage extended Kalman filter for nonlinear discrete-time system in presence of unknown inputs.

PubMed

Xiao, Mengli; Zhang, Yongbo; Wang, Zhihua; Fu, Huimin

2018-04-01

Considering the performances of conventional Kalman filter may seriously degrade when it suffers stochastic faults and unknown input, which is very common in engineering problems, a new type of adaptive three-stage extended Kalman filter (AThSEKF) is proposed to solve state and fault estimation in nonlinear discrete-time system under these conditions. The three-stage UV transformation and adaptive forgetting factor are introduced for derivation, and by comparing with the adaptive augmented state extended Kalman filter, it is proven to be uniformly asymptotically stable. Furthermore, the adaptive three-stage extended Kalman filter is applied to a two-dimensional radar tracking scenario to illustrate the effect, and the performance is compared with that of conventional three stage extended Kalman filter (ThSEKF) and the adaptive two-stage extended Kalman filter (ATEKF). The results show that the adaptive three-stage extended Kalman filter is more effective than these two filters when facing the nonlinear discrete-time systems with information of unknown inputs not perfectly known. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Characteristics of large three-dimensional heaps of particles produced by ballistic deposition from extended sources

NASA Astrophysics Data System (ADS)

Topic, Nikola; Gallas, Jason A. C.; Pöschel, Thorsten

2013-11-01

This paper reports a detailed numerical investigation of the geometrical and structural properties of three-dimensional heaps of particles. Our goal is the characterization of very large heaps produced by ballistic deposition from extended circular dropping areas. First, we provide an in-depth study of the formation of monodisperse heaps of particles. We find very large heaps to contain three new geometrical characteristics: they may display two external angles of repose, one internal angle of repose, and four distinct packing fraction (density) regions. Such features are found to be directly connected with the size of the dropping zone. We derive a differential equation describing the boundary of an unexpected triangular packing fraction zone formed under the dropping area. We investigate the impact that noise during the deposition has on the final heap structure. In addition, we perform two complementary experiments designed to test the robustness of the novel features found. The first experiment considers changes due to polydispersity. The second checks what happens when letting the extended dropping zone to become a point-like source of particles, the more common type of source.

Concordance of Gleason grading with three-dimensional ultrasound systematic biopsy and biopsy core pre-embedding.

PubMed

van der Aa, Anouk A M A; Mannaerts, Christophe K; van der Linden, Hans; Gayet, Maudy; Schrier, Bart Ph; Mischi, Massimo; Beerlage, Harrie P; Wijkstra, Hessel

2018-02-01

To determine the value of a three-dimensional (3D) greyscale transrectal ultrasound (TRUS)-guided prostate biopsy system and biopsy core pre-embedding method on concordance between Gleason scores of needle biopsies and radical prostatectomy (RP) specimens. Retrospective analysis of prostate biopsies and subsequent RP for PCa in the Jeroen Bosch Hospital, the Netherlands, from 2007 to 2016. Two cohorts were analysed: conventional 2D TRUS-guided biopsies and RP (2007-2013, n = 266) versus 3D TRUS-guided biopsies with pre-embedding (2013-2016, n = 129). The impact of 3D TRUS-guidance with pre-embedding on Gleason score (GS) concordance between biopsy and RP was evaluated using the κ-coefficient. Predictors of biopsy GS 6 upgrading were assessed using logistic regression models. Gleason concordance was comparable between the two cohorts with a κ = 0.44 for the 3D cohort, compared to κ = 0.42 for the 2D cohort. 3D TRUS-guidance with pre-embedding, did not significantly affect the risk of biopsy GS 6 upgrading in univariate and multivariate analysis. 3D TRUS-guidance with biopsy core pre-embedding did not improve Gleason concordance. Improved detection techniques are needed for recognition of low-grade disease upgrading.

One Size Does Not Fit All: Managing Radical and Incremental Creativity

ERIC Educational Resources Information Center

Gilson, Lucy L.; Lim, Hyoun Sook; D'Innocenzo, Lauren; Moye, Neta

2012-01-01

This research extends creativity theory by re-conceptualizing creativity as a two-dimensional construct (radical and incremental) and examining the differential effects of intrinsic motivation, extrinsic rewards, and supportive supervision on perceptions of creativity. We hypothesize and find two distinct types of creativity that are associated…

PCA-HOG symmetrical feature based diseased cell detection

NASA Astrophysics Data System (ADS)

Wan, Min-jie

2016-04-01

A histogram of oriented gradient (HOG) feature is applied to the field of diseased cell detection, which can detect diseased cells in high resolution tissue images rapidly, accurately and efficiently. Firstly, motivated by symmetrical cellular forms, a new HOG symmetrical feature based on the traditional HOG feature is proposed to meet the condition of cell detection. Secondly, considering the high feature dimension of traditional HOG feature leads to plenty of memory resources and long runtime in practical applications, a classical dimension reduction method called principal component analysis (PCA) is used to reduce the dimension of high-dimensional HOG descriptor. Because of that, computational speed is increased greatly, and the accuracy of detection can be controlled in a proper range at the same time. Thirdly, support vector machine (SVM) classifier is trained with PCA-HOG symmetrical features proposed above. At last, practical tissue images is detected and analyzed by SVM classifier. In order to verify the effectiveness of this new algorithm, it is practically applied to conduct diseased cell detection which takes 200 pieces of H&E (hematoxylin & eosin) high resolution staining histopathological images collected from 20 breast cancer patients as a sample. The experiment shows that the average processing rate can be 25 frames per second and the detection accuracy can be 92.1%.

No Association Between Variant N-acetyltransferase Genes, Cigarette Smoking and Prostate Cancer Susceptibility Among Men of African Descent

PubMed Central

Kidd, La Creis Renee; VanCleave, Tiva T.; Doll, Mark A.; Srivastava, Daya S.; Thacker, Brandon; Komolafe, Oyeyemi; Pihur, Vasyl; Brock, Guy N.; Hein, David W.

2011-01-01

Objective We evaluated the individual and combination effects of NAT1, NAT2 and tobacco smoking in a case-control study of 219 incident prostate cancer (PCa) cases and 555 disease-free men. Methods Allelic discriminations for 15 NAT1 and NAT2 loci were detected in germ-line DNA samples using Taqman polymerase chain reaction (PCR) assays. Single gene, gene-gene and gene-smoking interactions were analyzed using logistic regression models and multi-factor dimensionality reduction (MDR) adjusted for age and subpopulation stratification. MDR involves a rigorous algorithm that has ample statistical power to assess and visualize gene-gene and gene-environment interactions using relatively small samples sizes (i.e., 200 cases and 200 controls). Results Despite the relatively high prevalence of NAT1*10/*10 (40.1%), NAT2 slow (30.6%), and NAT2 very slow acetylator genotypes (10.1%) among our study participants, these putative risk factors did not individually or jointly increase PCa risk among all subjects or a subset analysis restricted to tobacco smokers. Conclusion Our data do not support the use of N-acetyltransferase genetic susceptibilities as PCa risk factors among men of African descent; however, subsequent studies in larger sample populations are needed to confirm this finding. PMID:21709725

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2013-01-08

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu-Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples. Copyright © 2012 Elsevier B.V. All rights reserved.

Application of UV-visible absorption spectroscopy combined with two-dimensional correlation for insight into DOM fractions from native halophyte soils in a larger estuarine delta.

PubMed

Wei, Huaibin; Yu, Huibin; Pan, Hongwei; Gao, Hongjie

2018-05-01

UV-visible absorption spectroscopy combined with principal component analysis (PCA) and two-dimensional correlation (2D correlation) is used to trace components of dissolved organic matter (DOM) extracted from soils in a larger estuarine delta and to investigate spatial variations of DOM fractions. Soil samples of different depths were collected from native halophyte soils along a saline gradient, i.e., Suaeda salsa Comm. (SSC), Chenopodium album Comm. (CAC), Phragmites australis Comm. (PAC), and Artemisia selengensis Comm. (ASC). Molecular weights of DOM within the SSC soil profile were the lowest, followed by the CAC, PAC, and ASC soil profiles. Humification degree of DOM within the ASC soil profile was the highest, followed by the PAC, SSC, and CAC soil profiles. DOM within the soil profiles mainly contained phenolic, carboxylic, microbial products, and aromatic and alkyl groups through the PCA, which presented the significant differentiation among the four native halophyte soil profiles. The 2D UV correlation spectra of DOM within the SSC soil profile indicated that the variations of the phenolic groups were the largest, followed by the carboxylic groups, microbial products, and humified organic materials according to the band changing order of 285 → 365 → 425 → 520 nm. The 2D UV correlation spectra of DOM within the CAC soil profiles determined that the decreasing order of the variations was phenolic groups > carboxylic groups > microbial products according the band changing order of 285 → 365 → 425 nm. The 2D UV correlation spectra of DOM within the PAC soil profile proved that the variations of the phenolic groups were larger than those of the carboxylic groups according to the band changing order of 285 → 365 nm. The 2D UV correlation spectra of DOM within the ASC soil profile demonstrated that the variations of the phenolic groups were larger than those of the other DOM fractions according to the broad cross-peak at 285/365-700 nm.

A fast semi-discrete Kansa method to solve the two-dimensional spatiotemporal fractional diffusion equation

NASA Astrophysics Data System (ADS)

Sun, HongGuang; Liu, Xiaoting; Zhang, Yong; Pang, Guofei; Garrard, Rhiannon

2017-09-01

Fractional-order diffusion equations (FDEs) extend classical diffusion equations by quantifying anomalous diffusion frequently observed in heterogeneous media. Real-world diffusion can be multi-dimensional, requiring efficient numerical solvers that can handle long-term memory embedded in mass transport. To address this challenge, a semi-discrete Kansa method is developed to approximate the two-dimensional spatiotemporal FDE, where the Kansa approach first discretizes the FDE, then the Gauss-Jacobi quadrature rule solves the corresponding matrix, and finally the Mittag-Leffler function provides an analytical solution for the resultant time-fractional ordinary differential equation. Numerical experiments are then conducted to check how the accuracy and convergence rate of the numerical solution are affected by the distribution mode and number of spatial discretization nodes. Applications further show that the numerical method can efficiently solve two-dimensional spatiotemporal FDE models with either a continuous or discrete mixing measure. Hence this study provides an efficient and fast computational method for modeling super-diffusive, sub-diffusive, and mixed diffusive processes in large, two-dimensional domains with irregular shapes.

On the modeling of the bottom particles segregation with non-linear diffusion equations: application to the marine sand ripples

NASA Astrophysics Data System (ADS)

Tiguercha, Djlalli; Bennis, Anne-claire; Ezersky, Alexander

2015-04-01

The elliptical motion in surface waves causes an oscillating motion of the sand grains leading to the formation of ripple patterns on the bottom. Investigation how the grains with different properties are distributed inside the ripples is a difficult task because of the segration of particle. The work of Fernandez et al. (2003) was extended from one-dimensional to two-dimensional case. A new numerical model, based on these non-linear diffusion equations, was developed to simulate the grain distribution inside the marine sand ripples. The one and two-dimensional models are validated on several test cases where segregation appears. Starting from an homogeneous mixture of grains, the two-dimensional simulations demonstrate different segregation patterns: a) formation of zones with high concentration of light and heavy particles, b) formation of «cat's eye» patterns, c) appearance of inverse Brazil nut effect. Comparisons of numerical results with the new set of field data and wave flume experiments show that the two-dimensional non-linear diffusion equations allow us to reproduce qualitatively experimental results on particles segregation.

Morphological image analysis for classification of gastrointestinal tissues using optical coherence tomography

NASA Astrophysics Data System (ADS)

Garcia-Allende, P. Beatriz; Amygdalos, Iakovos; Dhanapala, Hiruni; Goldin, Robert D.; Hanna, George B.; Elson, Daniel S.

2012-01-01

Computer-aided diagnosis of ophthalmic diseases using optical coherence tomography (OCT) relies on the extraction of thickness and size measures from the OCT images, but such defined layers are usually not observed in emerging OCT applications aimed at "optical biopsy" such as pulmonology or gastroenterology. Mathematical methods such as Principal Component Analysis (PCA) or textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric auto-correlation (CSAC) and spatial grey-level dependency matrices (SGLDM), as well as, quantitative measurements of the attenuation coefficient have been previously proposed to overcome this problem. We recently proposed an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technology for feature quantification. OCT images were first segmented in the axial direction in an automated manner according to intensity. Afterwards, a morphological analysis of the segmented OCT images was employed for quantifying the features that served for tissue classification. In this study, a PCA processing of the extracted features is accomplished to combine their discriminative power in a lower number of dimensions. Ready discrimination of gastrointestinal surgical specimens is attained demonstrating that the approach further surpasses the algorithms previously reported and is feasible for tissue classification in the clinical setting.

Estimation of three-dimensional radar tracking using modified extended kalman filter

NASA Astrophysics Data System (ADS)

Aditya, Prima; Apriliani, Erna; Khusnul Arif, Didik; Baihaqi, Komar

2018-03-01

Kalman filter is an estimation method by combining data and mathematical models then developed be extended Kalman filter to handle nonlinear systems. Three-dimensional radar tracking is one of example of nonlinear system. In this paper developed a modification method of extended Kalman filter from the direct decline of the three-dimensional radar tracking case. The development of this filter algorithm can solve the three-dimensional radar measurements in the case proposed in this case the target measured by radar with distance r, azimuth angle θ, and the elevation angle ϕ. Artificial covariance and mean adjusted directly on the three-dimensional radar system. Simulations result show that the proposed formulation is effective in the calculation of nonlinear measurement compared with extended Kalman filter with the value error at 0.77% until 1.15%.

External validation of urinary PCA3-based nomograms to individually predict prostate biopsy outcome.

PubMed

Auprich, Marco; Haese, Alexander; Walz, Jochen; Pummer, Karl; de la Taille, Alexandre; Graefen, Markus; de Reijke, Theo; Fisch, Margit; Kil, Paul; Gontero, Paolo; Irani, Jacques; Chun, Felix K-H

2010-11-01

Prior to safely adopting risk stratification tools, their performance must be tested in an external patient cohort. To assess accuracy and generalizability of previously reported, internally validated, prebiopsy prostate cancer antigen 3 (PCA3) gene-based nomograms when applied to a large, external, European cohort of men at risk of prostate cancer (PCa). Biopsy data, including urinary PCA3 score, were available for 621 men at risk of PCa who were participating in a European multi-institutional study. All patients underwent a ≥10-core prostate biopsy. Biopsy indication was based on suspicious digital rectal examination, persistently elevated prostate-specific antigen level (2.5-10 ng/ml) and/or suspicious histology (atypical small acinar proliferation of the prostate, >/= two cores affected by high-grade prostatic intraepithelial neoplasia in first set of biopsies). PCA3 scores were assessed using the Progensa assay (Gen-Probe Inc, San Diego, CA, USA). According to the previously reported nomograms, different PCA3 score codings were used. The probability of a positive biopsy was calculated using previously published logistic regression coefficients. Predicted outcomes were compared to the actual biopsy results. Accuracy was calculated using the area under the curve as a measure of discrimination; calibration was explored graphically. Biopsy-confirmed PCa was detected in 255 (41.1%) men. Median PCA3 score of biopsy-negative versus biopsy-positive men was 20 versus 48 in the total cohort, 17 versus 47 at initial biopsy, and 37 versus 53 at repeat biopsy (all p≤0.002). External validation of all four previously reported PCA3-based nomograms demonstrated equally high accuracy (0.73-0.75) and excellent calibration. The main limitations of the study reside in its early detection setting, referral scenario, and participation of only tertiary-care centers. In accordance with the original publication, previously developed PCA3-based nomograms achieved high accuracy and sufficient calibration. These novel nomograms represent robust tools and are thus generalizable to European men at risk of harboring PCa. Consequently, in presence of a PCA3 score, these nomograms may be safely used to assist clinicians when prostate biopsy is contemplated. Copyright © 2010 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Diagnostic Pathway with Multiparametric Magnetic Resonance Imaging Versus Standard Pathway: Results from a Randomized Prospective Study in Biopsy-naïve Patients with Suspected Prostate Cancer.

PubMed

Porpiglia, Francesco; Manfredi, Matteo; Mele, Fabrizio; Cossu, Marco; Bollito, Enrico; Veltri, Andrea; Cirillo, Stefano; Regge, Daniele; Faletti, Riccardo; Passera, Roberto; Fiori, Cristian; De Luca, Stefano

2017-08-01

An approach based on multiparametric magnetic resonance imaging (mpMRI) might increase the detection rate (DR) of clinically significant prostate cancer (csPCa). To compare an mpMRI-based pathway with the standard approach for the detection of prostate cancer (PCa) and csPCa. Between November 2014 and April 2016, 212 biopsy-naïve patients with suspected PCa (prostate specific antigen level ≤15 ng/ml and negative digital rectal examination results) were included in this randomized clinical trial. Patients were randomized into a prebiopsy mpMRI group (arm A, n=107) or a standard biopsy (SB) group (arm B, n=105). In arm A, patients with mpMRI evidence of lesions suspected for PCa underwent mpMRI/transrectal ultrasound fusion software-guided targeted biopsy (TB) (n=81). The remaining patients in arm A (n=26) with negative mpMRI results and patients in arm B underwent 12-core SB. The primary end point was comparison of the DR of PCa and csPCa between the two arms of the study; the secondary end point was comparison of the DR between TB and SB. The overall DRs were higher in arm A versus arm B for PCa (50.5% vs 29.5%, respectively; p=0.002) and csPCa (43.9% vs 18.1%, respectively; p<0.001). Concerning the biopsy approach, that is, TB in arm A, SB in arm A, and SB in arm B, the overall DRs were significantly different for PCa (60.5% vs 19.2% vs 29.5%, respectively; p<0.001) and for csPCa (56.8% vs 3.8% vs 18.1%, respectively; p<0.001). The reproducibility of the study could have been affected by the single-center nature. A diagnostic pathway based on mpMRI had a higher DR than the standard pathway in both PCa and csPCa. In this randomized trial, a pathway for the diagnosis of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI) was compared with the standard pathway based on random biopsy. The mpMRI-based pathway had better performance than the standard pathway. Copyright © 2016 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Exploring Secondary Students' Knowledge and Misconceptions about Influenza: Development, validation, and implementation of a multiple-choice influenza knowledge scale

NASA Astrophysics Data System (ADS)

Romine, William L.; Barrow, Lloyd H.; Folk, William R.

2013-07-01

Understanding infectious diseases such as influenza is an important element of health literacy. We present a fully validated knowledge instrument called the Assessment of Knowledge of Influenza (AKI) and use it to evaluate knowledge of influenza, with a focus on misconceptions, in Midwestern United States high-school students. A two-phase validation process was used. In phase 1, an initial factor structure was calculated based on 205 students of grades 9-12 at a rural school. In phase 2, one- and two-dimensional factor structures were analyzed from the perspectives of classical test theory and the Rasch model using structural equation modeling and principal components analysis (PCA) on Rasch residuals, respectively. Rasch knowledge measures were calculated for 410 students from 6 school districts in the Midwest, and misconceptions were verified through the χ 2 test. Eight items measured knowledge of flu transmission, and seven measured knowledge of flu management. While alpha reliability measures for the subscales were acceptable, Rasch person reliability measures and PCA on residuals advocated for a single-factor scale. Four misconceptions were found, which have not been previously documented in high-school students. The AKI is the first validated influenza knowledge assessment, and can be used by schools and health agencies to provide a quantitative measure of impact of interventions aimed at increasing understanding of influenza. This study also adds significantly to the literature on misconceptions about influenza in high-school students, a necessary step toward strategic development of educational interventions for these students.

Principal components analysis based control of a multi-DoF underactuated prosthetic hand.

PubMed

Matrone, Giulia C; Cipriani, Christian; Secco, Emanuele L; Magenes, Giovanni; Carrozza, Maria Chiara

2010-04-23

Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG). Driving a multi degrees of freedom (DoF) hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user. A Principal Components Analysis (PCA) based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand) with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs). Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control. Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture) may be achieved. This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.

Active elastic dimers: cells moving on rigid tracks.

PubMed

Lopez, J H; Das, Moumita; Schwarz, J M

2014-09-01

Experiments suggest that the migration of some cells in the three-dimensional extracellular matrix bears strong resemblance to one-dimensional cell migration. Motivated by this observation, we construct and study a minimal one-dimensional model cell made of two beads and an active spring moving along a rigid track. The active spring models the stress fibers with their myosin-driven contractility and α-actinin-driven extendability, while the friction coefficients of the two beads describe the catch and slip-bond behaviors of the integrins in focal adhesions. In the absence of active noise, net motion arises from an interplay between active contractility (and passive extendability) of the stress fibers and an asymmetry between the front and back of the cell due to catch-bond behavior of integrins at the front of the cell and slip-bond behavior of integrins at the back. We obtain reasonable cell speeds with independently estimated parameters. We also study the effects of hysteresis in the active spring, due to catch-bond behavior and the dynamics of cross linking, and the addition of active noise on the motion of the cell. Our model highlights the role of α-actinin in three-dimensional cell motility and does not require Arp2/3 actin filament nucleation for net motion.

The Make 2D-DB II package: conversion of federated two-dimensional gel electrophoresis databases into a relational format and interconnection of distributed databases.

PubMed

Mostaguir, Khaled; Hoogland, Christine; Binz, Pierre-Alain; Appel, Ron D

2003-08-01

The Make 2D-DB tool has been previously developed to help build federated two-dimensional gel electrophoresis (2-DE) databases on one's own web site. The purpose of our work is to extend the strength of the first package and to build a more efficient environment. Such an environment should be able to fulfill the different needs and requirements arising from both the growing use of 2-DE techniques and the increasing amount of distributed experimental data.

Incorporation of coupled nonequilibrium chemistry into a two-dimensional nozzle code (SEAGULL)

NASA Technical Reports Server (NTRS)

Ratliff, A. W.

1979-01-01

A two-dimensional multiple shock nozzle code (SEAGULL) was extended to include the effects of finite rate chemistry. The basic code that treats multiple shocks and contact surfaces was fully coupled with a generalized finite rate chemistry and vibrational energy exchange package. The modified code retains all of the original SEAGULL features plus the capability to treat chemical and vibrational nonequilibrium reactions. Any chemical and/or vibrational energy exchange mechanism can be handled as long as thermodynamic data and rate constants are available for all participating species.

Indirect Field Measurement of Wine-Grape Vineyard Canopy Leaf Area Index

NASA Technical Reports Server (NTRS)

Johnson, Lee F.; Pierce, Lars L.; Skiles, J. W. (Technical Monitor)

2002-01-01

Leaf area index (LAI) indirect measurements were made at 12 study plots in California's Napa Valley commercial wine-grape vineyards with a LI-COR LI-2000 Plant Canopy Analyzer (PCA). The plots encompassed different trellis systems, biological varieties, and planting densities. LAI ranged from 0.5 - 2.25 sq m leaf area/ sq m ground area according to direct (defoliation) measurements. Indirect LAI reported by the PCA was significantly related to direct LAI (r(exp 2) = 0.78, p less than 001). However, the PCA tended to underestimate direct LAI by about a factor of two. Narrowing the instrument's conical field of view from 148 deg to 56 deg served to increase readings by approximately 30%. The PCA offers a convenient way to discern relative differences in vineyard canopy density. Calibration by direct measurement (defoliation) is recommended in cases where absolute LAI is desired. Calibration equations provided herein may be inverted to retrieve actual vineyard LAI from PCA readings.

The psychosocial impact of prostate cancer on patients and their partners.

PubMed

Couper, Jeremy W; Bloch, Sidney; Love, Anthony; Duchesne, Gillian; Macvean, Michelle; Kissane, David W

2006-10-16

To assess the psychosocial impact of the diagnosis of either localised or metastatic prostate cancer (PCA) on patients and their female partners. Observational, prospective study at Time 1 and 6 months later at Time 2 of two groups of couples facing PCA. Time 1 was when patients were first diagnosed with histologically confirmed localised (potentially curable) PCA or metastatic (incurable) PCA. Depression and anxiety disorders according to the Diagnostic and statistical manual of mental disorders 4th edition (DSM-IV); psychological distress; marital satisfaction. At Time 1, partners had rates of DSM-IV major depression and generalised anxiety disorder twice those of women in the Australian community, and considerably higher than the patients' rates. At Time 2, psychological distress in partners had lessened but that in patients had increased. On the other hand, at Time 2, partners' marital satisfaction had deteriorated. To be fully effective, interventions aimed at reducing the psychosocial morbidity of PCA must involve both patient and partner, rather than the patient alone.

Resonant tunneling of 1-dimensional electrons across an array of 3-dimensionally confined potential wells

NASA Astrophysics Data System (ADS)

Allee, D. R.; Chou, S. Y.; Harris, J. S.; Pease, R. F. W.

A lateral resonant tunneling field effect transistor has been fabricated with a gate electrode in the form of a railway such that the two rails form a lateral double barrier potential at the GaAs/AlGaAs interface. The ties confine the electrons in the third dimension forming an array of potential boxes or three dimensionally confined potential wells. The width of the ties and rails is 50nm; the spacings between the ties and between the two rails are 230nm and 150nm respectively. The ties are 750nm long and extend beyond the the two rails forming one dimensional wires on either side. Conductance oscillations are observed in the drain current at 4.2K as the gate voltage is scanned. Comparison with devices with a solid gate, and with a monorail gate with ties fabricated on the same wafer suggest that these conductance oscillations are electron resonant tunneling from one dimensional wires through the quasi-bound states of the three dimensionally confined potential wells. Comparison with a device with a two rail gate without ties (previously published) indicates that additional confinement due to the ties enhances the strength of the conductance oscillations.

γ5 in the four-dimensional helicity scheme

NASA Astrophysics Data System (ADS)

Gnendiger, C.; Signer, A.

2018-05-01

We investigate the regularization-scheme dependent treatment of γ5 in the framework of dimensional regularization, mainly focusing on the four-dimensional helicity scheme (fdh). Evaluating distinctive examples, we find that for one-loop calculations, the recently proposed four-dimensional formulation (fdf) of the fdh scheme constitutes a viable and efficient alternative compared to more traditional approaches. In addition, we extend the considerations to the two-loop level and compute the pseudoscalar form factors of quarks and gluons in fdh. We provide the necessary operator renormalization and discuss at a practical level how the complexity of intermediate calculational steps can be reduced in an efficient way.

Hörmander multipliers on two-dimensional dyadic Hardy spaces

NASA Astrophysics Data System (ADS)

Daly, J.; Fridli, S.

2008-12-01

In this paper we are interested in conditions on the coefficients of a two-dimensional Walsh multiplier operator that imply the operator is bounded on certain of the Hardy type spaces Hp, 0

A Two-Dimensional Linear Bicharacteristic Scheme for Electromagnetics

NASA Technical Reports Server (NTRS)

Beggs, John H.

2002-01-01

The upwind leapfrog or Linear Bicharacteristic Scheme (LBS) has previously been implemented and demonstrated on one-dimensional electromagnetic wave propagation problems. This memorandum extends the Linear Bicharacteristic Scheme for computational electromagnetics to model lossy dielectric and magnetic materials and perfect electrical conductors in two dimensions. This is accomplished by proper implementation of the LBS for homogeneous lossy dielectric and magnetic media and for perfect electrical conductors. Both the Transverse Electric and Transverse Magnetic polarizations are considered. Computational requirements and a Fourier analysis are also discussed. Heterogeneous media are modeled through implementation of surface boundary conditions and no special extrapolations or interpolations at dielectric material boundaries are required. Results are presented for two-dimensional model problems on uniform grids, and the Finite Difference Time Domain (FDTD) algorithm is chosen as a convenient reference algorithm for comparison. The results demonstrate that the two-dimensional explicit LBS is a dissipation-free, second-order accurate algorithm which uses a smaller stencil than the FDTD algorithm, yet it has less phase velocity error.

Exploding dissipative solitons in the cubic-quintic complex Ginzburg-Landau equation in one and two spatial dimensions. A review and a perspective

NASA Astrophysics Data System (ADS)

Cartes, C.; Descalzi, O.; Brand, H. R.

2014-10-01

We review the work on exploding dissipative solitons in one and two spatial dimensions. Features covered include: the transition from modulated to exploding dissipative solitons, the analogue of the Ruelle-Takens scenario for dissipative solitons, inducing exploding dissipative solitons by noise, two classes of exploding dissipative solitons in two spatial dimensions, diffusing asymmetric exploding dissipative solitons as a model for a two-dimensional extended chaotic system. As a perspective we outline the interaction of exploding dissipative solitons with quasi one-dimensional dissipative solitons, breathing quasi one-dimensional solutions and their possible connection with experimental results on convection, and the occurence of exploding dissipative solitons in reaction-diffusion systems. It is a great pleasure to dedicate this work to our long-time friend Hans (Prof. Dr. Hans Jürgen Herrmann) on the occasion of his 60th birthday.

Mechanism of Superconductivity in Quasi-Two-Dimensional Organic Conductor β-(BDA-TTP) Salts

NASA Astrophysics Data System (ADS)

Nonoyama, Yoshito; Maekawa, Yukiko; Kobayashi, Akito; Suzumura, Yoshikazu; Ito, Hiroshi

2008-09-01

We investigate theoretically the superconductivity of two-dimensional organic conductors, β-(BDA-TTP)2SbF6 and β-(BDA-TTP)2AsF6, to understand the role of the spin and charge fluctuations. The transition temperature is estimated by applying random phase approximation to an extended Hubbard model wherein realistic transfer energies are estimated by extended Hückel calculation. We find a gapless superconducting state with a dxy-like symmetry, which is consistent with the experimental results obtained by specific heat and scanning tunneling microscope. In the present model with an effectively half-filled triangular lattice, spin fluctuation competes with charge fluctuation as a mechanism of pairing interaction since both fluctuations have the same characteristic momentum q=(π,0) for V being smaller than U. This is in contrast to a model with a quarter-filled square lattice, wherein both fluctuations contribute cooperatively to pairing interaction due to fluctuations having different characteristic momenta. The resultant difference in the superconductivity of these two materials is also discussed.

Intralaryngeal neuroanatomy of the recurrent laryngeal nerve of the rabbit

PubMed Central

Ryan, Stephen; McNicholas, Walter T; O'Regan, Ronan G; Nolan, Philip

2003-01-01

We undertook this study to determine the detailed neuroanatomy of the terminal branches of the recurrent laryngeal nerve (RLN) in the rabbit to facilitate future neurophysiological recordings from identified branches of this nerve. The whole larynx was isolated post mortem in 17 adult New Zealand White rabbits and prepared using a modified Sihler's technique, which stains axons and renders other tissues transparent so that nerve branches can be seen in whole mount preparations. Of the 34 hemi-laryngeal preparations processed, 28 stained well and these were dissected and used to characterize the neuroanatomy of the RLN. In most cases (23/28) the posterior cricoarytenoid muscle (PCA) was supplied by a single branch arising from the RLN, though in five PCA specimens there were two or three separate branches to the PCA. The interarytenoid muscle (IA) was supplied by two parallel filaments arising from the main trunk of the RLN rostral to the branch(es) to the PCA. The lateral cricoarytenoid muscle (LCA) commonly received innervation from two fine twigs branching from the RLN main trunk and travelling laterally towards the LCA. The remaining fibres of the RLN innervated the thyroarytenoid muscle (TA) and comprised two distinct branches, one supplying the pars vocalis and the other branching extensively to supply the remainder of the TA. No communicating anastomosis between the RLN and superior laryngeal nerve within the larynx was found. Our results suggest it is feasible to make electrophysiological recordings from identified terminal branches of the RLN supplying laryngeal adductor muscles separate from the branch or branches to the PCA. However, the very small size of the motor nerves to the IA and LCA suggests that it would be very difficult to record selectively from the nerve supply to individual laryngeal adductor muscles. PMID:12739619

Impact of a robotic surgical system on treatment choice for men with clinically organ-confined prostate cancer.

PubMed

Kobayashi, Takashi; Kanao, Kent; Araki, Motoo; Terada, Naoki; Kobayashi, Yasuyuki; Sawada, Atsuro; Inoue, Takahiro; Ebara, Shin; Watanabe, Toyohiko; Kamba, Tomomi; Sumitomo, Makoto; Nasu, Yasutomo; Ogawa, Osamu

2018-04-01

Introducing a new surgical technology may affect behaviors and attitudes of patients and surgeons about clinical practice. Robot-assisted laparoscopic radical prostatectomy (RALP) was approved in 2012 in Japan. We investigated whether the introduction of this system affected the treatment of organ-confined prostate cancer (PCa) and the use of radical prostatectomy (RP). We conducted a retrospective multicenter study on 718 patients with clinically determined organ-confined PCa treated at one of three Japanese academic institutions in 2011 (n = 338) or 2013 (n = 380). Two patient groups formed according to the treatment year were compared regarding the clinical characteristics of PCa, whether referred or screened at our hospital, comorbidities and surgical risk, and choice of primary treatment. Distribution of PCa risk was not changed by the introduction of RALP. Use of RP increased by 70% (from 127 to 221 cases, p < 0.0001), whereas the number of those undergoing radiotherapy or androgen deprivation therapy decreased irrespective of the disease risk of PCa. Increased use of RP (from 34 to 100 cases) for intermediate- or high-risk PCa patients with mild perioperative risk (American Society of Anesthesiologists score 2) accounted for 70% of the total RP increase, whereas the number of low- or very low-risk PCa patients with high comorbidity scores (Charlson Index ≥ 4) increased from 8 to 25 cases, accounting for 18%. Use of expectant management (active surveillance, watchful waiting) in very low-risk PCa patients was 15% in 2011 and 12% in 2013 (p = 0.791). Introduction of a robotic surgical system had little effect on the risk distribution of PCa. Use of RP increased, apparently due to increased indications in patients who are candidates for RP but have mild perioperative risk. Although small, there was an increase in the number of RPs performed on patients with severe comorbidities but with low-risk or very low-risk PCa.

Performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches in VQ codebook generation for image compression

NASA Astrophysics Data System (ADS)

Tsai, Jinn-Tsong; Chou, Ping-Yi; Chou, Jyh-Horng

2015-11-01

The aim of this study is to generate vector quantisation (VQ) codebooks by integrating principle component analysis (PCA) algorithm, Linde-Buzo-Gray (LBG) algorithm, and evolutionary algorithms (EAs). The EAs include genetic algorithm (GA), particle swarm optimisation (PSO), honey bee mating optimisation (HBMO), and firefly algorithm (FF). The study is to provide performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches. The PCA-EA-LBG approaches contain PCA-GA-LBG, PCA-PSO-LBG, PCA-HBMO-LBG, and PCA-FF-LBG, while the PCA-LBG-EA approaches contain PCA-LBG, PCA-LBG-GA, PCA-LBG-PSO, PCA-LBG-HBMO, and PCA-LBG-FF. All training vectors of test images are grouped according to PCA. The PCA-EA-LBG used the vectors grouped by PCA as initial individuals, and the best solution gained by the EAs was given for LBG to discover a codebook. The PCA-LBG approach is to use the PCA to select vectors as initial individuals for LBG to find a codebook. The PCA-LBG-EA used the final result of PCA-LBG as an initial individual for EAs to find a codebook. The search schemes in PCA-EA-LBG first used global search and then applied local search skill, while in PCA-LBG-EA first used local search and then employed global search skill. The results verify that the PCA-EA-LBG indeed gain superior results compared to the PCA-LBG-EA, because the PCA-EA-LBG explores a global area to find a solution, and then exploits a better one from the local area of the solution. Furthermore the proposed PCA-EA-LBG approaches in designing VQ codebooks outperform existing approaches shown in the literature.

Lateral supraorbital approach to ipsilateral PCA-P1 and ICA-PCoA aneurysms.

PubMed

Goehre, Felix; Jahromi, Behnam Rezai; Elsharkawy, Ahmed; Lehto, Hanna; Shekhtman, Oleg; Andrade-Barazarte, Hugo; Munoz, Francisco; Hijazy, Ferzat; Makhkamov, Makhkam; Hernesniemi, Juha

2015-01-01

Aneurysms of the posterior cerebral artery (PCA) are rare and often associated with anterior circulation aneurysms. The lateral supraorbital approach allows for a very fast and safe approach to the ipsilateral lesions Circle of Willis. A technical note on the successful clip occlusion of two aneurysms in the anterior and posterior Circle of Willis via this less invasive approach has not been published before. The objective of this technical note is to describe the simultaneous microsurgical clip occlusion of an ipsilateral PCA-P1 and an internal carotid artery - posterior communicating artery (ICA-PCoA) aneurysm via the lateral supraorbital approach. The authors present a technical report of successful clip occlusions of ipsilateral located PCA-P1 and ICA-PCoA aneurysms. A 59-year-old female patient was diagnosed with a PCA-P1 and an ipsilateral ICA-PCoA aneurysm by computed tomography angiography (CTA) after an ischemic stroke secondary to a contralateral ICA dissection. The patient underwent microsurgical clipping after a lateral supraorbital craniotomy. The intraoperative indocyanine green (ICG) videoangiography and the postoperative CTA showed a complete occlusion of both aneurysms; the parent vessels (ICA and PCA) were patent. The patient presents postoperative no new neurologic deficit. The lateral supraorbital approach is suitable for the simultaneous microsurgical treatment of proximal anterior circulation and ipsilateral proximal PCA aneurysms. Compared to endovascular treatment, direct visual control of brainstem perforators is possible.

A cadmium-transporting P1B-type ATPase in yeast Saccharomyces cerevisiae.

PubMed

Adle, David J; Sinani, Devis; Kim, Heejeong; Lee, Jaekwon

2007-01-12

Detoxification and homeostatic acquisition of metal ions are vital for all living organisms. We have identified PCA1 in yeast Saccharomyces cerevisiae as an overexpression suppressor of copper toxicity. PCA1 possesses signatures of a P1B-type heavy metal-transporting ATPase that is widely distributed from bacteria to humans. Copper resistance conferred by PCA1 is not dependent on catalytic activity, but it appears that a cysteine-rich region located in the N terminus sequesters copper. Unexpectedly, when compared with two independent natural isolates and an industrial S. cerevisiae strain, the PCA1 allele of the common laboratory strains we have examined possesses a missense mutation in a predicted ATP-binding residue conserved in P1B-type ATPases. Consistent with a previous report that identifies an equivalent mutation in a copper-transporting P1B-type ATPase of a Wilson disease patient, the PCA1 allele found in laboratory yeast strains is nonfunctional. Overexpression or deletion of the functional allele in yeast demonstrates that PCA1 is a cadmium efflux pump. Cadmium as well as copper and silver, but not other metals examined, dramatically increase PCA1 protein expression through post-transcriptional regulation and promote subcellular localization to the plasma membrane. Our study has revealed a novel metal detoxification mechanism in yeast mediated by a P1B-type ATPase that is unique in structure, substrate specificity, and mode of regulation.

Construction of high-dimensional universal quantum logic gates using a Λ system coupled with a whispering-gallery-mode microresonator.

PubMed

He, Ling Yan; Wang, Tie-Jun; Wang, Chuan

2016-07-11

High-dimensional quantum system provides a higher capacity of quantum channel, which exhibits potential applications in quantum information processing. However, high-dimensional universal quantum logic gates is difficult to achieve directly with only high-dimensional interaction between two quantum systems and requires a large number of two-dimensional gates to build even a small high-dimensional quantum circuits. In this paper, we propose a scheme to implement a general controlled-flip (CF) gate where the high-dimensional single photon serve as the target qudit and stationary qubits work as the control logic qudit, by employing a three-level Λ-type system coupled with a whispering-gallery-mode microresonator. In our scheme, the required number of interaction times between the photon and solid state system reduce greatly compared with the traditional method which decomposes the high-dimensional Hilbert space into 2-dimensional quantum space, and it is on a shorter temporal scale for the experimental realization. Moreover, we discuss the performance and feasibility of our hybrid CF gate, concluding that it can be easily extended to a 2n-dimensional case and it is feasible with current technology.

A novel nonparametric item response theory approach to measuring socioeconomic position: a comparison using household expenditure data from a Vietnam health survey, 2003

PubMed Central

2014-01-01

Background Measures of household socio-economic position (SEP) are widely used in health research. There exist a number of approaches to their measurement, with Principal Components Analysis (PCA) applied to a basket of household assets being one of the most common. PCA, however, carries a number of assumptions about the distribution of the data which may be untenable, and alternative, non-parametric, approaches may be preferred. Mokken scale analysis is a non-parametric, item response theory approach to scale development which appears never to have been applied to household asset data. A Mokken scale can be used to rank order items (measures of wealth) as well as households. Using data on household asset ownership from a national sample of 4,154 consenting households in the World Health Survey from Vietnam, 2003, we construct two measures of household SEP. Seventeen items asking about assets, and utility and infrastructure use were used. Mokken Scaling and PCA were applied to the data. A single item measure of total household expenditure is used as a point of contrast. Results An 11 item scale, out of the 17 items, was identified that conformed to the assumptions of a Mokken Scale. All the items in the scale were identified as strong items (Hi > .5). Two PCA measures of SEP were developed as a point of contrast. One PCA measure was developed using all 17 available asset items, the other used the reduced set of 11 items identified in the Mokken scale analaysis. The Mokken Scale measure of SEP and the 17 item PCA measure had a very high correlation (r = .98), and they both correlated moderately with total household expenditure: r = .59 and r = .57 respectively. In contrast the 11 item PCA measure correlated moderately with the Mokken scale (r = .68), and weakly with the total household expenditure (r = .18). Conclusion The Mokken scale measure of household SEP performed at least as well as PCA, and outperformed the PCA measure developed with the 11 items used in the Mokken scale. Unlike PCA, Mokken scaling carries no assumptions about the underlying shape of the distribution of the data, and can be used simultaneous to order household SEP and items. The approach, however, has not been tested with data from other countries and remains an interesting, but under researched approach. PMID:25126103

A novel nonparametric item response theory approach to measuring socioeconomic position: a comparison using household expenditure data from a Vietnam health survey, 2003.

PubMed

Reidpath, Daniel D; Ahmadi, Keivan

2014-01-01

Measures of household socio-economic position (SEP) are widely used in health research. There exist a number of approaches to their measurement, with Principal Components Analysis (PCA) applied to a basket of household assets being one of the most common. PCA, however, carries a number of assumptions about the distribution of the data which may be untenable, and alternative, non-parametric, approaches may be preferred. Mokken scale analysis is a non-parametric, item response theory approach to scale development which appears never to have been applied to household asset data. A Mokken scale can be used to rank order items (measures of wealth) as well as households. Using data on household asset ownership from a national sample of 4,154 consenting households in the World Health Survey from Vietnam, 2003, we construct two measures of household SEP. Seventeen items asking about assets, and utility and infrastructure use were used. Mokken Scaling and PCA were applied to the data. A single item measure of total household expenditure is used as a point of contrast. An 11 item scale, out of the 17 items, was identified that conformed to the assumptions of a Mokken Scale. All the items in the scale were identified as strong items (Hi > .5). Two PCA measures of SEP were developed as a point of contrast. One PCA measure was developed using all 17 available asset items, the other used the reduced set of 11 items identified in the Mokken scale analaysis. The Mokken Scale measure of SEP and the 17 item PCA measure had a very high correlation (r = .98), and they both correlated moderately with total household expenditure: r = .59 and r = .57 respectively. In contrast the 11 item PCA measure correlated moderately with the Mokken scale (r = .68), and weakly with the total household expenditure (r = .18). The Mokken scale measure of household SEP performed at least as well as PCA, and outperformed the PCA measure developed with the 11 items used in the Mokken scale. Unlike PCA, Mokken scaling carries no assumptions about the underlying shape of the distribution of the data, and can be used simultaneous to order household SEP and items. The approach, however, has not been tested with data from other countries and remains an interesting, but under researched approach.

A coherent discrete variable representation method on a sphere

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

Yu, Hua -Gen

Here, the coherent discrete variable representation (ZDVR) has been extended for construct- ing a multidimensional potential-optimized DVR basis on a sphere. In order to deal with the non-constant Jacobian in spherical angles, two direct product primitive basis methods are proposed so that the original ZDVR technique can be properly implemented. The method has been demonstrated by computing the lowest states of a two dimensional (2D) vibrational model. Results show that the extended ZDVR method gives accurate eigenval- ues and exponential convergence with increasing ZDVR basis size.

A coherent discrete variable representation method on a sphere

DOE PAGES

Yu, Hua -Gen

2017-09-05

Here, the coherent discrete variable representation (ZDVR) has been extended for construct- ing a multidimensional potential-optimized DVR basis on a sphere. In order to deal with the non-constant Jacobian in spherical angles, two direct product primitive basis methods are proposed so that the original ZDVR technique can be properly implemented. The method has been demonstrated by computing the lowest states of a two dimensional (2D) vibrational model. Results show that the extended ZDVR method gives accurate eigenval- ues and exponential convergence with increasing ZDVR basis size.

The self-trapping transition in the non-half-filled strongly correlated extended Holstein-Hubbard model in two-dimensions

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

Sankar, I. V., E-mail: ivshankar27@gmail.com; Chatterjee, Ashok, E-mail: ivshankar27@gmail.com

2014-04-24

The two-dimensional extended Holstein-Hubbard model (EHH) has been considered at strong correlation regime in the non-half-filled band case to understand the self-trapping transition of electrons in strongly correlated electron system. We have used the method of optimized canonical transformations to transform an EHH model into an effective extended Hubbard (EEH) model. In the strong on-site correlation limit an EH model can be transformed into a t-J model which is finally solved using Hartree-Fock approximation (HFA). We found that, for non-half-filled band case, the transition is abrupt in the adiabatic region whereas it is continuous in the anti-adiabatic region.

Androgen receptor and immune inflammation in benign prostatic hyperplasia and prostate cancer

PubMed Central

Izumi, Kouji; Li, Lei; Chang, Chawnshang

2014-01-01

Both benign prostatic hyperplasia (BPH) and prostate cancer (PCa) are frequent diseases in middle-aged to elderly men worldwide. While both diseases are linked to abnormal growth of the prostate, the epidemiological and pathological features of these two prostate diseases are different. BPH nodules typically arise from the transitional zone, and, in contrast, PCa arises from the peripheral zone. Androgen deprivation therapy alone may not be sufficient to cure these two prostatic diseases due to its undesirable side effects. The alteration of androgen receptor-mediated inflammatory signals from infiltrating immune cells and prostate stromal/epithelial cells may play key roles in those unwanted events. Herein, this review will focus on the roles of androgen/androgen receptor signals in the inflammation-induced progression of BPH and PCa. PMID:26594314

AlleleCoder: a PERL script for coding codominant polymorphism data for PCA analysis

USDA-ARS?s Scientific Manuscript database

A useful biological interpretation of diploid heterozygotes is in terms of the dose of the common allele (0, 1 or 2 copies). We have developed a PERL script that converts FASTA files into coded spreadsheets suitable for Principal Component Analysis (PCA). In combination with R and R Commander, two- ...

Three-dimensional ultrastructure of osteocytes assessed by focused ion beam-scanning electron microscopy (FIB-SEM).

PubMed

Hasegawa, Tomoka; Yamamoto, Tomomaya; Hongo, Hiromi; Qiu, Zixuan; Abe, Miki; Kanesaki, Takuma; Tanaka, Kawori; Endo, Takashi; de Freitas, Paulo Henrique Luiz; Li, Minqi; Amizuka, Norio

2018-04-01

The aim of this study is to demonstrate the application of focused ion beam-scanning electron microscopy, FIB-SEM for revealing the three-dimensional features of osteocytic cytoplasmic processes in metaphyseal (immature) and diaphyseal (mature) trabeculae. Tibiae of eight-week-old male mice were fixed with aldehyde solution, and treated with block staining prior to FIB-SEM observation. While two-dimensional backscattered SEM images showed osteocytes' cytoplasmic processes in a fragmented fashion, three-dimensional reconstructions of FIB-SEM images demonstrated that osteocytes in primary metaphyseal trabeculae extended their cytoplasmic processes randomly, thus maintaining contact with neighboring osteocytes and osteoblasts. In contrast, diaphyseal osteocytes extended thin cytoplasmic processes from their cell bodies, which ran perpendicular to the bone surface. In addition, these osteocytes featured thick processes that branched into thinner, transverse cytoplasmic processes; at some point, however, these transverse processes bend at a right angle to run perpendicular to the bone surface. Osteoblasts also possessed thicker cytoplasmic processes that branched off as thinner processes, which then connected with cytoplasmic processes of neighboring osteocytes. Thus, FIB-SEM is a useful technology for visualizing the three-dimensional structures of osteocytes and their cytoplasmic processes.

Comparative Proteomic Analysis of Two Varieties of Genetically Modified (GM) Embrapa 5.1 Common Bean (Phaseolus vulgaris L.) and Their Non-GM Counterparts.

PubMed

Balsamo, Geisi M; Valentim-Neto, Pedro A; Mello, Carla S; Arisi, Ana C M

2015-12-09

The genetically modified (GM) common bean event Embrapa 5.1 was commercially approved in Brazil in 2011; it is resistant to golden mosaic virus infection. In the present work grain proteome profiles of two Embrapa 5.1 common bean varieties, Pérola and Pontal, and their non-GM counterparts were compared by two-dimensional gel electrophoresis (2-DE) followed by mass spectrometry (MS). Analyses detected 23 spots differentially accumulated between GM Pérola and non-GM Pérola and 21 spots between GM Pontal and non-GM Pontal, although they were not the same proteins in Pérola and Pontal varieties, indicating that the variability observed may not be due to the genetic transformation. Among them, eight proteins were identified in Pérola varieties, and four proteins were identified in Pontal. Moreover, we applied principal component analysis (PCA) on 2-DE data, and variation between varieties was explained in the first two principal components. This work provides a first 2-DE-MS/MS-based analysis of Embrapa 5.1 common bean grains.

In-vivo detection of binary PKA network interactions upon activation of endogenous GPCRs

PubMed Central

Röck, Ruth; Bachmann, Verena; Bhang, Hyo-eun C; Malleshaiah, Mohan; Raffeiner, Philipp; Mayrhofer, Johanna E; Tschaikner, Philipp M; Bister, Klaus; Aanstad, Pia; Pomper, Martin G; Michnick, Stephen W; Stefan, Eduard

2015-01-01

Membrane receptor-sensed input signals affect and modulate intracellular protein-protein interactions (PPIs). Consequent changes occur to the compositions of protein complexes, protein localization and intermolecular binding affinities. Alterations of compartmentalized PPIs emanating from certain deregulated kinases are implicated in the manifestation of diseases such as cancer. Here we describe the application of a genetically encoded Protein-fragment Complementation Assay (PCA) based on the Renilla Luciferase (Rluc) enzyme to compare binary PPIs of the spatially and temporally controlled protein kinase A (PKA) network in diverse eukaryotic model systems. The simplicity and sensitivity of this cell-based reporter allows for real-time recordings of mutually exclusive PPIs of PKA upon activation of selected endogenous G protein-coupled receptors (GPCRs) in cancer cells, xenografts of mice, budding yeast, and zebrafish embryos. This extends the application spectrum of Rluc PCA for the quantification of PPI-based receptor-effector relationships in physiological and pathological model systems. PMID:26099953

Flow Injection Mass Spectral Fingerprints Demonstrate Chemical Differences in Rio Red Grapefruit with Respect to Year, Harvest Time, and Conventional versus Organic Farming

PubMed Central

Chen, Pei; Harnly, James M.; Lester, Gene E.

2013-01-01

Spectral fingerprints were acquired for Rio Red grapefruit using flow injection electrospray ionization with ion trap and time-of-flight mass spectrometry (FI-ESI-IT-MS and FI-ESI-TOF-MS). Rio Red grapefruits were harvested 3 times a year (early, mid, and late harvests) in 2005 and 2006 from conventionally and organically grown trees. Data analysis using analysis of variance principal component analysis (ANOVA-PCA) demonstrated that, for both MS systems, the chemical patterns were different as a function of farming mode (conventional vs organic), as well as growing year and time of harvest. This was visually obvious with PCA and was shown to be statistically significant using ANOVA. The spectral fingerprints provided a more inclusive view of the chemical composition of the grapefruit and extended previous conclusions regarding the chemical differences between conventionally and organically grown Rio Red grapefruit. PMID:20337420

Psychopathy, attention, and oddball target detection: New insights from PCL-R facet scores.

PubMed

Anderson, Nathaniel E; Steele, Vaughn R; Maurer, J Michael; Bernat, Edward M; Kiehl, Kent A

2015-09-01

Psychopathy is a disorder accompanied by cognitive deficits including abnormalities in attention. Prior studies examining cognitive features of psychopaths using ERPs have produced some inconsistent results. We examined psychopathy-related differences in ERPs during an auditory oddball task in a sample of incarcerated adult males. We extend previous work by deriving ERPs with principal component analysis (PCA) and relate these to the four facets of Hare's Psychopathy Checklist Revised (PCL-R). Features of psychopathy were associated with increased target N1 amplitude (facets 1, 4), decreased target P3 amplitude (facet 1), and reduced slow wave amplitude for frequent standard stimuli (facets 1, 3, 4). We conclude that employing PCA and examining PCL-R facets improve sensitivity and help clarify previously reported associations. Furthermore, attenuated slow wave during standards may be a novel marker for psychopaths' abnormalities in attention. © 2015 Society for Psychophysiological Research.

Numerical simulation of three-dimensional transonic turbulent projectile aerodynamics by TVD schemes

NASA Technical Reports Server (NTRS)

Shiau, Nae-Haur; Hsu, Chen-Chi; Chyu, Wei-Jao

1989-01-01

The two-dimensional symmetric TVD scheme proposed by Yee has been extended to and investigated for three-dimensional thin-layer Navier-Stokes simulation of complex aerodynamic problems. An existing three-dimensional Navier-stokes code based on the beam and warming algorithm is modified to provide an option of using the TVD algorithm and the flow problem considered is a transonic turbulent flow past a projectile with sting at ten-degree angle of attack. Numerical experiments conducted for three flow cases, free-stream Mach numbers of 0.91, 0.96 and 1.20 show that the symmetric TVD algorithm can provide surface pressure distribution in excellent agreement with measured data; moreover, the rate of convergence to attain a steady state solution is about two times faster than the original beam and warming algorithm.

Genome-wide copy number analysis reveals candidate gene loci that confer susceptibility to high-grade prostate cancer.

PubMed

Poniah, Prevathe; Mohd Zain, Shamsul; Abdul Razack, Azad Hassan; Kuppusamy, Shanggar; Karuppayah, Shankar; Sian Eng, Hooi; Mohamed, Zahurin

2017-09-01

Two key issues in prostate cancer (PCa) that demand attention currently are the need for a more precise and minimally invasive screening test owing to the inaccuracy of prostate-specific antigen and differential diagnosis to distinguish advanced vs. indolent cancers. This continues to pose a tremendous challenge in diagnosis and prognosis of PCa and could potentially lead to overdiagnosis and overtreatment complications. Copy number variations (CNVs) in the human genome have been linked to various carcinomas including PCa. Detection of these variants may improve clinical treatment as well as an understanding of the pathobiology underlying this complex disease. To this end, we undertook a pilot genome-wide CNV analysis approach in 36 subjects (18 patients with high-grade PCa and 18 controls that were matched by age and ethnicity) in search of more accurate biomarkers that could potentially explain susceptibility toward high-grade PCa. We conducted this study using the array comparative genomic hybridization technique. Array results were validated in 92 independent samples (46 high-grade PCa, 23 benign prostatic hyperplasia, and 23 healthy controls) using polymerase chain reaction-based copy number counting method. A total of 314 CNV regions were found to be unique to PCa subjects in this cohort (P<0.05). A log 2 ratio-based copy number analysis revealed 5 putative rare or novel CNV loci or both associated with susceptibility to PCa. The CNV gain regions were 1q21.3, 15q15, 7p12.1, and a novel CNV in PCa 12q23.1, harboring ARNT, THBS1, SLC5A8, and DDC genes that are crucial in the p53 and cancer pathways. A CNV loss and deletion event was observed at 8p11.21, which contains the SFRP1 gene from the Wnt signaling pathway. Cross-comparison analysis with genes associated to PCa revealed significant CNVs involved in biological processes that elicit cancer pathogenesis via cytokine production and endothelial cell proliferation. In conclusion, we postulated that the CNVs identified in this study could provide an insight into the development of advanced PCa. Copyright © 2017 Elsevier Inc. All rights reserved.

A two-dimensional bilayered Cd(II) coordination polymer with a three-dimensional supramolecular architecture incorporating 1,2-bis(pyridin-4-yl)ethene and 2,2'-(diazenediyl)dibenzoic acid.

PubMed

Liu, Lei-Lei; Zhou, Yan; Li, Ping; Tian, Jiang-Ya

2014-02-01

In poly[[μ2-1,2-bis(pyridin-4-yl)ethene-κ(2)N:N'][μ2-2,2'-(diazenediyl)dibenzoato-κ(3)O,O':O'']cadmium(II)], [Cd(C14H8N2O4)(C12H10N2)]n, the asymmetric unit contains one Cd(II) cation, one 2,2'-(diazenediyl)dibenzoate anion (denoted L(2-)) and one 1,2-bis(pyridin-4-yl)ethene ligand (denoted bpe). Each Cd(II) centre is six-coordinated by four O atoms of bridging/chelating carboxylate groups from three L(2-) ligands and by two N atoms from two bpe ligands, forming a distorted octahedron. The Cd(II) cations are bridged by L(2-) and bpe ligands to give a two-dimensional (4,4) layer. The layers are interlinked through bridging carboxylate O atoms from L(2-) ligands, generating a two-dimensional bilayered structure with a 3(6)4(13)6(2) topology. The bilayered structures are further extended to form a three-dimensional supramolecular architecture via a combination of hydrogen-bonding and aromatic stacking interactions.

Chemometric Analysis of Gas Chromatography – Mass Spectrometry Data using Fast Retention Time Alignment via a Total Ion Current Shift Function

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

Nadeau, Jeremy S.; Wright, Bob W.; Synovec, Robert E.

2010-04-15

A critical comparison of methods for correcting severely retention time shifted gas chromatography-mass spectrometry (GC-MS) data is presented. The method reported herein is an adaptation to the Piecewise Alignment Algorithm to quickly align severely shifted one-dimensional (1D) total ion current (TIC) data, then applying these shifts to broadly align all mass channels throughout the separation, referred to as a TIC shift function (SF). The maximum shift varied from (-) 5 s in the beginning of the chromatographic separation to (+) 20 s toward the end of the separation, equivalent to a maximum shift of over 5 peak widths. Implementing themore » TIC shift function (TIC SF) prior to Fisher Ratio (F-Ratio) feature selection and then principal component analysis (PCA) was found to be a viable approach to classify complex chromatograms, that in this study were obtained from GC-MS separations of three gasoline samples serving as complex test mixtures, referred to as types C, M and S. The reported alignment algorithm via the TIC SF approach corrects for large dynamic shifting in the data as well as subtle peak-to-peak shifts. The benefits of the overall TIC SF alignment and feature selection approach were quantified using the degree-of-class separation (DCS) metric of the PCA scores plots using the type C and M samples, since they were the most similar, and thus the most challenging samples to properly classify. The DCS values showed an increase from an initial value of essentially zero for the unaligned GC-TIC data to a value of 7.9 following alignment; however, the DCS was unchanged by feature selection using F-Ratios for the GC-TIC data. The full mass spectral data provided an increase to a final DCS of 13.7 after alignment and two-dimensional (2D) F-Ratio feature selection.« less

Differentiation of Microbial Species and Strains in Coculture Biofilms by Multivariate Analysis of Laser Desorption Postionization Mass Spectra

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

University of Illinois at Chicago; Montana State University; Bhardwaj, Chhavi

2013-04-01

7.87 to 10.5 eV vacuum ultraviolet (VUV) photon energies were used in laser desorption postionization mass spectrometry (LDPI-MS) to analyze biofilms comprised of binary cultures of interacting microorganisms. The effect of photon energy was examined using both tunable synchrotron and laser sources of VUV radiation. Principal components analysis (PCA) was applied to the MS data to differentiate species in Escherichia coli-Saccharomyces cerevisiae coculture biofilms. PCA of LDPI-MS also differentiated individual E. coli strains in a biofilm comprised of two interacting gene deletion strains, even though these strains differed from the wild type K-12 strain by no more than four genemore » deletions each out of approximately 2000 genes. PCA treatment of 7.87 eV LDPI-MS data separated the E. coli strains into three distinct groups two ?pure? groups and a mixed region. Furthermore, the ?pure? regions of the E. coli cocultures showed greater variance by PCA when analyzed by 7.87 eV photon energies than by 10.5 eV radiation. Comparison of the 7.87 and 10.5 eV data is consistent with the expectation that the lower photon energy selects a subset of low ionization energy analytes while 10.5 eV is more inclusive, detecting a wider range of analytes. These two VUV photon energies therefore give different spreads via PCA and their respective use in LDPI-MS constitute an additional experimental parameter to differentiate strains and species.« less

Intrathecal morphine for postoperative pain control following robot-assisted prostatectomy: a prospective randomized trial.

PubMed

Bae, Junyeol; Kim, Hyun-Chang; Hong, Deok Man

2017-08-01

Robot-assisted laparoscopic prostatectomy (RALP) is minimally invasive surgery, but also causes moderate to severe pain during the immediate postoperative period. We evaluated the efficacy and safety of intrathecal morphine (ITM) for postoperative pain control in patients undergoing RALP. Thirty patients scheduled for RALP were randomly assigned into one of two groups. In the ITM group (n = 15), postoperative pain was managed using 300 µg intrathecal morphine with intravenous patient-controlled analgesia (IV-PCA). In the IV-PCA group (n = 15), only intravenous patient-controlled analgesia was used. The numerical pain score (NPS; 0 = no pain, 100 = worst pain imaginable), postoperative IV-PCA requirements and opioid-related complications including nausea, vomiting, dizziness, headache and pruritus were compared between the two groups. The NPSs on coughing were 20 (IQR 10-50) in the ITM group and 60 (IQR 40-80) in the IV-PCA group at postoperative 24 h (p = 0.001). The NPSs were significantly lower in the ITM group up to postoperative 24 h. The ITM group showed less morphine consumption at postoperative 24 h in the ITM group than in the IV-PCA group [5 (IQR 3-15) mg vs 17 (IQR 11-24) mg, p = 0.001]. Complications associated with morphine were comparable between the two groups and respiratory depression was not reported in either group. Intrathecal morphine provided more satisfactory analgesia without serious complications during the early postoperative period in patients undergoing RALP.

Use of hyperbolic partial differential equations to generate body fitted coordinates

NASA Technical Reports Server (NTRS)

Steger, J. L.; Sorenson, R. L.

1980-01-01

The hyperbolic scheme is used to efficiently generate smoothly varying grids with good step size control near the body. Although only two dimensional applications are presented, the basic concepts are shown to extend to three dimensions.

Discrete models for the numerical analysis of time-dependent multidimensional gas dynamics

NASA Technical Reports Server (NTRS)

Roe, P. L.

1984-01-01

A possible technique is explored for extending to multidimensional flows some of the upwind-differencing methods that are highly successful in the one-dimensional case. Emphasis is on the two-dimensional case, and the flow domain is assumed to be divided into polygonal computational elements. Inside each element, the flow is represented by a local superposition of elementary solutions consisting of plane waves not necessarily aligned with the element boundaries.

An Invitation to the Mathematics of Topological Quantum Computation

NASA Astrophysics Data System (ADS)

Rowell, E. C.

2016-03-01

Two-dimensional topological states of matter offer a route to quantum computation that would be topologically protected against the nemesis of the quantum circuit model: decoherence. Research groups in industry, government and academic institutions are pursuing this approach. We give a mathematician's perspective on some of the advantages and challenges of this model, highlighting some recent advances. We then give a short description of how we might extend the theory to three-dimensional materials.

Three-dimensional unstructured grid refinement and optimization using edge-swapping

NASA Technical Reports Server (NTRS)

Gandhi, Amar; Barth, Timothy

1993-01-01

This paper presents a three-dimensional (3-D) 'edge-swapping method based on local transformations. This method extends Lawson's edge-swapping algorithm into 3-D. The 3-D edge-swapping algorithm is employed for the purpose of refining and optimizing unstructured meshes according to arbitrary mesh-quality measures. Several criteria including Delaunay triangulations are examined. Extensions from two to three dimensions of several known properties of Delaunay triangulations are also discussed.

SPINK1 Overexpression in Localized Prostate Cancer: a Rare Event Inversely Associated with ERG Expression and Exclusive of Homozygous PTEN Deletion.

PubMed

Huang, Kuo-Cheng; Evans, Andrew; Donnelly, Bryan; Bismar, Tarek A

2017-04-01

SPINK1 is proposed as potential prognostic marker in prostate cancer (PCA). However, its relation to PTEN and ERG in localized PCA remains unclear. The study population consisted of two independent cohorts of men treated by radical prostatectomy for localized PCA (discovery n = 218 and validation n = 129). Patterns of association between SPINK1 and each of ERG and PTEN were evaluated by immunohistochemistry and fluorescence in situ hybridization. Associations between SPINK1 expression and various pathologic parameters and clinical outcome were also investigated. SPINK1 was expressed in 15.3 % and 10.9 % of cases in the discovery and validation cohort, respectively. SPINK expression was observed in 5.56 % of high-grade prostatic intraepithelial neoplasia and 1.1 % of adjacent morphologically benign prostatic glands. SPINK1 and ERG expression were almost exclusive, with only 1.0 % of the cases co-expressing both in the same core sample. SPINK1 interfocal and within-core heterogeneity was noted in 29.2 % and 64.6 % of cases, respectively. SPINK1 expression was not significantly associated with PTEN deletion in the two cohorts (p = 0.871 for discovery cohort and p = 0.293 for validation cohort). While SPINK1 expression did occur with hemizygous PTEN deletion, there was a complete absence of SPINK1 expression in PCA showing homozygous PTEN deletion, which was confirmed in the validation cohort (p = 0.02). Despite SPINK1's association with higher Gleason score (>7) (p = 0.02), it was not associated with other pathological parameters or biochemical recurrence post-radical prostatectomy. We documented absolute exclusivity between SPINK1 overexpression and homozygous PTEN deletion in localized PCA. SPINK1 and ERG expressions are exclusive events in PCA. SPINK1 is not of added prognostic value in localized PCA.

Carcinogenic potential of hydrotreated petroleum aromatic extracts.

PubMed Central

Doak, S M; Hend, R W; van der Wiel, A; Hunt, P F

1985-01-01

Five experimental petroleum extracts were produced from luboil distillates derived from Middle East paraffinic crude by solvent extraction and severe hydrotreatment. The polycyclic aromatic content (PCA) of the extracts was determined by dimethyl sulphoxide extraction and ranged from 3.7-9.2% w/w. The five extracts were evaluated for their potential to induce cutaneous and systemic neoplasia in female mice derived from Carworth Farm No 1 strain (CF1). The test substances were applied undiluted (0.2 ml per application) to the shorn dorsal skin twice weekly for up to 78 weeks, with 48 mice in each treatment group and 96 in the untreated control group; two further groups, each of 48 mice, were similarly treated either with a non-hydrotreated commercial aromatic extract (PCA content, 19.7% w/v) or with a low dose of benzo(a)pyrene (12.5 micrograms/ml acetone). The mice were housed individually in polypropylene cages in specified pathogen free conditions. The incidence of cutaneous and systemic tumours was determined from histological analysis of haematoxylin and eosin stained tissue sections. The results were correlated with the PCA content of the extracts and compared with those from female mice exposed to a non-hydrotreated commercial aromatic extract. Four of the hydrotreated extracts were carcinogenic for murine skin; the two products with the lower PCA contents were less carcinogenic than the products with the higher PCA contents and all were less carcinogenic than the commercial extract. One extract with the lowest PCA content was non-carcinogenic. Thus refining by severe hydrotreatment was an effective method of reducing the carcinogenic potential of petroleum aromatic extracts. Although other physicochemical properties may influence the biological activity of oil products, the PCA content determined by dimethyl sulphoxide extraction may be a useful indicator of the potential of oil products to induce cutaneous tumours in experimental animals. There was no evidence that the commercial or hydrotreated extracts increased the incidence of systemic neoplasms when applied twice weekly to the dorsal skin. PMID:4005190

Assessment of WENO-extended two-fluid modelling in compressible multiphase flows

NASA Astrophysics Data System (ADS)

Kitamura, Keiichi; Nonomura, Taku

2017-03-01

The two-fluid modelling based on an advection-upwind-splitting-method (AUSM)-family numerical flux function, AUSM+-up, following the work by Chang and Liou [Journal of Computational Physics 2007;225: 840-873], has been successfully extended to the fifth order by weighted-essentially-non-oscillatory (WENO) schemes. Then its performance is surveyed in several numerical tests. The results showed a desired performance in one-dimensional benchmark test problems: Without relying upon an anti-diffusion device, the higher-order two-fluid method captures the phase interface within a fewer grid points than the conventional second-order method, as well as a rarefaction wave and a very weak shock. At a high pressure ratio (e.g. 1,000), the interpolated variables appeared to affect the performance: the conservative-variable-based characteristic-wise WENO interpolation showed less sharper but more robust representations of the shocks and expansions than the primitive-variable-based counterpart did. In two-dimensional shock/droplet test case, however, only the primitive-variable-based WENO with a huge void fraction realised a stable computation.

Vortex methods for separated flows

NASA Technical Reports Server (NTRS)

Spalart, Philippe R.

1988-01-01

The numerical solution of the Euler or Navier-Stokes equations by Lagrangian vortex methods is discussed. The mathematical background is presented and includes the relationship with traditional point-vortex studies, convergence to smooth solutions of the Euler equations, and the essential differences between two and three-dimensional cases. The difficulties in extending the method to viscous or compressible flows are explained. Two-dimensional flows around bluff bodies are emphasized. Robustness of the method and the assessment of accuracy, vortex-core profiles, time-marching schemes, numerical dissipation, and efficient programming are treated. Operation counts for unbounded and periodic flows are given, and two algorithms designed to speed up the calculations are described.

Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis.

PubMed

Matrone, Giulia C; Cipriani, Christian; Carrozza, Maria Chiara; Magenes, Giovanni

2012-06-15

In spite of the advances made in the design of dexterous anthropomorphic hand prostheses, these sophisticated devices still lack adequate control interfaces which could allow amputees to operate them in an intuitive and close-to-natural way. In this study, an anthropomorphic five-fingered robotic hand, actuated by six motors, was used as a prosthetic hand emulator to assess the feasibility of a control approach based on Principal Components Analysis (PCA), specifically conceived to address this problem. Since it was demonstrated elsewhere that the first two principal components (PCs) can describe the whole hand configuration space sufficiently well, the controller here employed reverted the PCA algorithm and allowed to drive a multi-DoF hand by combining a two-differential channels EMG input with these two PCs. Hence, the novelty of this approach stood in the PCA application for solving the challenging problem of best mapping the EMG inputs into the degrees of freedom (DoFs) of the prosthesis. A clinically viable two DoFs myoelectric controller, exploiting two differential channels, was developed and twelve able-bodied participants, divided in two groups, volunteered to control the hand in simple grasp trials, using forearm myoelectric signals. Task completion rates and times were measured. The first objective (assessed through one group of subjects) was to understand the effectiveness of the approach; i.e., whether it is possible to drive the hand in real-time, with reasonable performance, in different grasps, also taking advantage of the direct visual feedback of the moving hand. The second objective (assessed through a different group) was to investigate the intuitiveness, and therefore to assess statistical differences in the performance throughout three consecutive days. Subjects performed several grasp, transport and release trials with differently shaped objects, by operating the hand with the myoelectric PCA-based controller. Experimental trials showed that the simultaneous use of the two differential channels paradigm was successful. This work demonstrates that the proposed two-DoFs myoelectric controller based on PCA allows to drive in real-time a prosthetic hand emulator into different prehensile patterns with excellent performance. These results open up promising possibilities for the development of intuitive, effective myoelectric hand controllers.

Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification.

PubMed

Ramírez, J; Górriz, J M; Segovia, F; Chaves, R; Salas-Gonzalez, D; López, M; Alvarez, I; Padilla, P

2010-03-19

This letter shows a computer aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) by means of single photon emission computed tomography (SPECT) image classification. The proposed method is based on partial least squares (PLS) regression model and a random forest (RF) predictor. The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data by downscaling the SPECT images and extracting score features using PLS. A RF predictor then forms an ensemble of classification and regression tree (CART)-like classifiers being its output determined by a majority vote of the trees in the forest. A baseline principal component analysis (PCA) system is also developed for reference. The experimental results show that the combined PLS-RF system yields a generalization error that converges to a limit when increasing the number of trees in the forest. Thus, the generalization error is reduced when using PLS and depends on the strength of the individual trees in the forest and the correlation between them. Moreover, PLS feature extraction is found to be more effective for extracting discriminative information from the data than PCA yielding peak sensitivity, specificity and accuracy values of 100%, 92.7%, and 96.9%, respectively. Moreover, the proposed CAD system outperformed several other recently developed AD CAD systems. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Linking Existing Instruments to Develop an Activity of Daily Living Item Bank.

PubMed

Li, Chih-Ying; Romero, Sergio; Bonilha, Heather S; Simpson, Kit N; Simpson, Annie N; Hong, Ickpyo; Velozo, Craig A

2018-03-01

This study examined dimensionality and item-level psychometric properties of an item bank measuring activities of daily living (ADL) across inpatient rehabilitation facilities and community living centers. Common person equating method was used in the retrospective veterans data set. This study examined dimensionality, model fit, local independence, and monotonicity using factor analyses and fit statistics, principal component analysis (PCA), and differential item functioning (DIF) using Rasch analysis. Following the elimination of invalid data, 371 veterans who completed both the Functional Independence Measure (FIM) and minimum data set (MDS) within 6 days were retained. The FIM-MDS item bank demonstrated good internal consistency (Cronbach's α = .98) and met three rating scale diagnostic criteria and three of the four model fit statistics (comparative fit index/Tucker-Lewis index = 0.98, root mean square error of approximation = 0.14, and standardized root mean residual = 0.07). PCA of Rasch residuals showed the item bank explained 94.2% variance. The item bank covered the range of θ from -1.50 to 1.26 (item), -3.57 to 4.21 (person) with person strata of 6.3. The findings indicated the ADL physical function item bank constructed from FIM and MDS measured a single latent trait with overall acceptable item-level psychometric properties, suggesting that it is an appropriate source for developing efficient test forms such as short forms and computerized adaptive tests.

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Flux splitting algorithms for two-dimensional viscous flows with finite-rate chemistry

NASA Technical Reports Server (NTRS)

Shuen, Jian-Shun; Liou, Meng-Sing

1989-01-01

The Roe flux-difference splitting method has been extended to treat two-dimensional viscous flows with nonequilibrium chemistry. The derivations have avoided unnecessary assumptions or approximations. For spatial discretization, the second-order Roe upwind differencing is used for the convective terms and central differencing for the viscous terms. An upwind-based TVD scheme is applied to eliminate oscillations and obtain a sharp representation of discontinuities. A two-stage Runge-Kutta method is used to time integrate the discretized Navier-Stokes and species transport equations for the asymptotic steady solutions. The present method is then applied to two types of flows: the shock wave/boundary layer interaction problems and the jet in cross flows.

Linear canonical transformations of coherent and squeezed states in the Wigner phase space. III - Two-mode states

NASA Technical Reports Server (NTRS)

Han, D.; Kim, Y. S.; Noz, Marilyn E.

1990-01-01

It is shown that the basic symmetry of two-mode squeezed states is governed by the group SP(4) in the Wigner phase space which is locally isomorphic to the (3 + 2)-dimensional Lorentz group. This symmetry, in the Schroedinger picture, appears as Dirac's two-oscillator representation of O(3,2). It is shown that the SU(2) and SU(1,1) interferometers exhibit the symmetry of this higher-dimensional Lorentz group. The mathematics of two-mode squeezed states is shown to be applicable to other branches of physics including thermally excited states in statistical mechanics and relativistic extended hadrons in the quark model.

Dosimetric treatment course simulation based on a statistical model of deformable organ motion

NASA Astrophysics Data System (ADS)

Söhn, M.; Sobotta, B.; Alber, M.

2012-06-01

We present a method of modeling dosimetric consequences of organ deformation and correlated motion of adjacent organ structures in radiotherapy. Based on a few organ geometry samples and the respective deformation fields as determined by deformable registration, principal component analysis (PCA) is used to create a low-dimensional parametric statistical organ deformation model (Söhn et al 2005 Phys. Med. Biol. 50 5893-908). PCA determines the most important geometric variability in terms of eigenmodes, which represent 3D vector fields of correlated organ deformations around the mean geometry. Weighted sums of a few dominating eigenmodes can be used to simulate synthetic geometries, which are statistically meaningful inter- and extrapolations of the input geometries, and predict their probability of occurrence. We present the use of PCA as a versatile treatment simulation tool, which allows comprehensive dosimetric assessment of the detrimental effects that deformable geometric uncertainties can have on a planned dose distribution. For this, a set of random synthetic geometries is generated by a PCA model for each simulated treatment course, and the dose of a given treatment plan is accumulated in the moving tissue elements via dose warping. This enables the calculation of average voxel doses, local dose variability, dose-volume histogram uncertainties, marginal as well as joint probability distributions of organ equivalent uniform doses and thus of TCP and NTCP, and other dosimetric and biologic endpoints. The method is applied to the example of deformable motion of prostate/bladder/rectum in prostate IMRT. Applications include dosimetric assessment of the adequacy of margin recipes, adaptation schemes, etc, as well as prospective ‘virtual’ evaluation of the possible benefits of new radiotherapy schemes.

Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago.

PubMed

Binaku, Katrina; O'Brien, Timothy; Schmeling, Martina; Fosco, Tinamarie

2013-09-01

Both canonical correlation analysis (CCA) and principal component analysis (PCA) were applied to atmospheric aerosol and trace gas concentrations and meteorological data collected in Chicago during the summer months of 2002, 2003, and 2004. Concentrations of ammonium, calcium, nitrate, sulfate, and oxalate particulate matter, as well as, meteorological parameters temperature, wind speed, wind direction, and humidity were subjected to CCA and PCA. Ozone and nitrogen oxide mixing ratios were also included in the data set. The purpose of statistical analysis was to determine the extent of existing linear relationship(s), or lack thereof, between meteorological parameters and pollutant concentrations in addition to reducing dimensionality of the original data to determine sources of pollutants. In CCA, the first three canonical variate pairs derived were statistically significant at the 0.05 level. Canonical correlation between the first canonical variate pair was 0.821, while correlations of the second and third canonical variate pairs were 0.562 and 0.461, respectively. The first canonical variate pair indicated that increasing temperatures resulted in high ozone mixing ratios, while the second canonical variate pair showed wind speed and humidity's influence on local ammonium concentrations. No new information was uncovered in the third variate pair. Canonical loadings were also interpreted for information regarding relationships between data sets. Four principal components (PCs), expressing 77.0 % of original data variance, were derived in PCA. Interpretation of PCs suggested significant production and/or transport of secondary aerosols in the region (PC1). Furthermore, photochemical production of ozone and wind speed's influence on pollutants were expressed (PC2) along with overall measure of local meteorology (PC3). In summary, CCA and PCA results combined were successful in uncovering linear relationships between meteorology and air pollutants in Chicago and aided in determining possible pollutant sources.

Network Intrusion Detection Based on a General Regression Neural Network Optimized by an Improved Artificial Immune Algorithm

PubMed Central

Wu, Jianfa; Peng, Dahao; Li, Zhuping; Zhao, Li; Ling, Huanzhang

2015-01-01

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data. PMID:25807466

Dosimetric treatment course simulation based on a statistical model of deformable organ motion.

PubMed

Söhn, M; Sobotta, B; Alber, M

2012-06-21

We present a method of modeling dosimetric consequences of organ deformation and correlated motion of adjacent organ structures in radiotherapy. Based on a few organ geometry samples and the respective deformation fields as determined by deformable registration, principal component analysis (PCA) is used to create a low-dimensional parametric statistical organ deformation model (Söhn et al 2005 Phys. Med. Biol. 50 5893-908). PCA determines the most important geometric variability in terms of eigenmodes, which represent 3D vector fields of correlated organ deformations around the mean geometry. Weighted sums of a few dominating eigenmodes can be used to simulate synthetic geometries, which are statistically meaningful inter- and extrapolations of the input geometries, and predict their probability of occurrence. We present the use of PCA as a versatile treatment simulation tool, which allows comprehensive dosimetric assessment of the detrimental effects that deformable geometric uncertainties can have on a planned dose distribution. For this, a set of random synthetic geometries is generated by a PCA model for each simulated treatment course, and the dose of a given treatment plan is accumulated in the moving tissue elements via dose warping. This enables the calculation of average voxel doses, local dose variability, dose-volume histogram uncertainties, marginal as well as joint probability distributions of organ equivalent uniform doses and thus of TCP and NTCP, and other dosimetric and biologic endpoints. The method is applied to the example of deformable motion of prostate/bladder/rectum in prostate IMRT. Applications include dosimetric assessment of the adequacy of margin recipes, adaptation schemes, etc, as well as prospective 'virtual' evaluation of the possible benefits of new radiotherapy schemes.

Motion Estimation System Utilizing Point Cloud Registration

NASA Technical Reports Server (NTRS)

Chen, Qi (Inventor)

2016-01-01

A system and method of estimation motion of a machine is disclosed. The method may include determining a first point cloud and a second point cloud corresponding to an environment in a vicinity of the machine. The method may further include generating a first extended gaussian image (EGI) for the first point cloud and a second EGI for the second point cloud. The method may further include determining a first EGI segment based on the first EGI and a second EGI segment based on the second EGI. The method may further include determining a first two dimensional distribution for points in the first EGI segment and a second two dimensional distribution for points in the second EGI segment. The method may further include estimating motion of the machine based on the first and second two dimensional distributions.

Ranges of applicability for the continuum beam model in the mechanics of carbon nanotubes and nanorods

NASA Technical Reports Server (NTRS)

Harik, V. M.

2001-01-01

Limitations in the validity of the continuum beam model for carbon nanotubes (NTs) and nanorods are examined. Applicability of all assumptions used in the model is restricted by the two criteria for geometric parameters that characterize the structure of NTs. The key non-dimensional parameters that control the NT buckling behavior are derived via dimensional analysis of the nanomechanical problem. A mechanical law of geometric similitude for NT buckling is extended from continuum mechanics for different molecular structures. A model applicability map, where two classes of beam-like NTs are identified, is constructed for distinct ranges of non-dimensional parameters. Expressions for the critical buckling loads and strains are tailored for two classes of NTs and compared with the data provided by the molecular dynamics simulations. copyright 2001 Elsevier Science Ltd. All rights reserved.

Extension of modified power method to two-dimensional problems

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

Zhang, Peng; Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan 44919; Lee, Hyunsuk

2016-09-01

In this study, the generalized modified power method was extended to two-dimensional problems. A direct application of the method to two-dimensional problems was shown to be unstable when the number of requested eigenmodes is larger than a certain problem dependent number. The root cause of this instability has been identified as the degeneracy of the transfer matrix. In order to resolve this instability, the number of sub-regions for the transfer matrix was increased to be larger than the number of requested eigenmodes; and a new transfer matrix was introduced accordingly which can be calculated by the least square method. Themore » stability of the new method has been successfully demonstrated with a neutron diffusion eigenvalue problem and the 2D C5G7 benchmark problem. - Graphical abstract:.« less

State and group dynamics of world stock market by principal component analysis

NASA Astrophysics Data System (ADS)

Nobi, Ashadun; Lee, Jae Woo

2016-05-01

We study the dynamic interactions and structural changes by a principal component analysis (PCA) to cross-correlation coefficients of global financial indices in the years 1998-2012. The variances explained by the first PC increase with time and show a drastic change during the crisis. A sharp change in PC coefficient implies a transition of market state, a situation which occurs frequently in the American and Asian indices. However, the European indices remain stable over time. Using the first two PC coefficients, we identify indices that are similar and more strongly correlated than the others. We observe that the European indices form a robust group over the observation period. The dynamics of the individual indices within the group increase in similarity with time, and the dynamics of indices are more similar during the crises. Furthermore, the group formation of indices changes position in two-dimensional spaces due to crises. Finally, after a financial crisis, the difference of PCs between the European and American indices narrows.

Enhancing muconic acid production from glucose and lignin-derived aromatic compounds via increased protocatechuate decarboxylase activity

DOE PAGES

Johnson, Christopher W.; Salvachua, Davinia; Khanna, Payal; ...

2016-04-22

The conversion of biomass-derived sugars and aromatic molecules to cis,cis-muconic acid (referred to hereafter as muconic acid or muconate) has been of recent interest owing to its facile conversion to adipic acid, an important commodity chemical. Metabolic routes to produce muconate from both sugars and many lignin-derived aromatic compounds require the use of a decarboxylase to convert protocatechuate (PCA, 3,4-dihydroxybenzoate) to catechol (1,2-dihydroxybenzene), two central aromatic intermediates in this pathway. Several studies have identified the PCA decarboxylase as a metabolic bottleneck, causing an accumulation of PCA that subsequently reduces muconate production. A recent study showed that activity of the PCAmore » decarboxylase is enhanced by co-expression of two genetically associated proteins, one of which likely produces a flavin-derived cofactor utilized by the decarboxylase. Using entirely genome-integrated gene expression, we have engineered Pseudomonas putida KT2440-derived strains to produce muconate from either aromatic molecules or sugars and demonstrate in both cases that co-expression of these decarboxylase associated proteins reduces PCA accumulation and enhances muconate production relative to strains expressing the PCA decarboxylase alone. In bioreactor experiments, co-expression increased the specific productivity (mg/g cells/h) of muconate from the aromatic lignin monomer p-coumarate by 50% and resulted in a titer of >15 g/L. In strains engineered to produce muconate from glucose, co-expression more than tripled the titer, yield, productivity, and specific productivity, with the best strain producing 4.92+/-0.48 g/L muconate. Furthermore, this study demonstrates that overcoming the PCA decarboxylase bottleneck can increase muconate yields from biomass-derived sugars and aromatic molecules in industrially relevant strains and cultivation conditions.« less

Validation study of genes with hypermethylated promoter regions associated with prostate cancer recurrence

PubMed Central

Stott-Miller, Marni; Zhao, Shanshan; Wright, Jonathan L.; Kolb, Suzanne; Bibikova, Marina; Klotzle, Brandy; Ostrander, Elaine A.; Fan, Jian-Bing; Feng, Ziding; Stanford, Janet L.

2014-01-01

Background One challenge in prostate cancer (PCa) is distinguishing indolent from aggressive disease at diagnosis. DNA promoter hypermethylation is a frequent epigenetic event in PCa, but few studies of DNA methylation in relation to features of more aggressive tumors or PCa recurrence have been completed. Methods We used the Infinium® HumanMethylation450 BeadChip to assess DNA methylation in tumor tissue from 407 patients with clinically localized PCa who underwent radical prostatectomy. Recurrence status was determined by follow-up patient surveys, medical record review, and linkage with the SEER registry. The methylation status of 14 genes for which promoter hypermethylation was previously correlated with advanced disease or biochemical recurrence was evaluated. Average methylation level for promoter region CpGs in patients who recurred compared to those with no evidence of recurrence was analyzed. For two genes with differential methylation, time to recurrence was examined. Results During an average follow-up of 11.7 years, 104 (26%) patients recurred. Significant promoter hypermethylation in at least 50% of CpG sites in two genes, ABHD9 and HOXD3, was found in tumors from patients who recurred compared to those without recurrence. Evidence was strongest for HOXD3 (lowest P = 9.46x10−6), with higher average methylation across promoter region CpGs associated with reduced recurrence-free survival (P = 2×10−4). DNA methylation profiles did not differ by recurrence status for the other genes. Conclusions These results validate the association between promoter hypermethylation of ADHB9 and HOXD3 and PCa recurrence. Impact Tumor DNA methylation profiling may help distinguish PCa patients at higher risk for disease recurrence. PMID:24718283

XMRV: A New Virus in Prostate Cancer?

PubMed Central

Aloia, Amanda L.; Sfanos, Karen S.; Isaacs, William B.; Zheng, Qizhi; Maldarelli, Frank; De Marzo, Angelo M.; Rein, Alan

2010-01-01

Several recent papers have reported the presence of a gammaretrovirus, termed “XMRV” (xenotropic murine leukemia virus-related virus) in prostate cancers (PCa). If confirmed, this could have enormous implications for the detection, prevention, and treatment of PCa. However, other papers report failure to detect XMRV in PCa. We tested nearly 800 PCa samples, using a combination of real-time PCR and immunohistochemistry (IHC). The PCR reactions were simultaneously monitored for amplification of a single-copy human gene, in order to confirm the quality of the sample DNA and its suitability for PCR. Controls demonstrated that the PCR assay could detect the XMRV in a single infected cell, even in the presence of a 10,000-fold excess of uninfected human cells. The IHC used two rabbit polyclonal antisera, each prepared against a purified MLV protein. Both antisera always stained XMRV-infected or – transfected cells, but never stained control cells. No evidence for XMRV in PCa was obtained in these experiments. We discuss possible explanations for the discrepancies in the results from different laboratories. It is possible that XMRV is not actually circulating in the human population; even if it is, the data do not seem to support a causal role for this virus in PCa. PMID:20966126

Hawking radiation as tunneling from squashed Kaluza-Klein black hole

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

Matsuno, Ken; Umetsu, Koichiro

2011-03-15

We discuss Hawking radiation from a five-dimensional squashed Kaluza-Klein black hole on the basis of the tunneling mechanism. A simple method, which was recently suggested by Umetsu, may be used to extend the original derivation by Parikh and Wilczek to various black holes. That is, we use the two-dimensional effective metric, which is obtained by the dimensional reduction near the horizon, as the background metric. Using the same method, we derive both the desired result of the Hawking temperature and the effect of the backreaction associated with the radiation in the squashed Kaluza-Klein black hole background.

Nebula: reconstruction and visualization of scattering data in reciprocal space.

PubMed

Reiten, Andreas; Chernyshov, Dmitry; Mathiesen, Ragnvald H

2015-04-01

Two-dimensional solid-state X-ray detectors can now operate at considerable data throughput rates that allow full three-dimensional sampling of scattering data from extended volumes of reciprocal space within second to minute time-scales. For such experiments, simultaneous analysis and visualization allows for remeasurements and a more dynamic measurement strategy. A new software, Nebula , is presented. It efficiently reconstructs X-ray scattering data, generates three-dimensional reciprocal space data sets that can be visualized interactively, and aims to enable real-time processing in high-throughput measurements by employing parallel computing on commodity hardware.

Nebula: reconstruction and visualization of scattering data in reciprocal space

PubMed Central

Reiten, Andreas; Chernyshov, Dmitry; Mathiesen, Ragnvald H.

2015-01-01

Two-dimensional solid-state X-ray detectors can now operate at considerable data throughput rates that allow full three-dimensional sampling of scattering data from extended volumes of reciprocal space within second to minute time­scales. For such experiments, simultaneous analysis and visualization allows for remeasurements and a more dynamic measurement strategy. A new software, Nebula, is presented. It efficiently reconstructs X-ray scattering data, generates three-dimensional reciprocal space data sets that can be visualized interactively, and aims to enable real-time processing in high-throughput measurements by employing parallel computing on commodity hardware. PMID:25844083

Hierarchical classification in high dimensional numerous class cases

NASA Technical Reports Server (NTRS)

Kim, Byungyong; Landgrebe, D. A.

1990-01-01

As progress in new sensor technology continues, increasingly high resolution imaging sensors are being developed. These sensors give more detailed and complex data for each picture element and greatly increase the dimensionality of data over past systems. Three methods for designing a decision tree classifier are discussed: a top down approach, a bottom up approach, and a hybrid approach. Three feature extraction techniques are implemented. Canonical and extended canonical techniques are mainly dependent upon the mean difference between two classes. An autocorrelation technique is dependent upon the correlation differences. The mathematical relationship between sample size, dimensionality, and risk value is derived.

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

Nekrasov, Nikita; ITEP, Moscow; Shatashvili, Samson

Supersymmetric vacua of two dimensional N = 4 gauge theories with matter, softly broken by the twisted masses down to N = 2, are shown to be in one-to-one correspondence with the eigenstates of integrable spin chain Hamiltonians. Examples include: the Heisenberg SU(2)XXX spin chain which is mapped to the two dimensional U(N) theory with fundamental hypermultiplets, the XXZ spin chain which is mapped to the analogous three dimensional super-Yang-Mills theory compactified on a circle, the XYZ spin chain and eight-vertex model which are related to the four dimensional theory compactified on T{sup 2}. A consequence of our correspondence ismore » the isomorphism of the quantum cohomology ring of various quiver varieties, such as cotangent bundles to (partial) flag varieties and the ring of quantum integrals of motion of various spin chains. The correspondence extends to any spin group, representations, boundary conditions, and inhomogeneity, it includes Sinh-Gordon and non-linear Schroedinger models as well as the dynamical spin chains like Hubbard model. Compactifications of four dimensional N = 2 theories on a two-sphere lead to the instanton-corrected Bethe equations.« less

Two-stage Framework for a Topology-Based Projection and Visualization of Classified Document Collections

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

Oesterling, Patrick; Scheuermann, Gerik; Teresniak, Sven

During the last decades, electronic textual information has become the world's largest and most important information source available. People have added a variety of daily newspapers, books, scientific and governmental publications, blogs and private messages to this wellspring of endless information and knowledge. Since neither the existing nor the new information can be read in its entirety, computers are used to extract and visualize meaningful or interesting topics and documents from this huge information clutter. In this paper, we extend, improve and combine existing individual approaches into an overall framework that supports topological analysis of high dimensional document point cloudsmore » given by the well-known tf-idf document-term weighting method. We show that traditional distance-based approaches fail in very high dimensional spaces, and we describe an improved two-stage method for topology-based projections from the original high dimensional information space to both two dimensional (2-D) and three dimensional (3-D) visualizations. To show the accuracy and usability of this framework, we compare it to methods introduced recently and apply it to complex document and patent collections.« less

Lateral supraorbital approach to ipsilateral PCA-P1 and ICA-PCoA aneurysms

PubMed Central

Goehre, Felix; Jahromi, Behnam Rezai; Elsharkawy, Ahmed; Lehto, Hanna; Shekhtman, Oleg; Andrade-Barazarte, Hugo; Munoz, Francisco; Hijazy, Ferzat; Makhkamov, Makhkam; Hernesniemi, Juha

2015-01-01

Background: Aneurysms of the posterior cerebral artery (PCA) are rare and often associated with anterior circulation aneurysms. The lateral supraorbital approach allows for a very fast and safe approach to the ipsilateral lesions Circle of Willis. A technical note on the successful clip occlusion of two aneurysms in the anterior and posterior Circle of Willis via this less invasive approach has not been published before. The objective of this technical note is to describe the simultaneous microsurgical clip occlusion of an ipsilateral PCA-P1 and an internal carotid artery - posterior communicating artery (ICA-PCoA) aneurysm via the lateral supraorbital approach. Case Description: The authors present a technical report of successful clip occlusions of ipsilateral located PCA-P1 and ICA-PCoA aneurysms. A 59-year-old female patient was diagnosed with a PCA-P1 and an ipsilateral ICA-PCoA aneurysm by computed tomography angiography (CTA) after an ischemic stroke secondary to a contralateral ICA dissection. The patient underwent microsurgical clipping after a lateral supraorbital craniotomy. The intraoperative indocyanine green (ICG) videoangiography and the postoperative CTA showed a complete occlusion of both aneurysms; the parent vessels (ICA and PCA) were patent. The patient presents postoperative no new neurologic deficit. Conclusion: The lateral supraorbital approach is suitable for the simultaneous microsurgical treatment of proximal anterior circulation and ipsilateral proximal PCA aneurysms. Compared to endovascular treatment, direct visual control of brainstem perforators is possible. PMID:26060600

A Seven-Gene Locus for Synthesis of Phenazine-1-Carboxylic Acid by Pseudomonas fluorescens 2-79

PubMed Central

Mavrodi, Dmitri V.; Ksenzenko, Vladimir N.; Bonsall, Robert F.; Cook, R. James; Boronin, Alexander M.; Thomashow, Linda S.

1998-01-01

Pseudomonas fluorescens 2-79 produces the broad-spectrum antibiotic phenazine-1-carboxylic acid (PCA), which is active against a variety of fungal root pathogens. In this study, seven genes designated phzABCDEFG that are sufficient for synthesis of PCA were localized within a 6.8-kb BglII-XbaI fragment from the phenazine biosynthesis locus of strain 2-79. Polypeptides corresponding to all phz genes were identified by analysis of recombinant plasmids in a T7 promoter/polymerase expression system. Products of the phzC, phzD, and phzE genes have similarities to enzymes of shikimic acid and chorismic acid metabolism and, together with PhzF, are absolutely necessary for PCA production. PhzG is similar to pyridoxamine-5′-phosphate oxidases and probably is a source of cofactor for the PCA-synthesizing enzyme(s). Products of the phzA and phzB genes are highly homologous to each other and may be involved in stabilization of a putative PCA-synthesizing multienzyme complex. Two new genes, phzX and phzY, that are homologous to phzA and phzB, respectively, were cloned and sequenced from P. aureofaciens 30-84, which produces PCA, 2-hydroxyphenazine-1-carboxylic acid, and 2-hydroxyphenazine. Based on functional analysis of the phz genes from strains 2-79 and 30-84, we postulate that different species of fluorescent pseudomonads have similar genetic systems that confer the ability to synthesize PCA. PMID:9573209

Disparities at Presentation, Diagnosis, Treatment and Survival in African American Men, Affected by Prostate Cancer

PubMed Central

Chornokur, Ganna; Dalton, Kyle; Borysova, Meghan; Kumar, Nagi

2011-01-01

Background Prostate cancer (PCa) remains the most common malignancy and the second leading cause of cancer death among men in the United States. PCa exhibits the most striking racial disparity, as African American men are at 1.4 times higher risk of being diagnosed, and two to three times higher risk of dying of PCa, compared to Caucasian men. The etiology of the disparity has not been clearly elucidated. The objective of this paper is to critically review the literature and summarize the most prominent PCa racial disparities accompanied by proposed explanations. Methods The present literature on disparities at presentation, diagnosis, treatment and survival of African American men affected by PCa was systematically reviewed. Original research as well as relevant review articles were included. Results African American men recurrently present with more advanced disease than Caucasian men, are administered different treatment regimens than Caucasian men, and have shorter progression-free survival following treatment. In addition, African American men report more treatment related side-effects that translates to the diminished quality of life. Conclusions PCa racial disparity exists at stages of presentation, diagnosis, treatment regimens and subsequent survival, and the quality of life. The disparities are complex in involving biological, socio-economic and socio-cultural determinants. These mounting results highlight an urgent need for future clinical, scientific and socio-cultural research involving transdisciplinary teams to elucidate the causes for PCa racial disparities. PMID:21541975

Nanostructures formed by cyclodextrin covered procainamide through supramolecular self assembly - Spectral and molecular modeling study

NASA Astrophysics Data System (ADS)

Rajendiran, N.; Mohandoss, T.; Sankaranarayanan, R. K.

2015-02-01

Inclusion complexation behavior of procainamide (PCA) with two cyclodextrins (α-CD and β-CD) were analyzed by absorption, fluorescence, scanning electron microscope (SEM), transmission electron microscope (TEM), Raman image, FT-IR, differential scanning colorimeter (DSC), Powder X ray diffraction (XRD) and 1H NMR. Blue shift was observed in β-CD whereas no significant spectral shift observed in α-CD. The inclusion complex formation results suggest that water molecules also present in the inside of the CD cavity. The present study revealed that the phenyl ring of the PCA drug is entrapped in the CD cavity. Cyclodextrin studies show that PCA forms 1:2 inclusion complex with α-CD and β-CD. PCA:α-CD complex form nano-sized particles (46 nm) and PCA:β-CD complex form self-assembled to micro-sized tubular structures. The shape-shifting of 2D nanosheets into 1D microtubes by simple rolling mechanism were analysed by micro-Raman and TEM images. Thermodynamic parameters (ΔH, ΔG and ΔS) of inclusion process were determined from semiempirical PM3 calculations.

Differential principal component analysis of ChIP-seq.

PubMed

Ji, Hongkai; Li, Xia; Wang, Qian-fei; Ning, Yang

2013-04-23

We propose differential principal component analysis (dPCA) for analyzing multiple ChIP-sequencing datasets to identify differential protein-DNA interactions between two biological conditions. dPCA integrates unsupervised pattern discovery, dimension reduction, and statistical inference into a single framework. It uses a small number of principal components to summarize concisely the major multiprotein synergistic differential patterns between the two conditions. For each pattern, it detects and prioritizes differential genomic loci by comparing the between-condition differences with the within-condition variation among replicate samples. dPCA provides a unique tool for efficiently analyzing large amounts of ChIP-sequencing data to study dynamic changes of gene regulation across different biological conditions. We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein-DNA interactions.

Visibility graphs of random scalar fields and spatial data

NASA Astrophysics Data System (ADS)

Lacasa, Lucas; Iacovacci, Jacopo

2017-07-01

We extend the family of visibility algorithms to map scalar fields of arbitrary dimension into graphs, enabling the analysis of spatially extended data structures as networks. We introduce several possible extensions and provide analytical results on the topological properties of the graphs associated to different types of real-valued matrices, which can be understood as the high and low disorder limits of real-valued scalar fields. In particular, we find a closed expression for the degree distribution of these graphs associated to uncorrelated random fields of generic dimension. This result holds independently of the field's marginal distribution and it directly yields a statistical randomness test, applicable in any dimension. We showcase its usefulness by discriminating spatial snapshots of two-dimensional white noise from snapshots of a two-dimensional lattice of diffusively coupled chaotic maps, a system that generates high dimensional spatiotemporal chaos. The range of potential applications of this combinatorial framework includes image processing in engineering, the description of surface growth in material science, soft matter or medicine, and the characterization of potential energy surfaces in chemistry, disordered systems, and high energy physics. An illustration on the applicability of this method for the classification of the different stages involved in carcinogenesis is briefly discussed.

Clinical performance of serum [-2]proPSA derivatives, %p2PSA and PHI, in the detection and management of prostate cancer.

PubMed

Huang, Ya-Qiang; Sun, Tong; Zhong, Wei-De; Wu, Chin-Lee

2014-01-01

Prostate-specific antigen (PSA) has been widely used as a serum marker for prostate cancer (PCa) screening or progression monitoring, which dramatically increased rate of early detection while significantly reduced PCa-specific mortality. However, a number of limitations of PSA have been noticed. Low specificity of PSA may lead to overtreatment in men who presenting with a total PSA (tPSA) level of < 10 ng/mL. As a type of free PSA (fPSA), [-2]proPSA is differentially expressed in peripheral zone of prostate gland and found to be elevated in serum of men with PCa. Two p2PSA-based derivatives, prostate health index (PHI) and %p2PSA, which were defined as [(p2PSA/fPSA) × √ tPSA] and [(p2PSA/fPSA) × 100] respectively, have been suggested to be increased in PCa and can better distinguish PCa from benign prostatic diseases than tPSA or fPSA. We performed a systematic review of the available scientific evidences to evaluate the potentials of %p2PSA and PHI in clinical application. Mounting evidences suggested that both %p2PSA and PHI possess higher area under the ROC curve (AUC) and better specificity at a high sensitivity for PCa detection when compare with tPSA and %fPSA. It indicated that measurements of %p2PSA and PHI significantly improved the accuracy of PCa detection and diminished unnecessary biopsies. Furthermore, elevations of %p2PSA and PHI are related to more aggressive diseases. %p2PSA and PHI might be helpful in reducing overtreatment on indolent cases or assessing the progression of PCa in men who undergo active surveillance. Further studies are needed before being applied in routine clinical practice.

The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer.

PubMed

Zhang, Yu-Dong; Wang, Qing; Wu, Chen-Jiang; Wang, Xiao-Ning; Zhang, Jing; Liu, Hui; Liu, Xi-Sheng; Shi, Hai-Bin

2015-04-01

To evaluate histogram analysis of intravoxel incoherent motion (IVIM) for discriminating the Gleason grade of prostate cancer (PCa). A total of 48 patients pathologically confirmed as having clinically significant PCa (size > 0.5 cm) underwent preoperative DW-MRI (b of 0-900 s/mm(2)). Data was post-processed by monoexponential and IVIM model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analysis was performed by outlining entire-tumour regions of interest (ROIs) from histological-radiological correlation. The ability of imaging indices to differentiate low-grade (LG, Gleason score (GS) ≤6) from intermediate/high-grade (HG, GS > 6) PCa was analysed by ROC regression. Eleven patients had LG tumours (18 foci) and 37 patients had HG tumours (42 foci) on pathology examination. HG tumours had significantly lower ADCs and D in terms of mean, median, 10th and 75th percentiles, combined with higher histogram kurtosis and skewness for ADCs, D and f, than LG PCa (p < 0.05). Histogram D showed relatively higher correlations (ñ = 0.641-0.668 vs. ADCs: 0.544-0.574) with ordinal GS of PCa; and its mean, median and 10th percentile performed better than ADCs did in distinguishing LG from HG PCa. It is feasible to stratify the pathological grade of PCa by IVIM with histogram metrics. D performed better in distinguishing LG from HG tumour than conventional ADCs. • GS had relatively higher correlation with tumour D than ADCs. • Difference of histogram D among two-grade tumours was statistically significant. • D yielded better individual features in demonstrating tumour grade than ADC. • D* and f failed to determine tumour grade of PCa.

Dietary Consumption of Phenolic Acids and Prostate Cancer: A Case-Control Study in Sicily, Southern Italy.

PubMed

Russo, Giorgio Ivan; Campisi, Daniele; Di Mauro, Marina; Regis, Federica; Reale, Giulio; Marranzano, Marina; Ragusa, Rosalia; Solinas, Tatiana; Madonia, Massimo; Cimino, Sebastiano; Morgia, Giuseppe

2017-12-05

Dietary polyphenols gained the interest of the scientific community due to their wide content in a variety of plant-derived foods and beverages commonly consumed, such as fruits, vegetables, coffee, tea, and cocoa . We aimed to investigate whether there was an association between dietary phenolic acid consumption and prostate cancer (PCa) in South Italy. We conducted a population-based case-control study from January 2015 to December 2016 in a single institution of the municipality of Catania, southern Italy (Registration number: 41/2015). Patients with elevated PSA and/or suspicious PCa underwent transperineal prostate biopsy. A total of 118 histopathological-verified PCa cases were collected and a total of 222 controls were selected from a sample of 2044 individuals. Dietary data were collected by using two food frequency questionnaires and data on the phenolic acids content in foods was obtained from the Phenol-Explorer database (www.phenol-explorer.eu). Association between dietary intake of phenolic acids and PCa was calculated through logistic regression analysis. We found lower levels of caffeic acid (2.28 mg/day vs. 2.76 mg/day; p < 0.05) and ferulic acid (2.80 mg/day vs. 4.04 mg/day; p < 0.01) in PCa when compared to controls. The multivariate logistic regression showed that both caffeic acid (OR = 0.32; p < 0.05) and ferulic acid (OR = 0.30; p < 0.05) were associated with reduced risk of PCa. Higher intake of hydroxybenzoic acids and caffeic acids were associated with lower risk of advanced PCa. High intake of caffeic acid and ferulic acid may be associated with reduced risk of PCa.

Prostate-specific membrane antigen targeted imaging and therapy of prostate cancer using a PSMA inhibitor as a homing ligand.

PubMed

Kularatne, Sumith A; Wang, Kevin; Santhapuram, Hari-Krishna R; Low, Philip S

2009-01-01

Prostate cancer (PCa) is a major cause of mortality and morbidity in Western society today. Current methods for detecting PCa are limited, leaving most early malignancies undiagnosed and sites of metastasis in advanced disease undetected. Major deficiencies also exist in the treatment of PCa, especially metastatic disease. In an effort to improve both detection and therapy of PCa, we have developed a PSMA-targeted ligand that delivers attached imaging and therapeutic agents selectively to PCa cells without targeting normal cells. The PSMA-targeted radioimaging agent (DUPA-(99m)Tc) was found to bind PSMA-positive human PCa cells (LNCaP cell line) with nanomolar affinity (K(D) = 14 nM). Imaging and biodistribution studies revealed that DUPA-(99m)Tc localizes primarily to LNCaP cell tumor xenografts in nu/nu mice (% injected dose/gram = 11.3 at 4 h postinjection; tumor-to-muscle ratio = 75:1). Two PSMA-targeted optical imaging agents (DUPA-FITC and DUPA-rhodamine B) were also shown to efficiently label PCa cells and to internalize and traffic to intracellular endosomes. A PSMA-targeted chemotherapeutic agent (DUPA-TubH) was demonstrated to kill PSMA-positive LNCaP cells in culture (IC(50) = 3 nM) and to eliminate established tumor xenografts in nu/nu mice with no detectable weight loss. Blockade of tumor targeting upon administration of excess PSMA inhibitor (PMPA) and the absence of targeting to PSMA-negative tumors confirmed the specificity of each of the above targeted reagents for PSMA. Tandem use of the imaging and therapeutic agents targeted to the same receptor could allow detection, staging, monitoring, and treatment of PCa with improved accuracy and efficacy.