WorldWideScience

Sample records for pca

  1. The PCa Tumor Microenvironment.

    Science.gov (United States)

    Sottnik, Joseph L; Zhang, Jian; Macoska, Jill A; Keller, Evan T

    2011-12-01

    The tumor microenvironment (TME) is a very complex niche that consists of multiple cell types, supportive matrix and soluble factors. Cells in the TME consist of both host cells that are present at tumor site at the onset of tumor growth and cells that are recruited in either response to tumor- or host-derived factors. PCa (PCa) thrives on crosstalk between tumor cells and the TME. Crosstalk results in an orchestrated evolution of both the tumor and microenvironment as the tumor progresses. The TME reacts to PCa-produced soluble factors as well as direct interaction with PCa cells. In return, the TME produces soluble factors, structural support and direct contact interactions that influence the establishment and progression of PCa. In this review, we focus on the host side of the equation to provide a foundation for understanding how different aspects of the TME contribute to PCa progression. We discuss immune effector cells, specialized niches, such as the vascular and bone marrow, and several key protein factors that mediate host effects on PCa. This discussion highlights the concept that the TME offers a potentially very fertile target for PCa therapy.

  2. Volatilization from PCA steel alloy

    Energy Technology Data Exchange (ETDEWEB)

    Hagrman, D.L.; Smolik, G.R.; McCarthy, K.A.; Petti, D.A.

    1996-08-01

    The mobilizations of key components from Primary Candidate Alloy (PCA) steel alloy have been measured with laboratory-scale experiments. The experiments indicate most of the mobilization from PCA steel is due to oxide formation and spalling but that the spalled particles are large enough to settle rapidly. Based on the experiments, models for the volatization of iron, manganese, and cobalt from PCA steel in steam and molybdenum from PCA steel in air have been derived.

  3. Petrology of Antarctic Eucrites PCA 91078 and PCA 91245

    Science.gov (United States)

    Howard, L. M.; Domanik, K. J.; Drake, M. J.; Mittlefehldt, D. W.

    2002-01-01

    Antarctic eucrites PCA 91078 and PCA 91245, are petrographically characterized and found to be unpaired, type 6, basaltic eucrites. Observed textures that provide insight into the petrogenesis of these meteorites are also discussed. Additional information is contained in the original extended abstract.

  4. Circle of Willis Variants: Fetal PCA

    Directory of Open Access Journals (Sweden)

    Amir Shaban

    2013-01-01

    Full Text Available We sought to determine the prevalence of fetal posterior cerebral artery (fPCA and if fPCA was associated with specific stroke etiology and vessel territory affected. This paper is a retrospective review of prospectively identified patients with acute ischemic stroke from July 2008 to December 2010. We defined complete fPCA as absence of a P1 segment linking the basilar with the PCA and partial fPCA as small segment linking the basilar with the PCA. Patients without intracranial vascular imaging were excluded. We compared patients with complete fPCA, partial fPCA, and without fPCA in terms of demographics, stroke severity, distribution, and etiology and factored in whether the stroke was ipsilateral to the fPCA. Of the 536 included patients, 9.5% ( had complete fPCA and 15.1% ( had partial fPCA. Patients with complete fPCA were older and more often female than partial fPCA and no fPCA patients, and significant variation in TOAST classification was detected across groups (. Patients with complete fPCA had less small vessel and more large vessel strokes than patients with no fPCA and partial fPCA. Fetal PCA may predispose to stroke mechanism, but is not associated with vascular distribution, stroke severity, or early outcome.

  5. PCaPAC 2006 Proceedings

    Energy Technology Data Exchange (ETDEWEB)

    Pavel Chevtsov; Matthew Bickley (Eds.)

    2007-03-30

    The 6-th international PCaPAC (Personal Computers and Particle Accelerator Controls) workshop was held at Jefferson Lab, Newport News, Virginia, from October 24-27, 2006. The main objectives of the conference were to discuss the most important issues of the use of PCs and modern IT technologies for controls of accelerators and to give scientists, engineers, and technicians a forum to exchange the ideas on control problems and their solutions. The workshop consisted of plenary sessions and poster sessions. No parallel sessions were held.Totally, more than seventy oral and poster presentations as well as tutorials were made during the conference, on the basis of which about fifty papers were submitted by the authors and included in this publication. This printed version of the PCaPAC 2006 Proceedings is published at Jefferson Lab according to the decision of the PCaPAC International Program Committee of October 26, 2006.

  6. Circle of Willis Variants: Fetal PCA

    OpenAIRE

    Amir Shaban; Karen C. Albright; Amelia K. Boehme; Sheryl Martin-Schild

    2013-01-01

    We sought to determine the prevalence of fetal posterior cerebral artery (fPCA) and if fPCA was associated with specific stroke etiology and vessel territory affected. This paper is a retrospective review of prospectively identified patients with acute ischemic stroke from July 2008 to December 2010. We defined complete fPCA as absence of a P1 segment linking the basilar with the PCA and partial fPCA as small segment linking the basilar with the PCA. Patients without intracranial vascular ima...

  7. Sparse PCA with Oracle Property.

    Science.gov (United States)

    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.

  8. Tensile properties of unirradiated path A PCA

    International Nuclear Information System (INIS)

    Braski, D.N.; Maziasz, P.J.

    1983-01-01

    The tensile properties of PCA in the Al (solution annealed), A3 (25%-cold worked), and B2 (aged, cold worked, and reaged) conditions were determined from room temperature to 600 0 C. The tensile behavior of PCA-A1 and -A3 was generally similar to that of titanium-modified type 316 stainless steel with similar microstructures. The PCA-B2 was weaker than PCA-A3, especially above 500 0 C, but demonstrated slightly better ducility

  9. Semi-Supervised Kernel PCA

    DEFF Research Database (Denmark)

    Walder, Christian; Henao, Ricardo; Mørup, Morten

    We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least...... squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets....

  10. International assessment of PCA codes

    International Nuclear Information System (INIS)

    Neymotin, L.; Lui, C.; Glynn, J.; Archarya, S.

    1993-11-01

    Over the past three years (1991-1993), an extensive international exercise for intercomparison of a group of six Probabilistic Consequence Assessment (PCA) codes was undertaken. The exercise was jointly sponsored by the Commission of European Communities (CEC) and OECD Nuclear Energy Agency. This exercise was a logical continuation of a similar effort undertaken by OECD/NEA/CSNI in 1979-1981. The PCA codes are currently used by different countries for predicting radiological health and economic consequences of severe accidents at nuclear power plants (and certain types of non-reactor nuclear facilities) resulting in releases of radioactive materials into the atmosphere. The codes participating in the exercise were: ARANO (Finland), CONDOR (UK), COSYMA (CEC), LENA (Sweden), MACCS (USA), and OSCAAR (Japan). In parallel with this inter-code comparison effort, two separate groups performed a similar set of calculations using two of the participating codes, MACCS and COSYMA. Results of the intercode and inter-MACCS comparisons are presented in this paper. The MACCS group included four participants: GREECE: Institute of Nuclear Technology and Radiation Protection, NCSR Demokritos; ITALY: ENEL, ENEA/DISP, and ENEA/NUC-RIN; SPAIN: Universidad Politecnica de Madrid (UPM) and Consejo de Seguridad Nuclear; USA: Brookhaven National Laboratory, US NRC and DOE

  11. Parallel GPU implementation of iterative PCA algorithms.

    Science.gov (United States)

    Andrecut, M

    2009-11-01

    Principal component analysis (PCA) is a key statistical technique for multivariate data analysis. For large data sets, the common approach to PCA computation is based on the standard NIPALS-PCA algorithm, which unfortunately suffers from loss of orthogonality, and therefore its applicability is usually limited to the estimation of the first few components. Here we present an algorithm based on Gram-Schmidt orthogonalization (called GS-PCA), which eliminates this shortcoming of NIPALS-PCA. Also, we discuss the GPU (Graphics Processing Unit) parallel implementation of both NIPALS-PCA and GS-PCA algorithms. The numerical results show that the GPU parallel optimized versions, based on CUBLAS (NVIDIA), are substantially faster (up to 12 times) than the CPU optimized versions based on CBLAS (GNU Scientific Library).

  12. MD-11 PCA - Research flight team photo

    Science.gov (United States)

    1995-01-01

    On Aug. 30, 1995, a the McDonnell Douglas MD-11 transport aircraft landed equipped with a computer-assisted engine control system that has the potential to increase flight safety. In landings at NASA Dryden Flight Research Center, Edwards, California, on August 29 and 30, the aircraft demonstrated software used in the aircraft's flight control computer that essentially landed the MD-11 without a need for the pilot to manipulate the flight controls significantly. In partnership with McDonnell Douglas Aerospace (MDA), with Pratt & Whitney and Honeywell helping to design the software, NASA developed this propulsion-controlled aircraft (PCA) system following a series of incidents in which hydraulic failures resulted in the loss of flight controls. This new system enables a pilot to operate and land the aircraft safely when its normal, hydraulically-activated control surfaces are disabled. This August 29, 1995, photo shows the MD-11 team. Back row, left to right: Tim Dingen, MDA pilot; John Miller, MD-11 Chief pilot (MDA); Wayne Anselmo, MD-11 Flight Test Engineer (MDA); Gordon Fullerton, PCA Project pilot; Bill Burcham, PCA Chief Engineer; Rudey Duran, PCA Controls Engineer (MDA); John Feather, PCA Controls Engineer (MDA); Daryl Townsend, Crew Chief; Henry Hernandez, aircraft mechanic; Bob Baron, PCA Project Manager; Don Hermann, aircraft mechanic; Jerry Cousins, aircraft mechanic; Eric Petersen, PCA Manager (Honeywell); Trindel Maine, PCA Data Engineer; Jeff Kahler, PCA Software Engineer (Honeywell); Steve Goldthorpe, PCA Controls Engineer (MDA). Front row, left to right: Teresa Hass, Senior Project Management Analyst; Hollie Allingham (Aguilera), Senior Project Management Analyst; Taher Zeglum, PCA Data Engineer (MDA); Drew Pappas, PCA Project Manager (MDA); John Burken, PCA Control Engineer.

  13. An efficient algorithm for weighted PCA

    NARCIS (Netherlands)

    Krijnen, W.P.; Kiers, H.A.L.

    1995-01-01

    The method for analyzing three-way data where one of the three components matrices in TUCKALS3 is chosen to have one column is called Replicated PCA. The corresponding algorithm is relatively inefficient. This is shown by offering an alternative algorithm called Weighted PCA. Specifically it is

  14. Model selection for Gaussian kernel PCA denoising

    DEFF Research Database (Denmark)

    Jørgensen, Kasper Winther; Hansen, Lars Kai

    2012-01-01

    We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

  15. Performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches in VQ codebook generation for image compression

    Science.gov (United States)

    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.

  16. PcaO Positively Regulates pcaHG of the β-Ketoadipate Pathway in Corynebacterium glutamicum▿

    OpenAIRE

    Zhao, Ke-Xin; Huang, Yan; Chen, Xi; Wang, Nan-Xi; Liu, Shuang-Jiang

    2010-01-01

    We identified a new regulator, PcaO, which is involved in regulation of the protocatechuate (PCA) branch of the β-ketoadipate pathway in Corynebacterium glutamicum. PcaO is an atypical large ATP-binding LuxR family (LAL)-type regulator and does not have a Walker A motif. A mutant of C. glutamicum in which pcaO was disrupted (RES167ΔpcaO) was unable to grow on PCA, and growth on PCA was restored by complementation with pcaO. Both an enzymatic assay of PCA 3,4-dioxygenase activity (encoded by p...

  17. Simultaneous Estimation of Hydrochlorothiazide, Hydralazine Hydrochloride, and Reserpine Using PCA, NAS, and NAS-PCA.

    Science.gov (United States)

    Sharma, Chetan; Badyal, Pragya Nand; Rawal, Ravindra K

    2015-01-01

    In this study, new and feasible UV-visible spectrophotometric and multivariate spectrophotometric methods were described for the simultaneous determination of hydrochlorothiazide (HCTZ), hydralazine hydrochloride (H.HCl), and reserpine (RES) in combined pharmaceutical tablets. Methanol was used as a solvent for analysis and the whole UV region was scanned from 200-400 nm. The resolution was obtained by using multivariate methods such as the net analyte signal method (NAS), principal component analysis (PCA), and net analyte signal-principal component analysis (NAS-PCA) applied to the UV spectra of the mixture. The results obtained from all of the three methods were compared. NAS-PCA showed a lot of resolved data as compared to NAS and PCA. Thus, the NAS-PCA technique is a combination of NAS and PCA methods which is advantageous to obtain the information from overlapping results.

  18. PCA3 and PCA3-Based Nomograms Improve Diagnostic Accuracy in Patients Undergoing First Prostate Biopsy

    Directory of Open Access Journals (Sweden)

    Virginie Vlaeminck-Guillem

    2013-08-01

    Full Text Available While now recognized as an aid to predict repeat prostate biopsy outcome, the urinary PCA3 (prostate cancer gene 3 test has also been recently advocated to predict initial biopsy results. The objective is to evaluate the performance of the PCA3 test in predicting results of initial prostate biopsies and to determine whether its incorporation into specific nomograms reinforces its diagnostic value. A prospective study included 601 consecutive patients addressed for initial prostate biopsy. The PCA3 test was performed before ≥12-core initial prostate biopsy, along with standard risk factor assessment. Diagnostic performance of the PCA3 test was evaluated. The three available nomograms (Hansen’s and Chun’s nomograms, as well as the updated Prostate Cancer Prevention Trial risk calculator; PCPT were applied to the cohort, and their predictive accuracies were assessed in terms of biopsy outcome: the presence of any prostate cancer (PCa and high-grade prostate cancer (HGPCa. The PCA3 score provided significant predictive accuracy. While the PCPT risk calculator appeared less accurate; both Chun’s and Hansen’s nomograms provided good calibration and high net benefit on decision curve analyses. When applying nomogram-derived PCa probability thresholds ≤30%, ≤6% of HGPCa would have been missed, while avoiding up to 48% of unnecessary biopsies. The urinary PCA3 test and PCA3-incorporating nomograms can be considered as reliable tools to aid in the initial biopsy decision.

  19. Theoretical analysis of the PCA experiment

    International Nuclear Information System (INIS)

    Minsart, G.

    1980-01-01

    A very brief description of the PCA-PVF facility is given, and the studied configurations are mentioned. The analysis of the experiment has been divided into two parts: study of the fission density distribution across the PCA core and neutronic analysis of the flux spectra and spatial distributions in the whole facility. For both parts, the procedure of calculation is explained: cross section sets, one- and two-dimensional models, group collapsing, choice of bucklings, ... . The obtained results are shortly compared with the measured values, and illustrated by a figure and several tables. The computations of the fission map in the PCA core yield results in good agreement with the experimental ones (within a few percents for nearly all points). The discrepancies observed for relative reaction rates and spectral indices of a series of threshold detectors at the selected locations in and between steel and iron layers in the water reflector are briefly discussed

  20. PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

    Directory of Open Access Journals (Sweden)

    Kanokmon Rujirakul

    2014-01-01

    Full Text Available Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

  1. PEM-PCA: a parallel expectation-maximization PCA face recognition architecture.

    Science.gov (United States)

    Rujirakul, Kanokmon; So-In, Chakchai; Arnonkijpanich, Banchar

    2014-01-01

    Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages' complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

  2. MD-11 PCA - First Landing at Edwards

    Science.gov (United States)

    1995-01-01

    This McDonnell Douglas MD-11 approaches the first landing ever of a transport aircraft under engine power only on Aug. 29, 1995, at NASA's Dryden Flight Research Center, Edwards, California. The milestone flight, flown by NASA research pilot and former astronaut Gordon Fullerton, was part of a NASA project to develop a computer-assisted engine control system that enables a pilot to land a plane safely when it normal control surfaces are disabled. The Propulsion-Controlled Aircraft (PCA) system uses standard autopilot controls already present in the cockpit, together with the new programming in the aircraft's flight control computers. The PCA concept is simple--for pitch control, the program increases thrust to climb and reduces thrust to descend. To turn right, the autopilot increases the left engine thrust while decreasing the right engine thrust. The initial Propulsion-Controlled Aircraft studies by NASA were carried out at Dryden with a modified twin-engine F-15 research aircraft.

  3. MD-11 PCA - Research flight team egress

    Science.gov (United States)

    1995-01-01

    This McDonnell Douglas MD-11 has parked on the flightline at NASA's Dryden Flight Research Center, Edwards, California, following its completion of the first and second landings ever performed by a transport aircraft under engine power only (on Aug. 29, 1995). The milestone flight, with NASA research pilot and former astronaut Gordon Fullerton at the controls, was part of a NASA project to develop a computer-assisted engine control system that enables a pilot to land a plane safely when its normal control surfaces are disabled. Coming down the steps from the aircraft are Gordon Fullerton (in front), followed by Bill Burcham, Propulsion Controlled Aircraft (PCA) project engineer at Dryden; NASA Dryden controls engineer John Burken; John Feather of McDonnell Douglas; and Drew Pappas, McDonnell Douglas' project manager for PCA.

  4. Improvements to the RXTE/PCA Calibration

    Science.gov (United States)

    Jahoda, K.

    2009-01-01

    The author presents the current status of the RXTE/PCA Calibration, with emphasis on recent updates to the energy scale and the background subtraction. A new treatment of the Xenon K-escape line removes the largest remaining residual in the previously distributed matrices. Observations of Sco X-1 made simultaneously with Swift XRT, expressly for the purpose of cross calibrating the response to bright sources, are presented.

  5. Sequential combination of k-t principle component analysis (PCA) and partial parallel imaging: k-t PCA GROWL.

    Science.gov (United States)

    Qi, Haikun; Huang, Feng; Zhou, Hongmei; Chen, Huijun

    2017-03-01

    k-t principle component analysis (k-t PCA) is a distinguished method for high spatiotemporal resolution dynamic MRI. To further improve the accuracy of k-t PCA, a combination with partial parallel imaging (PPI), k-t PCA/SENSE, has been tested. However, k-t PCA/SENSE suffers from long reconstruction time and limited improvement. This study aims to improve the combination of k-t PCA and PPI on both reconstruction speed and accuracy. A sequential combination scheme called k-t PCA GROWL (GRAPPA operator for wider readout line) was proposed. The GRAPPA operator was performed before k-t PCA to extend each readout line into a wider band, which improved the condition of the encoding matrix in the following k-t PCA reconstruction. k-t PCA GROWL was tested and compared with k-t PCA and k-t PCA/SENSE on cardiac imaging. k-t PCA GROWL consistently resulted in better image quality compared with k-t PCA/SENSE at high acceleration factors for both retrospectively and prospectively undersampled cardiac imaging, with a much lower computation cost. The improvement in image quality became greater with the increase of acceleration factor. By sequentially combining the GRAPPA operator and k-t PCA, the proposed k-t PCA GROWL method outperformed k-t PCA/SENSE in both reconstruction speed and accuracy, suggesting that k-t PCA GROWL is a better combination scheme than k-t PCA/SENSE. Magn Reson Med 77:1058-1067, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

  6. 24 CFR 401.451 - PAE Physical Condition Analysis (PCA).

    Science.gov (United States)

    2010-04-01

    ... (PCA). 401.451 Section 401.451 Housing and Urban Development Regulations Relating to Housing and Urban... PROGRAM (MARK-TO-MARKET) Restructuring Plan § 401.451 PAE Physical Condition Analysis (PCA). (a) Review... of the project by means of a PCA. If the PAE finds any immediate threats to health and safety, the...

  7. Grassmann Averages for Scalable Robust PCA

    DEFF Research Database (Denmark)

    Hauberg, Søren; Feragen, Aasa; Black, Michael J.

    2014-01-01

    As the collection of large datasets becomes increasingly automated, the occurrence of outliers will increase—“big data” implies “big outliers”. While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can...... to vectors (subspaces) or elements of vectors; we focus on the latter and use a trimmed average. The resulting Trimmed Grassmann Average (TGA) is particularly appropriate for computer vision because it is robust to pixel outliers. The algorithm has low computational complexity and minimal memory requirements...

  8. PCA: Principal Component Analysis for spectra modeling

    Science.gov (United States)

    Hurley, Peter D.; Oliver, Seb; Farrah, Duncan; Wang, Lingyu; Efstathiou, Andreas

    2012-07-01

    The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.

  9. Nonlinear peculiar-velocity analysis and PCA

    Energy Technology Data Exchange (ETDEWEB)

    Dekel, A. [and others

    2001-02-20

    We allow for nonlinear effects in the likelihood analysis of peculiar velocities, and obtain {approximately}35%-lower values for the cosmological density parameter and for the amplitude of mass-density fluctuations. The power spectrum in the linear regime is assumed to be of the flat {Lambda}CDM model (h = 0:65, n = 1) with only {Omega}{sub m} free. Since the likelihood is driven by the nonlinear regime, we break the power spectrum at k{sub b} {approximately} 0.2 (h{sup {minus}1} Mpc){sup {minus}1} and fit a two-parameter power-law at k > k{sub b} . This allows for an unbiased fit in the linear regime. Tests using improved mock catalogs demonstrate a reduced bias and a better fit. We find for the Mark III and SFI data {Omega}{sub m} = 0.35 {+-} 0.09 with {sigma}{sub 8}{Omega}P{sub m}{sup 0.6} = 0.55 {+-} 0.10 (90% errors). When allowing deviations from {Lambda}CDM, we find an indication for a wiggle in the power spectrum in the form of an excess near k {approximately} 0.05 and a deficiency at k {approximately} 0.1 (h{sup {minus}1} Mpc){sup {minus}1}--a cold flow which may be related to a feature indicated from redshift surveys and the second peak in the CMB anisotropy. A {chi}{sup 2} test applied to principal modes demonstrates that the nonlinear procedure improves the goodness of fit. The Principal Component Analysis (PCA) helps identifying spatial features of the data and fine-tuning the theoretical and error models. We address the potential for optimal data compression using PCA.

  10. Beyond textbook neuroanatomy: The syndrome of malignant PCA infarction.

    Science.gov (United States)

    Gogela, Steven L; Gozal, Yair M; Rahme, Ralph; Zuccarello, Mario; Ringer, Andrew J

    2015-01-01

    Given its limited vascular territory, occlusion of the posterior cerebral artery (PCA) usually does not result in malignant infarction. Challenging this concept, we present 3 cases of unilateral PCA infarction with secondary malignant progression, resulting from extension into what would classically be considered the posterior middle cerebral artery (MCA) territory. Interestingly, these were true PCA infarctions, not "MCA plus" strokes, since the underlying occlusive lesion was in the PCA. We hypothesize that congenital and/or acquired variability in the distribution and extent of territory supplied by the PCA may underlie this rare clinical entity. Patients with a PCA infarction should thus be followed closely and offered early surgical decompression in the event of malignant progression.

  11. Nonlinear PCA: characterizing interactions between modes of brain activity.

    OpenAIRE

    Friston, K; Phillips, J; Chawla, D; Büchel, C

    2000-01-01

    This paper presents a nonlinear principal component analysis (PCA) that identifies underlying sources causing the expression of spatial modes or patterns of activity in neuroimaging time-series. The critical aspect of this technique is that, in relation to conventional PCA, the sources can interact to produce (second-order) spatial modes that represent the modulation of one (first-order) spatial mode by another. This nonlinear PCA uses a simple neural network architecture that embodies a spec...

  12. SVD vs PCA: Comparison of Performance in an Imaging Spectrometer

    Directory of Open Access Journals (Sweden)

    Wilma Oblefias

    2004-12-01

    Full Text Available The calculation of basis spectra from a spectral library is an important prerequisite of any compact imaging spectrometer. In this paper, we compare the basis spectra computed by singular-value decomposition (SVD and principal component analysis (PCA in terms of estimation performance with respect to resolution, presence of noise, intensity variation, and quantization error. Results show that SVD is robust in intensity variation while PCA is not. However, PCA performs better with signals of low signal-to-noise ratio. No significant difference is seen between SVD and PCA in terms of resolution and quantization error.

  13. Preliminary Design Review: PCA Integrated Radar-Tracker Application

    National Research Council Canada - National Science Library

    Lebak, J

    2002-01-01

    The DARPA Polymorphous Computing Architecture (PCA) program is building advanced computer architectures that can reorganize their computation and communication structure to achieve better overall application performance...

  14. Denoising by semi-supervised kernel PCA preimaging

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai

    2014-01-01

    Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality reduction, prior to the so-called pre-imag...

  15. Condition Monitoring of Sensors in a NPP Using Optimized PCA

    Directory of Open Access Journals (Sweden)

    Wei Li

    2018-01-01

    Full Text Available An optimized principal component analysis (PCA framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.

  16. Gas-Chromatographic Determination Of Water In Freon PCA

    Science.gov (United States)

    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.

  17. Memory Efficient PCA Methods for Large Group ICA.

    Science.gov (United States)

    Rachakonda, Srinivas; Silva, Rogers F; Liu, Jingyu; Calhoun, Vince D

    2016-01-01

    Principal component analysis (PCA) is widely used for data reduction in group independent component analysis (ICA) of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components. This work focuses on reducing very high dimensional temporally concatenated datasets into its group PCA space. Existing randomized PCA methods can determine the PCA subspace with minimal memory requirements and, thus, are ideal for solving large PCA problems. Since the number of dataloads is not typically optimized, we extend one of these methods to compute PCA of very large datasets with a minimal number of dataloads. This method is coined multi power iteration (MPOWIT). The key idea behind MPOWIT is to estimate a subspace larger than the desired one, while checking for convergence of only the smaller subset of interest. The number of iterations is reduced considerably (as well as the number of dataloads), accelerating convergence without loss of accuracy. More importantly, in the proposed implementation of MPOWIT, the memory required for successful recovery of the group principal components becomes independent of the number of subjects analyzed. Highly efficient subsampled eigenvalue decomposition techniques are also introduced, furnishing excellent PCA subspace approximations that can be used for intelligent initialization of randomized methods such as MPOWIT. Together, these developments enable efficient estimation of accurate principal components, as we illustrate by solving a 1600-subject group-level PCA of fMRI with standard acquisition parameters, on a regular desktop computer with only 4 GB RAM, in just a few hours. MPOWIT is also highly scalable and could realistically solve group-level PCA of fMRI on thousands of subjects, or more, using standard hardware, limited only by time, not memory. Also, the MPOWIT algorithm is highly parallelizable, which would enable fast, distributed implementations ideal for big

  18. Memory efficient PCA methods for large group ICA

    Directory of Open Access Journals (Sweden)

    Srinivas eRachakonda

    2016-02-01

    Full Text Available Principal component analysis (PCA is widely used for data reduction in group independent component analysis (ICA of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components. This work focuses on reducing very high dimensional temporally concatenated datasets into its group PCA space. Existing randomized PCA methods can determine the PCA subspace with minimal memory requirements and, thus, are ideal for solving large PCA problems. Since the number of dataloads is not typically optimized, we extend one of these methods to compute PCA of very large datasets with a minimal number of dataloads. This method is coined multi power iteration (MPOWIT. The key idea behind MPOWIT is to estimate a subspace larger than the desired one, while checking for convergence of only the smaller subset of interest. The number of iterations is reduced considerably (as well as the number of dataloads, accelerating convergence without loss of accuracy. More importantly, in the proposed implementation of MPOWIT, the memory required for successful recovery of the group principal components becomes independent of the number of subjects analyzed. Highly efficient subsampled eigenvalue decomposition techniques are also introduced, furnishing excellent PCA subspace approximations that can be used for intelligent initialization of randomized methods such as MPOWIT. Together, these developments enable efficient estimation of accurate principal components, as we illustrate by solving a 1600-subject group-level PCA of fMRI with standard acquisition parameters, on a regular desktop computer with only 4GB RAM, in just a few hours. MPOWIT is also highly scalable and could realistically solve group-level PCA of fMRI on thousands of subjects, or more, using standard hardware, limited only by time, not memory. Also, the MPOWIT algorithm is highly parallelizable, which would enable fast, distributed implementations

  19. Comparative study of PCA in classification of multichannel EMG signals.

    Science.gov (United States)

    Geethanjali, P

    2015-06-01

    Electromyographic (EMG) signals are abundantly used in the field of rehabilitation engineering in controlling the prosthetic device and significantly essential to find fast and accurate EMG pattern recognition system, to avoid intrusive delay. The main objective of this paper is to study the influence of Principal component analysis (PCA), a transformation technique, in pattern recognition of six hand movements using four channel surface EMG signals from ten healthy subjects. For this reason, time domain (TD) statistical as well as auto regression (AR) coefficients are extracted from the four channel EMG signals. The extracted statistical features as well as AR coefficients are transformed using PCA to 25, 50 and 75 % of corresponding original feature vector space. The classification accuracy of PCA transformed and non-PCA transformed TD statistical features as well as AR coefficients are studied with simple logistic regression (SLR), decision tree (DT) with J48 algorithm, logistic model tree (LMT), k nearest neighbor (kNN) and neural network (NN) classifiers in the identification of six different movements. The Kruskal-Wallis (KW) statistical test shows that there is a significant reduction (P PCA transformed features compared to non-PCA transformed features. SLR with non-PCA transformed time domain (TD) statistical features performs better in accuracy and computational power compared to other features considered in this study. In addition, the motion control of three drives for six movements of the hand is implemented with SLR using TD statistical features in off-line with TMSLF2407 digital signal controller (DSC).

  20. PRINCIPAL COMPONENT ANALYSIS (PCA DAN APLIKASINYA DENGAN SPSS

    Directory of Open Access Journals (Sweden)

    Hermita Bus Umar

    2009-03-01

    Full Text Available PCA (Principal Component Analysis are statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the setform coherent subset that are relativity independent of one another.Variables that are correlated with one another but largely independent of other subset of variables are combined into factors. The Coals of PCA to which each variables is explained by each dimension. Step in PCA include selecting and mean measuring a set of variables, preparing the correlation matrix, extracting a set offactors from the correlation matrixs. Rotating the factor to increase interpretabilitv and interpreting the result.

  1. Stability and chaos of LMSER PCA learning algorithm

    International Nuclear Information System (INIS)

    Lv Jiancheng; Y, Zhang

    2007-01-01

    LMSER PCA algorithm is a principal components analysis algorithm. It is used to extract principal components on-line from input data. The algorithm has both stability and chaotic dynamic behavior under some conditions. This paper studies the local stability of the LMSER PCA algorithm via a corresponding deterministic discrete time system. Conditions for local stability are derived. The paper also explores the chaotic behavior of this algorithm. It shows that the LMSER PCA algorithm can produce chaos. Waveform plots, Lyapunov exponents and bifurcation diagrams are presented to illustrate the existence of chaotic behavior of this algorithm

  2. Elevated YKL40 is associated with advanced prostate cancer (PCa) and positively regulates invasion and migration of PCa cells.

    Science.gov (United States)

    Jeet, Varinder; Tevz, Gregor; Lehman, Melanie; Hollier, Brett; Nelson, Colleen

    2014-10-01

    Chitinase 3-like 1 (CHI3L1 or YKL40) is a secreted glycoprotein highly expressed in tumours from patients with advanced stage cancers, including prostate cancer (PCa). The exact function of YKL40 is poorly understood, but it has been shown to play an important role in promoting tumour angiogenesis and metastasis. The therapeutic value and biological function of YKL40 are unknown in PCa. The objective of this study was to examine the expression and function of YKL40 in PCa. Gene expression analysis demonstrated that YKL40 was highly expressed in metastatic PCa cells when compared with less invasive and normal prostate epithelial cell lines. In addition, the expression was primarily limited to androgen receptor-positive cell lines. Evaluation of YKL40 tissue expression in PCa patients showed a progressive increase in patients with aggressive disease when compared with those with less aggressive cancers and normal controls. Treatment of LNCaP and C4-2B cells with androgens increased YKL40 expression, whereas treatment with an anti-androgen agent decreased the gene expression of YKL40 in androgen-sensitive LNCaP cells. Furthermore, knockdown of YKL40 significantly decreased invasion and migration of PCa cells, whereas overexpression rendered them more invasive and migratory, which was commensurate with an enhancement in the anchorage-independent growth of cells. To our knowledge, this study characterises the role of YKL40 for the first time in PCa. Together, these results suggest that YKL40 plays an important role in PCa progression and thus inhibition of YKL40 may be a potential therapeutic strategy for the treatment of PCa. © 2014 The authors.

  3. Quantitation of passive cutaneous anaphylaxis (PCA) by using radiolabelled antigen

    International Nuclear Information System (INIS)

    Ring, J.; Seifert, J.; Brendel, W.

    1978-01-01

    The major problem of detecting reaginic antibody by passive cutaneous anaphylaxis (PCA) is the quantitation of the dye reaction. Radiolabelled antigen was used in an attempt to quantitate the PCA reaction (Radio-PCA). Antisera containing reaginic antibody against human serum albumin (HSA) were produced in rabbits. These antisera were injected into normal rabbit skin in different dilutions. Twentyfour hours later HSA was injected intravenously either with Evans Blue or as 125-I-HSA. Radioactivity found in antibody-containing skin was significantly higher than in control specimens containing saline or normal rabbit serum, as low as antiserum dilutions of 1:1,000. Compared with Evans Blue technique Radio-PCA was able to distinguish quantitatively between different antiserum dilutions at a higher level of statistical significance. (author)

  4. Geochemical Constraints for Mercury's PCA-Derived Geochemical Terranes

    Science.gov (United States)

    Stockstill-Cahill, K. R.; Peplowski, P. N.

    2018-05-01

    PCA-derived geochemical terranes provide a robust, analytical means of defining these terranes using strictly geochemical inputs. Using the end members derived in this way, we are able to assess the geochemical implications for Mercury.

  5. EEG frequency PCA in EEG-ERP dynamics.

    Science.gov (United States)

    Barry, Robert J; De Blasio, Frances M

    2018-05-01

    Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG. © 2017 Society for Psychophysiological Research.

  6. On the Link Between L1-PCA and ICA.

    Science.gov (United States)

    Martin-Clemente, Ruben; Zarzoso, Vicente

    2017-03-01

    Principal component analysis (PCA) based on L1-norm maximization is an emerging technique that has drawn growing interest in the signal processing and machine learning research communities, especially due to its robustness to outliers. The present work proves that L1-norm PCA can perform independent component analysis (ICA) under the whitening assumption. However, when the source probability distributions fulfil certain conditions, the L1-norm criterion needs to be minimized rather than maximized, which can be accomplished by simple modifications on existing optimal algorithms for L1-PCA. If the sources have symmetric distributions, we show in addition that L1-PCA is linked to kurtosis optimization. A number of numerical experiments illustrate the theoretical results and analyze the comparative performance of different algorithms for ICA via L1-PCA. Although our analysis is asymptotic in the sample size, this equivalence opens interesting new perspectives for performing ICA using optimal algorithms for L1-PCA with guaranteed global convergence while inheriting the increased robustness to outliers of the L1-norm criterion.

  7. A PCA3 gene-based transcriptional amplification system targeting primary prostate cancer

    OpenAIRE

    Neveu, Bertrand; Jain, Pallavi; T?tu, Bernard; Wu, Lily; Fradet, Yves; Pouliot, Fr?d?ric

    2015-01-01

    Targeting specifically primary prostate cancer (PCa) cells for immune therapy, gene therapy or molecular imaging is of high importance. The PCA3 long non-coding RNA is a unique PCa biomarker and oncogene that has been widely studied. This gene has been mainly exploited as an accurate diagnostic urine biomarker for PCa detection. In this study, the PCA3 promoter was introduced into a new transcriptional amplification system named the 3-Step Transcriptional Amplification System (PCA3-3STA) and ...

  8. Joint Group Sparse PCA for Compressed Hyperspectral Imaging.

    Science.gov (United States)

    Khan, Zohaib; Shafait, Faisal; Mian, Ajmal

    2015-12-01

    A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition.

  9. PCA and Postoperative Pain Management After Orthopedic Surgeries

    Directory of Open Access Journals (Sweden)

    S.M. Hashemi

    2016-08-01

    Full Text Available Background: Patients often suffer from inadequate treatment of postoperative pain. The aim of this study was to investigate effect of PCA on postoperative pain management and patients’ satisfaction from use of PCA. Materials and Methods: In this prospective study, between 2010 to 2011, patients presented by orthopedic specialists to acute and chronic pain service of Akhtar Hospital. A satisfaction questionnaire was given on discharge to this patients, were asked to fill out it . Then collected by ward nurse. Results: patients’ satisfaction from pain relief with use of PCA was high ( 94.9% . In this patient pain relief at third day after surgery and require analgesic was low, significantly (p=0.0001. Significant patients’ satisfaction from effect of PCA in pain control and products support was high (p=0.0001.     Conclusion: Patient controlled analgesia is a safe, effective and noninvasive method for post operative pain management and in this study patients’ satisfaction for pain management was high for use of PCA and pain service. 

  10. [A Quantitative Verification for Operability of Three PCA Devices Attached to the Disposable Infusion Pumps].

    Science.gov (United States)

    Tadokoro, Takahiro; Fuchibe, Makoto; Odo, Yuichiro; Kakinohana, Manabu

    2015-11-01

    In this study using 3 different PCA devices (Baxter infuser LVBB +PCM 2 ml: Pump B, Coopdech Balloonjector +PCA 3 ml: Pump C, Rakuraku fuser +PCA 3 ml: Pump S), we investigated how easily PCA devices could be handled. In this study with 42 volunteers (14 elders and 28 nurses), we compared 3 PCA ejection volume and ejection rate among three PCA devices. PCA ejection rate was defined as the ratio of actual ejection volume to the maximum ejection volume (MEV) of each PCA device. Although not only elders but also nurses failed to produce accurate PCA ejection volume in the Pump B, Pump S could provide the MEV even by elders. In the Pump C, approximately 80% of MEV could be achieved by nurses, but 60% of MEV by elders (P PCA ejection volume might be dependent on PCA device.

  11. On a PCA-based lung motion model

    Energy Technology Data Exchange (ETDEWEB)

    Li Ruijiang; Lewis, John H; Jia Xun; Jiang, Steve B [Department of Radiation Oncology and Center for Advanced Radiotherapy Technologies, University of California San Diego, 3855 Health Sciences Dr, La Jolla, CA 92037-0843 (United States); Zhao Tianyu; Wuenschel, Sara; Lamb, James; Yang Deshan; Low, Daniel A [Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Pl, St. Louis, MO 63110-1093 (United States); Liu Weifeng, E-mail: sbjiang@ucsd.edu [Amazon.com Inc., 701 5th Ave. Seattle, WA 98104 (United States)

    2011-09-21

    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

  12. On a PCA-based lung motion model.

    Science.gov (United States)

    Li, Ruijiang; Lewis, John H; Jia, Xun; Zhao, Tianyu; Liu, Weifeng; Wuenschel, Sara; Lamb, James; Yang, Deshan; Low, Daniel A; Jiang, Steve B

    2011-09-21

    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

  13. Synthesis and antifungal evaluation of PCA amide analogues.

    Science.gov (United States)

    Qin, Chuan; Yu, Di-Ya; Zhou, Xu-Dong; Zhang, Min; Wu, Qing-Lai; Li, Jun-Kai

    2018-04-18

    To improve the physical and chemical properties of phenazine-1-carboxylic acid (PCA) and find higher antifungal compounds, a series of PCA amide analogues were designed and synthesized and their structures were confirmed by 1 H NMR, HRMS, and X-ray. Most compounds showed some antifungal activities in vitro. Particularly, compound 3d exhibited inhibition effect against Pyriculariaoryzac Cavgra with EC 50 value of 28.7 μM and compound 3q exhibited effect against Rhizoctonia solani with EC 50 value of 24.5 μM, more potently active than that of the positive control PCA with its EC 50 values of 37.3 μM (Pyriculariaoryzac Cavgra) and 33.2 μM (Rhizoctonia solani), respectively.

  14. On a PCA-based lung motion model

    International Nuclear Information System (INIS)

    Li Ruijiang; Lewis, John H; Jia Xun; Jiang, Steve B; Zhao Tianyu; Wuenschel, Sara; Lamb, James; Yang Deshan; Low, Daniel A; Liu Weifeng

    2011-01-01

    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

  15. Adaptive PCA based fault diagnosis scheme in imperial smelting process.

    Science.gov (United States)

    Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin

    2014-09-01

    In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Recent Improvements to the Calibration Models for RXTE/PCA

    Science.gov (United States)

    Jahoda, K.

    2008-01-01

    We are updating the calibration of the PCA to correct for slow variations, primarily in energy to channel relationship. We have also improved the physical model in the vicinity of the Xe K-edge, which should increase the reliability of continuum fits above 20 keV. The improvements to the matrix are especially important to simultaneous observations, where the PCA is often used to constrain the continuum while other higher resolution spectrometers are used to study the shape of lines and edges associated with Iron.

  17. Principal Component Analysis Based Two-Dimensional (PCA-2D) Correlation Spectroscopy: PCA Denoising for 2D Correlation Spectroscopy

    International Nuclear Information System (INIS)

    Jung, Young Mee

    2003-01-01

    Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra

  18. Metoclopramide improves the quality of tramadol PCA indistinguishable to morphine PCA: a prospective, randomized, double blind clinical comparison.

    Science.gov (United States)

    Pang, Weiwu; Liu, Yu-Cheng; Maboudou, Edgard; Chen, Tom Xianxiu; Chois, John M; Liao, Cheng-Chun; Wu, Rick Sai-Chuen

    2013-09-01

    Multimodal analgesia has been effectively used in postoperative pain control. Tramadol can be considered "multimodal" because it has two main mechanisms of action, an opioid agonist and a reuptake inhibitor of norepinephrine and serotonin. Tramadol is not as commonly used as morphine due to the increased incidence of postoperative nausea and vomiting (PONV). As metoclopramide is an antiemetic and an analgesic, it was hypothesized that when added to reduce PONV, metoclopromide may enhance the multimodal feature of tramadol by the analgesic property of metoclopramide. Therefore, the effectiveness of postoperative patient-controlled analgesia (PCA) with morphine was compared against PCA with combination of tramadol and metoclopramide. A prospective, randomized, double blind clinical trial. Academic pain service of a university hospital. Sixty patients undergoing elective total knee arthroplasty with general anesthesia. Sixty patients were randomly divided into Group M and Group T. In a double-blinded fashion, Group M received intraoperative 0.2 mg/kg morphine and postoperative PCA with 1 mg morphine per bolus, whereas Group T received intraoperative tramadol 2.5 mg/kg and postoperative PCA with 20 mg tramadol plus 1 mg metoclopramide per bolus. Lockout interval was 5 minutes in both groups. Pain scale, satisfaction rate, analgesic consumption, PCA demand, and side effects were recorded by a blind investigator. These two groups displayed no statistically significant difference between the items and variables evaluated. This combination provides analgesia equivalent to that of morphine and can be used as an alternative to morphine PCA. Wiley Periodicals, Inc.

  19. Linking PCA and time derivatives of dynamic systems

    NARCIS (Netherlands)

    Stanimirovic, Olja; Hoefsloot, Huub C. J.; de Bokx, Pieter K.; Smilde, Age K.

    2006-01-01

    Low dimensional approximate descriptions of the high dimensional phase space of dynamic processes are very useful. Principal component analysis (PCA) is the most used technique to find the low dimensional subspace of interest. Here, it will be shown that mean centering of the process data across

  20. Copenhagen uPAR prostate cancer (CuPCa) database

    DEFF Research Database (Denmark)

    Lippert, Solvej; Berg, Kasper D; Høyer-Hansen, Gunilla

    2016-01-01

    AIM: Urokinase plasminogen activator receptor (uPAR) plays a central role during cancer invasion by facilitating pericellular proteolysis. We initiated the prospective 'Copenhagen uPAR Prostate Cancer' study to investigate the significance of uPAR levels in prostate cancer (PCa) patients. METHODS...

  1. The in-reactor deformation of the PCA alloy

    International Nuclear Information System (INIS)

    Puigh, R.J.

    1986-04-01

    The swelling and in-reactor creep behaviors of the PCA alloy have been determined from the irradiation of pressurized tube specimens in the FFTF reactor. These data have been obtained to a peak neutron fluence corresponding to approximately 80 dpa in the FFTF reactor for irradiation temperatures between 400 and 750 0 C. Diametral measurements performed on the unstressed specimens indicate the possible onset of swelling in the PCA alloy for irradiation temperatures between 400 and 550 0 C and at a neutron fluence corresponding to ∼50 dpa. The creep data suggest a non-linear fluence dependence and linear stress dependence (for hoop stresses less than 100 MPa) which is consistent with the in-reactor creep behavior of many cold worked austenitic stainless steels. These PCA creep data are compared to available 316 SS in-reactor creep data. The in-reactor creep strains for PCA are significantly less than observed in 20% cold worked 316 SS over the temperature ranges and fluences investigated

  2. Neutron spectral characterization of the PCA-PV benchmark facility

    International Nuclear Information System (INIS)

    Stallmann, F.W.; Kam, F.B.K.; Fabry, A.

    1980-01-01

    The Pool Critical Assembly (PCA) at the Oak Ridge National Laboratory is being used to generate the PCA-PV benchmark neutron field. A configuration consisting of steel blocks and water gaps is used to simulate the thermal shield pressure vessel configurations in power reactors. The distances between the steel blocks can be changed so that the penetration of neutrons through water and steel can be determined and compared for many different configurations. Easy access and low flux levels make it possible to conduct extensive measurements using active and passive neutron dosimetry, which are impossible to perform in commercial reactors. The clean core and simple geometry facilitates neutron transport calculations which can be validated in detail by comparison with measurements. A facility which has the same configuration of water and steel as the PCA-PV facility but contains test specimens for materials testing, will be irradiated in the higher fluxes at the Oak Ridge Research Reactor. Using the results from the PCA-PV facility, the correlation between neutron flux-fluences and radiation damage in steel can be established. This facility is being discussed in a separate paper

  3. ECG-derived respiration methods: adapted ICA and PCA.

    Science.gov (United States)

    Tiinanen, Suvi; Noponen, Kai; Tulppo, Mikko; Kiviniemi, Antti; Seppänen, Tapio

    2015-05-01

    Respiration is an important signal in early diagnostics, prediction, and treatment of several diseases. Moreover, a growing trend toward ambulatory measurements outside laboratory environments encourages developing indirect measurement methods such as ECG derived respiration (EDR). Recently, decomposition techniques like principal component analysis (PCA), and its nonlinear version, kernel PCA (KPCA), have been used to derive a surrogate respiration signal from single-channel ECG. In this paper, we propose an adapted independent component analysis (AICA) algorithm to obtain EDR signal, and extend the normal linear PCA technique based on the best principal component (PC) selection (APCA, adapted PCA) to improve its performance further. We also demonstrate that the usage of smoothing spline resampling and bandpass-filtering improve the performance of all EDR methods. Compared with other recent EDR methods using correlation coefficient and magnitude squared coherence, the proposed AICA and APCA yield a statistically significant improvement with correlations 0.84, 0.82, 0.76 and coherences 0.90, 0.91, 0.85 between reference respiration and AICA, APCA and KPCA, respectively. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

  4. A Hold-out method to correct PCA variance inflation

    DEFF Research Database (Denmark)

    Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Hansen, Lars Kai

    2012-01-01

    In this paper we analyze the problem of variance inflation experienced by the PCA algorithm when working in an ill-posed scenario where the dimensionality of the training set is larger than its sample size. In an earlier article a correction method based on a Leave-One-Out (LOO) procedure...

  5. Effects of PCA and DMAE on the namatode Caenorhabditis briggsae.

    Science.gov (United States)

    Zuckerman, B M; Barrett, K A

    1978-04-01

    Concentration of 6.8 mM DMAE did not retard age pigment accumulation in Caenorhabditis briggsae. However, when the nematodes were exposed to 6.8 mM PCA + 6.8 mM DMAE combined, the accumulation of age pigment was significantly retarded. A combination of 3.4 mM DMAE + 3.4 mM PCA had no effect on age pigment. It is concluded from this study that PCA and DMAE act in concert to produce the observed effect on age pigment. In respect to this parameter neither molecule was effective alone. The results indicate that the effect of centrophenoxine on age pigment might be enhanced by retarding the hydrolysis of centrophenoxine. The accumulation of electron dense aggregates, thought to be aggregates of cross-linked molecules, was reduced by 6.8 PCA + 6.8 DMAE. It is suggested that centrophenoxine be tested for its ability to remove random, unwanted cross-linkages in higher animals.

  6. Tracking image features with PCA-SURF descriptors

    CSIR Research Space (South Africa)

    Pancham, A

    2015-05-01

    Full Text Available IAPR International Conference on Machine Vision Applications, May 18-22, 2015, Tokyo, JAPAN Tracking Image Features with PCA-SURF Descriptors Ardhisha Pancham CSIR, UKZN South Africa apancham@csir.co.za Daniel Withey CSIR South Africa...

  7. Optimization of CNC end milling process parameters using PCA ...

    African Journals Online (AJOL)

    Optimization of CNC end milling process parameters using PCA-based Taguchi method. ... International Journal of Engineering, Science and Technology ... To meet the basic assumption of Taguchi method; in the present work, individual response correlations have been eliminated first by means of Principal Component ...

  8. [An improved low spectral distortion PCA fusion method].

    Science.gov (United States)

    Peng, Shi; Zhang, Ai-Wu; Li, Han-Lun; Hu, Shao-Xing; Meng, Xian-Gang; Sun, Wei-Dong

    2013-10-01

    Aiming at the spectral distortion produced in PCA fusion process, the present paper proposes an improved low spectral distortion PCA fusion method. This method uses NCUT (normalized cut) image segmentation algorithm to make a complex hyperspectral remote sensing image into multiple sub-images for increasing the separability of samples, which can weaken the spectral distortions of traditional PCA fusion; Pixels similarity weighting matrix and masks were produced by using graph theory and clustering theory. These masks are used to cut the hyperspectral image and high-resolution image into some sub-region objects. All corresponding sub-region objects between the hyperspectral image and high-resolution image are fused by using PCA method, and all sub-regional integration results are spliced together to produce a new image. In the experiment, Hyperion hyperspectral data and Rapid Eye data were used. And the experiment result shows that the proposed method has the same ability to enhance spatial resolution and greater ability to improve spectral fidelity performance.

  9. Chemical fingerprinting of petroleum biomakers using time warping and PCA

    DEFF Research Database (Denmark)

    Christensen, Jan H.; Tomasi, Giorgio; Hansen, Asger B.

    2005-01-01

    A new method for chemical fingerprinting of petroleum biomakers is described. The method consists of GC-MS analysis, preprocessing of GC-MS chromatograms, and principal component analysis (PCA) of selected regions. The preprocessing consists of baseline removal by derivatization, normalization...

  10. Parent-controlled PCA for pain management in pediatric oncology: is it safe?

    Science.gov (United States)

    Anghelescu, Doralina L; Faughnan, Lane G; Oakes, Linda L; Windsor, Kelley B; Pei, Deqing; Burgoyne, Laura L

    2012-08-01

    Patient-controlled analgesia offers safe and effective pain control for children who can self-administer medication. Some children may not be candidates for patient-controlled analgesia (PCA) unless a proxy can administer doses. The safety of proxy-administered PCA has been studied, but the safety of parent-administered PCA in children with cancer has not been reported. In this study, we compare the rate of complications in PCA by parent proxy versus PCA by clinician (nurse) proxy and self-administered PCA. Our pediatric institution's quality improvement database was reviewed for adverse events associated with PCA from 2004 through 2010. Each PCA day was categorized according to patient or proxy authorization. Data from 6151 PCA observation days were included; 61.3% of these days were standard PCA, 23.5% were parent-proxy PCA, and 15.2% were clinician-proxy PCA days. The mean duration of PCA use was 12.1 days, and the mean patient age was 12.3 years. The mean patient age was lower in the clinician-proxy (9.4 y) and parent-proxy (5.1 y) groups, respectively. The complication rate was lowest in the parent-proxy group (0.62%). We found that proxy administration of PCA by authorized parents is as safe as clinician administered and standard PCA at our pediatric institution.

  11. Parent-Controlled PCA for Pain Management in Pediatric Oncology: Is it Safe?

    OpenAIRE

    Anghelescu, Doralina L.; Faughnan, Lane G.; Oakes, Linda L.; Windsor, Kelley B.; Pei, Deqing; Burgoyne, Laura L.

    2012-01-01

    Patient-controlled analgesia offers safe and effective pain control for children who can self-administer medication. Some children may not be candidates for PCA unless a proxy can administer doses. The safety of proxy-administered PCA has been studied, but the safety of parent-administered PCA in children with cancer has not been reported. In this study we compare the rate of complications in PCA by parent proxy versus PCA by clinician (nurse) proxy and self-administered PCA. Our pediatric in...

  12. F-15 PCA (Propulsion Controlled Aircraft) Simulation Cockpit

    Science.gov (United States)

    1990-01-01

    The F-15 PCA (Propulsion Controlled Aircraft) simulation was used from 1990 to 1993. It was used for the development of propulsion algorithms and piloting techniques (using throttles only) to be used for emergency flight control in the advent of a major flight control system failure on a multi-engine aircraft. Following this program with the Dryden F-15, similiar capabilities were developed for other aircraft, such as the B-720, Lear 24, B-727, C-402, and B-747.

  13. Technology Marketing using PCA , SOM, and STP Strategy Modeling

    OpenAIRE

    Sunghae Jun

    2011-01-01

    Technology marketing is a total processing about identifying and meeting the technological needs of human society. Most technology results exist in intellectual properties like patents. In our research, we consider patent document as a technology. So patent data are analyzed by Principal Component Analysis (PCA) and Self Organizing Map (SOM) for STP(Segmentation, Targeting, and Positioning) strategy modeling. STP is a popular approach for developing marketing strategies. We use STP strategy m...

  14. Imaging network level language recovery after left PCA stroke.

    Science.gov (United States)

    Sebastian, Rajani; Long, Charltien; Purcell, Jeremy J; Faria, Andreia V; Lindquist, Martin; Jarso, Samson; Race, David; Davis, Cameron; Posner, Joseph; Wright, Amy; Hillis, Argye E

    2016-05-11

    The neural mechanisms that support aphasia recovery are not yet fully understood. Our goal was to evaluate longitudinal changes in naming recovery in participants with posterior cerebral artery (PCA) stroke using a case-by-case analysis. Using task based and resting state functional magnetic resonance imaging (fMRI) and detailed language testing, we longitudinally studied the recovery of the naming network in four participants with PCA stroke with naming deficits at the acute (0 week), sub acute (3-5 weeks), and chronic time point (5-7 months) post stroke. Behavioral and imaging analyses (task related and resting state functional connectivity) were carried out to elucidate longitudinal changes in naming recovery. Behavioral and imaging analysis revealed that an improvement in naming accuracy from the acute to the chronic stage was reflected by increased connectivity within and between left and right hemisphere "language" regions. One participant who had persistent moderate naming deficit showed weak and decreasing connectivity longitudinally within and between left and right hemisphere language regions. These findings emphasize a network view of aphasia recovery, and show that the degree of inter- and intra- hemispheric balance between the language-specific regions is necessary for optimal recovery of naming, at least in participants with PCA stroke.

  15. PCA3 Silencing Sensitizes Prostate Cancer Cells to Enzalutamide-mediated Androgen Receptor Blockade.

    Science.gov (United States)

    Özgür, Emre; Celik, Ayca Iribas; Darendeliler, Emin; Gezer, Ugur

    2017-07-01

    Prostate cancer (PCa) is an androgen-dependent disease. Novel anti-androgens (i.e. enzalutamide) have recently been developed for the treatment of patients with metastatic castration-resistant prostate cancer (CRPC). Evidence is accumulating that prostate cancer antigen 3 (PCA3) is involved in androgen receptor (AR) signaling. Here, in combination with enzalutamide-mediated AR blockade, we investigated the effect of PCA3 targeting on the viability of PCa cells. In hormone-sensitive LNCaP cells, AR-overexpressing LNCaP-AR + cells and VCaP cells (representing CRPC), PCA3 was silenced using siRNA oligonucleotides. Gene expression and cell viability was assessed in PCA3-silenced and/or AR-blocked cells. PCA3 targeting reduced the expression of AR-related genes (i.e. prostate-specific antigen (PSA) and prostate-specific transcript 1 (non-protein coding) (PCGEM1)) and potentiated the effect of enzalutamide. Proliferation of PCa cells was suppressed upon PCA3 silencing with a greater effect in LNCaP-AR + cells. Furthermore, PCA3 silencing sensitized PCa cells to enzalutamide-induced loss of cell growth. PCA3, as a therapeutic target in PCa, might be used to potentiate AR antagonists. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

  16. Prostate cancer (PCa) risk variants and risk of fatal PCa in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium.

    Science.gov (United States)

    Shui, Irene M; Lindström, Sara; Kibel, Adam S; Berndt, Sonja I; Campa, Daniele; Gerke, Travis; Penney, Kathryn L; Albanes, Demetrius; Berg, Christine; Bueno-de-Mesquita, H Bas; Chanock, Stephen; Crawford, E David; Diver, W Ryan; Gapstur, Susan M; Gaziano, J Michael; Giles, Graham G; Henderson, Brian; Hoover, Robert; Johansson, Mattias; Le Marchand, Loic; Ma, Jing; Navarro, Carmen; Overvad, Kim; Schumacher, Fredrick R; Severi, Gianluca; Siddiq, Afshan; Stampfer, Meir; Stevens, Victoria L; Travis, Ruth C; Trichopoulos, Dimitrios; Vineis, Paolo; Mucci, Lorelei A; Yeager, Meredith; Giovannucci, Edward; Kraft, Peter

    2014-06-01

    Screening and diagnosis of prostate cancer (PCa) is hampered by an inability to predict who has the potential to develop fatal disease and who has indolent cancer. Studies have identified multiple genetic risk loci for PCa incidence, but it is unknown whether they could be used as biomarkers for PCa-specific mortality (PCSM). To examine the association of 47 established PCa risk single-nucleotide polymorphisms (SNPs) with PCSM. We included 10 487 men who had PCa and 11 024 controls, with a median follow-up of 8.3 yr, during which 1053 PCa deaths occurred. The main outcome was PCSM. The risk allele was defined as the allele associated with an increased risk for PCa in the literature. We used Cox proportional hazards regression to calculate the hazard ratios of each SNP with time to progression to PCSM after diagnosis. We also used logistic regression to calculate odds ratios for each risk SNP, comparing fatal PCa cases to controls. Among the cases, we found that 8 of the 47 SNPs were significantly associated (pPCa, but most did not differentiate between fatal and nonfatal PCa. Rs11672691 and rs10993994 were associated with both fatal and nonfatal PCa, while rs6465657, rs7127900, rs2735839, and rs13385191 were associated with nonfatal PCa only. Eight established risk loci were associated with progression to PCSM after diagnosis. Twenty-two SNPs were associated with fatal PCa incidence, but most did not differentiate between fatal and nonfatal PCa. The relatively small magnitudes of the associations do not translate well into risk prediction, but these findings merit further follow-up, because they may yield important clues about the complex biology of fatal PCa. In this report, we assessed whether established PCa risk variants could predict PCSM. We found eight risk variants associated with PCSM: One predicted an increased risk of PCSM, while seven were associated with decreased risk. Larger studies that focus on fatal PCa are needed to identify more markers that

  17. New genomic structure for prostate cancer specific gene PCA3 within BMCC1: implications for prostate cancer detection and progression.

    Directory of Open Access Journals (Sweden)

    Raymond A Clarke

    Full Text Available The prostate cancer antigen 3 (PCA3/DD3 gene is a highly specific biomarker upregulated in prostate cancer (PCa. In order to understand the importance of PCA3 in PCa we investigated the organization and evolution of the PCA3 gene locus.We have employed cDNA synthesis, RTPCR and DNA sequencing to identify 4 new transcription start sites, 4 polyadenylation sites and 2 new differentially spliced exons in an extended form of PCA3. Primers designed from these novel PCA3 exons greatly improve RT-PCR based discrimination between PCa, PCa metastases and BPH specimens. Comparative genomic analyses demonstrated that PCA3 has only recently evolved in an anti-sense orientation within a second gene, BMCC1/PRUNE2. BMCC1 has been shown previously to interact with RhoA and RhoC, determinants of cellular transformation and metastasis, respectively. Using RT-PCR we demonstrated that the longer BMCC1-1 isoform - like PCA3 - is upregulated in PCa tissues and metastases and in PCa cell lines. Furthermore PCA3 and BMCC1-1 levels are responsive to dihydrotestosterone treatment.Upregulation of two new PCA3 isoforms in PCa tissues improves discrimination between PCa and BPH. The functional relevance of this specificity is now of particular interest given PCA3's overlapping association with a second gene BMCC1, a regulator of Rho signalling. Upregulation of PCA3 and BMCC1 in PCa has potential for improved diagnosis.

  18. 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

    OpenAIRE

    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

    2011-01-01

    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 const...

  19. Clinical utility of the PCA3 urine assay in European men scheduled for repeat biopsy.

    NARCIS (Netherlands)

    Haese, A.; Taille, A. De La; Poppel, H. van; Marberger, M.; Stenzl, A.; Mulders, P.F.A.; Huland, H.; Abbou, C.C.; Remzi, M.; Tinzl, M.; Feyerabend, S.; Stillebroer, A.B.; Gils, M.P.M.Q.; Schalken, J.A.

    2008-01-01

    BACKGROUND: The Prostate CAncer gene 3 (PCA3) assay has shown promise as an aid in prostate cancer (pCA) diagnosis in identifying men with a high probability of a positive (repeat) biopsy. OBJECTIVE: This study evaluated the clinical utility of the PROGENSA PCA3 assay. DESIGN, SETTING, AND

  20. Biometric identification based on feature fusion with PCA and SVM

    Science.gov (United States)

    Lefkovits, László; Lefkovits, Szidónia; Emerich, Simina

    2018-04-01

    Biometric identification is gaining ground compared to traditional identification methods. Many biometric measurements may be used for secure human identification. The most reliable among them is the iris pattern because of its uniqueness, stability, unforgeability and inalterability over time. The approach presented in this paper is a fusion of different feature descriptor methods such as HOG, LIOP, LBP, used for extracting iris texture information. The classifiers obtained through the SVM and PCA methods demonstrate the effectiveness of our system applied to one and both irises. The performances measured are highly accurate and foreshadow a fusion system with a rate of identification approaching 100% on the UPOL database.

  1. Innovative Comparison of Transient Ignition Temperature at the Booster Interface, New Stainless Steel Pyrovalve Primer Chamber Assembly "V" (PCA) Design Versus the Current Aluminum "Y" PCA Design

    Science.gov (United States)

    Saulsberry, Regor L.; McDougle, Stephen H.; Garcia,Roberto; Johnson, Kenneth L.; Sipes, William; Rickman, Steven; Hosangadi, Ashvin

    2011-01-01

    An assessment of four spacecraft pyrovalve anomalies that occurred during ground testing was conducted by the NASA Engineering & Safety Center (NESC) in 2008. In all four cases, a common aluminum (Al) primer chamber assembly (PCA) was used with dual NASA Standard Initiators (NSIs) and the nearly simultaneous (separated by less than 80 microseconds) firing of both initiators failed to ignite the booster charge. The results of the assessment and associated test program were reported in AIAA Paper AIAA-2008-4798, NESC Independent Assessment of Pyrovalve Ground Test Anomalies. As a result of the four Al PCA anomalies, and the test results and findings of the NESC assessment, the Mars Science Laboratory (MSL) project team decided to make changes to the PCA. The material for the PCA body was changed from aluminum (Al) to stainless steel (SS) to avoid melting, distortion, and potential leakage of the NSI flow passages when the device functioned. The flow passages, which were interconnected in a Y-shaped configuration (Y-PCA) in the original design, were changed to a V-shaped configuration (V-PCA). The V-shape was used to more efficiently transfer energy from the NSIs to the booster. Development and qualification testing of the new design clearly demonstrated faster booster ignition times compared to the legacy AL Y-PCA design. However, the final NESC assessment report recommended that the SS V-PCA be experimentally characterized and quantitatively compared to the Al Y-PCA design. This data was deemed important for properly evaluating the design options for future NASA projects. This test program has successfully quantified the improvement of the SS V-PCA over the Al Y-PCA. A phase B of the project was also conducted and evaluated the effect of firing command skew and enlargement of flame channels to further assist spacecraft applications.

  2. On the use of A PCA as a multichannel time analyzer

    International Nuclear Information System (INIS)

    Adib, M.; Abdelkawy, A.; Abuelela, M.; Habib, N.; Wahba, M.; Salama, F.

    1992-01-01

    PCA and PCA-11 software programmes have been used to utilize the operation of the nucleus personal computer analyzer PCA-8000 in its multichannel scaler (MCS) mode. The operating condition of PCA-8000 were selected to match the time-of-flight (TOF) spectrometer which is in operation at the ET-RR-1 reactor. The results of measuring the main parameters of PCA-8000 operating in its MCS mode showed that it can be successfully used as a multichannel time analyzer.5 fig

  3. Performance evaluation of PCA-based spike sorting algorithms.

    Science.gov (United States)

    Adamos, Dimitrios A; Kosmidis, Efstratios K; Theophilidis, George

    2008-09-01

    Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

  4. MD-11 PCA - View of aircraft on ramp

    Science.gov (United States)

    1995-01-01

    This McDonnell Douglas MD-11 is taxiing to a position on the flightline at NASA's Dryden Flight Research Center, Edwards, California, following its completion of the first and second landings ever performed by a transport aircraft under engine power only (on Aug. 29, 1995). The milestone flight, with NASA research pilot and former astronaut Gordon Fullerton at the controls, was part of a NASA project to develop a computer-assisted engine control system that enables a pilot to land a plane safely when its normal control surfaces are disabled. The Propulsion-Controlled Aircraft (PCA) system uses standard autopilot controls already present in the cockpit, together with the new programming in the aircraft's flight control computers. The PCA concept is simple. For pitch control, the program increases thrust to climb and reduces thrust to descend. To turn right, the autopilot increases the left engine thrust while decreasing the right engine thrust. The initial Propulsion-Controlled Aircraft studies by NASA were carried out at Dryden with a modified twin-engine F-15 research aircraft.

  5. MD-11 PCA - Closeup view of aircraft on ramp

    Science.gov (United States)

    1995-01-01

    This McDonnell Douglas MD-11 has taxied to a position on the flightline at NASA's Dryden Flight Research Center, Edwards, California, following its completion of the first and second landings ever performed by a transport aircraft under engine power only (on Aug. 29, 1995). The milestone flight, with NASA research pilot and former astronaut Gordon Fullerton at the controls, was part of a NASA project to develop a computer-assisted engine control system that enables a pilot to land a plane safely when its normal control surfaces are disabled. The Propulsion-Controlled Aircraft (PCA) system uses standard autopilot controls already present in the cockpit, together with the new programming in the aircraft's flight control computers. The PCA concept is simple. For pitch control, the program increases thrust to climb and reduces thrust to descend. To turn right, the autopilot increases the left engine thrust while decreasing the right engine thrust. The initial Propulsion-Controlled Aircraft studies by NASA were carried out at Dryden with a modified twin-engine F-15 research aircraft.

  6. PCA safety data review after clinical decision support and smart pump technology implementation.

    Science.gov (United States)

    Prewitt, Judy; Schneider, Susan; Horvath, Monica; Hammond, Julia; Jackson, Jason; Ginsberg, Brian

    2013-06-01

    Medication errors account for 20% of medical errors in the United States with the largest risk at prescribing and administration. Analgesics or opioids are frequently used medications that can be associated with patient harm when prescribed or administered improperly. In an effort to decrease medication errors, Duke University Hospital implemented clinical decision support via computer provider order entry (CPOE) and "smart pump" technology, 2/2008, with the goal to decrease patient-controlled analgesia (PCA) adverse events. This project evaluated PCA safety events, reviewing voluntary report system and adverse drug events via surveillance (ADE-S), on intermediate and step-down units preimplementation and postimplementation of clinical decision support via CPOE and PCA smart pumps for the prescribing and administration of opioids therapy in the adult patient requiring analgesia for acute pain. Voluntary report system and ADE-S PCA events decreased based upon 1000 PCA days; ADE-S PCA events per 1000 PCA days decreased 22%, from 5.3 (pre) to 4.2 (post) (P = 0.09). Voluntary report system events decreased 72%, from 2.4/1000 PCA days (pre) to 0.66/1000 PCA days (post) and was statistically significant (P PCA events between time periods in both the ADE-S and voluntary report system data, thus supporting the recommendation of clinical decision support via CPOE and PCA smart pump technology.

  7. Fault detection of feed water treatment process using PCA-WD with parameter optimization.

    Science.gov (United States)

    Zhang, Shirong; Tang, Qian; Lin, Yu; Tang, Yuling

    2017-05-01

    Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T 2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA-WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T 2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an automatic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Posterior cerebral artery involvement in moyamoya disease: initial infarction and angle between PCA and basilar artery.

    Science.gov (United States)

    Lee, Ji Yeoun; Kim, Seung-Ki; Cheon, Jung-Eun; Choi, Jung Won; Phi, Ji Hoon; Kim, In-One; Cho, Byung-Kyu; Wang, Kyu-Chang

    2013-12-01

    Moyamoya disease (MMD) is a chronic cerebrovascular occlusive disease, and progressive involvement of the posterior cerebral artery (PCA) has been reported. However, majority of MMD articles are presenting classic anterior circulation related issues. This study investigates the preoperative factors related to the long-term outcome of posterior circulation in MMD. Retrospective review of 88 MMD patients (166 PCAs in either hemisphere) without symptomatic disease involvement of PCA at initial diagnosis was done. Data at initial diagnosis regarding age, presence of infarction, status of the PCA, type of posterior communicating artery, and the angle between PCA and basilar artery were reviewed. Progressive stenosis of PCA was evaluated by symptom or radiological imaging during follow up. During an average follow up of 8.3 years, 29 out of 166 (18 %) evaluated PCAs showed progressive disease involvement. The average time of progression from the initial operation was 4.9 years, with the latest onset at 10.8 years. The patients who showed progressive stenosis of the PCA tended to be younger, present with infarction, have smaller angle between PCA and basilar artery, and have asymptomatic stenosis of the PCA at initial presentation. However, multivariate analysis confirmed only the presence of initial infarction and a smaller angle between PCA and basilar artery to be significantly associated with progressive stenosis of PCA. Involvement of PCA in MMD may occur in a delayed fashion, years after the completion of revascularization of anterior circulation. Persistent long-term follow-up regarding the posterior circulation is recommended.

  9. Identification of an IL-1-induced gene expression pattern in AR+ PCa cells that mimics the molecular phenotype of AR- PCa cells.

    Science.gov (United States)

    Thomas-Jardin, Shayna E; Kanchwala, Mohammed S; Jacob, Joan; Merchant, Sana; Meade, Rachel K; Gahnim, Nagham M; Nawas, Afshan F; Xing, Chao; Delk, Nikki A

    2018-06-01

    In immunosurveillance, bone-derived immune cells infiltrate the tumor and secrete inflammatory cytokines to destroy cancer cells. However, cancer cells have evolved mechanisms to usurp inflammatory cytokines to promote tumor progression. In particular, the inflammatory cytokine, interleukin-1 (IL-1), is elevated in prostate cancer (PCa) patient tissue and serum, and promotes PCa bone metastasis. IL-1 also represses androgen receptor (AR) accumulation and activity in PCa cells, yet the cells remain viable and tumorigenic; suggesting that IL-1 may also contribute to AR-targeted therapy resistance. Furthermore, IL-1 and AR protein levels negatively correlate in PCa tumor cells. Taken together, we hypothesize that IL-1 reprograms AR positive (AR + ) PCa cells into AR negative (AR - ) PCa cells that co-opt IL-1 signaling to ensure AR-independent survival and tumor progression in the inflammatory tumor microenvironment. LNCaP and PC3 PCa cells were treated with IL-1β or HS-5 bone marrow stromal cell (BMSC) conditioned medium and analyzed by RNA sequencing and RT-QPCR. To verify genes identified by RNA sequencing, LNCaP, MDA-PCa-2b, PC3, and DU145 PCa cell lines were treated with the IL-1 family members, IL-1α or IL-1β, or exposed to HS-5 BMSC in the presence or absence of Interleukin-1 Receptor Antagonist (IL-1RA). Treated cells were analyzed by western blot and/or RT-QPCR. Comparative analysis of sequencing data from the AR + LNCaP PCa cell line versus the AR - PC3 PCa cell line reveals an IL-1-conferred gene suite in LNCaP cells that is constitutive in PC3 cells. Bioinformatics analysis of the IL-1 regulated gene suite revealed that inflammatory and immune response pathways are primarily elicited; likely facilitating PCa cell survival and tumorigenicity in an inflammatory tumor microenvironment. Our data supports that IL-1 reprograms AR + PCa cells to mimic AR - PCa gene expression patterns that favor AR-targeted treatment resistance and cell survival. © 2018 Wiley

  10. Cost effectiveness analysis of screening in the early diagnosis of prostate cancer (PCA)

    International Nuclear Information System (INIS)

    Mueller-Lisse, U.G.; Mueller-Lisse, U.L.

    2002-01-01

    Purpose. The authors attempted to provide an overview of current concepts and the status of research in the field of cost effectiveness analysis (CEA) of screening for prostate cancer (PCA).Material and methods. Basic concepts and methods of CEA were reviewed. Examples of CEA-related studies of PCA were obtained from pertinent literature through medical databases.Results. Screening for PCA has so far been restricted to limited groups of health care recipients, usually within the framework of clinical trials. In those trials, screening for PCA usually results in higher numbers of PCAs being detected at lower average stages in a given population. As a consequence of screening, the rate of potentially curable PCAs increases. However, it has not yet been demonstrated that screening for PCA decreases PCA-related mortality or morbidity from metastatic PCA. On the other hand, additional costs are associated with the screening measure and with increased use of resources for diagnosis and treatment of the additional PCAs detected through screening.Conclusions. Throughout the European Union and North America, mass screening for PCA has not been implemented. This may chiefly be due to the current lack of information on long term benefits of PCA screening, particularly disease-specific survival. Currently, major studies are underway to assess the effects of PCA screening and its cost effectiveness. These studies include the US-American prostate, lung, colon and ovary trials (PLCO) and the European randomised study of Screening for Prostate Cancer (ERSPC). (orig.) [de

  11. Preliminary PCA/TT Results on MRO CRISM Multispectral Images

    Science.gov (United States)

    Klassen, David R.; Smith, M. D.

    2010-10-01

    Mars Reconnaissance Orbiter arrived at Mars in March 2006 and by September had achieved its science-phase orbit with the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) beginning its visible to near-infrared (VIS/NIR) spectral imaging shortly thereafter. One goal of CRISM is to fill in the spatial gaps between the various targeted observations, eventually mapping the entire surface. Due to the large volume of data this would create, the instrument works in a reduced spectral sampling mode creating "multispectral” images. From these data we can create image cubes using 64 wavelengths from 0.410 to 3.923 µm. We present here our analysis of these multispectral mode data products using Principal Components Analysis (PCA) and Target Transformation (TT) [1]. Previous work with ground-based images [2-5] has shown that over an entire visible hemisphere, there are only three to four meaningful components using 32-105 wavelengths over 1.5-4.1 µm the first two are consistent over all temporal scales. The TT retrieved spectral endmembers show nearly the same level of consistency [5]. The preliminary work on the CRISM images cubes implies similar results; three to four significant principal components that are fairly consistent over time. These components are then used in TT to find spectral endmembers which can be used to characterize the surface reflectance for future use in radiative transfer cloud optical depth retrievals. We present here the PCA/TT results comparing the principal components and recovered endmembers from six reconstructed CRISM multi-spectral image cubes. References: [1] Bandfield, J. L., et al. (2000) JGR, 105, 9573. [2] Klassen, D. R. and Bell III, J. F. (2001) BAAS 33, 1069. [3] Klassen, D. R. and Bell III, J. F. (2003) BAAS, 35, 936. [4] Klassen, D. R., Wark, T. J., Cugliotta, C. G. (2005) BAAS, 37, 693. [5] Klassen, D. R. (2009) Icarus, 204, 32.

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

    Science.gov (United States)

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

    2014-04-01

    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.

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

    Science.gov (United States)

    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

  14. PCA-derived factors that may be predictive of postoperative pain in pediatric patients: a possible role for the PCA ratio.

    Science.gov (United States)

    McDonnell, Conor; Pehora, Carolyne; Crawford, Mark W

    2012-01-01

    No method exists to reliably predict which patients will develop severe postoperative pain. The authors hypothesized that data derived from patient-controlled analgesia (PCA) pumps (specifically the ratio of patient demands to pump deliveries) may predict which patients would develop severe pain after scoliosis repair. Quaternary, university-affiliated, pediatric hospital. Forty American Society of Anesthesiologists I-Il pediatric patients who had undergone elective scoliosis repair and had consented to recruitment to a randomized clinical trial investigating the effects of early morphine administration on remifentanil-induced hyperalgesia. To test the hypothesis of the current study, the authors calculated the PCA ratio of demand to delivery at every 4 hours throughout the first 24 hours after surgery for all the patients recruited to the original study. The authors compared calculated PCA ratios, numeric rating scale pain scores, and cumulative morphine consumption for those patients who developed severe postoperative pain and met the criteria for opioid rotation versus those patients who did not. Seven patients required opioid rotation from PCA morphine to PCA hydromorphone. Eight hours after surgery, the median PCA ratio for those seven patients (2.5[range, 1.8-4.3]) was significantly greater than that for all other recruited patients (1.3 [range, 0-2.7]; p PCA ratios of demand to delivery as early as 8 hours after surgery.

  15. 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

    Science.gov (United States)

    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

  16. Shallow-Land Buriable PCA-type austenitic stainless steel for fusion application

    International Nuclear Information System (INIS)

    Zucchetti, M.

    1991-01-01

    Neutron-induced activity in the PCA (Primary Candidate Alloy) austenitic stainless steel is examined, when used for first-wall components in a DEMO fusion reactor. Some low-activity definitions, based on different waste management and disposal concepts, are introduced. Activity in the PCA is so high that any recycling of the irradiated material can be excluded. Disposal of PCA radioactive wastes in Shallow-Land Buriable (SLB) is prevented as well. Mo, Nb and some impurity elements have to be removed or limited, in order to reduce the radioactivity of the PCA. Possible low-activity versions of the PCA are introduced (PCA-la); they meet the requirements for SLB and may also be recycled under certain conditions. (author)

  17. Improved k-t PCA Algorithm Using Artificial Sparsity in Dynamic MRI.

    Science.gov (United States)

    Wang, Yiran; Chen, Zhifeng; Wang, Jing; Yuan, Lixia; Xia, Ling; Liu, Feng

    2017-01-01

    The k - t principal component analysis ( k - t PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise amplification when the reduction factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse k - t PCA that combines the k - t PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional k - t PCA method by further eliminating the reconstruction error derived from complex subtraction of the sampled k - t space from the original reconstructed k - t space. The proposed method is tested through both simulations and in vivo datasets with different reduction factors. Compared to the standard k - t PCA algorithm, the sparse k - t PCA can improve the normalized root-mean-square error performance and the accuracy of temporal resolution. It is thus useful for rapid dynamic MR imaging.

  18. Patient perspectives of patient-controlled analgesia (PCA) and methods for improving pain control and patient satisfaction.

    Science.gov (United States)

    Patak, Lance S; Tait, Alan R; Mirafzali, Leela; Morris, Michelle; Dasgupta, Sunavo; Brummett, Chad M

    2013-01-01

    This study aimed to (1) identify patient-controlled analgesia (PCA) attributes that negatively impact patient satisfaction and ability to control pain while using PCA and (2) obtain data on patient perceptions of new PCA design features. We conducted a prospective survey study of postoperative pain control among patients using a PCA device. The survey was designed to evaluate patient satisfaction with pain control, understanding of PCA, difficulties using PCA, lockout-period management, and evaluation of new PCA design features. A total of 350 eligible patients completed the survey (91%). Patients who had difficulties using PCA were less satisfied (P PCA. Forty-nine percent of patients reported not knowing if they would receive medicine when they pushed the PCA button, and of these, 22% believed that this uncertainty made their pain worse. The majority of patients preferred the proposed PCA design features for easier use, including a light on the button, making it easier to find (57%), and a PCA button that vibrates (55%) or lights up (70%), alerting the patient that the PCA pump is able to deliver more medicine. A majority of patients, irrespective of their satisfaction with PCA, preferred a new PCA design. Certain attributes of current PCA technology may negatively impact patient experience, and modifications could potentially address these concerns and improve patient outcomes.

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

    Science.gov (United States)

    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

  20. Comparative analysis of the PCA3 gene expression in sediments and exosomes isolated from urine

    Directory of Open Access Journals (Sweden)

    D. S. Mikhaylenko

    2017-01-01

    Full Text Available Introduction. Prostate cancer (PCa is one of the common oncological diseases in men. Expression of the PCA3 gene in urine is currently used as a molecular genetic marker of PCa.Objective: to comparative analysis of the PCA3 expression in urine sediments and exosomes for the determination of the biomaterial, which allows detecting the PCA3 expression in more efficient manner.Materials and methods. The 12 patients with different stages of PCa and 8 control samples were examined.Results. The diagnostic accuracy of the PCA3 gene expression analysis in this cohort exceeded 90 %. We had not obtained significant differences in the sensitivity and specificity of the PCA3 hyperexpression in the urine sediments compared with exosomes. This result indicates in favor to using urine sediment for the PCA3 analysis as a biomaterial with less time-consuming sample preparation, although the possible advantage of exosomes for the analysis of the expression marker panels requires further studies.

  1. A Multi-Time Scale Morphable Software Milieu for Polymorphous Computing Architectures (PCA) - Composable, Scalable Systems

    National Research Council Canada - National Science Library

    Skjellum, Anthony

    2004-01-01

    Polymorphous Computing Architectures (PCA) rapidly "morph" (reorganize) software and hardware configurations in order to achieve high performance on computation styles ranging from specialized streaming to general threaded applications...

  2. PCA-1/ALKBH3 contributes to pancreatic cancer by supporting apoptotic resistance and angiogenesis.

    Science.gov (United States)

    Yamato, Ichiro; Sho, Masayuki; Shimada, Keiji; Hotta, Kiyohiko; Ueda, Yuko; Yasuda, Satoshi; Shigi, Naoko; Konishi, Noboru; Tsujikawa, Kazutake; Nakajima, Yoshiyuki

    2012-09-15

    The PCA-1/ALKBH3 gene implicated in DNA repair is expressed in several human malignancies but its precise contributions to cancer remain mainly unknown. In this study, we have determined its functions and clinical importance in pancreatic cancer. PCA-1/ALKBH3 functions in proliferation, apoptosis and angiogenesis were evaluated in human pancreatic cancer cells in vitro and in vivo. Further, PCA-1/ALKBH3 expression in 116 patients with pancreatic cancer was evaluated by immunohistochemistry. siRNA-mediated silencing of PCA-1/ALKBH3 expression induced apoptosis and suppressed cell proliferation. Conversely, overexpression of PCA-1/ALKBH3 increased anchorage-independent growth and invasiveness. In addition, PCA-1/ALKBH3 silencing downregulated VEGF expression and inhibited angiogenesis in vivo. Furthermore, immunohistochemical analysis showed that PCA-1/ALKBH3 expression was abundant in pancreatic cancer tissues, where it correlated with advanced tumor status, pathological stage and VEGF intensity. Importantly, patients with low positivity of PCA-1/ALKBH3 expression had improved postoperative prognosis compared with those with high positivity. Our results establish PCA-1/ALKBH3 as important gene in pancreatic cancer with potential utility as a therapeutic target in this fatal disease.

  3. Tensile properties of unirradiated PCA from room temperature to 7000C

    International Nuclear Information System (INIS)

    Braski, D.N.; Maziasz, P.J.

    1983-01-01

    The tensile properties of Prime Candidate Alloy (PCA) austenitic stainless steel after three different thermomechanical treatments were determined from room temperature to 700 0 C. The solution-annealed PCA had the lowest strength and highest ductility, while the reverse was true for the 25%-cold-worked material. The PCA containing titanium-rich MC particles fell between the other two heats. The cold-worked PCA had nearly the same tensile properties as cold-worked type 316 stainless steel. Both alloys showed ductility minima at 300 0 C

  4. Quality of Life and Sexual Health in the Aging of PCa Survivors.

    Science.gov (United States)

    Gacci, Mauro; Baldi, Elisabetta; Tamburrino, Lara; Detti, Beatrice; Livi, Lorenzo; De Nunzio, Cosimo; Tubaro, Andrea; Gravas, Stavros; Carini, Marco; Serni, Sergio

    2014-01-01

    Prostate cancer (PCa) is the most common malignancy in elderly men. The progressive ageing of the world male population will further increase the need for tailored assessment and treatment of PCa patients. The determinant role of androgens and sexual hormones for PCa growth and progression has been established. However, several trials on androgens and PCa are recently focused on urinary continence, quality of life, and sexual function, suggesting a new point of view on the whole endocrinological aspect of PCa. During aging, metabolic syndrome, including diabetes, hypertension, dyslipidemia, and central obesity, can be associated with a chronic, low-grade inflammation of the prostate and with changes in the sex steroid pathways. These factors may affect both the carcinogenesis processes and treatment outcomes of PCa. Any treatment for PCa can have a long-lasting negative impact on quality of life and sexual health, which should be assessed by validated self-reported questionnaires. In particular, sexual health, urinary continence, and bowel function can be worsened after prostatectomy, radiotherapy, or hormone treatment, mostly in the elderly population. In the present review we summarized the current knowledge on the role of hormones, metabolic features, and primary treatments for PCa on the quality of life and sexual health of elderly Pca survivors.

  5. Polymorphous Computing Architecture (PCA) Application Benchmark 1: Three-Dimensional Radar Data Processing

    National Research Council Canada - National Science Library

    Lebak, J

    2001-01-01

    The DARPA Polymorphous Computing Architecture (PCA) program is building advanced computer architectures that can reorganize their computation and communication structures to achieve better overall application performance...

  6. PCA/HEXTE Observations of Coma and A2319

    Science.gov (United States)

    Rephaeli, Yoel

    1998-01-01

    The Coma cluster was observed in 1996 for 90 ks by the PCA and HEXTE instruments aboard the RXTE satellite, the first simultaneous, pointing measurement of Coma in the broad, 2-250 keV, energy band. The high sensitivity achieved during this long observation allows precise determination of the spectrum. Our analysis of the measurements clearly indicates that in addition to the main thermal emission from hot intracluster gas at kT=7.5 keV, a second spectral component is required to best-fit the data. If thermal, it can be described with a temperature of 4.7 keV contributing about 20% of the total flux. The additional spectral component can also be described by a power-law, possibly due to Compton scattering of relativistic electrons by the CMB. This interpretation is based on the diffuse radio synchrotron emission, which has a spectral index of 2.34, within the range allowed by fits to the RXTE spectral data. A Compton origin of the measured nonthermal component would imply that the volume-averaged magnetic field in the central region of Coma is B =0.2 micro-Gauss, a value deduced directly from the radio and X-ray measurements (and thus free of the usual assumption of energy equipartition). Barring the presence of unknown systematic errors in the RXTE source or background measurements, our spectral analysis yields considerable evidence for Compton X-ray emission in the Coma cluster.

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

    Science.gov (United States)

    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.

  8. Developing and Evaluating Creativity Gamification Rehabilitation System: The Application of PCA-ANFIS Based Emotions Model

    Science.gov (United States)

    Su, Chung-Ho; Cheng, Ching-Hsue

    2016-01-01

    This study aims to explore the factors in a patient's rehabilitation achievement after a total knee replacement (TKR) patient exercises, using a PCA-ANFIS emotion model-based game rehabilitation system, which combines virtual reality (VR) and motion capture technology. The researchers combine a principal component analysis (PCA) and an adaptive…

  9. The relationship between Prostate CAncer gene 3 (PCA3) and prostate cancer significance

    NARCIS (Netherlands)

    van Poppel, Hein; Haese, Alexander; Graefen, Markus; de la Taille, Alexandre; Irani, Jacques; de Reijke, Theo; Remzi, Mesut; Marberger, Michael

    2012-01-01

    OBJECTIVE To evaluate the relationship between Prostate CAncer gene 3 (PCA3) and prostate cancer significance. PATIENTS AND METHODS Clinical data from two multi-centre European open-label, prospective studies evaluating the clinical utility of the PCA3 assay in guiding initial and repeat biopsy

  10. The role of PCA3 in the diagnosis of prostate cancer.

    NARCIS (Netherlands)

    Hessels, D.

    2010-01-01

    Serum PSA has shown to be the most valuable tool in the detection, staging and monitoring of prostate cancer (PCa). However, the substantial overlap in serum PSA values between men with non-malignant prostatic diseases and PCa is the limitation of PSA as a prostate tumor marker. In patients with

  11. Protocatechuic acid (PCA) induced a better antiviral effect by immune enhancement in SPF chickens.

    Science.gov (United States)

    Guo, Yongxia; Zhang, Qiang; Zuo, Zonghui; Chu, Jun; Xiao, Hongzhi; Javed, M Tariq; He, Cheng

    2018-01-01

    Protocatechuic acid (PCA) is an antiviral agent against Avian Influenza virus (AIV) and Infectious Bursal Disease (IBD) virus, but its antiviral mechanism is unknown. In this study, we evaluated the humoral and cellular responses to PCA in specific pathogen-free (SPF) chickens. One hundred forty 35-day-old SPF chickens were randomly divided into 7 groups. The birds were inoculated with the commercial, attenuated Newcastle Disease Virus (NDV) vaccine and then received orally with 10, 20 or 40 mg/kg body weight of PCA for 30 days. Immune organ indexes, anti-Newcastle Disease Virus (NDV) antibodies and lymphocyte proliferation, but not body weight, were significantly increased in chicken treated with 40 mg/kg PCA, compared to the control birds treated with Astragalus polysaccharide (ASP). Survival rate was 70% and 60%, respectively, in the chickens with 40 mg/kg PCA, 20 mg/kg PCA while 50% survival was found in the birds treated with 125 mg/kg ASP. PCA treatment resulted in significantly lower viral load and reduced shedding. These results indicate that PCA may improve poultry health by enhancing both the humoral and cellular immune response. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  12. GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

    Science.gov (United States)

    Wagner, Florian

    2015-01-01

    Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.

  13. k-t PCA: temporally constrained k-t BLAST reconstruction using principal component analysis

    DEFF Research Database (Denmark)

    Pedersen, Henrik; Kozerke, Sebastian; Ringgaard, Steffen

    2009-01-01

    in applications exhibiting a broad range of temporal frequencies such as free-breathing myocardial perfusion imaging. We show that temporal basis functions calculated by subjecting the training data to principal component analysis (PCA) can be used to constrain the reconstruction such that the temporal resolution...... is improved. The presented method is called k-t PCA....

  14. The biological knowledge discovery by PCCF measure and PCA-F projection.

    Science.gov (United States)

    Jia, Xingang; Zhu, Guanqun; Han, Qiuhong; Lu, Zuhong

    2017-01-01

    In the process of biological knowledge discovery, PCA is commonly used to complement the clustering analysis, but PCA typically gives the poor visualizations for most gene expression data sets. Here, we propose a PCCF measure, and use PCA-F to display clusters of PCCF, where PCCF and PCA-F are modeled from the modified cumulative probabilities of genes. From the analysis of simulated and experimental data sets, we demonstrate that PCCF is more appropriate and reliable for analyzing gene expression data compared to other commonly used distances or similarity measures, and PCA-F is a good visualization technique for identifying clusters of PCCF, where we aim at such data sets that the expression values of genes are collected at different time points.

  15. [The value of PHI/PCA3 in the early diagnosis of prostate cancer].

    Science.gov (United States)

    Tan, S J; Xu, L W; Xu, Z; Wu, J P; Liang, K; Jia, R P

    2016-01-12

    To investigate the value of prostate health index (PHI) and prostate cancer gene 3 (PCA3) in the early diagnosis of prostate cancer (PCa). A total of 190 patients with abnormal serum prostate specific antigen (PSA) or abnormal digital rectal examination were enrolled. They were all underwent initial biopsy and 11 of them were also underwent repeated biopsy. In addition, 25 healthy cases (with normal digital rectal examination and PSAPHI and PCA3 were detected by using immunofluorescence and Loop-Mediated Isothermal Amplification (LAMP). The sensitivity and specificity of diagnosis were determined by ROC curve.In addition, the relationship between PHI/PSA and the Gleason score and clinical stage were analyzed. A total of 89 patients were confirmed PCa by Pathological diagnosis. The other 101 patients were diagnosed as benign prostatic hyperplasia (BPH). The sensitivity and specificity of PCA3 test were 85.4% was 92.1%. Area under curve (AUC) of PHI is higher than AUC of PSA (0.727>0.699). The PHI in peripheral blood was positively correlated with Gleason score and clinical stage. The detection of PCA3 and PHI shows excellent detecting effectiveness. Compared with single PSA, the combined detection of PHI and PCA3 improved the diagnostic specificity. It can provide a new method for the early diagnosis in prostate cancer and avoid unnecessary biopsies.

  16. GND-PCA-based statistical modeling of diaphragm motion extracted from 4D MRI.

    Science.gov (United States)

    Swastika, Windra; Masuda, Yoshitada; Xu, Rui; Kido, Shoji; Chen, Yen-Wei; Haneishi, Hideaki

    2013-01-01

    We analyzed a statistical model of diaphragm motion using regular principal component analysis (PCA) and generalized N-dimensional PCA (GND-PCA). First, we generate 4D MRI of respiratory motion from 2D MRI using an intersection profile method. We then extract semiautomatically the diaphragm boundary from the 4D-MRI to get subject-specific diaphragm motion. In order to build a general statistical model of diaphragm motion, we normalize the diaphragm motion in time and spatial domains and evaluate the diaphragm motion model of 10 healthy subjects by applying regular PCA and GND-PCA. We also validate the results using the leave-one-out method. The results show that the first three principal components of regular PCA contain more than 98% of the total variation of diaphragm motion. However, validation using leave-one-out method gives up to 5.0 mm mean of error for right diaphragm motion and 3.8 mm mean of error for left diaphragm motion. Model analysis using GND-PCA provides about 1 mm margin of error and is able to reconstruct the diaphragm model by fewer samples.

  17. The applications of PCA in QSAR studies: A case study on CCR5 antagonists.

    Science.gov (United States)

    Yoo, ChangKyoo; Shahlaei, Mohsen

    2018-01-01

    Principal component analysis (PCA), as a well-known multivariate data analysis and data reduction technique, is an important and useful algebraic tool in drug design and discovery. PCA, in a typical quantitative structure-activity relationship (QSAR) study, analyzes an original data matrix in which molecules are described by several intercorrelated quantitative dependent variables (molecular descriptors). Although extensively applied, there is disparity in the literature with respect to the applications of PCA in the QSAR studies. This study investigates the different applications of PCA in QSAR studies using a dataset including CCR5 inhibitors. The different types of preprocessing are used to compare the PCA performances. The use of PC plots in the exploratory investigation of matrix of descriptors is described. This work is also proved PCA analysis to be a powerful technique for exploring complex datasets in QSAR studies for identification of outliers. This study shows that PCA is able to easily apply to the pool of calculated structural descriptors and also the extracted information can be used to help decide upon an appropriate harder model for further analysis. © 2017 John Wiley & Sons A/S.

  18. Periarticular infiltration for pain relief after total hip arthroplasty: a comparison with epidural and PCA analgesia.

    Science.gov (United States)

    Pandazi, Ageliki; Kanellopoulos, Ilias; Kalimeris, Konstantinos; Batistaki, Chrysanthi; Nikolakopoulos, Nikolaos; Matsota, Paraskevi; Babis, George C; Kostopanagiotou, Georgia

    2013-11-01

    Epidural and intravenous patient-controlled analgesia (PCA) are established methods for pain relief after total hip arthroplasty (THA). Periarticular infiltration is an alternative method that is gaining ground due to its simplicity and safety. Our study aims to assess the efficacy of periarticular infiltration in pain relief after THA. Sixty-three patients undergoing THA under spinal anaesthesia were randomly assigned to receive postoperative analgesia with continuous epidural infusion with ropivacaine (epidural group), intraoperative periarticular infiltration with ropivacaine, clonidine, morphine, epinephrine and corticosteroids (infiltration group) or PCA with morphine (PCA group). PCA morphine provided rescue analgesia in all groups. We recorded morphine consumption, visual analog scale (VAS) scores at rest and movement, blood loss from wound drainage, mean arterial pressure (MAP) and adverse effects at 1, 6, 12, 24 h postoperatively. Morphine consumption at all time points, VAS scores at rest, 6, 12 and 24 h and at movement, 6 and 12 h postoperatively were lower in infiltration group compared to PCA group (p PCA group (p PCA with morphine after THA, providing better pain relief and lower opioid consumption postoperatively. Infiltration seems to be equally effective to epidural analgesia without having the potential side effects of the latter.

  19. Distinct clinical and metabolic deficits in PCA and AD are not related to amyloid distribution.

    Science.gov (United States)

    Rosenbloom, M H; Alkalay, A; Agarwal, N; Baker, S L; O'Neil, J P; Janabi, M; Yen, I V; Growdon, M; Jang, J; Madison, C; Mormino, E C; Rosen, H J; Gorno-Tempini, M L; Weiner, M W; Miller, B L; Jagust, W J; Rabinovici, G D

    2011-05-24

    Patients with posterior cortical atrophy (PCA) often have Alzheimer disease (AD) at autopsy, yet are cognitively and anatomically distinct from patients with clinical AD. We sought to compare the distribution of β-amyloid and glucose metabolism in PCA and AD in vivo using Pittsburgh compound B (PiB) and FDG-PET. Patients with PCA (n = 12, age 57.5 ± 7.4, Mini-Mental State Examination [MMSE] 22.2 ± 5.1), AD (n = 14, age 58.8 ± 9.6, MMSE 23.8 ± 6.7), and cognitively normal controls (NC, n = 30, age 73.6 ± 6.4) underwent PiB and FDG-PET. Group differences in PiB distribution volume ratios (DVR, cerebellar reference) and FDG uptake (pons-averaged) were assessed on a voxel-wise basis and by comparing binding in regions of interest (ROIs). Compared to NC, both patients with AD and patients with PCA showed diffuse PiB uptake throughout frontal, temporoparietal, and occipital cortex (p PCA and AD even after correcting for atrophy. FDG patterns in PCA and AD were distinct: while both groups showed hypometabolism compared to NC in temporoparietal cortex and precuneus/posterior cingulate, patients with PCA further showed hypometabolism in inferior occipitotemporal cortex compared to both NC and patients with AD (p PCA. Fibrillar amyloid deposition in PCA is diffuse and similar to AD, while glucose hypometabolism extends more posteriorly into occipital cortex. Further studies are needed to determine the mechanisms of selective network degeneration in focal variants of AD.

  20. Can we use PCA to detect small signals in noisy data?

    Science.gov (United States)

    Spiegelberg, Jakob; Rusz, Ján

    2017-01-01

    Principal component analysis (PCA) is among the most commonly applied dimension reduction techniques suitable to denoise data. Focusing on its limitations to detect low variance signals in noisy data, we discuss how statistical and systematical errors occur in PCA reconstructed data as a function of the size of the data set, which extends the work of Lichtert and Verbeeck, (2013) [16]. Particular attention is directed towards the estimation of bias introduced by PCA and its influence on experiment design. Aiming at the denoising of large matrices, nullspace based denoising (NBD) is introduced. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Improved swelling resistance for PCA austenitic stainless steel under HFIR irradiation through microstructural control

    International Nuclear Information System (INIS)

    Maziasz, P.J.; Braski, D.N.

    1984-01-01

    Swelling evaluation of PCA variants and 20%-cold-worked (N-Lot) type 316 stainless steel (CW 316) at 300 to 600 0 C was extended to 44 dpa. Swelling was negligible in all the steels at 300 0 C after approx. 44 dpa. At 500 to 600 0 C 25%-cold-worked PCA showed better void swelling resistance than type 316 at approx. 44 dpa. There was less swelling variation among alloys at 400 0 C, but again 25%-cold-worked PCA was the best

  2. Prostate health index (phi) and prostate cancer antigen 3 (PCA3) significantly improve diagnostic accuracy in patients undergoing prostate biopsy.

    Science.gov (United States)

    Perdonà, Sisto; Bruzzese, Dario; Ferro, Matteo; Autorino, Riccardo; Marino, Ada; Mazzarella, Claudia; Perruolo, Giuseppe; Longo, Michele; Spinelli, Rosa; Di Lorenzo, Giuseppe; Oliva, Andrea; De Sio, Marco; Damiano, Rocco; Altieri, Vincenzo; Terracciano, Daniela

    2013-02-15

    Prostate health index (phi) and prostate cancer antigen 3 (PCA3) have been recently proposed as novel biomarkers for prostate cancer (PCa). We assessed the diagnostic performance of these biomarkers, alone or in combination, in men undergoing first prostate biopsy for suspicion of PCa. One hundred sixty male subjects were enrolled in this prospective observational study. PSA molecular forms, phi index (Beckman coulter immunoassay), PCA3 score (Progensa PCA3 assay), and other established biomarkers (tPSA, fPSA, and %fPSA) were assessed before patients underwent a 18-core first prostate biopsy. The discriminating ability between PCa-negative and PCa-positive biopsies of Beckman coulter phi and PCA3 score and other used biomarkers were determined. One hundred sixty patients met inclusion criteria. %p2PSA (p2PSA/fPSA × 100), phi and PCA3 were significantly higher in patients with PCa compared to PCa-negative group (median values: 1.92 vs. 1.55, 49.97 vs. 36.84, and 50 vs. 32, respectively, P ≤ 0.001). ROC curve analysis showed that %p2PSA, phi, and PCA3 are good indicator of malignancy (AUCs = 0.68, 0.71, and 0.66, respectively). A multivariable logistic regression model consisting of both the phi index and PCA3 score allowed to reach an overall diagnostic accuracy of 0.77. Decision curve analysis revealed that this "combined" marker achieved the highest net benefit over the examined range of the threshold probability. phi and PCA3 showed no significant difference in the ability to predict PCa diagnosis in men undergoing first prostate biopsy. However, diagnostic performance is significantly improved by combining phi and PCA3. Copyright © 2012 Wiley Periodicals, Inc.

  3. Using a cross-model loadings plot to identify protein spots causing 2-DE gels to become outliers in PCA

    DEFF Research Database (Denmark)

    Kristiansen, Luise Cederkvist; Jacobsen, Susanne; Jessen, Flemming

    2010-01-01

    The multivariate method PCA is an exploratory tool often used to get an overview of multivariate data, such as the quantified spot volumes of digitized 2-DE gels. PCA can reveal hidden structures present in the data, and thus enables identification of potential outliers and clustering. Based on PCA...

  4. Faults detection approach using PCA and SOM algorithm in PMSG-WT system

    Directory of Open Access Journals (Sweden)

    Mohamed Lamine FADDA

    2016-07-01

    Full Text Available In this paper, a new approach for faults detection in observable data system wind turbine - permanent magnet synchronous generator (WT-PMSG, the studying objective, illustrate the combination (SOM-PCA to build Multi-local-PCA models faults detection in system (WT-PMSG, the performance of the method suggested to faults detection in system data, finding good results in simulation experiment.

  5. Autophagosomal Sequestration of Mitochondria as an Indicator of Antiandrogen Therapy Resistance of Prostate Cancer (PCa)

    Science.gov (United States)

    2017-11-01

    Prostate Cancer (PCa) PRINCIPAL INVESTIGATOR: George Wilding, M.D. CONTRACTING ORGANIZATION: University of Texas MD Anderson Cancer Center Houston, TX...Indicator of Antiandrogen Therapy Resistance of Prostate Cancer (PCa) 5b. GRANT NUMBER W81XWH-15-1-0509 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S...AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER The University of Texas MD Anderson Cancer Center 1515 Holcombe Blvd. Houston, TX 77030-4009

  6. Lactate Oxidation Coupled to Iron or Electrode Reduction by Geobacter sulfurreducens PCA

    KAUST Repository

    Call, D. F.

    2011-10-14

    Geobacter sulfurreducens PCA completely oxidized lactate and reduced iron or an electrode, producing pyruvate and acetate intermediates. Compared to the current produced by Shewanella oneidensis MR-1, G. sulfurreducens PCA produced 10-times-higher current levels in lactate-fed microbial electrolysis cells. The kinetic and comparative analyses reported here suggest a prominent role of G. sulfurreducens strains in metaland electrode-reducing communities supplied with lactate. © 2011, American Society for Microbiology.

  7. Lactate Oxidation Coupled to Iron or Electrode Reduction by Geobacter sulfurreducens PCA

    KAUST Repository

    Call, D. F.; Logan, B. E.

    2011-01-01

    Geobacter sulfurreducens PCA completely oxidized lactate and reduced iron or an electrode, producing pyruvate and acetate intermediates. Compared to the current produced by Shewanella oneidensis MR-1, G. sulfurreducens PCA produced 10-times-higher current levels in lactate-fed microbial electrolysis cells. The kinetic and comparative analyses reported here suggest a prominent role of G. sulfurreducens strains in metaland electrode-reducing communities supplied with lactate. © 2011, American Society for Microbiology.

  8. Lateral supraorbital approach to ipsilateral PCA-P1 and ICA-PCoA aneurysms.

    Science.gov (United States)

    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.

  9. Efficacy and tolerability of intravenous morphine patient-controlled analgesia (PCA) in women undergoing cesarean delivery.

    Science.gov (United States)

    Andziak, Marta; Beta, Jarosław; Barwijuk, Michal; Issat, Tadeusz; Jakimiuk, Artur J

    2015-06-01

    The aim of the study was to evaluate analgesic efficacy and tolerability of patient-controlled analgesia (PCA) with intravenous morphine. Our observational study included 50 women who underwent a Misgav-Ladach or modified Misgav-Ladach cesarean section. Automated PCA infusion device (Medima S-PCA Syringe Pump, Medima, Krakow, Poland) was used for postoperative pain control. Time of morphine administration or initiation of intravenous patient-controlled analgesia (IV PCA) with morphine was recorded, as well as post-operative pain at rest assessed by a visual analogue scale (VAS). All patients were followed up for 24 hours after discharge from the operating room, taking into account patient records, worst pain score at rest, number of IV PCA attempts, and drug consumption. Median of total morphine doses used during the postoperative period was 42.9mg (IQR 35.6-48.5), with median infusion time of 687.0 min. (IQR 531.0-757.5). Pain severity and total drug consumption improved after the first 3 hours following cesarean delivery (p PCA attempts per patient was 33 (IQR: 24-37), with median of 11 placebo attempts (IQR: 3-27). Patient-controlled analgesia with morphine is an efficient and acceptable analgesic method in women undergoing cesarean section.

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

    Science.gov (United States)

    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.

  11. PCA based clustering for brain tumor segmentation of T1w MRI images.

    Science.gov (United States)

    Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

    2017-03-01

    Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  12. 2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm

    Directory of Open Access Journals (Sweden)

    Patrik Kamencay

    2014-03-01

    Full Text Available This paper presents a proposed methodology for face recognition based on an information theory approach to coding and decoding face images. In this paper, we propose a 2D-3D face-matching method based on a principal component analysis (PCA algorithm using canonical correlation analysis (CCA to learn the mapping between a 2D face image and 3D face data. This method makes it possible to match a 2D face image with enrolled 3D face data. Our proposed fusion algorithm is based on the PCA method, which is applied to extract base features. PCA feature-level fusion requires the extraction of different features from the source data before features are merged together. Experimental results on the TEXAS face image database have shown that the classification and recognition results based on the modified CCA-PCA method are superior to those based on the CCA method. Testing the 2D-3D face match results gave a recognition rate for the CCA method of a quite poor 55% while the modified CCA method based on PCA-level fusion achieved a very good recognition score of 85%.

  13. Differential research of inflammatory and related mediators in BPH, histological prostatitis and PCa.

    Science.gov (United States)

    Huang, T R; Wang, G C; Zhang, H M; Peng, B

    2018-02-14

    Prostate cancer (PCa) is one of the most common male malignancies in the world. It was aimed to investigate differential expression of inflammatory and related factors in benign prostatic hyperplasia (BPH), prostate cancer (PCa), histological prostatitis (HP) and explore the role of Inducible nitric oxide synthase (iNOS), (VEGF) Vascular endothelial growth factor, androgen receptor (AR) and IL-2, IL-8 and TNF-α in the occurrence and development of prostate cancer. RT-PCR was used to detect the mRNA expression level of iNOS, VEGF, AR and IL-2, IL-8 and TNF-α in BPH, PCa and BPH+HP. Western blotting and immunohistochemical staining were used to detect the protein levels of various proteins in three diseases. The results showed the mRNA and protein levels of iNOS, VEGF and IL-2, IL-8 and TNF-α were significantly increased in PCa and BPH+HP groups compared with BPH group (p BPH+HP groups (p BPH+HP groups (p > .05). iNOS, VEGF, AR and IL-2, IL-8 and TNF-α are involved in the malignant transformation of prostate tissue and play an important role in the development and progression of Prostate cancer (PCa). © 2018 Blackwell Verlag GmbH.

  14. PCA criterion for SVM (MLP) classifier for flavivirus biomarker from salivary SERS spectra at febrile stage.

    Science.gov (United States)

    Radzol, A R M; Lee, Khuan Y; Mansor, W; Omar, I S

    2016-08-01

    Non-structural protein (NS1) has been conceded as one of the biomarkers for flavivirus that causes diseases with life threatening consequences. NS1 is an antigen that allows detection of the illness at febrile stage, mostly from blood samples currently. Our work here intends to define an optimum model for PCA-SVM with MLP kernel for classification of flavivirus biomarker, NS1 molecule, from SERS spectra of saliva, which to the best of our knowledge has never been explored. Since performance of the model depends on the PCA criterion and MLP parameters, both are examined in tandem. Input vector to classifier determined by each PCA criterion is subjected to brute force tuning of MLP parameters for entirety. Its performance is also compared to our previous works where a Linear and RBF kernel are used. It is found that the best PCA-SVM (MLP) model can be defined by 5 PCs from Cattel's Scree test for PCA, together with P1 and P2 values of 0.1 and -0.2 respectively, with a classification performance of [96.9%, 93.8%, 100.0%].

  15. Avoiding Optimal Mean ℓ2,1-Norm Maximization-Based Robust PCA for Reconstruction.

    Science.gov (United States)

    Luo, Minnan; Nie, Feiping; Chang, Xiaojun; Yang, Yi; Hauptmann, Alexander G; Zheng, Qinghua

    2017-04-01

    Robust principal component analysis (PCA) is one of the most important dimension-reduction techniques for handling high-dimensional data with outliers. However, most of the existing robust PCA presupposes that the mean of the data is zero and incorrectly utilizes the average of data as the optimal mean of robust PCA. In fact, this assumption holds only for the squared [Formula: see text]-norm-based traditional PCA. In this letter, we equivalently reformulate the objective of conventional PCA and learn the optimal projection directions by maximizing the sum of projected difference between each pair of instances based on [Formula: see text]-norm. The proposed method is robust to outliers and also invariant to rotation. More important, the reformulated objective not only automatically avoids the calculation of optimal mean and makes the assumption of centered data unnecessary, but also theoretically connects to the minimization of reconstruction error. To solve the proposed nonsmooth problem, we exploit an efficient optimization algorithm to soften the contributions from outliers by reweighting each data point iteratively. We theoretically analyze the convergence and computational complexity of the proposed algorithm. Extensive experimental results on several benchmark data sets illustrate the effectiveness and superiority of the proposed method.

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

    Science.gov (United States)

    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 (pphi 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.

  17. COMBINING PCA ANALYSIS AND ARTIFICIAL NEURAL NETWORKS IN MODELLING ENTREPRENEURIAL INTENTIONS OF STUDENTS

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2013-02-01

    Full Text Available Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA and artificial neural networks (ANNs. PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.

  18. Enhancement of noisy EDX HRSTEM spectrum-images by combination of filtering and PCA.

    Science.gov (United States)

    Potapov, Pavel; Longo, Paolo; Okunishi, Eiji

    2017-05-01

    STEM spectrum-imaging with collecting EDX signal is considered in view of the extraction of maximum information from very noisy data. It is emphasized that spectrum-images with weak EDX signal often suffer from information loss in the course of PCA treatment. The loss occurs when the level of random noise exceeds a certain threshold. Weighted PCA, though potentially helpful in isolation of meaningful variations from noise, might provoke the complete loss of information in the situation of weak EDX signal. Filtering datasets prior PCA can improve the situation and recover the lost information. In particular, Gaussian kernel filters are found to be efficient. A new filter useful in the case of sparse atomic-resolution EDX spectrum-images is suggested. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Fundamental flow and fracture analysis of prime candidate alloy (PCA) for path a (austenitics)

    International Nuclear Information System (INIS)

    Lucas, G.E.; Jayakumar, M.; Maziasz, P.J.

    1982-01-01

    Room temperature microhardness tests have been performed on samples of Prime Candidate Alloy (PCA) for the austenitics (Path A) subjected to various thermomechanical treatments (TMT). The TMTs have effected various microstructures, which have been well characterized by optical metallography and TEM. For comparison, microhardness tests have been performed on samples of N-lot, DO heat and MFE 316 stainless steel with similar TMTs. The results indicate that the TMTs investigated can significantly alter the microhardness of the PCA in a manner which is consistent with microstructural changes. Moreover, while PCA had the lowest microhardness of the four alloys types after cold working, its microhardness increased while the others decreased to comparable values after aging for 2 h at 750 0 C

  20. Improved swelling resistance for PCA austenitic stainless steel under HFIR irradiation through microstructural control

    International Nuclear Information System (INIS)

    Maziasz, P.J.; Braski, D.N.

    1983-01-01

    Six microstructural variants of Prime Candidate Alloy (PCA) were evaluated for swelling resistance during HFIR irradiation, together with several heats of type 316 stainless steel (316). Swelling was negligible in all the steels at 300 0 C after approx. 44 dpa. At 500 to 600 0 C 25%-cold-worked PCA showed better void swelling resistance than type 316 at approx. 44 dpa. There was less swelling variability among alloys at 400 0 C, but again 25%-cold-worked PCA was the best. Microstructurally, swelling resistance correlated with development of fine, stable bubbles whereas high swelling was due to coarser distributions of bubbles becoming unstable and converting to voids (bias-driven cavities)

  1. A comparative study of PCA, SIMCA and Cole model for classification of bioimpedance spectroscopy measurements.

    Science.gov (United States)

    Nejadgholi, Isar; Bolic, Miodrag

    2015-08-01

    Due to safety and low cost of bioimpedance spectroscopy (BIS), classification of BIS can be potentially a preferred way of detecting changes in living tissues. However, for longitudinal datasets linear classifiers fail to classify conventional Cole parameters extracted from BIS measurements because of their high variability. In some applications, linear classification based on Principal Component Analysis (PCA) has shown more accurate results. Yet, these methods have not been established for BIS classification, since PCA features have neither been investigated in combination with other classifiers nor have been compared to conventional Cole features in benchmark classification tasks. In this work, PCA and Cole features are compared in three synthesized benchmark classification tasks which are expected to be detected by BIS. These three tasks are classification of before and after geometry change, relative composition change and blood perfusion in a cylindrical organ. Our results show that in all tasks the features extracted by PCA are more discriminant than Cole parameters. Moreover, a pilot study was done on a longitudinal arm BIS dataset including eight subjects and three arm positions. The goal of the study was to compare different methods in arm position classification which includes all three synthesized changes mentioned above. Our comparative study on various classification methods shows that the best classification accuracy is obtained when PCA features are classified by a K-Nearest Neighbors (KNN) classifier. The results of this work suggest that PCA+KNN is a promising method to be considered for classification of BIS datasets that deal with subject and time variability. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Controversies in using urine samples for prostate cancer detection: PSA and PCA3 expression analysis

    Directory of Open Access Journals (Sweden)

    S. Fontenete

    2011-12-01

    Full Text Available PURPOSE: Prostate cancer (PCa is one of the most commonly diagnosed malignancies in the world. Although PSA utilization as a serum marker has improved prostate cancer detection it still presents some limitations, mainly regarding its specificity. The expression of this marker, along with the detection of PCA3 mRNA in urine samples, has been suggested as a new approach for PCa detection. The goal of this work was to evaluate the efficacy of the urinary detection of PCA3 mRNA and PSA mRNA without performing the somewhat embarrassing prostate massage. It was also intended to optimize and implement a methodological protocol for this kind of sampling. MATERIALS AND METHODS: Urine samples from 57 patients with suspected prostate disease were collected, without undergoing prostate massage. Increased serum PSA levels were confirmed by medical records review. RNA was extracted by different methods and a preamplification step was included in order to improve gene detection by Real-Time PCR. RESULTS: An increase in RNA concentration with the use of TriPure Isolation Reagent. Despite this optimization, only 15.8% of the cases showed expression of PSA mRNA and only 3.8% of prostate cancer patients presented detectable levels of PCA3 mRNA. The use of a preamplification step revealed no improvement in the results obtained. CONCLUSION: This work confirms that prostate massage is important before urine collection for gene expression analysis. Since PSA and PCA3 are prostate specific, it is necessary to promote the passage of cells from prostate to urinary tract, in order to detect these genetic markers in urine samples.

  3. Modification of the grain boundary microstructure of the austenitic PCA stainless steel to improve helium embrittlement resistance

    International Nuclear Information System (INIS)

    Maziasz, P.J.; Braski, D.N.

    1986-01-01

    Grain boundary MC precipitation was produced by a modified thermal-mechanical pretreatment in 25% cold worked (CW) austenitic prime candidate alloy (PCA) stainless steel prior to HFIR irradiation. Postirradiation tensile results and fracture analysis showed that the modified material (B3) resisted helium embrittlement better than either solution annealed (SA) or 25% CW PCA irradiated at 500 to 600 0 C to approx.21 dpa and 1370 at. ppM He. PCA SA and 25% CW were not embrittled at 300 to 400 0 C. Grain boundary MC survives in PCA-B3 during HFIR irradiation at 500 0 C but dissolves at 600 0 C; it does not form in either SA or 25% CW PCA during similar irradiation. The grain boundary MC appears to play an important role in the helium embrittlement resistance of PCA-B3

  4. Biopsy and treatment decisions in the initial management of prostate cancer and the role of PCA3; a systematic analysis of expert opinion

    NARCIS (Netherlands)

    Tombal, Bertrand; Ameye, Filip; de la Taille, Alexandre; de Reijke, Theo; Gontero, Paolo; Haese, Alexander; Kil, Paul; Perrin, Paul; Remzi, Mesut; Schröder, Jörg; Speakman, Mark; Volpe, Alessandro; Meesen, Bianca; Stoevelaar, Herman

    2012-01-01

    The Prostate CAncer gene 3 (PCA3) assay may guide prostate biopsy decisions and predict prostate cancer (PCa) aggressiveness. This study explored the appropriateness of (1) PCA3 testing; (2) biopsy; (3) active surveillance (AS) and the value of the PCA3 Score for biopsy and AS decisions. Using the

  5. PCA3 noncoding RNA is involved in the control of prostate-cancer cell survival and modulates androgen receptor signaling

    International Nuclear Information System (INIS)

    Ferreira, Luciana Bueno; Gimba, Etel Rodrigues Pereira; Palumbo, Antonio; Mello, Kivvi Duarte de; Sternberg, Cinthya; Caetano, Mauricio S; Oliveira, Felipe Leite de; Neves, Adriana Freitas; Nasciutti, Luiz Eurico; Goulart, Luiz Ricardo

    2012-01-01

    PCA3 is a non-coding RNA (ncRNA) that is highly expressed in prostate cancer (PCa) cells, but its functional role is unknown. To investigate its putative function in PCa biology, we used gene expression knockdown by small interference RNA, and also analyzed its involvement in androgen receptor (AR) signaling. LNCaP and PC3 cells were used as in vitro models for these functional assays, and three different siRNA sequences were specifically designed to target PCA3 exon 4. Transfected cells were analyzed by real-time qRT-PCR and cell growth, viability, and apoptosis assays. Associations between PCA3 and the androgen-receptor (AR) signaling pathway were investigated by treating LNCaP cells with 100 nM dihydrotestosterone (DHT) and with its antagonist (flutamide), and analyzing the expression of some AR-modulated genes (TMPRSS2, NDRG1, GREB1, PSA, AR, FGF8, CdK1, CdK2 and PMEPA1). PCA3 expression levels were investigated in different cell compartments by using differential centrifugation and qRT-PCR. LNCaP siPCA3-transfected cells significantly inhibited cell growth and viability, and increased the proportion of cells in the sub G0/G1 phase of the cell cycle and the percentage of pyknotic nuclei, compared to those transfected with scramble siRNA (siSCr)-transfected cells. DHT-treated LNCaP cells induced a significant upregulation of PCA3 expression, which was reversed by flutamide. In siPCA3/LNCaP-transfected cells, the expression of AR target genes was downregulated compared to siSCr-transfected cells. The siPCA3 transfection also counteracted DHT stimulatory effects on the AR signaling cascade, significantly downregulating expression of the AR target gene. Analysis of PCA3 expression in different cell compartments provided evidence that the main functional roles of PCA3 occur in the nuclei and microsomal cell fractions. Our findings suggest that the ncRNA PCA3 is involved in the control of PCa cell survival, in part through modulating AR signaling, which may raise new

  6. Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures

    Directory of Open Access Journals (Sweden)

    Anders E. Berglund

    2017-01-01

    Full Text Available Background. Many gene-expression signatures exist for describing the biological state of profiled tumors. Principal Component Analysis (PCA can be used to summarize a gene signature into a single score. Our hypothesis is that gene signatures can be validated when applied to new datasets, using inherent properties of PCA. Results. This validation is based on four key concepts. Coherence: elements of a gene signature should be correlated beyond chance. Uniqueness: the general direction of the data being examined can drive most of the observed signal. Robustness: if a gene signature is designed to measure a single biological effect, then this signal should be sufficiently strong and distinct compared to other signals within the signature. Transferability: the derived PCA gene signature score should describe the same biology in the target dataset as it does in the training dataset. Conclusions. The proposed validation procedure ensures that PCA-based gene signatures perform as expected when applied to datasets other than those that the signatures were trained upon. Complex signatures, describing multiple independent biological components, are also easily identified.

  7. Decision tree and PCA-based fault diagnosis of rotating machinery

    Science.gov (United States)

    Sun, Weixiang; Chen, Jin; Li, Jiaqing

    2007-04-01

    After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

  8. PCA-MLP SVM distinction of salivary Raman spectra of dengue fever infection.

    Science.gov (United States)

    Radzol, A R M; Lee, Khuan Y; Mansor, W; Wong, P S; Looi, I

    2017-07-01

    Dengue fever (DF) is a disease of major concern caused by flavivirus infection. Delayed diagnosis leads to severe stages, which could be deadly. Of recent, non-structural protein (NS1) has been acknowledged as a biomarker, alternative to immunoglobulins for early detection of dengue in blood. Further, non-invasive detection of NS1 in saliva makes the approach more appealing. However, since its concentration in saliva is less than blood, a sensitive and specific technique, Surface Enhanced Raman Spectroscopy (SERS), is employed. Our work here intends to define an optimal PCA-SVM (Principal Component Analysis-Support Vector Machine) with Multilayer Layer Perceptron (MLP) kernel model to distinct between positive and negative NS1 infected samples from salivary SERS spectra, which, to the best of our knowledge, has never been explored. Salivary samples of DF positive and negative subjects were collected, pre-processed and analyzed. PCA and SVM classifier were then used to differentiate the SERS analyzed spectra. Since performance of the model depends on the PCA criterion and MLP parameters, both are examined in tandem. Its performance is also compared to our previous works on simulated NS1 salivary samples. It is found that the best PCA-SVM (MLP) model can be defined by 95 PCs from CPV criterion with P1 and P2 values of 0.01 and -0.2 respectively. A classification performance of [76.88%, 85.92%, 67.83%] is achieved.

  9. AlleleCoder: a PERL script for coding codominant polymorphism data for PCA analysis

    Science.gov (United States)

    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- ...

  10. Statistical Significance of the Contribution of Variables to the PCA Solution: An Alternative Permutation Strategy

    Science.gov (United States)

    Linting, Marielle; van Os, Bart Jan; Meulman, Jacqueline J.

    2011-01-01

    In this paper, the statistical significance of the contribution of variables to the principal components in principal components analysis (PCA) is assessed nonparametrically by the use of permutation tests. We compare a new strategy to a strategy used in previous research consisting of permuting the columns (variables) of a data matrix…

  11. Investigation of domain walls in PPLN by confocal raman microscopy and PCA analysis

    Science.gov (United States)

    Shur, Vladimir Ya.; Zelenovskiy, Pavel; Bourson, Patrice

    2017-07-01

    Confocal Raman microscopy (CRM) is a powerful tool for investigation of ferroelectric domains. Mechanical stresses and electric fields existed in the vicinity of neutral and charged domain walls modify frequency, intensity and width of spectral lines [1], thus allowing to visualize micro- and nanodomain structures both at the surface and in the bulk of the crystal [2,3]. Stresses and fields are naturally coupled in ferroelectrics due to inverse piezoelectric effect and hardly can be separated in Raman spectra. PCA is a powerful statistical method for analysis of large data matrix providing a set of orthogonal variables, called principal components (PCs). PCA is widely used for classification of experimental data, for example, in crystallization experiments, for detection of small amounts of components in solid mixtures etc. [4,5]. In Raman spectroscopy PCA was applied for analysis of phase transitions and provided critical pressure with good accuracy [6]. In the present work we for the first time applied Principal Component Analysis (PCA) method for analysis of Raman spectra measured in periodically poled lithium niobate (PPLN). We found that principal components demonstrate different sensitivity to mechanical stresses and electric fields in the vicinity of the domain walls. This allowed us to separately visualize spatial distribution of fields and electric fields at the surface and in the bulk of PPLN.

  12. Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Shieh

    2014-01-01

    Full Text Available This paper incorporates principal component analysis (PCA with support vector machine-particle swarm optimization (SVM-PSO for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

  13. Pre-processing data using wavelet transform and PCA based on ...

    Indian Academy of Sciences (India)

    Abazar Solgi

    2017-07-14

    Jul 14, 2017 ... Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation. Abazar Solgi1,*, Amir Pourhaghi1, Ramin Bahmani2 and Heidar Zarei3. 1. Department of Water Resources Engineering, Shahid Chamran University of ...

  14. Elemental concentration analysis in PCa, BPH and normal prostate tissues using SR-TXRF

    International Nuclear Information System (INIS)

    Leitao, Roberta G.; Anjos, Marcelino J.; Canellas, Catarine G.L.; Lopes, Ricardo T.

    2009-01-01

    Prostate cancer (PCa) is one of the main causes of illness and death all over the world. In Brazil, prostate cancer currently represents the second most prevalent malignant neoplasia in men, representing 21% of all cancer cases. Benign Prostate Hyperplasia (BPH) is an illness prevailing in men above the age of 50, close to 90% after the age of 80. The prostate presents a high zinc concentration, about 10-fold higher than any other body tissue. In this work, samples of human prostate tissues with cancer (PCa), BPH and normal tissue were analyzed utilizing the total reflection X-ray fluorescence spectroscopy using synchrotron radiation technique (SRTXRF) to investigate the differences in the elemental concentrations in these tissues. SR-TXRF analyses were performed at the X-Ray fluorescence beamline at Brazilian National Synchrotron Light Laboratory (LNLS), in Campinas, Sao Paulo. It was possible to determine the concentrations of the following elements: P, S, K, Ca, Fe, Cu, Zn, Br and Rb. By using Mann-Whitney U test it was observed that almost all elements presented concentrations with significant differences α = 0.05) between the groups studied. The elements and groups were: S, K, Ca, Fe, Zn, Br and Rb (PCa X Normal); S, Fe, Zn and Br (PCa X BPH); K, Ca, Fe, Zn, Br and Rb (BPH X Normal). (author)

  15. PCA-based bootstrap confidence interval tests for gene-disease association involving multiple SNPs

    Directory of Open Access Journals (Sweden)

    Xue Fuzhong

    2010-01-01

    Full Text Available Abstract Background Genetic association study is currently the primary vehicle for identification and characterization of disease-predisposing variant(s which usually involves multiple single-nucleotide polymorphisms (SNPs available. However, SNP-wise association tests raise concerns over multiple testing. Haplotype-based methods have the advantage of being able to account for correlations between neighbouring SNPs, yet assuming Hardy-Weinberg equilibrium (HWE and potentially large number degrees of freedom can harm its statistical power and robustness. Approaches based on principal component analysis (PCA are preferable in this regard but their performance varies with methods of extracting principal components (PCs. Results PCA-based bootstrap confidence interval test (PCA-BCIT, which directly uses the PC scores to assess gene-disease association, was developed and evaluated for three ways of extracting PCs, i.e., cases only(CAES, controls only(COES and cases and controls combined(CES. Extraction of PCs with COES is preferred to that with CAES and CES. Performance of the test was examined via simulations as well as analyses on data of rheumatoid arthritis and heroin addiction, which maintains nominal level under null hypothesis and showed comparable performance with permutation test. Conclusions PCA-BCIT is a valid and powerful method for assessing gene-disease association involving multiple SNPs.

  16. Regularized Pre-image Estimation for Kernel PCA De-noising

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    The main challenge in de-noising by kernel Principal Component Analysis (PCA) is the mapping of de-noised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed...

  17. Global Clustering Quality Coefficient Assessing the Efficiency of PCA Class Assignment

    Directory of Open Access Journals (Sweden)

    Mirela Praisler

    2014-01-01

    Full Text Available An essential factor influencing the efficiency of the predictive models built with principal component analysis (PCA is the quality of the data clustering revealed by the score plots. The sensitivity and selectivity of the class assignment are strongly influenced by the relative position of the clusters and by their dispersion. We are proposing a set of indicators inspired from analytical geometry that may be used for an objective quantitative assessment of the data clustering quality as well as a global clustering quality coefficient (GCQC that is a measure of the overall predictive power of the PCA models. The use of these indicators for evaluating the efficiency of the PCA class assignment is illustrated by a comparative study performed for the identification of the preprocessing function that is generating the most efficient PCA system screening for amphetamines based on their GC-FTIR spectra. The GCQC ranking of the tested feature weights is explained based on estimated density distributions and validated by using quadratic discriminant analysis (QDA.

  18. [Interest of evaluation of professional practice for the improvement of the management of postoperative pain with patient controlled analgesia (PCA)].

    Science.gov (United States)

    Baumann, A; Cuignet-Royer, E; Cornet, C; Trueck, S; Heck, M; Taron, F; Peignier, C; Chastel, A; Gervais, P; Bouaziz, H; Audibert, G; Mertes, P-M

    2010-10-01

    To evaluate the daily practice of postoperative PCA in Nancy University Hospital, in continuity with a quality program of postoperative pain (POP) care conducted in 2003. A retrospective audit of patient medical records. A review of all the medical records of consecutive surgical patients managed by PCA over a 5-week period in six surgical services. Criteria studied: Evaluation of hospital means (eight criteria) and of medical and nursing staff practice (16 criteria). A second audit was conducted 6 months after the implementation of quality improvement measures. Assessment of the hospital means: temperature chart including pain scores and PCA drug consumption, patient information leaflet, PCA protocol, postoperative pre-filled prescription form (PFPF) for post-anaesthesia care including PCA, and optional training of nurses in postoperative pain management. EVALUATION OF PRACTICES: One hundred and fifty-nine files of a total of 176 patients were analyzed (88%). Improvements noted after 6 months: trace of POP evaluation progressed from 73 to 87%, advance prescription of PCA adjustment increased from 56 to 68% and of the treatment of adverse effects from 54 to 68%, trace of PCA adaptation by attending nurse from 15 to 43%, trace of the administration of the treatment of adverse effects by attending nurse from 24% to 64%, as did the use of PFPF from 59 to 70%. The usefulness of a pre-filled prescription form for post-anaesthesia care including PCA prescription is demonstrated. Quality improvement measures include: poster information and pocket guides on PCA for nurses, training of 3 nurses per service to act as "PCA advisers" who will in turn train their ward colleagues in PCA management and the use of equipment until an acute pain team is established. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

  19. Effect of process control agent (PCA) on the characteristics of mechanically alloyed Ti-Mg powders [Journal article

    CSIR Research Space (South Africa)

    Machio, Christopher N

    2011-03-01

    Full Text Available This paper reports the results of a study to determine the effect of process control agent (PCA) on the characteristics of Ti-Mg powders during milling. It has been shown that a 2% increase in PCA content leads to up to a 40% increase in yield...

  20. RXTE PCA and Swift BAT detects the millisecond pulsar Swift J1756.9-2508 in outburst

    NARCIS (Netherlands)

    Patruno, A.; Markwardt, C.B.; Strohmayer, T.E.; Swank, J.H.; Smith, S.E.; Pereira, D.

    2009-01-01

    We report a detection of increased activity of the accreting millisecond X-ray pulsar Swift J1756.9-2508 observed with the RXTE-PCA monitoring on July 8, 9hr UTC. Increased flux is detected simultaneously on the Swift-BAT camera. RXTE-PCA follow up observations starting on July 13, 23hr UTC,

  1. Amorphization of Fe-based alloy via wet mechanical alloying assisted by PCA decomposition

    Energy Technology Data Exchange (ETDEWEB)

    Neamţu, B.V., E-mail: Bogdan.Neamtu@stm.utcluj.ro [Materials Science and Engineering Department, Technical University of Cluj-Napoca, 103-105, Muncii Avenue, 400641, Cluj-Napoca (Romania); Chicinaş, H.F.; Marinca, T.F. [Materials Science and Engineering Department, Technical University of Cluj-Napoca, 103-105, Muncii Avenue, 400641, Cluj-Napoca (Romania); Isnard, O. [Université Grenoble Alpes, Institut NEEL, F-38042, Grenoble (France); CNRS, Institut NEEL, 25 rue des martyrs, BP166, F-38042, Grenoble (France); Pană, O. [National Institute for Research and Development of Isotopic and Molecular Technologies, 65-103 Donath Street, 400293, Cluj-Napoca (Romania); Chicinaş, I. [Materials Science and Engineering Department, Technical University of Cluj-Napoca, 103-105, Muncii Avenue, 400641, Cluj-Napoca (Romania)

    2016-11-01

    Amorphization of Fe{sub 75}Si{sub 20}B{sub 5} (at.%) alloy has been attempted both by wet and dry mechanical alloying starting from a mixture of elemental powders. Powder amorphization was not achieved even after 140 hours of dry mechanical alloying. Using the same milling parameters, when wet mechanical alloying was used, the powder amorphization was achieved after 40 h of milling. Our assumption regarding the powder amorphization capability enhancement by contamination with carbon was proved by X-ray Photoelectron Spectroscopy (XPS) measurements which revealed the presence of carbon in the chemical composition of the wet mechanically alloyed sample. Using shorter milling times and several process control agents (PCA) (ethanol, oleic acid and benzene) with different carbon content it was proved that the milling duration required for powder amorphization is linked to the carbon content of the PCA. Differential Scanning Calorimetry (DSC), thermomagnetic (TG) and X-ray Diffraction (XRD) measurements performed to the heated samples revealed the fact that, the crystallisation occurs at 488 °C, thus leading to the formation of Fe{sub 3}Si and Fe{sub 2}B. Thermogravimetry measurements performed under H{sub 2} atmosphere, showed the same amount of contamination with C, which is about 2.3 wt%, for the amorphous samples regardless of the type of PCA. Saturation magnetisation of the wet milled samples decreases upon increasing milling time. In the case of the amorphous samples wet milled with benzene up to 20 h and with oleic acid up to 30 h, the saturation magnetisation has roughly the same value, indicating the same degree of contamination. The XRD performed on the samples milled using the same parameters, revealed that powder amorphization can be achieved even via dry milling, just by adding the equivalent amount of elemental C calculated from the TG plots. This proves that in this system by considering the atomic species which can contaminate the powder, they can be

  2. Use of principal components analysis (PCA) on estuarine sediment datasets: The effect of data pre-treatment

    International Nuclear Information System (INIS)

    Reid, M.K.; Spencer, K.L.

    2009-01-01

    Principal components analysis (PCA) is a multivariate statistical technique capable of discerning patterns in large environmental datasets. Although widely used, there is disparity in the literature with respect to data pre-treatment prior to PCA. This research examines the influence of commonly reported data pre-treatment methods on PCA outputs, and hence data interpretation, using a typical environmental dataset comprising sediment geochemical data from an estuary in SE England. This study demonstrated that applying the routinely used log (x + 1) transformation skewed the data and masked important trends. Removing outlying samples and correcting for the influence of grain size had the most significant effect on PCA outputs and data interpretation. Reducing the influence of grain size using granulometric normalisation meant that other factors affecting metal variability, including mineralogy, anthropogenic sources and distance along the salinity transect could be identified and interpreted more clearly. - Data pre-treatment can have a significant influence on the outcome of PCA.

  3. Antineoplastic and immunomodulatory effect of polyphenolic components of Achyranthes aspera (PCA) extract on urethane induced lung cancer in vivo.

    Science.gov (United States)

    Narayan, Chandradeo; Kumar, Arvind

    2014-01-01

    Polyphenolic compounds of Achyranthes aspera (PCA) extract is evaluated for anti-cancerous and cytokine based immunomodulatory effects. The PCA extract contains known components of phenolic acid and flavonoids such as mixture of quinic acid, chlorogenic acid, kaempferol, quercetin and chrysin along with many unknown components. PCA has been orally feed to urethane (ethyl carbamate) primed lung cancerous mice at a dosage of 100 mg/kg body weight for 30 consecutive days. 100 mg powder of A. aspera contains 2.4 mg phenolic acid and 1.1 mg flavonoid (2:1 ratio). Enhanced activities and expression of antioxidant enzymes GST, GR, CAT, SOD, while down regulated expression and activation of LDH enzymes in PCA feed urethane primed lung cancerous tissues as compared to PCA non-feed urethane primed lung cancerous tissues were observed. PCA feed urethane primed lung tissues showed down regulated expression of pro-inflammatory cytokines IL-1β, IL-6 and TNF-α along with TFs, NF-κB and Stat3 while the expression of pro-apoptotic proteins Bax and p53 were enhanced in PCA feed urethane primed lung tissues. FTIR and CD spectroscopy data revealed that PCA resisted the urethane mediated conformational changes of DNA which is evident by the shift in guanine and thymine bands in FTIR from 1,708 to 1,711 cm(-1) and 1,675 to 1,671 cm(-1), respectively in PCA feed urethane primed lung cancerous tissues DNA in comparison to urethane primed lung cancerous tissues DNA. The present study suggests that PCA components have synergistic anti-cancerous and cytokine based immunomodulatory role and DNA conformation restoring effects. However, more research is required to show the effects of each component separately and in combination for effective therapeutic use to cure and prevent lung cancer including other cancers.

  4. A Study of Wind Turbine Comprehensive Operational Assessment Model Based on EM-PCA Algorithm

    Science.gov (United States)

    Zhou, Minqiang; Xu, Bin; Zhan, Yangyan; Ren, Danyuan; Liu, Dexing

    2018-01-01

    To assess wind turbine performance accurately and provide theoretical basis for wind farm management, a hybrid assessment model based on Entropy Method and Principle Component Analysis (EM-PCA) was established, which took most factors of operational performance into consideration and reach to a comprehensive result. To verify the model, six wind turbines were chosen as the research objects, the ranking obtained by the method proposed in the paper were 4#>6#>1#>5#>2#>3#, which are completely in conformity with the theoretical ranking, which indicates that the reliability and effectiveness of the EM-PCA method are high. The method could give guidance for processing unit state comparison among different units and launching wind farm operational assessment.

  5. Short-term PV/T module temperature prediction based on PCA-RBF neural network

    Science.gov (United States)

    Li, Jiyong; Zhao, Zhendong; Li, Yisheng; Xiao, Jing; Tang, Yunfeng

    2018-02-01

    Aiming at the non-linearity and large inertia of temperature control in PV/T system, short-term temperature prediction of PV/T module is proposed, to make the PV/T system controller run forward according to the short-term forecasting situation to optimize control effect. Based on the analysis of the correlation between PV/T module temperature and meteorological factors, and the temperature of adjacent time series, the principal component analysis (PCA) method is used to pre-process the original input sample data. Combined with the RBF neural network theory, the simulation results show that the PCA method makes the prediction accuracy of the network model higher and the generalization performance stronger than that of the RBF neural network without the main component extraction.

  6. Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

    Science.gov (United States)

    Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

  7. Thermogravimetry/mass spectrometry study of woody residues and an herbaceous biomass crop using PCA techniques

    Energy Technology Data Exchange (ETDEWEB)

    Gomez, C.J.; Velo, E.; Puigjaner, L. [Department of Chemical Engineering, ETSEIB, Universitat Politecnica de Catalunya, Avinguda Diagonal 647, G2, E-08028 Barcelona (Spain); Meszaros, E.; Jakab, E. [Institute of Materials and Environmental Chemistry, Chemical Research Center, Hungarian Academy of Sciences, P.O. Box 17, Budapest 1525 (Hungary)

    2007-10-15

    The devolatilization behaviour of pine and beech wood from carpentry residuals and an herbaceous product from an energy plantation (artichoke thistle) was investigated by thermogravimetry/mass spectrometry (TG/MS). The effect of three pre-treatments, hot-water washing, ethanol extraction and their combination, was also studied. Principal component analysis (PCA) was employed to help in the evaluation of the large data set of results. The characteristics of the thermal decomposition of the herbaceous crop are considerably different from that of the woody biomass samples. The evolution profiles of some characteristic pyrolysis products revealed that the thermal behaviour of wood and thistle is still considerably different after the elimination of some of the inorganic ions and extractive compounds, although the macromolecular components of the samples decompose at similar temperatures. With the help of the PCA calculations, the effect of the different pre-treatments on the production of the main pyrolysis products was evidenced. (author)

  8. Microstructural design of PCA austenitic stainless steel for improved resistance to helium embrittlement under HFIR irradiation

    International Nuclear Information System (INIS)

    Maziasz, P.J.; Braski, D.N.

    1983-01-01

    Several variants of Prime Candidate Alloy (PCA) with different preirradiation thermal-mechanical treatments were irradiated in HFIR and were evaluated for embrittlement resistance via disk-bend tensile testing. Comparison tests were made on two heats of 20%-cold-worked type 316 stainless steel. None of the alloys were brittle after irradiation at 300 to 400 0 C to approx. 44 dpa and helium levels of 3000 to approx.3600 at. ppm. However, all were quite brittle after similar exposure at 600 0 C. Embrittlement varied with alloy and pretreatment for irradiation to 44 dpa at 500 0 C and to 22 dpa at 600 0 C. Better relative embrittlement resistance among PCA variants was found in alloys which contained prior grain boundary MC carbide particles that remained stable under irradiation

  9. Statistical Fractal Models Based on GND-PCA and Its Application on Classification of Liver Diseases

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2013-01-01

    Full Text Available A new method is proposed to establish the statistical fractal model for liver diseases classification. Firstly, the fractal theory is used to construct the high-order tensor, and then Generalized -dimensional Principal Component Analysis (GND-PCA is used to establish the statistical fractal model and select the feature from the region of liver; at the same time different features have different weights, and finally, Support Vector Machine Optimized Ant Colony (ACO-SVM algorithm is used to establish the classifier for the recognition of liver disease. In order to verify the effectiveness of the proposed method, PCA eigenface method and normal SVM method are chosen as the contrast methods. The experimental results show that the proposed method can reconstruct liver volume better and improve the classification accuracy of liver diseases.

  10. The effects of multidisciplinary rehabilitation: RePCa-a randomised study among primary prostate cancer patients

    DEFF Research Database (Denmark)

    Dieperink, K B; Johansen, C; Hansen, Steinbjørn

    2013-01-01

    Background:The objective of this study is the effectiveness of multidisciplinary rehabilitation on treatment-related adverse effects after completed radiotherapy in patients with prostate cancer (PCa).Methods:In a single-centre oncology unit in Odense, Denmark, 161 PCa patients treated...... with radiotherapy and androgen deprivation therapy were randomly assigned to either a programme of two nursing counselling sessions and two instructive sessions with a physical therapist (n=79) or to usual care (n=82). Primary outcome was Expanded Prostate Cancer Index Composite (EPIC-26) urinary irritative sum......-score.Before radiotherapy, pre-intervention 4 weeks after radiotherapy, and after a 20-week intervention, measurements included self-reported disease-specific quality of life (QoL; EPIC-26, including urinary, bowel, sexual, and hormonal symptoms), general QoL (Short-form-12, SF-12), pelvic floor muscle strength (Modified...

  11. Application of principal component analysis (PCA) as a sensory assessment tool for fermented food products.

    Science.gov (United States)

    Ghosh, Debasree; Chattopadhyay, Parimal

    2012-06-01

    The objective of the work was to use the method of quantitative descriptive analysis (QDA) to describe the sensory attributes of the fermented food products prepared with the incorporation of lactic cultures. Panellists were selected and trained to evaluate various attributes specially color and appearance, body texture, flavor, overall acceptability and acidity of the fermented food products like cow milk curd and soymilk curd, idli, sauerkraut and probiotic ice cream. Principal component analysis (PCA) identified the six significant principal components that accounted for more than 90% of the variance in the sensory attribute data. Overall product quality was modelled as a function of principal components using multiple least squares regression (R (2) = 0.8). The result from PCA was statistically analyzed by analysis of variance (ANOVA). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring the fermented food product attributes that are important for consumer acceptability.

  12. Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

    Science.gov (United States)

    Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D. Joshua

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi. PMID:21461364

  13. [Identification of varieties of cashmere by Vis/NIR spectroscopy technology based on PCA-SVM].

    Science.gov (United States)

    Wu, Gui-Fang; He, Yong

    2009-06-01

    One mixed algorithm was presented to discriminate cashmere varieties with principal component analysis (PCA) and support vector machine (SVM). Cashmere fiber has such characteristics as threadlike, softness, glossiness and high tensile strength. The quality characters and economic value of each breed of cashmere are very different. In order to safeguard the consumer's rights and guarantee the quality of cashmere product, quickly, efficiently and correctly identifying cashmere has significant meaning to the production and transaction of cashmere material. The present research adopts Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere. The near infrared fingerprint of cashmere was acquired by principal component analysis (PCA), and support vector machine (SVM) methods were used to further identify the cashmere material. The result of PCA indicated that the score map made by the scores of PC1, PC2 and PC3 was used, and 10 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99.99%. One hundred cashmere samples were used for calibration and the remaining 75 cashmere samples were used for validation. A one-against-all multi-class SVM model was built, the capabilities of SVM with different kernel function were comparatively analyzed, and the result showed that SVM possessing with the Gaussian kernel function has the best identification capabilities with the accuracy of 100%. This research indicated that the data mining method of PCA-SVM has a good identification effect, and can work as a new method for rapid identification of cashmere material varieties.

  14. SU-C-BRF-03: PCA Modeling of Anatomical Changes During Head and Neck Radiation Therapy

    International Nuclear Information System (INIS)

    Chetvertkov, M; Kim, J; Siddiqui, F; Kumarasiri, A; Chetty, I; Gordon, J

    2014-01-01

    Purpose: To develop principal component analysis (PCA) models from daily cone beam CTs (CBCTs) of head and neck (H and N) patients that could be used prospectively in adaptive radiation therapy (ART). Methods: : For 7 H and N patients, Pinnacle Treatment Planning System (Philips Healthcare) was used to retrospectively deformably register daily CBCTs to the planning CT. The number N of CBCTs per treatment course ranged from 14 to 22. For each patient a PCA model was built from the deformation vector fields (DVFs), after first subtracting the mean DVF, producing N eigen-DVFs (EDVFs). It was hypothesized that EDVFs with large eigenvalues represent the major anatomical deformations during the course of treatment, and that it is feasible to relate each EDVF to a clinically meaningful systematic or random change in anatomy, such as weight loss, neck flexion, etc. Results: DVFs contained on the order of 3×87×87×58=1.3 million scalar values (3 times the number of voxels in the registered volume). The top 3 eigenvalues accounted for ∼90% of variance. Anatomical changes corresponding to an EDVF were evaluated by generating a synthetic DVF, and applying that DVF to the CT to produce a synthetic CBCT. For all patients, the EDVF for the largest eigenvalue was interpreted to model weight loss. The EDVF for other eigenvalues appeared to represented quasi-random fraction-to-fraction changes. Conclusion: The leading EDVFs from single-patient PCA models have tentatively been identified with weight loss changes during treatment. Other EDVFs are tentatively identified as quasi-random inter-fraction changes. Clean separation of systematic and random components may require further work. This work is expected to facilitate development of population-based PCA models that can be used to prospectively identify significant anatomical changes, such as weight loss, early in treatment, triggering replanning where beneficial

  15. Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms.

    Science.gov (United States)

    Reena Benjamin, J; Jayasree, T

    2018-02-01

    In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms. A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details. The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations. In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

  16. Wavelet Compressed PCA Models for Real-Time Image Registration in Augmented Reality Applications

    OpenAIRE

    Christopher Cooper; Kent Wise; John Cooper; Makarand Deo

    2015-01-01

    The use of augmented reality (AR) has shown great promise in enhancing medical training and diagnostics via interactive simulations. This paper presents a novel method to perform accurate and inexpensive image registration (IR) utilizing a pre-constructed database of reference objects in conjunction with a principal component analysis (PCA) model. In addition, a wavelet compression algorithm is utilized to enhance the speed of the registration process. The proposed method is used to perform r...

  17. In Vivo Imaging of Experimental Melanoma Tumors using the Novel Radiotracer 68Ga-NODAGA-Procainamide (PCA).

    Science.gov (United States)

    Kertész, István; Vida, András; Nagy, Gábor; Emri, Miklós; Farkas, Antal; Kis, Adrienn; Angyal, János; Dénes, Noémi; Szabó, Judit P; Kovács, Tünde; Bai, Péter; Trencsényi, György

    2017-01-01

    The most aggressive form of skin cancer is the malignant melanoma. Because of its high metastatic potential the early detection of primary melanoma tumors and metastases using non-invasive PET imaging determines the outcome of the disease. Previous studies have already shown that benzamide derivatives, such as procainamide (PCA) specifically bind to melanin pigment. The aim of this study was to synthesize and investigate the melanin specificity of the novel 68 Ga-labeled NODAGA-PCA molecule in vitro and in vivo using PET techniques. Procainamide (PCA) was conjugated with NODAGA chelator and was labeled with Ga-68 ( 68 Ga-NODAGA-PCA). The melanin specificity of 68 Ga-NODAGA-PCA was tested in vitro , ex vivo and in vivo using melanotic B16-F10 and amelanotic Melur melanoma cell lines. By subcutaneous and intravenous injection of melanoma cells tumor-bearing mice were prepared, on which biodistribution studies and small animal PET/CT scans were performed for 68 Ga-NODAGA-PCA and 18 FDG tracers. 68 Ga-NODAGA-PCA was produced with high specific activity (14.9±3.9 GBq/µmol) and with excellent radiochemical purity (98%PCA uptake of B16-F10 cells was significantly ( p ≤0.01) higher than Melur cells. Ex vivo biodistribution and in vivo PET/CT studies using subcutaneous and metastatic tumor models showed significantly ( p ≤0.01) higher 68 Ga-NODAGA-PCA uptake in B16-F10 primary tumors and lung metastases in comparison with amelanotic Melur tumors. In experiments where 18 FDG and 68 Ga-NODAGA-PCA uptake of B16-F10 tumors was compared, we found that the tumor-to-muscle (T/M) and tumor-to-lung (T/L) ratios were significantly ( p ≤0.05 and p ≤0.01) higher using 68 Ga-NODAGA-PCA than the 18 FDG accumulation. Our novel radiotracer 68 Ga-NODAGA-PCA showed specific binding to the melanin producing experimental melanoma tumors. Therefore, 68 Ga-NODAGA-PCA is a suitable diagnostic radiotracer for the detection of melanoma tumors and metastases in vivo .

  18. Automatic detection of optic disc based on PCA and mathematical morphology.

    Science.gov (United States)

    Morales, Sandra; Naranjo, Valery; Angulo, Us; Alcaniz, Mariano

    2013-04-01

    The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology along with principal component analysis (PCA). It makes use of different operations such as generalized distance function (GDF), a variant of the watershed transformation, the stochastic watershed, and geodesic transformations. The input of the segmentation method is obtained through PCA. The purpose of using PCA is to achieve the grey-scale image that better represents the original RGB image. The implemented algorithm has been validated on five public databases obtaining promising results. The average values obtained (a Jaccard's and Dice's coefficients of 0.8200 and 0.8932, respectively, an accuracy of 0.9947, and a true positive and false positive fractions of 0.9275 and 0.0036) demonstrate that this method is a robust tool for the automatic segmentation of the optic disc. Moreover, it is fairly reliable since it works properly on databases with a large degree of variability and improves the results of other state-of-the-art methods.

  19. Identifikasi Wajah Manusia untuk Sistem Monitoring Kehadiran Perkuliahan menggunakan Ekstraksi Fitur Principal Component Analysis (PCA

    Directory of Open Access Journals (Sweden)

    Cucu Suhery

    2017-04-01

    Full Text Available Berbagai sistem monitoring presensi yang ada memiliki kekurangan dan kelebihan masing-masing, dan perlu  untuk terus dikembangkan sehingga memudahkan dalam proses pengolahan datanya. Pada penelitian ini dikembangkan suatu sistem monitoring presensi menggunakan deteksi wajah manusia yang diintegrasikan dengan basis data menggunakan bahasa pemrograman Python dan library opencv. Akuisisi data citra dilakukan dengan ponsel android, kemudian citra tersebut dideteksi dan dipotong sehingga hanya didapat bagian wajah saja.  Deteksi wajah menggunakan metode Haar-Cascade Classifier, kemudian ekstraksi fitur dilakukan menggunakan metode Principal Component Analysis (PCA. Hasil dari PCA diberi label sesuai dengan data manusia yang ada pada basis data. Semua citra yang telah memiliki nilai PCA dan tersimpan di basis data akan dicari kemiripannya dengan citra wajah pada proses pengujian menggunakan metoda Euclidian Distance. Pada penelitian ini basis data yang digunakan yaitu MySQL. Hasil deteksi citra wajah pada proses pelatihan memiliki tingkat keberhasilan 100% dan hasil identifikasi wajah pada proses pengujian memiliki tingkat keberhasilan 90%..   Kata kunci— android, haar-cascade classifier, principal component analysis, euclidian distance, MySQL, sistem monitoring presensi, deteksi wajah

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

    Science.gov (United States)

    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.

  1. A neuro-fuzzy inference system for sensor failure detection using wavelet denoising, PCA and SPRT

    International Nuclear Information System (INIS)

    Na, Man Gyun

    2001-01-01

    In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA(principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system. The PCA is used to reduce the dimension of an input space without losing a significant amount of information, The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors

  2. Synthesis and bioactivities of Phenazine-1-carboxylic acid derivatives based on the modification of PCA carboxyl group.

    Science.gov (United States)

    Xiong, Zhipeng; Niu, Junfan; Liu, Hao; Xu, Zhihong; Li, Junkai; Wu, Qinglai

    2017-05-01

    Phenazine-1-carboxylic acid (PCA) as a natural product widely exists in microbial metabolites of Pseudomonads and Streptomycetes and has been registered for the fungicide against rice sheath blight in China. To find higher fungicidal activities compounds and study the effects on fungicidal activities after changing the carboxyl group of PCA, we synthesized a series of PCA derivatives by modifying the carboxyl group of PCA and their structures were confirmed by 1 H NMR and HRMS. Most compounds exhibited significant fungicidal activities in vitro. In particular, compound 6 exhibited inhibition effect against Rhizoctonia solani with EC 50 values of 4.35mg/L and compound 3b exhibited effect against Fusarium graminearum with EC 50 values of 8.30mg/L, compared to the positive control PCA with its EC 50 values of 7.88mg/L (Rhizoctonia solani) and 127.28mg/L (Fusarium graminearum), respectively. The results indicated that the carboxyl group of PCA could be modified to be amide group, acylhydrazine group, ester group, methyl, hydroxymethyl, chloromethyl and ether group etc. And appropriate modifications on carboxyl group of PCA were useful to extend the fungicidal scope. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Treatment of a patient with posterior cortical atrophy (PCA) with chiropractic manipulation and Dynamic Neuromuscular Stabilization (DNS): A case report.

    Science.gov (United States)

    Francio, Vinicius T; Boesch, Ron; Tunning, Michael

    2015-03-01

    Posterior cortical atrophy (PCA) is a rare progressive neurodegenerative syndrome which unusual symptoms include deficits of balance, bodily orientation, chronic pain syndrome and dysfunctional motor patterns. Current research provides minimal guidance on support, education and recommended evidence-based patient care. This case reports the utilization of chiropractic spinal manipulation, dynamic neuromuscular stabilization (DNS), and other adjunctive procedures along with medical treatment of PCA. A 54-year-old male presented to a chiropractic clinic with non-specific back pain associated with visual disturbances, slight memory loss, and inappropriate cognitive motor control. After physical examination, brain MRI and PET scan, the diagnosis of PCA was recognized. Chiropractic spinal manipulation and dynamic neuromuscular stabilization were utilized as adjunctive care to conservative pharmacological treatment of PCA. Outcome measurements showed a 60% improvement in the patient's perception of health with restored functional neuromuscular pattern, improvements in locomotion, posture, pain control, mood, tolerance to activities of daily living (ADLs) and overall satisfactory progress in quality of life. Yet, no changes on memory loss progression, visual space orientation, and speech were observed. PCA is a progressive and debilitating condition. Because of poor awareness of PCA by physicians, patients usually receive incomplete care. Additional efforts must be centered on the musculoskeletal features of PCA, aiming enhancement in quality of life and functional improvements (FI). Adjunctive rehabilitative treatment is considered essential for individuals with cognitive and motor disturbances, and manual medicine procedures may be consider a viable option.

  4. [The role of a single PCA3 test before a first negative prostate biopsy: 5-year follow-up].

    Science.gov (United States)

    Bernardeau, S; Charles, T; Fromont-Hankard, G; Irani, J

    2017-04-01

    We report a 5-year follow-up of a cohort of patients who underwent a first prostate biopsy following a prostate cancer antigen 3 (PCA3) test. We reviewed consecutive patients who had in 2008 a single urinary PCA3 test using the Gen-Probe ® assay before a first prostate biopsy for a prostate-specific antigen (PSA) between 3 and 20ng/mL and/or a suspicious digital rectal examination. PCA3 performances were analyzed in 2008 and then in 2013 after taking into account the results of repeat biopsies. At initial biopsy in 2008, among the 125 patients study cohort, prostate cancer was diagnosed in 47 patients (37.6%). Abnormal digital rectal exam, PSA density, prostate volume and PCA3 score were significantly associated with prostate cancer diagnosis. PCA3 area under the curve of the receiver operating curve was 0.67 [95%CI: 0.57-0.76] with an optimal threshold of PCA3 in this sample of 24 units. During the 5-year follow-up, among the 78 patients with a negative prostate biopsy in 2008, 23 (29.5%) had a repeat prostate biopsy of whom 14 were diagnosed with prostate cancer. PCA3 score measured in 2008 was associated with prostate cancer diagnosis (P=0.002). All 9 patients with a negative repeat prostate biopsy had a PCA3 score below the cut-off while this was the case in only 2 patients among the 14 with a positive repeat prostate biopsy. The results of a single PCA3 test before a first prostate biopsy seems to be a useful aid in deciding whether to perform a repeat biopsy. 4. Copyright © 2017. Published by Elsevier Masson SAS.

  5. Arabidopsis PCaP2 Functions as a Linker Between ABA and SA Signals in Plant Water Deficit Tolerance

    Directory of Open Access Journals (Sweden)

    Xianling Wang

    2018-05-01

    Full Text Available Water stress has a major influence on plant growth, development, and productivity. However, the cross-talk networks involved in drought tolerance are not well understood. Arabidopsis PCaP2 is a plasma membrane-associated Ca2+-binding protein. In this study, we employ qRT-PCR and β-glucuronidase (GUS histochemical staining to demonstrate that PCaP2 expression was strongly induced in roots, cotyledons, true leaves, lateral roots, and whole plants under water deficit conditions. Compared with the wild type (WT plants, PCaP2-overexpressing (PCaP2-OE plants displayed enhanced water deficit tolerance in terms of seed germination, seedling growth, and plant survival status. On the contrary, PCaP2 mutation and reduction via PCaP2-RNAi rendered plants more sensitive to water deficit. Furthermore, PCaP2-RNAi and pcap2 seedlings showed shorter root hairs and lower relative water content compared to WT under normal conditions and these phenotypes were exacerbated under water deficit. Additionally, the expression of PCaP2 was strongly induced by exogenous abscisic acid (ABA and salicylic acid (SA treatments. PCaP2-OE plants showed insensitive to exogenous ABA and SA treatments, in contrast to the susceptible phenotypes of pcap2 and PCaP2-RNAi. It is well-known that SNF1-related kinase 2s (SnRK2s and pathogenesis-related (PRs are major factors that influence plant drought tolerance by ABA- and SA-mediated pathways, respectively. Interestingly, PCaP2 positively regulated the expression of drought-inducible genes (RD29A, KIN1, and KIN2, ABA-mediated drought responsive genes (SnRK2.2, -2.3, -2.6, ABF1, -2, -3, -4, and SA-mediated drought responsive genes (PR1, -2, -5 under water deficit, ABA, or SA treatments. Taken together, our results showed that PCaP2 plays an important and positive role in Arabidopsis water deficit tolerance by involving in response to both ABA and SA signals and regulating root hair growth. This study provides novel insights into the

  6. 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

    Science.gov (United States)

    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.

  7. Swelling and microstructural development in path A PCA and type 316 stainless steel irradiated in HFIR to about 22 dpa

    International Nuclear Information System (INIS)

    Maziasz, P.J.; Braski, D.N.

    1983-01-01

    Irradiation of several microstructural variants of PCA and 20%-cold-worked N-lot type 316 stainess steel (CW 316) in HFIR to about 10 dpa produced no visible cavities at 300 0 C, bubbles at 400 0 C, and varying distributions of bubbles and voids at 500 and 600 0 C. The PCA-B1 swells the most and CW 316 (N-lot) the least at 600 0 C. Irradiations have been extended to about 22 dpa. The PCA-Al swells 0.06%/dpa at 600 0 C but at a much lower rate at 500 0 C. The PCA-A3 shows the lowest swelling at 600 0 C, about the half the swelling rate of type 316 stainless steel

  8. Testing a Modified PCA-Based Sharpening Approach for Image Fusion

    Directory of Open Access Journals (Sweden)

    Jan Jelének

    2016-09-01

    Full Text Available Image data sharpening is a challenging field of remote sensing science, which has become more relevant as high spatial-resolution satellites and superspectral sensors have emerged. Although the spectral property is crucial for mineral mapping, spatial resolution is also important as it allows targeted minerals/rocks to be identified/interpreted in a spatial context. Therefore, improving the spatial context, while keeping the spectral property provided by the superspectral sensor, would bring great benefits for geological/mineralogical mapping especially in arid environments. In this paper, a new concept was tested using superspectral data (ASTER and high spatial-resolution panchromatic data (WorldView-2 for image fusion. A modified Principal Component Analysis (PCA-based sharpening method, which implements a histogram matching workflow that takes into account the real distribution of values, was employed to test whether the substitution of Principal Components (PC1–PC4 can bring a fused image which is spectrally more accurate. The new approach was compared to those most widely used—PCA sharpening and Gram–Schmidt sharpening (GS, both available in ENVI software (Version 5.2 and lower as well as to the standard approach—sharpening Landsat 8 multispectral bands (MUL using its own panchromatic (PAN band. The visual assessment and the spectral quality indicators proved that the spectral performance of the proposed sharpening approach employing PC1 and PC2 improve the performance of the PCA algorithm, moreover, comparable or better results are achieved compared to the GS method. It was shown that, when using the PC1, the visible-near infrared (VNIR part of the spectrum was preserved better, however, if the PC2 was used, the short-wave infrared (SWIR part was preserved better. Furthermore, this approach improved the output spectral quality when fusing image data from different sensors (e.g., ASTER and WorldView-2 while keeping the proper albedo

  9. PCA-based algorithm for calibration of spectrophotometric analysers of food

    International Nuclear Information System (INIS)

    Morawski, Roman Z; Miekina, Andrzej

    2013-01-01

    Spectrophotometric analysers of food, being instruments for determination of the composition of food products and ingredients, are today of growing importance for food industry, as well as for food distributors and consumers. Their metrological performance significantly depends of the numerical performance of available means for spectrophotometric data processing; in particular – the means for calibration of analysers. In this paper, a new algorithm for this purpose is proposed, viz. the algorithm using principal components analysis (PCA). It is almost as efficient as PLS-based algorithms of calibration, but much simpler

  10. Research on distributed heterogeneous data PCA algorithm based on cloud platform

    Science.gov (United States)

    Zhang, Jin; Huang, Gang

    2018-05-01

    Principal component analysis (PCA) of heterogeneous data sets can solve the problem that centralized data scalability is limited. In order to reduce the generation of intermediate data and error components of distributed heterogeneous data sets, a principal component analysis algorithm based on heterogeneous data sets under cloud platform is proposed. The algorithm performs eigenvalue processing by using Householder tridiagonalization and QR factorization to calculate the error component of the heterogeneous database associated with the public key to obtain the intermediate data set and the lost information. Experiments on distributed DBM heterogeneous datasets show that the model method has the feasibility and reliability in terms of execution time and accuracy.

  11. Convergence analysis of Chauvin's PCA learning algorithm with a constant learning rate

    International Nuclear Information System (INIS)

    Lv Jiancheng; Yi Zhang

    2007-01-01

    The convergence of Chauvin's PCA learning algorithm with a constant learning rate is studied in this paper by using a DDT method (deterministic discrete-time system method). Different from the DCT method (deterministic continuous-time system method), the DDT method does not require that the learning rate converges to zero. An invariant set of Chauvin's algorithm with a constant learning rate is obtained so that the non-divergence of this algorithm can be guaranteed. Rigorous mathematic proofs are provided to prove the local convergence of this algorithm

  12. Convergence analysis of Chauvin's PCA learning algorithm with a constant learning rate

    Energy Technology Data Exchange (ETDEWEB)

    Lv Jiancheng [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China); Yi Zhang [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)]. E-mail: zhangyi@uestc.edu.cn

    2007-05-15

    The convergence of Chauvin's PCA learning algorithm with a constant learning rate is studied in this paper by using a DDT method (deterministic discrete-time system method). Different from the DCT method (deterministic continuous-time system method), the DDT method does not require that the learning rate converges to zero. An invariant set of Chauvin's algorithm with a constant learning rate is obtained so that the non-divergence of this algorithm can be guaranteed. Rigorous mathematic proofs are provided to prove the local convergence of this algorithm.

  13. FUZZY FUSION OF PCA, ICA AND ILDA FACE ALGORITHMS FOR ENHANCED USER AUTHENTICATION

    Directory of Open Access Journals (Sweden)

    PRASHANT KUMAR JAIN

    2017-09-01

    Full Text Available Use of biometrics has increased over last few years due to its inherent advantages over customary identification tools such as token card and password, etc. In biometrics, after fingerprint, face recognition is second most preferred method with reasonably good accuracy. In some applications like CCTV cameras where face of a person is available for processing, face recognition techniques can to be very useful. In this paper, integration of face recognition techniques PCA, ICA and ILDA using fuzzy fusion method is detailed. The preliminary results clearly reveal that the fusion of methods improves the accuracy of the user identification.

  14. Effect of Spatial Alignment Transformations in PCA and ICA of Functional Neuroimages

    DEFF Research Database (Denmark)

    Lukic, Ana S.; Wernick, Miles N.; Yang, Yongui

    2007-01-01

    this observation is true, not only for spatial ICA, but also for temporal ICA and for principal component analysis (PCA). In each case we find conditions that the spatial alignment operator must satisfy to ensure invariance of the results. We illustrate our findings using functional magnetic-resonance imaging (f......It has been previously observed that spatial independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial alignment of scans in a functional neuroimaging study. In this paper we seek to determine analytically the conditions under which...

  15. PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods

    Directory of Open Access Journals (Sweden)

    Yukai Yao

    2015-01-01

    Full Text Available We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.

  16. A g-factor metric for k-t SENSE and k-t PCA based parallel imaging.

    Science.gov (United States)

    Binter, Christian; Ramb, Rebecca; Jung, Bernd; Kozerke, Sebastian

    2016-02-01

    To propose and validate a g-factor formalism for k-t SENSE, k-t PCA and related k-t methods for assessing SNR and temporal fidelity. An analytical gxf -factor formulation in the spatiotemporal frequency domain is derived, enabling assessment of noise and depiction fidelity in both the spatial and frequency domain. Using pseudoreplica analysis of cardiac cine data the gxf -factor description is validated and example data are used to analyze the performance of k-t methods for various parameter settings. Analytical gxf -factor maps were found to agree well with pseudoreplica analysis for 3x, 5x, and 7x k-t SENSE and k-t PCA. While k-t SENSE resulted in lower average gxf values (gx (avg) ) in static regions when compared with k-t PCA, k-t PCA yielded lower gx (avg) values in dynamic regions. Temporal transfer was better preserved with k-t PCA for increasing undersampling factors. The proposed gxf -factor and temporal transfer formalism allows assessing noise performance and temporal depiction fidelity of k-t methods including k-t SENSE and k-t PCA. The framework enables quantitative comparison of different k-t methods relative to frame-by-frame parallel imaging reconstruction. © 2015 Wiley Periodicals, Inc.

  17. Ketamine PCA for treatment of end-of-life neuropathic pain in pediatrics.

    Science.gov (United States)

    Taylor, Matthew; Jakacki, Regina; May, Carol; Howrie, Denise; Maurer, Scott

    2015-12-01

    Control of neuropathic pain (NP) for children at end of life is challenging. Ketamine improves control of NP, but its use in children is not well described. We describe a retrospective case review of 14 children with terminal prognoses treated with ketamine patient-controlled analgesia (PCA) for management of opioid-refractory NP at the end of life. Median ketamine dose was 0.06 mg/kg/h (range 0.014-0.308 mg/kg/h) with a 0.05 mg/kg (range 0.03-0.5mg/kg) demand dose available every 15 minutes (range 10-60 minutes). All patients noted subjective pain relief with ketamine, and 79% had no adverse effects. Benzodiazepines limited neuropsychiatric side effects. Ketamine treatment arrested dose escalation of opioids in 64% of patients, and 79% were discharged to home hospice. Ketamine PCA is an effective, well-tolerated therapy for opioid-refractory NP in pediatric end-of-life care. © The Author(s) 2014.

  18. Location optimization of solar plants by an integrated hierarchical DEA PCA approach

    International Nuclear Information System (INIS)

    Azadeh, A.; Ghaderi, S.F.; Maghsoudi, A.

    2008-01-01

    Unique features of renewable energies such as solar energy has caused increasing demands for such resources. In order to use solar energy as a natural resource, environmental circumstances and geographical location related to solar intensity must be considered. Different factors may affect on the selection of a suitable location for solar plants. These factors must be considered concurrently for optimum location identification of solar plants. This article presents an integrated hierarchical approach for location of solar plants by data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT). Furthermore, an integrated hierarchical DEA approach incorporating the most relevant parameters of solar plants is introduced. Moreover, 2 multivariable methods namely, PCA and NT are used to validate the results of DEA model. The prescribed approach is tested for 25 different cities in Iran with 6 different regions within each city. This is the first study that considers an integrated hierarchical DEA approach for geographical location optimization of solar plants. Implementation of the proposed approach would enable the energy policy makers to select the best-possible location for construction of a solar power plant with lowest possible costs

  19. PCA-based ANN approach to leak classification in the main pipes of VVER-1000

    International Nuclear Information System (INIS)

    Hadad, Kamal; Jabbari, Masoud; Tabadar, Z.; Hashemi-Tilehnoee, Mehdi

    2012-01-01

    This paper presents a neural network based fault diagnosing approach which allows dynamic crack and leaks fault identification. The method utilizes the Principal Component Analysis (PCA) technique to reduce the problem dimension. Such a dimension reduction approach leads to faster diagnosing and allows a better graphic presentation of the results. To show the effectiveness of the proposed approach, two methodologies are used to train the neural network (NN). At first, a training matrix composed of 14 variables is used to train a Multilayer Perceptron neural network (MLP) with Resilient Backpropagation (RBP) algorithm. Employing the proposed method, a more accurate and simpler network is designed where the input size is reduced from 14 to 6 variables for training the NN. In short, the application of PCA highly reduces the network topology and allows employing more efficient training algorithms. The accuracy, generalization ability, and reliability of the designed networks are verified using 10 simulated events data from a VVER-1000 simulation using DINAMIKA-97 code. Noise is added to the data to evaluate the robustness of the method and the method again shows to be effective and powerful. (orig.)

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

    Directory of Open Access Journals (Sweden)

    Jabid Quiroga

    2017-12-01

    Full Text Available 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.

  1. The PCA learning effect: An emerging correlate of face memory during childhood.

    Science.gov (United States)

    Gao, Xiaoqing; Maurer, Daphne; Wilson, Hugh R

    2015-10-01

    Human adults implicitly learn the prototype and the principal components of the variability distinguishing faces (Gao & Wilson, 2014). Here we measured the implicit learning effect in adults and 9-year-olds, and with a modified child-friendly procedure, in 7-year-olds. All age groups showed the implicit learning effect by falsely recognizing the average (the prototype effect) and the principal component faces as having been seen (the PCA learning effect). The PCA learning effect, but not the prototype effect increased between 9years of age and adulthood and at both ages was the better predictor of memory for the actually studied faces. In contrast, for the 7-year-olds, the better predictor of face memory was the prototype effect. The pattern suggests that there may be a developmental change between ages 7 and 9 in the mechanism underlying memory for faces. We provide the first evidence that children as young as age 7 can extract the most important dimensions of variation represented by principal components among individual faces, a key ability that grows stronger with age and comes to underlie memory for faces. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. Features and performance of PCa board which cuts off the electromagnetic wave. Electromagnetic shield building using the carbon fiber contamination PCa board; Denjiha wo shadansuru PCa ban no tokucho to seino. Tanso sen'i konnyu PCa ban wo mochiita denji shirudobiru

    Energy Technology Data Exchange (ETDEWEB)

    Yoshida, Katsuo; Kasai, Yasuaki [Obayashi Corp., Osaka (Japan); Okada, Shin' ichiro; Sakamoto, Shin [Osaka Gas Corp., Osaka (Japan)

    1999-03-10

    With the rapid popularization of public radio information communication equipment, portable telephones, wireless LAN, etc., the interception (building shield) of the electromagnetic wave internal and external the building becomes large problem. As process equal to the convention and the method in which the cost is possible, they did do not shield the whole building, and also ensure the comfort as an office, and PCa board which mixed the carbon fiber into the mortar was developed. They described the survey result of the electromagnetic shield performance of the building which constructed by using this in external wall. They explained electromagnetism characteristics of the contamination mortar and application to the PCa board and method. They carried out the measurement in the electromagnetic shield room laboratory, and they obtained next result. 1) There is seldom on the effect both only the concrete and only by the carbon fiber mesh. 2) They considered the effect that carbon fiber chop 1% are mixed into the concrete. 3) The effect became a maximum, when carbon fiber chop and carbon fiber mesh were mixed, and they confirmed being excellent cost-concerned. (NEDO)

  3. A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM

    Directory of Open Access Journals (Sweden)

    Wei-Li Qin

    2016-01-01

    Full Text Available Aileron actuators are pivotal components for aircraft flight control system. Thus, the fault diagnosis of aileron actuators is vital in the enhancement of the reliability and fault tolerant capability. This paper presents an aileron actuator fault diagnosis approach combining principal component analysis (PCA, grid search (GS, 10-fold cross validation (CV, and one-versus-one support vector machine (SVM. This method is referred to as PGC-SVM and utilizes the direct drive valve input, force motor current, and displacement feedback signal to realize fault detection and location. First, several common faults of aileron actuators, which include force motor coil break, sensor coil break, cylinder leakage, and amplifier gain reduction, are extracted from the fault quadrantal diagram; the corresponding fault mechanisms are analyzed. Second, the data feature extraction is performed with dimension reduction using PCA. Finally, the GS and CV algorithms are employed to train a one-versus-one SVM for fault classification, thus obtaining the optimal model parameters and assuring the generalization of the trained SVM, respectively. To verify the effectiveness of the proposed approach, four types of faults are introduced into the simulation model established by AMESim and Simulink. The results demonstrate its desirable diagnostic performance which outperforms that of the traditional SVM by comparison.

  4. Learning binary code via PCA of angle projection for image retrieval

    Science.gov (United States)

    Yang, Fumeng; Ye, Zhiqiang; Wei, Xueqi; Wu, Congzhong

    2018-01-01

    With benefits of low storage costs and high query speeds, binary code representation methods are widely researched for efficiently retrieving large-scale data. In image hashing method, learning hashing function to embed highdimensions feature to Hamming space is a key step for accuracy retrieval. Principal component analysis (PCA) technical is widely used in compact hashing methods, and most these hashing methods adopt PCA projection functions to project the original data into several dimensions of real values, and then each of these projected dimensions is quantized into one bit by thresholding. The variances of different projected dimensions are different, and with real-valued projection produced more quantization error. To avoid the real-valued projection with large quantization error, in this paper we proposed to use Cosine similarity projection for each dimensions, the angle projection can keep the original structure and more compact with the Cosine-valued. We used our method combined the ITQ hashing algorithm, and the extensive experiments on the public CIFAR-10 and Caltech-256 datasets validate the effectiveness of the proposed method.

  5. External validation of a PCA-3-based nomogram for predicting prostate cancer and high-grade cancer on initial prostate biopsy.

    Science.gov (United States)

    Greene, Daniel J; Elshafei, Ahmed; Nyame, Yaw A; Kara, Onder; Malkoc, Ercan; Gao, Tianming; Jones, J Stephen

    2016-08-01

    The aim of this study was to externally validate a previously developed PCA3-based nomogram for the prediction of prostate cancer (PCa) and high-grade (intermediate and/or high-grade) prostate cancer (HGPCa) at the time of initial prostate biopsy. A retrospective review was performed on a cohort of 336 men from a large urban academic medical center. All men had serum PSA PCa, PSA at diagnosis, PCA3, total prostate volume (TPV), and abnormal finding on digital rectal exam (DRE). These variables were used to test the accuracy (concordance index) and calibration of a previously published PCA3 nomogram. Biopsy confirms PCa and HGPCa in 51.0% and 30.4% of validation patients, respectively. This differed from the original cohort in that it had significantly more PCa and HGPCA (51% vs. 44%, P = 0.019; and 30.4% vs. 19.1%, P PCa detection the concordance index was 75% and 77% for overall PCa and HGPCa, respectively. Calibration for overall PCa was good. This represents the first external validation of a PCA3-based prostate cancer predictive nomogram in a North American population. Prostate 76:1019-1023, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  6. PCA and vTEC climatology at midnight over mid-latitude regions

    Science.gov (United States)

    Natali, M. P.; Meza, A.

    2017-12-01

    The effect of the thermospheric vertical neutral wind on vertical total electron content (vTEC) variations including longitudinal anomaly, remaining winter anomaly, mid-latitude summer night anomaly, and semiannual anomaly is studied at mid-latitude regions around zero magnetic declination at midnight during high solar activity. By using the principal component analysis (PCA) numerical technique, this work studies the spatial and temporal variations of the ionosphere at midnight over mid-latitude regions during 2000-2002. PCA is applied to a time series of global vTEC maps produced by the International Global Navigation Satellite System (GNSS) Service. Four regions were studied in particular, each located at mid-latitude and approximately centered at zero magnetic declination, with two in the northern hemisphere and two in southern hemisphere, and all are located near and far from geomagnetic poles in each case. This technique provides an effective method to analyze the main ionospheric variabilities at mid-latitudes. PCA is also applied to the vTEC computed using the International Reference Ionosphere (IRI) 2012 model, to analyze the capability of this model to represent ionospheric variabilities at mid-latitude. Also, the Horizontal Wind Model 2007 (HWM07) is used to improve our climatology interpretation, by analyzing the relationship between vTEC and thermospheric wind, both quantitatively and qualitatively. At midnight, the behavior of mean vTEC values strongly responds to vertical wind variation, experiencing a decrease of about 10-15% with the action of the positive vertical component of the field-aligned neutral wind lasting for 2 h in all regions except for Oceania. Notable results include: a significant increase toward higher latitudes during summer in the South America and Asia regions, associated with the mid-latitude summer night anomaly, and an increase toward higher latitudes in winter in the North America and Oceania regions, highlighting the

  7. Pain management in patients with adolescent idiopathic scoliosis undergoing posterior spinal fusion: combined intrathecal morphine and continuous epidural versus PCA.

    Science.gov (United States)

    Ravish, Matthew; Muldowney, Bridget; Becker, Aimee; Hetzel, Scott; McCarthy, James J; Nemeth, Blaise A; Noonan, Kenneth J

    2012-12-01

    A retrospective case-comparison study. Compare efficacy and safety of combined intrathecal morphine (ITM) and epidural analgesia (EPI) to that of conventional intravenous patient-controlled analgesia (IV-PCA) after posterior spinal fusion (PSF) for adolescent idiopathic scoliosis (AIS). Pain control after PSF in AIS has been managed traditionally with IV-PCA. More recently studies have shown improvement in pain control with the use of continuous EPI or intraoperative ITM. No studies to our knowledge have compared the use of both ITM and EPI analgesia to that of IV-PCA. An Institutional Review Board-approved retrospective case-comparison study was performed from 1989 to 2009 of all patients undergoing PSF for AIS. Patients received either IV-PCA or ITM/EPI. Daily pain scores were recorded along with total opioid and benzodiazepine use. Adverse events were recorded for all the patients. A total of 146 patients were initially included in the study; 95 patients received ITM/EPI and 51 received IV-PCA as a historical control. Eight patients from the ITM/EPI group were excluded from the pain comparison portion of the study. There were no statistical differences in age, sex, weight, or hospital stay between the 2 groups. The ITM/EPI group had, on average, 1 additional level of fusion (P = 0.001). Daily average pain scores were lower in the ITM/EPI group on all hospital days, and statistically lower in days 1 and 3 to 5. Total opioid requirement was significantly lower in the ITM/EPI patients, although oral opioid use was higher among this group. Total benzodiazepine use was lower among the IV-PCA group. A total of 15.7% of the IV-PCA patients had bladder hypotonia, compared with 1.1% of the ITM/EPI group (P = 0.002). The rate of illeus was 15.7% in the IV-PCA patients and 5.7% in the ITM/EPI (P = 0.071). Respiratory depression was reported in 4 ITM/EPI patients, 0 in our PCA group. Technical catheter malfunction was reported in 8.5% of the EPI group. The use of ITM

  8. Improved algorithms for the classification of rough rice using a bionic electronic nose based on PCA and the Wilks distribution.

    Science.gov (United States)

    Xu, Sai; Zhou, Zhiyan; Lu, Huazhong; Luo, Xiwen; Lan, Yubin

    2014-03-19

    Principal Component Analysis (PCA) is one of the main methods used for electronic nose pattern recognition. However, poor classification performance is common in classification and recognition when using regular PCA. This paper aims to improve the classification performance of regular PCA based on the existing Wilks Λ-statistic (i.e., combined PCA with the Wilks distribution). The improved algorithms, which combine regular PCA with the Wilks Λ-statistic, were developed after analysing the functionality and defects of PCA. Verification tests were conducted using a PEN3 electronic nose. The collected samples consisted of the volatiles of six varieties of rough rice (Zhongxiang1, Xiangwan13, Yaopingxiang, WufengyouT025, Pin 36, and Youyou122), grown in same area and season. The first two principal components used as analysis vectors cannot perform the rough rice varieties classification task based on a regular PCA. Using the improved algorithms, which combine the regular PCA with the Wilks Λ-statistic, many different principal components were selected as analysis vectors. The set of data points of the Mahalanobis distance between each of the varieties of rough rice was selected to estimate the performance of the classification. The result illustrates that the rough rice varieties classification task is achieved well using the improved algorithm. A Probabilistic Neural Networks (PNN) was also established to test the effectiveness of the improved algorithms. The first two principal components (namely PC1 and PC2) and the first and fifth principal component (namely PC1 and PC5) were selected as the inputs of PNN for the classification of the six rough rice varieties. The results indicate that the classification accuracy based on the improved algorithm was improved by 6.67% compared to the results of the regular method. These results prove the effectiveness of using the Wilks Λ-statistic to improve the classification accuracy of the regular PCA approach. The results

  9. Comparative evaluation of urinary PCA3 and TMPRSS2: ERG scores and serum PHI in predicting prostate cancer aggressiveness.

    Science.gov (United States)

    Tallon, Lucile; Luangphakdy, Devillier; Ruffion, Alain; Colombel, Marc; Devonec, Marian; Champetier, Denis; Paparel, Philippe; Decaussin-Petrucci, Myriam; Perrin, Paul; Vlaeminck-Guillem, Virginie

    2014-07-30

    It has been suggested that urinary PCA3 and TMPRSS2:ERG fusion tests and serum PHI correlate to cancer aggressiveness-related pathological criteria at prostatectomy. To evaluate and compare their ability in predicting prostate cancer aggressiveness, PHI and urinary PCA3 and TMPRSS2:ERG (T2) scores were assessed in 154 patients who underwent radical prostatectomy for biopsy-proven prostate cancer. Univariate and multivariate analyses using logistic regression and decision curve analyses were performed. All three markers were predictors of a tumor volume≥0.5 mL. Only PHI predicted Gleason score≥7. T2 score and PHI were both independent predictors of extracapsular extension(≥pT3), while multifocality was only predicted by PCA3 score. Moreover, when compared to a base model (age, digital rectal examination, serum PSA, and Gleason sum at biopsy), the addition of both PCA3 score and PHI to the base model induced a significant increase (+12%) when predicting tumor volume>0.5 mL. PHI and urinary PCA3 and T2 scores can be considered as complementary predictors of cancer aggressiveness at prostatectomy.

  10. Comparative Evaluation of Urinary PCA3 and TMPRSS2: ERG Scores and Serum PHI in Predicting Prostate Cancer Aggressiveness

    Directory of Open Access Journals (Sweden)

    Lucile Tallon

    2014-07-01

    Full Text Available It has been suggested that urinary PCA3 and TMPRSS2:ERG fusion tests and serum PHI correlate to cancer aggressiveness-related pathological criteria at prostatectomy. To evaluate and compare their ability in predicting prostate cancer aggressiveness, PHI and urinary PCA3 and TMPRSS2:ERG (T2 scores were assessed in 154 patients who underwent radical prostatectomy for biopsy-proven prostate cancer. Univariate and multivariate analyses using logistic regression and decision curve analyses were performed. All three markers were predictors of a tumor volume ≥0.5 mL. Only PHI predicted Gleason score ≥7. T2 score and PHI were both independent predictors of extracapsular extension (≥pT3, while multifocality was only predicted by PCA3 score. Moreover, when compared to a base model (age, digital rectal examination, serum PSA, and Gleason sum at biopsy, the addition of both PCA3 score and PHI to the base model induced a significant increase (+12% when predicting tumor volume >0.5 mL. PHI and urinary PCA3 and T2 scores can be considered as complementary predictors of cancer aggressiveness at prostatectomy.

  11. Human Classification Based on Gestural Motions by Using Components of PCA

    International Nuclear Information System (INIS)

    Aziz, Azri A; Wan, Khairunizam; Za'aba, S K; Shahriman A B; Asyekin H; Zuradzman M R; Adnan, Nazrul H

    2013-01-01

    Lately, a study of human capabilities with the aim to be integrated into machine is the famous topic to be discussed. Moreover, human are bless with special abilities that they can hear, see, sense, speak, think and understand each other. Giving such abilities to machine for improvement of human life is researcher's aim for better quality of life in the future. This research was concentrating on human gesture, specifically arm motions for differencing the individuality which lead to the development of the hand gesture database. We try to differentiate the human physical characteristic based on hand gesture represented by arm trajectories. Subjects are selected from different type of the body sizes, and then acquired data undergo resampling process. The results discuss the classification of human based on arm trajectories by using Principle Component Analysis (PCA)

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

    Science.gov (United States)

    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.

  13. Evaluation of the application of BIM technology based on PCA - Q Clustering Algorithm and Choquet Integral

    Directory of Open Access Journals (Sweden)

    Wei Xiaozhao

    2016-03-01

    Full Text Available For the development of the construction industry, the construction of data era is approaching, BIM (building information model with the actual needs of the construction industry has been widely used as a building information clan system software, different software for the practical application of different maturity, through the expert scoring method for the application of BIM technology maturity index mark, establish the evaluation index system, using PCA - Q clustering algorithm for the evaluation index system of classification, comprehensive evaluation in combination with the Choquet integral on the classification of evaluation index system, to achieve a reasonable assessment of the application of BIM technology maturity index. To lay a foundation for the future development of BIM Technology in various fields of construction, at the same time provides direction for the comprehensive application of BIM technology.

  14. Visual tracking based on the sparse representation of the PCA subspace

    Science.gov (United States)

    Chen, Dian-bing; Zhu, Ming; Wang, Hui-li

    2017-09-01

    We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis (PCA) subspace, and then we employ an L 1 regularization to restrict the sparsity of the residual term, an L 2 regularization term to restrict the sparsity of the representation coefficients, and an L 2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.

  15. Oxidation/volatilization rates in air for candidate fusion reactor blanket materials, PCA and HT-9

    International Nuclear Information System (INIS)

    Piet, S.J.; Kraus, H.G.; Neilson, R.M. Jr.; Jones, J.L.

    1986-01-01

    Large uncertainties exist in the quantity of neutron-induced activation products that can be mobilized in potential fusion accidents. The accidental combination of high temperatures and oxidizing conditions might lead to mobilization of a significant amount of activation products from structural materials. Here, the volatilization of constituents of PCA and HT-9 resulting from oxidation in air was investigated. Tests were conducted in flowing air at temperatures from 600 to 1300 0 C for 1, 5, or 20 h. Elemental volatility was calculated in terms of the weight fraction of the element volatilized from the initial alloy. Molybdenum and manganese were the radiologically significant primary constituents most volatilizized, suggesting that molybdenum and manganese should be minimized in fusion steel compositions. Higher chromium content appears beneficial in reducing hazards from mobile activation products. Scanning electron microscopy and energy dispersive spectroscopy were used to study the oxide layer on samples. (orig.)

  16. Oxidation/volatilization rates in air for candidate fusion reactor blanket materials, PCA and HT-9

    International Nuclear Information System (INIS)

    Piet, S.J.; Kraus, H.G.; Neilson, R.M. Jr.; Jones, J.L.

    1986-01-01

    Large uncertainties exist in the quantity of neutron-induced activation products that can be mobilized in potential fusion accidents. The accidental combination of high temperatures and oxidizing conditions might lead to mobilization of a significant amount of activation products from structural materials. Here, the volatilization of constituents of PCA and HT-9 resulting from oxidation in air was investigated. Tests were conducted in flowing air at temperatures from 600 to 1300 0 C for 1, 5, or 20 hours. Elemental volatility was calculated in terms of the weight fraction of the element volatilized from the initial alloy. Molybdenum and manganese were the radiologically significant primary constituents most volatilized, suggesting that molybdenum and manganese should be minimized in fusion steel compositions. Higher chromium content appears beneficial in reducing hazards from mobile activation products. Scanning electron microscopy and energy dispersive spectroscopy were used to study the oxide layer on samples

  17. Coarse-to-fine markerless gait analysis based on PCA and Gauss-Laguerre decomposition

    Science.gov (United States)

    Goffredo, Michela; Schmid, Maurizio; Conforto, Silvia; Carli, Marco; Neri, Alessandro; D'Alessio, Tommaso

    2005-04-01

    Human movement analysis is generally performed through the utilization of marker-based systems, which allow reconstructing, with high levels of accuracy, the trajectories of markers allocated on specific points of the human body. Marker based systems, however, show some drawbacks that can be overcome by the use of video systems applying markerless techniques. In this paper, a specifically designed computer vision technique for the detection and tracking of relevant body points is presented. It is based on the Gauss-Laguerre Decomposition, and a Principal Component Analysis Technique (PCA) is used to circumscribe the region of interest. Results obtained on both synthetic and experimental tests provide significant reduction of the computational costs, with no significant reduction of the tracking accuracy.

  18. Insights on the Spectral Signatures of Stellar Activity and Planets from PCA

    Energy Technology Data Exchange (ETDEWEB)

    Davis, Allen B.; Fischer, Debra A. [Department of Astronomy, Yale University, 52 Hillhouse Avenue, New Haven, CT 06511 (United States); Cisewski, Jessi [Department of Statistics, Yale University, 24 Hillhouse Avenue, New Haven, CT 06511 (United States); Dumusque, Xavier [Observatoire de Genève, Université de Genève, 51 ch. des Maillettes, 1290 Versoix (Switzerland); Ford, Eric B., E-mail: allen.b.davis@yale.edu [Center for Exoplanets and Habitable Worlds, The Pennsylvania State University, University Park, PA 16802 (United States)

    2017-09-01

    Photospheric velocities and stellar activity features such as spots and faculae produce measurable radial velocity signals that currently obscure the detection of sub-meter-per-second planetary signals. However, photospheric velocities are imprinted differently in a high-resolution spectrum than are Keplerian Doppler shifts. Photospheric activity produces subtle differences in the shapes of absorption lines due to differences in how temperature or pressure affects the atomic transitions. In contrast, Keplerian Doppler shifts affect every spectral line in the same way. With a high enough signal-to-noise (S/N) and resolution, statistical techniques can exploit differences in spectra to disentangle the photospheric velocities and detect lower-amplitude exoplanet signals. We use simulated disk-integrated time-series spectra and principal component analysis (PCA) to show that photospheric signals introduce spectral line variability that is distinct from that of Doppler shifts. We quantify the impact of instrumental resolution and S/N for this work.

  19. Dimensionality Reduction Methods: Comparative Analysis of methods PCA, PPCA and KPCA

    Directory of Open Access Journals (Sweden)

    Jorge Arroyo-Hernández

    2016-01-01

    Full Text Available The dimensionality reduction methods are algorithms mapping the set of data in subspaces derived from the original space, of fewer dimensions, that allow a description of the data at a lower cost. Due to their importance, they are widely used in processes associated with learning machine. This article presents a comparative analysis of PCA, PPCA and KPCA dimensionality reduction methods. A reconstruction experiment of worm-shape data was performed through structures of landmarks located in the body contour, with methods having different number of main components. The results showed that all methods can be seen as alternative processes. Nevertheless, thanks to the potential for analysis in the features space and the method for calculation of its preimage presented, KPCA offers a better method for recognition process and pattern extraction

  20. Intrathecal morphine is superior to intravenous PCA in patients undergoing minimally invasive cardiac surgery

    Directory of Open Access Journals (Sweden)

    Chirojit Mukherjee

    2012-01-01

    Full Text Available Aim of our study was to evaluate the beneficial effect of low dose intrathecal morphine on postoperative analgesia, over the use of intravenous patient controlled anesthesia (PCA, in patients undergoing fast track anesthesia during minimally invasive cardiac surgical procedures. A randomized controlled trial was undertaken after approval from local ethical committee. Written informed consent was obtained from 61 patients receiving mitral or tricuspid or both surgical valve repair in minimal invasive technique. Patients were assigned randomly to 2 groups. Group 1 received general anesthesia and intravenous patient controlled analgesia (PCA pump with Piritramide (GA group. Group 2 received a single shot of intrathecal morphine (1.5 μg/kg body weight prior to the administration of general anesthesia (ITM group. Site of puncture was confined to lumbar (L1-2 or L2-3 intrathecal space. The amount of intravenous piritramide used in post anesthesia care unit (PACU and the first postoperative day was defined as primary end point. Secondary end points included: time for tracheal extubation, pain and sedation scores in PACU upto third postoperative day. For statistical analysis Mann-Whitney-U Test and Fishers exact test (SPSS were used. We found that the demand for intravenous opioids in PACU was significantly reduced in ITM group (P <0.001. Pain scores were significantly decreased in ITM group until second postoperative day (P <0.01. There was no time delay for tracheal extubation in ITM group, and sedation scores did not differ in either group. We conclude that low dose single shot intrathecal morphine provides adequate postoperative analgesia, reduces the intravenous opioid consumption during the early postoperative period and does not defer early extubation.

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

    Science.gov (United States)

    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

  2. Design and synthesis of prostate cancer antigen-1 (PCA-1/ALKBH3) inhibitors as anti-prostate cancer drugs.

    Science.gov (United States)

    Nakao, Syuhei; Mabuchi, Miyuki; Shimizu, Tadashi; Itoh, Yoshihiro; Takeuchi, Yuko; Ueda, Masahiro; Mizuno, Hiroaki; Shigi, Naoko; Ohshio, Ikumi; Jinguji, Kentaro; Ueda, Yuko; Yamamoto, Masatatsu; Furukawa, Tatsuhiko; Aoki, Shunji; Tsujikawa, Kazutake; Tanaka, Akito

    2014-02-15

    A series of 1-aryl-3,4-substituted-1H-pyrazol-5-ol derivatives was synthesized and evaluated as prostate cancer antigen-1 (PCA-1/ALKBH3) inhibitors to obtain a novel anti-prostate cancer drug. After modifying 1-(1H-benzimidazol-2-yl)-3,4-dimethyl-1H-pyrazol-5-ol (1), a hit compound found during random screening using a recombinant PCA-1/ALKBH3, 1-(1H-5-methylbenzimidazol-2-yl)-4-benzyl-3-methyl-1H-pyrazol-5-ol (35, HUHS015), was obtained as a potent PCA-1/ALKBH3 inhibitor both in vitro and in vivo. The bioavailability (BA) of 35 was 7.2% in rats after oral administration. As expected, continuously administering 35 significantly suppressed the growth of DU145 cells, which are human hormone-independent prostate cancer cells, in a mouse xenograft model without untoward effects. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. Comparative Performance Of Using PCA With K-Means And Fuzzy C Means Clustering For Customer Segmentation

    Directory of Open Access Journals (Sweden)

    Fahmida Afrin

    2015-08-01

    Full Text Available Abstract Data mining is the process of analyzing data and discovering useful information. Sometimes it is called knowledge Discovery. Clustering refers to groups whereas data are grouped in such a way that the data in one cluster are similar data in different clusters are dissimilar. Many data mining technologies are developed for customer segmentation. PCA is working as a preprocessor of Fuzzy C means and K- means for reducing the high dimensional and noisy data. There are many clustering method apply on customer segmentation. In this paper the performance of Fuzzy C means and K-means after implementing Principal Component Analysis is analyzed. We analyze the performance on a standard dataset for these algorithms. The results indicate that PCA based fuzzy clustering produces better results than PCA based K-means and is a more stable method for customer segmentation.

  4. Investigação da qualidade de farinhas enriquecidas utilizando Análise por Componentes Principais (PCA Enriched flour quality investigation using Principal Component Analysis (PCA

    Directory of Open Access Journals (Sweden)

    Bruno Thiago Soeiro

    2010-09-01

    Full Text Available Alguns países, incluindo o Brasil (RDC 344, 2002, instituíram uma regulamentação indicando que farinhas de milho e trigo devem ser enriquecidas com ácido fólico e ferro. O principal objetivo deste trabalho foi a avaliação de algumas características de farinhas enriquecidas usando a Análise por Componentes Principais (PCA. Parâmetros como o teor de ácido fólico, ferro, proteína, lipídios, umidade, cinzas e carboidratos foram avaliados em 30 embalagens de farinhas adquiridas em comércio local. As farinhas de trigo e milho apresentaram, em média, composição centesimal aceitável de acordo com a Legislação Brasileira. Para as farinhas de trigo, a concentração de ácido fólico estava, em média, próxima ao esperado. As farinhas de milho continham quantidade superior da vitamina. Para os dois tipos de farinha, constatou-se teor de ferro acima do valor declarado no rótulo dos produtos. Uma matriz com 30 linhas (amostras e 7 colunas (variáveis foi organizada e os dados foram autoescalados. A primeira informação observada foi uma clara diferenciação entre os tipos de farinhas. As farinhas de trigo foram caracterizadas por maior quantidade de proteínas, umidade e cinzas. Por outro lado, as farinhas de milho apresentaram maior concentração de ferro, lipídios, carboidratos e ácido fólico. Foi possível notar também que farinhas acondicionadas em embalagens de plástico apresentaram menor quantidade de ácido fólico (152 µg.100 g-1, em média, quando comparadas às amostras armazenadas em embalagens de papel (259 µg.100 g-1, em média. Esse estudo pode fornecer ferramentas importantes para a avaliação dos programas de enriquecimento de alimentos com ácido fólico, principalmente, por apontar, preliminarmente, para a importância do tipo de embalagem para o acondicionamento das farinhas enriquecidas com a vitamina.Some countries, including Brazil (resolution - RDC # 344, 2004, have issued a regulation stipulating

  5. PCA3 Reference Set Application: T2-Erg-Martin Sanda-Emory (2014) — EDRN Public Portal

    Science.gov (United States)

    We hypothesize that combining T2:erg (T2:erg) fusion and PCA3 detection in urine collected after digital rectal exam can improve the specificity of identifying clinically significant prostate cancer presence over the standard PSA and DRE. To address this hypothesis we propose to validate the performance of the urinary T2:erg in a multiplex model predicting the diagnosis of clinically significant prostate cancer on subsequent prostate biopsy using post-DRE pre biopsy urine specimens from a cohort of 900 men on the EDRN’s PCA3 trial.

  6. Age-related postoperative morphine requirements in children following major surgery--an assessment using patient-controlled analgesia (PCA)

    DEFF Research Database (Denmark)

    Hansen, Tom Giedsing; Henneberg, Steen Winther; Hole, P

    1996-01-01

    To investigate if small children require less morphine for postoperative analgesia than do older children and adolescents we analysed the morphine consumption pattern of 28 consecutive children on intravenous patient-controlled analgesia (PCA) following major surgery. The median age-specific morp......To investigate if small children require less morphine for postoperative analgesia than do older children and adolescents we analysed the morphine consumption pattern of 28 consecutive children on intravenous patient-controlled analgesia (PCA) following major surgery. The median age...

  7. The effectiveness of Patient Controlled Analgesia (PCA morphine-ketamine compared to Patient Controlled Analgesia (PCA morphine to reduce total dose of morphine and Visual Analog Scale (VAS in postoperative laparotomy surgery

    Directory of Open Access Journals (Sweden)

    I Gusti Ngurah Mahaalit Aribawa

    2017-05-01

    Full Text Available Background: Laparotomy may cause moderate to severe after surgery pain, thus adequate pain management is needed. The addition of ketamine in patient controlled analgesia (PCA morphine after surgery can be the option. This study aims to evaluate the effectiveness of PCA morphine-ketamine compared to PCA morphine in patient postoperative laparotomy surgery to reduce total dose of morphine requirement and pain intensity evaluated with visual analog scale (VAS. Methods: This study was a double-blind RCT in 58 patients of ASA I and II, age 18-64 years, underwent an elective laparotomy at Sanglah General Hospital. Patients were divided into 2 groups. Group A, got addition of ketamine (1mg/ml in PCA morphine (1mg/ml and patients in group B received morphine (1mg/ml by PCA. Prior to surgical incision both group were given a bolus ketamine 0,15mg/ kg and ketorolac 0,5mg/kg. The total dose of morphine and VAS were measured at 6, 12, and 24 hours postoperatively. Result: Total dose of morphine in the first 24 hours postoperatively at morphine-ketamine group (5,1±0,8mg is lower than morphine only group (6,5±0,9mg p<0,001. VAS (resting 6 and 12 hour postoperative in morphine-ketamine group (13,4±4,8 mm and (10,7±2,6 mm are lower than morphine (17,9±4,1mm p≤0,05 and (12,8±5,3mm p≤0,05. VAS (moving 6, 12, and 24 hour postoperative morphineketamine group (24,8±5,1mm, (18±5,6mm and (9±5,6mm are lower than morphine (28,7±5,2mm p≤0,05, (23,1±6,0mm p≤0,05, and (12,8±5,3mm p≤0,05. Conclusions: Addition of ketamine in PCA morphine for postoperative laparotomy surgery reduces total morphine requirements in 24 hours compared to PCA morphine alone.

  8. Neuroendocrine prostate cancer (NEPCa) increased the neighboring PCa chemo-resistance via altering the PTHrP/p38/Hsp27/androgen receptor (AR)/p21 signals

    Science.gov (United States)

    Cui, Yun; Sun, Yin; Hu, Shuai; Luo, Jie; Li, Lei; Li, Xin; Yeh, Shuyuan; Jin, Jie; Chang, Chawnshang

    2016-01-01

    Prostatic neuroendocrine cells (NE) are an integral part of prostate cancer (PCa) that are associated with PCa progression. As the current androgen-deprivation therapy (ADT) with anti-androgens may promote the neuroendocrine PCa (NEPCa) development, and few therapies can effectively suppress NEPCa, understanding the impact of NEPCa on PCa progression may help us to develop better therapies to battle PCa. Here we found NEPCa cells could increase the docetaxel-resistance of their neighboring PCa cells. Mechanism dissection revealed that through secretion of PTHrP, NEPCa cells could alter the p38/MAPK/Hsp27 signals in their neighboring PCa cells that resulted in increased androgen receptor (AR) activity via promoting AR nuclear translocation. The consequences of increased AR function might then increase docetaxel-resistance via increasing p21 expression. In vivo xenograft mice experiments also confirmed NEPCa could increase the docetaxel-resistance of neighboring PCa, and targeting this newly identified PTHrP/p38/Hsp27/AR/p21 signaling pathway with either p38 inhibitor (SB203580) or sh-PTHrP may result in improving/restoring the docetaxel sensitivity to better suppress PCa. PMID:27375022

  9. pcaH, a molecular marker for estimating the diversity of the protocatechuate-degrading bacterial community in the soil environment

    DEFF Research Database (Denmark)

    El Azhari, Najoi

    2007-01-01

    Microorganisms degrading phenolic compounds play an important role in soil carbon cycling as well as in pesticide degradation. The pcaH gene encoding a key ring-cleaving enzyme of the β-ketoadipate pathway was selected as a functional marker. Using a degenerate primer pair, pcaH fragments were cl......H sequences from Actinobacteria and Proteobacteria phyla. This confirms that the developed primer pair targets a wide diversity of pcaH sequences, thereby constituting a suitable molecular marker to estimate the response of the pca community to agricultural practices....

  10. PCA-based approach for subtracting thermal background emission in high-contrast imaging data

    Science.gov (United States)

    Hunziker, S.; Quanz, S. P.; Amara, A.; Meyer, M. R.

    2018-03-01

    Aims.Ground-based observations at thermal infrared wavelengths suffer from large background radiation due to the sky, telescope and warm surfaces in the instrument. This significantly limits the sensitivity of ground-based observations at wavelengths longer than 3 μm. The main purpose of this work is to analyse this background emission in infrared high-contrast imaging data as illustrative of the problem, show how it can be modelled and subtracted and demonstrate that it can improve the detection of faint sources, such as exoplanets. Methods: We used principal component analysis (PCA) to model and subtract the thermal background emission in three archival high-contrast angular differential imaging datasets in the M' and L' filter. We used an M' dataset of β Pic to describe in detail how the algorithm works and explain how it can be applied. The results of the background subtraction are compared to the results from a conventional mean background subtraction scheme applied to the same dataset. Finally, both methods for background subtraction are compared by performing complete data reductions. We analysed the results from the M' dataset of HD 100546 only qualitatively. For the M' band dataset of β Pic and the L' band dataset of HD 169142, which was obtained with an angular groove phase mask vortex vector coronagraph, we also calculated and analysed the achieved signal-to-noise ratio (S/N). Results: We show that applying PCA is an effective way to remove spatially and temporarily varying thermal background emission down to close to the background limit. The procedure also proves to be very successful at reconstructing the background that is hidden behind the point spread function. In the complete data reductions, we find at least qualitative improvements for HD 100546 and HD 169142, however, we fail to find a significant increase in S/N of β Pic b. We discuss these findings and argue that in particular datasets with strongly varying observing conditions or

  11. Comparison of PCA and ICA based clutter reduction in GPR systems for anti-personal landmine detection

    DEFF Research Database (Denmark)

    Karlsen, Brian; Larsen, Jan; Sørensen, Helge Bjarup Dissing

    2001-01-01

    This paper presents statistical signal processing approaches for clutter reduction in stepped-frequency ground penetrating radar (SF-GPR) data. In particular, we suggest clutter/signal separation techniques based on principal and independent component analysis (PCA/ICA). The approaches...

  12. Accounting for baryonic effects in cosmic shear tomography: Determining a minimal set of nuisance parameters using PCA

    Energy Technology Data Exchange (ETDEWEB)

    Eifler, Tim; Krause, Elisabeth; Dodelson, Scott; Zentner, Andrew; Hearin, Andrew; Gnedin, Nickolay

    2014-05-28

    Systematic uncertainties that have been subdominant in past large-scale structure (LSS) surveys are likely to exceed statistical uncertainties of current and future LSS data sets, potentially limiting the extraction of cosmological information. Here we present a general framework (PCA marginalization) to consistently incorporate systematic effects into a likelihood analysis. This technique naturally accounts for degeneracies between nuisance parameters and can substantially reduce the dimension of the parameter space that needs to be sampled. As a practical application, we apply PCA marginalization to account for baryonic physics as an uncertainty in cosmic shear tomography. Specifically, we use CosmoLike to run simulated likelihood analyses on three independent sets of numerical simulations, each covering a wide range of baryonic scenarios differing in cooling, star formation, and feedback mechanisms. We simulate a Stage III (Dark Energy Survey) and Stage IV (Large Synoptic Survey Telescope/Euclid) survey and find a substantial bias in cosmological constraints if baryonic physics is not accounted for. We then show that PCA marginalization (employing at most 3 to 4 nuisance parameters) removes this bias. Our study demonstrates that it is possible to obtain robust, precise constraints on the dark energy equation of state even in the presence of large levels of systematic uncertainty in astrophysical processes. We conclude that the PCA marginalization technique is a powerful, general tool for addressing many of the challenges facing the precision cosmology program.

  13. PASS Reference Set Application: Lin UW (2010) TMPRSS2-ERG-PCA-PASS — EDRN Public Portal

    Science.gov (United States)

    Active surveillance is used to manage low-risk prostate cancer. Both PCA3 and TMPRSS2:ERG are promising biomarkers that may be associated with aggressive disease. This study examines the correlation of these biomarkers with higher cancer volume and grade determined at the time of biopsy in an active surveillance cohort.

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

    Science.gov (United States)

    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.

  15. Contrasting Effects of Dissolved Organic Matter on Mercury Methylation by Geobacter sulfurreducens PCA and Desulfovibrio desulfuricans ND132.

    Science.gov (United States)

    Zhao, Linduo; Chen, Hongmei; Lu, Xia; Lin, Hui; Christensen, Geoff A; Pierce, Eric M; Gu, Baohua

    2017-09-19

    Natural dissolved organic matter (DOM) affects mercury (Hg) redox reactions and anaerobic microbial methylation in the environment. Several studies have shown that DOM can enhance Hg methylation, especially under sulfidic conditions, whereas others show that DOM inhibits Hg methylation due to strong Hg-DOM complexation. In this study, we investigated and compared the effects of DOM on Hg methylation by an iron-reducing bacterium Geobacter sulfurreducens PCA and a sulfate-reducing bacterium Desulfovibrio desulfuricans ND132 under nonsulfidic conditions. The methylation experiment was performed with washed cells either in the absence or presence of DOM or glutathione, both of which form strong complexes with Hg via thiol-functional groups. DOM was found to greatly inhibit Hg methylation by G. Sulfurreducens PCA but enhance Hg methylation by D. desulfuricans ND132 cells with increasing DOM concentration. These strain-dependent opposing effects of DOM were also observed with glutathione, suggesting that thiols in DOM likely played an essential role in affecting microbial Hg uptake and methylation. Additionally, DOM and glutathione greatly decreased Hg sorption by G. sulfurreducens PCA but showed little effect on D. desulfuricans ND132 cells, demonstrating that ND132 has a higher affinity to sorb or take up Hg than the PCA strain. These observations indicate that DOM effects on Hg methylation are bacterial strain specific, depend on the DOM:Hg ratio or site-specific conditions, and may thus offer new insights into the role of DOM in methylmercury production in the environment.

  16. Relationship between swelling and irradiation creep in cold worked PCA stainless steel to 178 DPA at∼400 degrees C

    International Nuclear Information System (INIS)

    Toloczko, M.B.; Garner, F.A.

    1993-01-01

    At 178 dpa and ∼400 degrees C, the irradiation creep behavior of 20% cold-worked PCA has become dominated by the creep disappearance phenomenon. The total diametral deformation rate has reached the limiting value of 0.33%/dpa at the three highest stress levels. The stress-enhancement of swelling tends to camouflage the onset of creep disappearance, however

  17. Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

    Directory of Open Access Journals (Sweden)

    Sumaiya Thaseen Ikram

    2016-06-01

    Full Text Available Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA and support vector machine (SVM. The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C and kernel parameter gamma (γ, thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed.

  18. PCA-based detection of damage in time-varying systems

    Science.gov (United States)

    Bellino, A.; Fasana, A.; Garibaldi, L.; Marchesiello, S.

    2010-10-01

    When performing Structural Health Monitoring, it is well known that the natural frequencies do not depend only on the damage but also on environmental conditions, such as temperature and humidity. The Principal Component Analysis is used to take this problem into account, because it allows eliminating the effect of external factors. The purpose of the present work is to show that this technique can be successfully used not only for time-invariant systems, but also for time-varying ones. Referring to the latter, one of the most studied systems which shows these characteristics is the bridge with crossing loads, such as the case of the railway bridge studied in present paper; in this case, the mass and the velocity of the train can be considered as "environmental" factors.This paper, after a brief description of the PCA method and one example of its application on time-invariant systems, presents the great potentialities of the methodology when applied to time-varying systems. The results show that this method is able to better detect the presence of damage and also to properly distinguish among different levels of crack depths.

  19. Statistical Downscaling Output GCM Modeling with Continuum Regression and Pre-Processing PCA Approach

    Directory of Open Access Journals (Sweden)

    Sutikno Sutikno

    2010-08-01

    Full Text Available One of the climate models used to predict the climatic conditions is Global Circulation Models (GCM. GCM is a computer-based model that consists of different equations. It uses numerical and deterministic equation which follows the physics rules. GCM is a main tool to predict climate and weather, also it uses as primary information source to review the climate change effect. Statistical Downscaling (SD technique is used to bridge the large-scale GCM with a small scale (the study area. GCM data is spatial and temporal data most likely to occur where the spatial correlation between different data on the grid in a single domain. Multicollinearity problems require the need for pre-processing of variable data X. Continuum Regression (CR and pre-processing with Principal Component Analysis (PCA methods is an alternative to SD modelling. CR is one method which was developed by Stone and Brooks (1990. This method is a generalization from Ordinary Least Square (OLS, Principal Component Regression (PCR and Partial Least Square method (PLS methods, used to overcome multicollinearity problems. Data processing for the station in Ambon, Pontianak, Losarang, Indramayu and Yuntinyuat show that the RMSEP values and R2 predict in the domain 8x8 and 12x12 by uses CR method produces results better than by PCR and PLS.

  20. Hybrid Pixel-Based Method for Cardiac Ultrasound Fusion Based on Integration of PCA and DWT

    Directory of Open Access Journals (Sweden)

    Samaneh Mazaheri

    2015-01-01

    Full Text Available Medical image fusion is the procedure of combining several images from one or multiple imaging modalities. In spite of numerous attempts in direction of automation ventricle segmentation and tracking in echocardiography, due to low quality images with missing anatomical details or speckle noises and restricted field of view, this problem is a challenging task. This paper presents a fusion method which particularly intends to increase the segment-ability of echocardiography features such as endocardial and improving the image contrast. In addition, it tries to expand the field of view, decreasing impact of noise and artifacts and enhancing the signal to noise ratio of the echo images. The proposed algorithm weights the image information regarding an integration feature between all the overlapping images, by using a combination of principal component analysis and discrete wavelet transform. For evaluation, a comparison has been done between results of some well-known techniques and the proposed method. Also, different metrics are implemented to evaluate the performance of proposed algorithm. It has been concluded that the presented pixel-based method based on the integration of PCA and DWT has the best result for the segment-ability of cardiac ultrasound images and better performance in all metrics.

  1. PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras.

    Science.gov (United States)

    Zheng, Lei; Lukac, Rastislav; Wu, Xiaolin; Zhang, David

    2009-04-01

    Single-sensor digital color cameras use a process called color demosiacking to produce full color images from the data captured by a color filter array (CAF). The quality of demosiacked images is degraded due to the sensor noise introduced during the image acquisition process. The conventional solution to combating CFA sensor noise is demosiacking first, followed by a separate denoising processing. This strategy will generate many noise-caused color artifacts in the demosiacking process, which are hard to remove in the denoising process. Few denoising schemes that work directly on the CFA images have been presented because of the difficulties arisen from the red, green and blue interlaced mosaic pattern, yet a well-designed "denoising first and demosiacking later" scheme can have advantages such as less noise-caused color artifacts and cost-effective implementation. This paper presents a principle component analysis (PCA)-based spatially-adaptive denoising algorithm, which works directly on the CFA data using a supporting window to analyze the local image statistics. By exploiting the spatial and spectral correlations existing in the CFA image, the proposed method can effectively suppress noise while preserving color edges and details. Experiments using both simulated and real CFA images indicate that the proposed scheme outperforms many existing approaches, including those sophisticated demosiacking and denoising schemes, in terms of both objective measurement and visual evaluation.

  2. PCA based feature reduction to improve the accuracy of decision tree c4.5 classification

    Science.gov (United States)

    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.

  3. Biological image construction by using Raman radiation and Pca: preliminary results

    International Nuclear Information System (INIS)

    Martinez E, J. C.; Cordova F, T.; Hugo R, V.

    2015-10-01

    Full text: In the last years, the Raman spectroscopy (Rs) technique has had some applications in the study and analysis of biological samples, due to it is able to detect concentrations or presence of certain organic and inorganic compounds of medical interest. In this work, raw data were obtained through measurements in selected points on a square regions in order to detect specific organic / inorganic compounds on biological samples. Gold nano stars samples were prepared and coated with membrane markers (CD 10+ and CD 19+) and diluted in leukemic B lymphocytes. Each data block was evaluated independently by the method of principal component analysis (Pca) in order to find representative dimensionless values (Cp) for each Raman spectrum in a specific coordinate. Each Cp was normalized in a range of 0-255 in order to generate a representative image of 8 bits of the region under study. Data acquisition was performed with Raman microscopy system Renishaw in Via in the range of 550 to 1700 cm-1 with a 785 nm laser source, with a power of 17 m W and 15 s of exposure time were used for each spectrum. In preliminary results could detect the presence of molecular markers CD 10+ and CD 19+ with gold nano stars and discrimination between both markers. The results suggest conducting studies with specific concentrations organic and inorganic materials. (Author)

  4. Biological image construction by using Raman radiation and Pca: preliminary results

    Energy Technology Data Exchange (ETDEWEB)

    Martinez E, J. C. [IPN, Unidad Profesional Interdisciplinaria de Ingenieria, Campus Guanajuato, Av. Mineral de Valenciana 200, Col. Fracc. Industrial Puerto Interior, 36275 Silao, Guanajuato (Mexico); Cordova F, T. [Universidad de Guanajuato, DIC, Departamento de Ingenieria Fisica, Loma del Bosque No. 103, Col. Lomas del Campestre, 37150 Leon, Guanajuato (Mexico); Hugo R, V., E-mail: jcmartineze@ipn.mx [Universidad de Guadalajara, Centro Universitario de Tonala, Morelos No. 180, 69584 Tonala, Jalisco (Mexico)

    2015-10-15

    Full text: In the last years, the Raman spectroscopy (Rs) technique has had some applications in the study and analysis of biological samples, due to it is able to detect concentrations or presence of certain organic and inorganic compounds of medical interest. In this work, raw data were obtained through measurements in selected points on a square regions in order to detect specific organic / inorganic compounds on biological samples. Gold nano stars samples were prepared and coated with membrane markers (CD 10+ and CD 19+) and diluted in leukemic B lymphocytes. Each data block was evaluated independently by the method of principal component analysis (Pca) in order to find representative dimensionless values (Cp) for each Raman spectrum in a specific coordinate. Each Cp was normalized in a range of 0-255 in order to generate a representative image of 8 bits of the region under study. Data acquisition was performed with Raman microscopy system Renishaw in Via in the range of 550 to 1700 cm-1 with a 785 nm laser source, with a power of 17 m W and 15 s of exposure time were used for each spectrum. In preliminary results could detect the presence of molecular markers CD 10+ and CD 19+ with gold nano stars and discrimination between both markers. The results suggest conducting studies with specific concentrations organic and inorganic materials. (Author)

  5. Comprehensive analysis and evaluation of big data for main transformer equipment based on PCA and Apriority

    Science.gov (United States)

    Guo, Lijuan; Yan, Haijun; Hao, Yongqi; Chen, Yun

    2018-01-01

    With the power supply level of urban power grid toward high reliability development, it is necessary to adopt appropriate methods for comprehensive evaluation of existing equipment. Considering the wide and multi-dimensional power system data, the method of large data mining is used to explore the potential law and value of power system equipment. Based on the monitoring data of main transformer and the records of defects and faults, this paper integrates the data of power grid equipment environment. Apriori is used as an association identification algorithm to extract the frequent correlation factors of the main transformer, and the potential dependence of the big data is analyzed by the support and confidence. Then, the integrated data is analyzed by PCA, and the integrated quantitative scoring model is constructed. It is proved to be effective by using the test set to validate the evaluation algorithm and scheme. This paper provides a new idea for data fusion of smart grid, and provides a reference for further evaluation of big data of power grid equipment.

  6. Application of EOF/PCA-based methods in the post-processing of GRACE derived water variations

    Science.gov (United States)

    Forootan, Ehsan; Kusche, Jürgen

    2010-05-01

    Two problems that users of monthly GRACE gravity field solutions face are 1) the presence of correlated noise in the Stokes coefficients that increases with harmonic degree and causes ‘striping', and 2) the fact that different physical signals are overlaid and difficult to separate from each other in the data. These problems are termed the signal-noise separation problem and the signal-signal separation problem. Methods that are based on principal component analysis and empirical orthogonal functions (PCA/EOF) have been frequently proposed to deal with these problems for GRACE. However, different strategies have been applied to different (spatial: global/regional, spectral: global/order-wise, geoid/equivalent water height) representations of the GRACE level 2 data products, leading to differing results and a general feeling that PCA/EOF-based methods are to be applied ‘with care'. In addition, it is known that conventional EOF/PCA methods force separated modes to be orthogonal, and that, on the other hand, to either EOFs or PCs an arbitrary orthogonal rotation can be applied. The aim of this paper is to provide a common theoretical framework and to study the application of PCA/EOF-based methods as a signal separation tool due to post-process GRACE data products. In order to investigate and illustrate the applicability of PCA/EOF-based methods, we have employed them on GRACE level 2 monthly solutions based on the Center for Space Research, University of Texas (CSR/UT) RL04 products and on the ITG-GRACE03 solutions from the University of Bonn, and on various representations of them. Our results show that EOF modes do reveal the dominating annual, semiannual and also long-periodic signals in the global water storage variations, but they also show how choosing different strategies changes the outcome and may lead to unexpected results.

  7. Piper-PCA-Fisher Recognition Model of Water Inrush Source: A Case Study of the Jiaozuo Mining Area

    Directory of Open Access Journals (Sweden)

    Pinghua Huang

    2018-01-01

    Full Text Available Source discrimination of mine water plays an important role in guiding mine water prevention in mine water management. To accurately determine water inrush source from a mine in the Jiaozuo mining area, a Piper trilinear diagram based on hydrochemical experimental data of stratified underground water in the area was utilized to determine typical water samples. Additionally, principal component analysis (PCA was used for dimensionality reduction of conventional hydrochemical variables, after which mutually independent variables were extracted. The Piper-PCA-Fisher water inrush source recognition model was established by combining the Piper trilinear diagram and Fisher discrimination theory. Screened typical samples were used to conduct back-discriminate verification of the model. Results showed that 28 typical water samples in different aquifers were determined through the Piper trilinear diagram as a water sample set for training. Before PCA was carried out, the first five factors covered 98.92% of the information quantity of the original data and could effectively represent the data information of the original samples. During the one-by-one rediscrimination process of 28 groups of training samples using the Piper-PCA-Fisher water inrush source model, 100% correct discrimination rate was achieved. During the prediction and discrimination process of 13 samples, one water sample was misdiscriminated; hence, the correct prediscrimination rate was 92.3%. Compared with the traditional Fisher water source recognition model, the Piper-PCA-Fisher water source recognition model established in this study had higher accuracy in both rediscrimination and prediscrimination processes. Thus it had a strong ability to discriminate water inrush sources.

  8. Improving the prediction of pathologic outcomes in patients undergoing radical prostatectomy: the value of prostate cancer antigen 3 (PCA3), prostate health index (phi) and sarcosine.

    Science.gov (United States)

    Ferro, Matteo; Lucarelli, Giuseppe; Bruzzese, Dario; Perdonà, Sisto; Mazzarella, Claudia; Perruolo, Giuseppe; Marino, Ada; Cosimato, Vincenzo; Giorgio, Emilia; Tagliamonte, Virginia; Bottero, Danilo; De Cobelli, Ottavio; Terracciano, Daniela

    2015-02-01

    Several efforts have been made to find biomarkers that could help clinicians to preoperatively determine prostate cancer (PCa) pathological characteristics and choose the best therapeutic approach, avoiding over-treatment. On this effort, prostate cancer antigen 3 (PCA3), prostate health index (phi) and sarcosine have been presented as promising tools. We evaluated the ability of these biomarkers to predict the pathologic PCa characteristics within a prospectively collected contemporary cohort of patients who underwent radical prostatectomy (RP) for clinically localized PCa at a single high-volume Institution. The prognostic performance of PCA3, phi and sarcosine were evaluated in 78 patients undergoing RP for biopsy-proven PCa. Receiver operating characteristic (ROC) curve analyses tested the accuracy (area under the curve (AUC)) in predicting PCa pathological characteristics. Decision curve analyses (DCA) were used to assess the clinical benefit of the three biomarkers. We found that PCA3, phi and sarcosine levels were significantly higher in patients with tumor volume (TV)≥0.5 ml, pathologic Gleason sum (GS)≥7 and pT3 disease (all p-values≤0.01). ROC curve analysis showed that phi is an accurate predictor of high-stage (AUC 0.85 [0.77-0.93]), high-grade (AUC 0.83 [0.73-0.93]) and high-volume disease (AUC 0.94 [0.88-0.99]). Sarcosine showed a comparable AUC (0.85 [0.76-0.94]) only for T3 stage prediction, whereas PCA3 score showed lower AUCs, ranging from 0.74 (for GS) to 0.86 (for TV). PCA3, phi and sarcosine are predictors of PCa characteristics at final pathology. Successful clinical translation of these findings would reduce the frequency of surveillance biopsies and may enhance acceptance of active surveillance (AS). Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

  9. Interleukin-6: a bone marrow stromal cell paracrine signal that induces neuroendocrine differentiation and modulates autophagy in bone metastatic PCa cells.

    Science.gov (United States)

    Delk, Nikki A; Farach-Carson, Mary C

    2012-04-01

    Autophagy reallocates nutrients and clears normal cells of damaged proteins and organelles. In the context of metastatic disease, invading cancer cells hijack autophagic processes to survive and adapt in the host microenvironment. We sought to understand how autophagy is regulated in the metastatic niche for prostate cancer (PCa) cells where bone marrow stromal cell (BMSC) paracrine signaling induces PCa neuroendocrine differentiation (NED). In PCa, this transdifferentiation of metastatic PCa cells to neuronal-like cells correlates with advanced disease. Because autophagy provides a survival advantage for cancer cells and promotes cell differentiation, we hypothesized that autophagy mediates PCa NED in the bone. Thus, we determined the ability of paracrine factors in conditioned media (CM) from two separate BMSC subtypes, HS5 and HS27a, to induce autophagy in C4-2 and C4-2B bone metastatic PCa cells by characterizing the autophagy marker, LC3. Unlike HS27a CM, HS5 CM induced LC3 accumulation in PCa cells, suggesting autophagy was induced and indicating that HS5 and HS27a secrete a different milieu of paracrine factors that influence PCa autophagy. We identified interleukin-6 (IL-6), a cytokine more highly expressed in HS5 cells than in HS27a cells, as a paracrine factor that regulates PCa autophagy. Pharmacological inhibition of STAT3 activity did not attenuate LC3 accumulation, implying that IL-6 regulates NED and autophagy through different pathways. Finally, chloroquine inhibition of autophagic flux blocked PCa NED; hence autophagic flux maintains NED. Our studies imply that autophagy is cytoprotective for PCa cells in the bone, thus targeting autophagy is a potential therapeutic strategy.

  10. Baryonic effects in cosmic shear tomography: PCA parametrization and importance of extreme baryonic models

    Energy Technology Data Exchange (ETDEWEB)

    Mohammed, Irshad [Fermilab; Gnedin, Nickolay Y. [Fermilab

    2017-07-07

    Baryonic effects are amongst the most severe systematics to the tomographic analysis of weak lensing data which is the principal probe in many future generations of cosmological surveys like LSST, Euclid etc.. Modeling or parameterizing these effects is essential in order to extract valuable constraints on cosmological parameters. In a recent paper, Eifler et al. (2015) suggested a reduction technique for baryonic effects by conducting a principal component analysis (PCA) and removing the largest baryonic eigenmodes from the data. In this article, we conducted the investigation further and addressed two critical aspects. Firstly, we performed the analysis by separating the simulations into training and test sets, computing a minimal set of principle components from the training set and examining the fits on the test set. We found that using only four parameters, corresponding to the four largest eigenmodes of the training set, the test sets can be fitted thoroughly with an RMS $\\sim 0.0011$. Secondly, we explored the significance of outliers, the most exotic/extreme baryonic scenarios, in this method. We found that excluding the outliers from the training set results in a relatively bad fit and degraded the RMS by nearly a factor of 3. Therefore, for a direct employment of this method to the tomographic analysis of the weak lensing data, the principle components should be derived from a training set that comprises adequately exotic but reasonable models such that the reality is included inside the parameter domain sampled by the training set. The baryonic effects can be parameterized as the coefficients of these principle components and should be marginalized over the cosmological parameter space.

  11. Change detection of medical images using dictionary learning techniques and PCA

    Science.gov (United States)

    Nika, Varvara; Babyn, Paul; Zhu, Hongmei

    2014-03-01

    Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of MRI scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. In this paper we present the Eigen-Block Change Detection algorithm (EigenBlockCD). It performs local registration and identifies the changes between consecutive MR images of the brain. Blocks of pixels from baseline scan are used to train local dictionaries that are then used to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the EigenBlockCD. We show the advantages of L2 norm over L1 norm theoretically and numerically. We also demonstrate the performance of the EigenBlockCD algorithm for detecting changes of MR images and compare our results with those provided in recent literature. Experimental results with both simulated and real MRI scans show that the EigenBlockCD outperforms the previous methods. It detects clinical changes while ignoring the changes due to patient's position and other acquisition artifacts.

  12. SU-F-R-41: Regularized PCA Can Model Treatment-Related Changes in Head and Neck Patients Using Daily CBCTs

    International Nuclear Information System (INIS)

    Chetvertkov, M; Siddiqui, F; Chetty, I; Kumarasiri, A; Liu, C; Gordon, J

    2016-01-01

    Purpose: To use daily cone beam CTs (CBCTs) to develop regularized principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients, to guide replanning decisions in adaptive radiation therapy (ART). Methods: Known deformations were applied to planning CT (pCT) images of 10 H&N patients to model several different systematic anatomical changes. A Pinnacle plugin was used to interpolate systematic changes over 35 fractions, generating a set of 35 synthetic CTs for each patient. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CTs and random fraction-to-fraction changes were superimposed on the DVFs. Standard non-regularized and regularized patient-specific PCA models were built using the DVFs. The ability of PCA to extract the known deformations was quantified. PCA models were also generated from clinical CBCTs, for which the deformations and DVFs were not known. It was hypothesized that resulting eigenvectors/eigenfunctions with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: As demonstrated with quantitative results in the supporting document regularized PCA is more successful than standard PCA at capturing systematic changes early in the treatment. Regularized PCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes. To be successful at guiding ART, regularized PCA should be coupled with models of when anatomical changes occur: early, late or throughout the treatment course. Conclusion: The leading eigenvector/eigenfunction from the both PCA approaches can tentatively be identified as a major systematic change during radiotherapy course when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the regularized PCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are

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

    Science.gov (United States)

    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.

  14. SU-F-R-41: Regularized PCA Can Model Treatment-Related Changes in Head and Neck Patients Using Daily CBCTs

    Energy Technology Data Exchange (ETDEWEB)

    Chetvertkov, M [Wayne State University, Detroit, MI (United States); Henry Ford Health System, Detroit, MI (United States); Siddiqui, F; Chetty, I; Kumarasiri, A; Liu, C; Gordon, J [Henry Ford Health System, Detroit, MI (United States)

    2016-06-15

    Purpose: To use daily cone beam CTs (CBCTs) to develop regularized principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients, to guide replanning decisions in adaptive radiation therapy (ART). Methods: Known deformations were applied to planning CT (pCT) images of 10 H&N patients to model several different systematic anatomical changes. A Pinnacle plugin was used to interpolate systematic changes over 35 fractions, generating a set of 35 synthetic CTs for each patient. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CTs and random fraction-to-fraction changes were superimposed on the DVFs. Standard non-regularized and regularized patient-specific PCA models were built using the DVFs. The ability of PCA to extract the known deformations was quantified. PCA models were also generated from clinical CBCTs, for which the deformations and DVFs were not known. It was hypothesized that resulting eigenvectors/eigenfunctions with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: As demonstrated with quantitative results in the supporting document regularized PCA is more successful than standard PCA at capturing systematic changes early in the treatment. Regularized PCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes. To be successful at guiding ART, regularized PCA should be coupled with models of when anatomical changes occur: early, late or throughout the treatment course. Conclusion: The leading eigenvector/eigenfunction from the both PCA approaches can tentatively be identified as a major systematic change during radiotherapy course when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the regularized PCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are

  15. The Feasibility Study for Multigeometries Identification of Uranium Components Using PCA-LSSVM Based on Correlation Measurements

    Directory of Open Access Journals (Sweden)

    Mi Zhou

    2018-01-01

    Full Text Available The geometry of uranium components is one of the key characteristics and strictly confidential. The geometry identification of metal uranium components was studied using 252Cf source-driven correlation measurement method. For the 3 uranium samples with the same mass and enrichment, there are subtle differences in neutron signals. Even worse, the correlation functions were disturbed by scatter neutrons and include “accidental” coincidence, which is not conductive to the geometry identification. In this paper, we proposed an identification method combining principal component analysis and least-square support vector machine (PCA-LSSVM. The results based on PCA-LSSVM showed that the training precision was 100% and the test precision was 95.83% of the identification model. The total precision of the identification model was 98.41%, which indicated that the identification model was an effective way to identify the geometry properties with the correlation functions.

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

    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.

  18. News Schemes for Activity Recognition Systems Using PCA-WSVM, ICA-WSVM, and LDA-WSVM

    Directory of Open Access Journals (Sweden)

    M’hamed Bilal Abidine

    2015-08-01

    Full Text Available Feature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we used three methods for feature extraction: Principal Component Analysis (PCA, Independent Component Analysis (ICA, and Linear Discriminant Analysis (LDA. The new features selected by each method are then used as the inputs for a Weighted Support Vector Machines (WSVM classifier. This classifier is used to handle the problem of imbalanced activity data from the sensor readings. The experiments were implemented on multiple real-world datasets with Conditional Random Fields (CRF, standard Support Vector Machines (SVM, Weighted SVM, and combined methods PCA+WSVM, ICA+WSVM, and LDA+WSVM showed that LDA+WSVM had a higher recognition rate than other methods for activity recognition.

  19. Corrosion of path A PCA and 12 Cr-1 MoVW steel in thermally convective lithium

    International Nuclear Information System (INIS)

    Tortorelli, P.F.; DeVan, J.H.

    1984-01-01

    Exposure of path A PCA alloys to thermally convective lithium for 6700 h at 600 and 570 0 C resulted in corrosion reactions that were similar to what is observed for other austenitic alloys exposed under similar conditions. It corroded more rapidly than type 316 stainless steel, and the presence of nitride stringers in PCA did not affect the measured weight losses. Consideration of the weight change and surface analysis data for 12 Cr-1 MoVW steel exposed to thermally convective lithium between 500 and 350 0 C for 10,088 h revealed that reactions with carbon and nitrogen were probably the principal corrosion processes for this alloy in this temperature range. Corrosion was not severe

  20. An EEMD-PCA approach to extract heart rate, respiratory rate and respiratory activity from PPG signal.

    Science.gov (United States)

    Motin, Mohammod Abdul; Karmakar, Chandan Kumar; Palaniswami, Marimuthu

    2016-08-01

    The pulse oximeter's photoplethysmographic (PPG) signals, measure the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), respiratory rate (RR) and respiratory activity (RA) and this will reduce the number of sensors connected to the patient's body for recording vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR, RR and RA simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 45 epochs of PPG, electrocardiogram (ECG) and respiratory signal extracted from the MIMIC database (Physionet ATM data bank). The ECG and capnograph based respiratory signal were used as the ground truth and several metrics such as magnitude squared coherence (MSC), correlation coefficients (CC) and root mean square (RMS) error were used to compare the performance of EEMD-PCA algorithm with most of the existing methods in the literature. Results of EEMD-PCA based extraction of HR, RR and RA from PPG signal showed that the median RMS error (quartiles) obtained for RR was 0 (0, 0.89) breaths/min, for HR was 0.62 (0.56, 0.66) beats/min and for RA the average value of MSC and CC was 0.95 and 0.89 respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR, RR and RA than other existing methods.

  1. Epigenetic Signature: A New Player as Predictor of Clinically Significant Prostate Cancer (PCa) in Patients on Active Surveillance (AS).

    Science.gov (United States)

    Ferro, Matteo; Ungaro, Paola; Cimmino, Amelia; Lucarelli, Giuseppe; Busetto, Gian Maria; Cantiello, Francesco; Damiano, Rocco; Terracciano, Daniela

    2017-05-27

    Widespread prostate-specific antigen (PSA) testing notably increased the number of prostate cancer (PCa) diagnoses. However, about 30% of these patients have low-risk tumors that are not lethal and remain asymptomatic during their lifetime. Overtreatment of such patients may reduce quality of life and increase healthcare costs. Active surveillance (AS) has become an accepted alternative to immediate treatment in selected men with low-risk PCa. Despite much progress in recent years toward identifying the best candidates for AS in recent years, the greatest risk remains the possibility of misclassification of the cancer or missing a high-risk cancer. This is particularly worrisome in men with a life expectancy of greater than 10-15 years. The Prostate Cancer Research International Active Surveillance (PRIAS) study showed that, in addition to age and PSA at diagnosis, both PSA density (PSA-D) and the number of positive cores at diagnosis (two compared with one) are the strongest predictors for reclassification biopsy or switching to deferred treatment. However, there is still no consensus upon guidelines for placing patients on AS. Each institution has its own protocol for AS that is based on PRIAS criteria. Many different variables have been proposed as tools to enrol patients in AS: PSA-D, the percentage of freePSA, and the extent of cancer on biopsy (number of positive cores or percentage of core involvement). More recently, the Prostate Health Index (PHI), the 4 Kallikrein (4K) score, and other patient factors, such as age, race, and family history, have been investigated as tools able to predict clinically significant PCa. Recently, some reports suggested that epigenetic mapping differs significantly between cancer patients and healthy subjects. These findings indicated as future prospect the use of epigenetic markers to identify PCa patients with low-grade disease, who are likely candidates for AS. This review explores literature data about the potential of

  2. Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm.

    Science.gov (United States)

    Saba, Luca; Jain, Pankaj K; Suri, Harman S; Ikeda, Nobutaka; Araki, Tadashi; Singh, Bikesh K; Nicolaides, Andrew; Shafique, Shoaib; Gupta, Ajay; Laird, John R; Suri, Jasjit S

    2017-06-01

    Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings.

  3. CT-guided thin needles percutaneous cryoablation (PCA) in patients with primary and secondary lung tumors: A preliminary experience

    Energy Technology Data Exchange (ETDEWEB)

    Pusceddu, Claudio, E-mail: clapusceddu@gmail.com [Division of Interventional Radiology, Department of Oncological Radiology, Businco Hospital, Regional Referral Center for Oncologic Diseases, Cagliari, Zip code 09100 (Italy); Sotgia, Barbara, E-mail: barbara.sotgia@gmail.com [Department of Oncological Radiology, Businco Hospital, Regional Referral Center for Oncological Diseases, Cagliari, Zip code 09100 (Italy); Fele, Rosa Maria, E-mail: rosellafele@tiscali.it [Department of Oncological Radiology, Businco Hospital, Regional Referral Center for Oncological Diseases, Cagliari, Zip code 09100 (Italy); Melis, Luca, E-mail: doclucamelis@tiscali.it [Department of Oncological Radiology, Businco Hospital, Regional Referral Center for Oncological Diseases, Cagliari, Zip code 09100 (Italy)

    2013-05-15

    Purpose: To report the data of our initial experience with CT-guided thin cryoprobes for percutaneous cryoablation (PCA) in patients with primary and secondary pulmonary tumors. Material and methods: CT-guided thin needles PCA was performed on 34 lung masses (11 NSCLC = 32%; 23 secondary lung malignancies = 68%) in 32 consecutive patients (24 men and 8 women; mean age 67 ± 10 years) not suitable for surgical resection. Lung masses were treated using two types of cryoprobes: IceRod and IceSeed able to obtain different size of iceball. The number of probes used ranged from 1 to 5 depending on the size of the tumor. After insertion of the cryoprobes into the lesion, the PCA were performed with two 2 (91%) or 3 (9%) cycles each of 12 min of freezing followed by a 4 min active thawing phase and a 4 min passive thawing phase for each one for all treatments. Results: All cryoablation sessions were successfully completed. All primary and metastatic lung tumors were ablated. No procedure-related deaths occurred. Morbidity consisted of 21% (7 of 34) pneumothorax and 3% (1 of 34) cases asymptomatic small pulmonary hemorrhage, respectively, all of CTCAE grade 1 (Common Terminology Criteria for Adverse Events). Low density of entire lesion, central necrosis and solid mass appearance were identify in 21 (62%), 7 (21%) and 6 (17%) of cryoablated tumors, respectively. No lymphadenopathy developed in the region of treated lesions. Technical success (complete lack of enhancement) was achieved in 82%, 97% and 91% of treated lesions at 1-, 3- and 6-months CT follow-up scan, respectively (p < .000). Comparing the tumor longest diameter between the baseline and at 6 month CT images, technical success was revealed in 92% cases (p < .000). Conclusion: Our preliminary experience suggests that PCA is a feasible treatment option. Well-designed clinical trials with a larger patient population are necessary to further investigate the long-term results and prognostic factors.

  4. A Principal Component Analysis (PCA Approach to Seasonal and Zooplankton Diversity Relationships in Fishing Grounds of Mannar Gulf, India

    Directory of Open Access Journals (Sweden)

    Selvin J. PITCHAIKANI

    2017-06-01

    Full Text Available Principal component analysis (PCA is a technique used to emphasize variation and bring out strong patterns in a dataset. It is often used to make data easy to explore and visualize. The primary objective of the present study was to record information of zooplankton diversity in a systematic way and to study the variability and relationships among seasons prevailed in Gulf of Mannar. The PCA for the zooplankton seasonal diversity was investigated using the four seasonal datasets to understand the statistical significance among the four seasons. Two different principal components (PC were segregated in all the seasons homogeneously. PCA analyses revealed that Temora turbinata is an opportunistic species and zooplankton diversity was significantly different from season to season and principally, the zooplankton abundance and its dynamics in Gulf of Mannar is structured by seasonal current patterns. The factor loadings of zooplankton for different seasons in Tiruchendur coastal water (GOM is different compared with the Southwest coast of India; particularly, routine and opportunistic species were found within the positive and negative factors. The copepods Acrocalanus gracilis and Acartia erythrea were dominant in summer and Southwest monsoon due to the rainfall and freshwater discharge during the summer season; however, these species were replaced by Temora turbinata during Northeast monsoon season.

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

    Directory of Open Access Journals (Sweden)

    Alessandra Caggiano

    2018-03-01

    Full Text Available 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.

  6. An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors

    International Nuclear Information System (INIS)

    Azadeh, A.; Amalnick, M.S.; Ghaderi, S.F.; Asadzadeh, S.M.

    2007-01-01

    This paper introduces an integrated approach based on data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT) for total energy efficiency assessment and optimization in energy intensive manufacturing sectors. Total energy efficiency assessment and optimization of the proposed approach considers structural indicators in addition conventional consumption and manufacturing sector output indicators. The validity of the DEA model is verified and validated by PCA and NT through Spearman correlation experiment. Moreover, the proposed approach uses the measure-specific super-efficiency DEA model for sensitivity analysis to determine the critical energy carriers. Four energy intensive manufacturing sectors are discussed in this paper: iron and steel, pulp and paper, petroleum refining and cement manufacturing sectors. To show superiority and applicability, the proposed approach has been applied to refinery sub-sectors of some OECD (Organization for Economic Cooperation and Development) countries. This study has several unique features which are: (1) a total approach which considers structural indicators in addition to conventional energy efficiency indicators; (2) a verification and validation mechanism for DEA by PCA and NT and (3) utilization of DEA for total energy efficiency assessment and consumption optimization of energy intensive manufacturing sectors

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

    Science.gov (United States)

    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.

  8. Improving the bioavailability and anticancer effect of the PCA-1/ALKBH3 inhibitor HUHS015 using sodium salt.

    Science.gov (United States)

    Mabuchi, Miyuki; Shimizu, Tadashi; Ueda, Masahiro; Sasakawa, Yuka; Nakao, Syuhei; Ueda, Yuko; Kawamura, Akio; Tsujikawa, Kazutake; Tanaka, Akito

    2015-01-01

    Prostate cancer antigen (PCA)-1/AlkB homologue 3 (ALKBH3) has been identified as a clinically significant factor and siRNA of PCA-1 inhibits DU145 proliferation both in vitro and in vivo. HUHS015 ( 1: ), a previous reported PCA-1 small-molecule inhibitor, was also effective without any obvious side-effects or toxicity. The potency of HUHS015, however, is not satisfying. We thought the reason is poor solubility of HUHS015 because insoluble material remained at the injection site after subcutaneous administration. To improve this inhibitor's solubility, we prepared various salts of HUHS015 and examined their solubility, which resulted in the selection of HUHS015 sodium salt ( 2: ) for further studies in vivo. Next, we compared the pharmacokinetics of 1: and 2: via several administration routes. We observed significant improvements in the pharmacokinetic parameters. For example, subcutaneous administration of 2: increased the area under the curve (AUC)0-24 by 8-fold compared to 1 and increased the suppressive effect on the proliferation of DU145 cells in a xenograft model. Copyright © 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

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

    Science.gov (United States)

    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

  10. A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

    Science.gov (United States)

    Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

    2017-07-01

    Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time

  11. 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.

    Science.gov (United States)

    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.

  12. SU-G-BRA-03: PCA Based Imaging Angle Optimization for 2D Cine MRI Based Radiotherapy Guidance

    Energy Technology Data Exchange (ETDEWEB)

    Chen, T; Yue, N; Jabbour, S; Zhang, M [Rutgers University, New Brunswick, NJ (United States)

    2016-06-15

    Purpose: To develop an imaging angle optimization methodology for orthogonal 2D cine MRI based radiotherapy guidance using Principal Component Analysis (PCA) of target motion retrieved from 4DCT. Methods: We retrospectively analyzed 4DCT of 6 patients with lung tumor. A radiation oncologist manually contoured the target volume at the maximal inhalation phase of the respiratory cycle. An object constrained deformable image registration (DIR) method has been developed to track the target motion along the respiration at ten phases. The motion of the center of the target mass has been analyzed using the PCA to find out the principal motion components that were uncorrelated with each other. Two orthogonal image planes for cineMRI have been determined using this method to minimize the through plane motion during MRI based radiotherapy guidance. Results: 3D target respiratory motion for all 6 patients has been efficiently retrieved from 4DCT. In this process, the object constrained DIR demonstrated satisfactory accuracy and efficiency to enable the automatic motion tracking for clinical application. The average motion amplitude in the AP, lateral, and longitudinal directions were 3.6mm (min: 1.6mm, max: 5.6mm), 1.7mm (min: 0.6mm, max: 2.7mm), and 5.6mm (min: 1.8mm, max: 16.1mm), respectively. Based on PCA, the optimal orthogonal imaging planes were determined for cineMRI. The average angular difference between the PCA determined imaging planes and the traditional AP and lateral imaging planes were 47 and 31 degrees, respectively. After optimization, the average amplitude of through plane motion reduced from 3.6mm in AP images to 2.5mm (min:1.3mm, max:3.9mm); and from 1.7mm in lateral images to 0.6mm (min: 0.2mm, max:1.5mm), while the principal in plane motion amplitude increased from 5.6mm to 6.5mm (min: 2.8mm, max: 17mm). Conclusion: DIR and PCA can be used to optimize the orthogonal image planes of cineMRI to minimize the through plane motion during radiotherapy

  13. SU-G-BRA-03: PCA Based Imaging Angle Optimization for 2D Cine MRI Based Radiotherapy Guidance

    International Nuclear Information System (INIS)

    Chen, T; Yue, N; Jabbour, S; Zhang, M

    2016-01-01

    Purpose: To develop an imaging angle optimization methodology for orthogonal 2D cine MRI based radiotherapy guidance using Principal Component Analysis (PCA) of target motion retrieved from 4DCT. Methods: We retrospectively analyzed 4DCT of 6 patients with lung tumor. A radiation oncologist manually contoured the target volume at the maximal inhalation phase of the respiratory cycle. An object constrained deformable image registration (DIR) method has been developed to track the target motion along the respiration at ten phases. The motion of the center of the target mass has been analyzed using the PCA to find out the principal motion components that were uncorrelated with each other. Two orthogonal image planes for cineMRI have been determined using this method to minimize the through plane motion during MRI based radiotherapy guidance. Results: 3D target respiratory motion for all 6 patients has been efficiently retrieved from 4DCT. In this process, the object constrained DIR demonstrated satisfactory accuracy and efficiency to enable the automatic motion tracking for clinical application. The average motion amplitude in the AP, lateral, and longitudinal directions were 3.6mm (min: 1.6mm, max: 5.6mm), 1.7mm (min: 0.6mm, max: 2.7mm), and 5.6mm (min: 1.8mm, max: 16.1mm), respectively. Based on PCA, the optimal orthogonal imaging planes were determined for cineMRI. The average angular difference between the PCA determined imaging planes and the traditional AP and lateral imaging planes were 47 and 31 degrees, respectively. After optimization, the average amplitude of through plane motion reduced from 3.6mm in AP images to 2.5mm (min:1.3mm, max:3.9mm); and from 1.7mm in lateral images to 0.6mm (min: 0.2mm, max:1.5mm), while the principal in plane motion amplitude increased from 5.6mm to 6.5mm (min: 2.8mm, max: 17mm). Conclusion: DIR and PCA can be used to optimize the orthogonal image planes of cineMRI to minimize the through plane motion during radiotherapy

  14. PHI and PCA3 improve the prognostic performance of PRIAS and Epstein criteria in predicting insignificant prostate cancer in men eligible for active surveillance.

    Science.gov (United States)

    Cantiello, Francesco; Russo, Giorgio Ivan; Cicione, Antonio; Ferro, Matteo; Cimino, Sebastiano; Favilla, Vincenzo; Perdonà, Sisto; De Cobelli, Ottavio; Magno, Carlo; Morgia, Giuseppe; Damiano, Rocco

    2016-04-01

    To assess the performance of prostate health index (PHI) and prostate cancer antigen 3 (PCA3) when added to the PRIAS or Epstein criteria in predicting the presence of pathologically insignificant prostate cancer (IPCa) in patients who underwent radical prostatectomy (RP) but eligible for active surveillance (AS). An observational retrospective study was performed in 188 PCa patients treated with laparoscopic or robot-assisted RP but eligible for AS according to Epstein or PRIAS criteria. Blood and urinary specimens were collected before initial prostate biopsy for PHI and PCA3 measurements. Multivariate logistic regression analyses and decision curve analysis were carried out to identify predictors of IPCa using the updated ERSPC definition. At the multivariate analyses, the inclusion of both PCA3 and PHI significantly increased the accuracy of the Epstein multivariate model in predicting IPCa with an increase of 17 % (AUC = 0.77) and of 32 % (AUC = 0.92), respectively. The inclusion of both PCA3 and PHI also increased the predictive accuracy of the PRIAS multivariate model with an increase of 29 % (AUC = 0.87) and of 39 % (AUC = 0.97), respectively. DCA revealed that the multivariable models with the addition of PHI or PCA3 showed a greater net benefit and performed better than the reference models. In a direct comparison, PHI outperformed PCA3 performance resulting in higher net benefit. In a same cohort of patients eligible for AS, the addition of PHI and PCA3 to Epstein or PRIAS models improved their prognostic performance. PHI resulted in greater net benefit in predicting IPCa compared to PCA3.

  15. A comparison of PCA and PMF models for source identification of fugitive methane emissions

    Science.gov (United States)

    Assan, Sabina; Baudic, Alexia; Bsaibes, Sandy; Gros, Valerie; Ciais, Philippe; Staufer, Johannes; Robinson, Rod; Vogel, Felix

    2017-04-01

    Methane (CH_4) is a greenhouse gas with a global warming potential 28-32 times that of carbon dioxide (CO_2) on a 100 year period, and even greater on shorter timescales [Etminan, et al., 2016, Allen, 2014]. Thus, despite its relatively short life time and smaller emission quantities compared to CO_2, CH4 emissions contribute to approximately 20{%} of today's anthropogenic greenhouse gas warming [Kirschke et al., 2013]. Major anthropogenic sources include livestock (enteric fermentation), oil and gas production and distribution, landfills, and wastewater emissions [EPA, 2011]. Especially in densely populated areas multiple CH4 sources can be found in close vicinity. Thus, when measuring CH4 emissions at local scales it is necessary to distinguish between different CH4 source categories to effectively quantify the contribution of each sector and aid the implementation of greenhouse gas reduction strategies. To this end, source apportionment models can be used to aid the interpretation of spatial and temporal patterns in order to identify and characterise emission sources. The focus of this study is to evaluate two common linear receptor models, namely Principle Component Analysis (PCA) and Positive Matrix Factorisation (PMF) for CH4 source apportionment. The statistical models I will present combine continuous in-situ CH4 , C_2H_6, δ^1^3CH4 measured using a Cavity Ring Down Spectroscopy (CRDS) instrument [Assan et al. 2016] with volatile organic compound (VOC) observations performed using Gas Chromatography (GC) in order to explain the underlying variance of the data. The strengths and weaknesses of both models are identified for data collected in multi-source environments in the vicinity of four different types of sites; an agricultural farm with cattle, a natural gas compressor station, a wastewater treatment plant, and a pari-urban location in the Ile de France region impacted by various sources. To conclude, receptor model results to separate statistically the

  16. Molecular Characterization of the Genes pcaG and pcaH, Encoding Protocatechuate 3,4-Dioxygenase, Which Are Essential for Vanillin Catabolism in Pseudomonas sp. Strain HR199

    Science.gov (United States)

    Overhage, Jörg; Kresse, Andreas U.; Priefert, Horst; Sommer, Horst; Krammer, Gerhard; Rabenhorst, Jürgen; Steinbüchel, Alexander

    1999-01-01

    Pseudomonas sp. strain HR199 is able to utilize eugenol (4-allyl-2-methoxyphenol), vanillin (4-hydroxy-3-methoxybenzaldehyde), or protocatechuate as the sole carbon source for growth. Mutants of this strain which were impaired in the catabolism of vanillin but retained the ability to utilize eugenol or protocatechuate were obtained after nitrosoguanidine mutagenesis. One mutant (SK6169) was used as recipient of a Pseudomonas sp. strain HR199 genomic library in cosmid pVK100, and phenotypic complementation was achieved with a 5.8-kbp EcoRI fragment (E58). The amino acid sequences deduced from two corresponding open reading frames (ORF) identified on E58 revealed high degrees of homology to pcaG and pcaH, encoding the two subunits of protocatechuate 3,4-dioxygenase. Three additional ORF most probably encoded a 4-hydroxybenzoate 3-hydroxylase (PobA) and two putative regulatory proteins, which exhibited homology to PcaQ of Agrobacterium tumefaciens and PobR of Pseudomonas aeruginosa, respectively. Since mutant SK6169 was also complemented by a subfragment of E58 that harbored only pcaH, this mutant was most probably lacking a functional β subunit of the protocatechuate 3,4-dioxygenase. Since this mutant was still able to grow on protocatechuate and lacked protocatechuate 4,5-dioxygenase and protocatechuate 2,3-dioxygenase, the degradation had to be catalyzed by different enzymes. Two other mutants (SK6184 and SK6190), which were also impaired in the catabolism of vanillin, were not complemented by fragment E58. Since these mutants accumulated 3-carboxy muconolactone during cultivation on eugenol, they most probably exhibited a defect in a step of the catabolic pathway following the ortho cleavage. Moreover, in these mutants cyclization of 3-carboxymuconic acid seems to occur by a syn absolute stereochemical course, which is normally only observed for cis,cis-muconate lactonization in pseudomonads. In conclusion, vanillin is degraded through the ortho-cleavage pathway

  17. Comparative Effectiveness of Semantic Feature Analysis (SFA and Phonological Components Analysis (PCA for Anomia Treatment in Persian Speaking Patients With Aphasia

    Directory of Open Access Journals (Sweden)

    Zahra Sadeghi

    2017-09-01

    Discussion: While PCA is more effective for participants with phonological impairments, SFA is more effective for participants with semantic impairments. Therefore, a direct relationship between underlying functional deficit and response to specific treatment was established for all participants.

  18. NOAA TIFF Image - 4m Bathymetric Principal Component Analysis (PCA) of Red Snapper Research Areas in the South Atlantic Bight, 2010

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset contains unified Bathymetric PCA GeoTiffs with 4x4 meter cell resolution describing the topography of 15 areas along the shelf edge off the South...

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

    Science.gov (United States)

    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 ...

  20. The singular value filter: a general filter design strategy for PCA-based signal separation in medical ultrasound imaging.

    Science.gov (United States)

    Mauldin, F William; Lin, Dan; Hossack, John A

    2011-11-01

    A general filtering method, called the singular value filter (SVF), is presented as a framework for principal component analysis (PCA) based filter design in medical ultrasound imaging. The SVF approach operates by projecting the original data onto a new set of bases determined from PCA using singular value decomposition (SVD). The shape of the SVF weighting function, which relates the singular value spectrum of the input data to the filtering coefficients assigned to each basis function, is designed in accordance with a signal model and statistical assumptions regarding the underlying source signals. In this paper, we applied SVF for the specific application of clutter artifact rejection in diagnostic ultrasound imaging. SVF was compared to a conventional PCA-based filtering technique, which we refer to as the blind source separation (BSS) method, as well as a simple frequency-based finite impulse response (FIR) filter used as a baseline for comparison. The performance of each filter was quantified in simulated lesion images as well as experimental cardiac ultrasound data. SVF was demonstrated in both simulation and experimental results, over a wide range of imaging conditions, to outperform the BSS and FIR filtering methods in terms of contrast-to-noise ratio (CNR) and motion tracking performance. In experimental mouse heart data, SVF provided excellent artifact suppression with an average CNR improvement of 1.8 dB with over 40% reduction in displacement tracking error. It was further demonstrated from simulation and experimental results that SVF provided superior clutter rejection, as reflected in larger CNR values, when filtering was achieved using complex pulse-echo received data and non-binary filter coefficients.

  1. A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

    Directory of Open Access Journals (Sweden)

    Fernando Sánchez Lasheras

    2015-03-01

    Full Text Available Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS technique with the principal component analysis (PCA, dendrograms and classification and regression trees (CARTs. Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.. Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

  2. Evaluation of significant sources influencing the variation of water quality of Kandla creek, Gulf of Katchchh, using PCA

    Digital Repository Service at National Institute of Oceanography (India)

    Dalal, S.G.; Shirodkar, P.V.; Jagtap, T.G.; Naik, B.G.; Rao, G.S.

    and Marhaba 2003). The use of PCA for wa- ter quality assessment has increased in the last few years, mainly due to the need to obtain appreciable data reduction for analysis and decision (Morales et al. 1999). Bartlett’s sphericity test (χ 2 with degrees....). Florida: CRC. Morales, M. M., Mart, P., Llopis, A., Campos, L., & Sagrado, J. (1999). An environmental study by fac- tor analysis of surface seawater in the Gulf of Valen- cia (western Mediterranean). Analytica Chimica Acta, 394, 109–117. doi:10.1016/S0003...

  3. PCA/INCREMENT MEMORY interface for analog processors on-line with PC-XT/AT IBM

    International Nuclear Information System (INIS)

    Biri, S.; Buttsev, V.S.; Molnar, J.; Samojlov, V.N.

    1989-01-01

    The functional and operational descriptions on PCA/INCREMENT MEMORY interface are discussed. The following is solved with this unit: connection between the analogue signal processor and PC, nuclear spectrum acquisition up to 2 24 -1 counts/channel using increment or decrement method, data read/write from or to memory via data bus PC during the spectrum acquisition. Dual ported memory organization is 4096x24 bit, increment cycle time at 4.77 MHz system clock frequency is 1.05 μs. 6 refs.; 2 figs

  4. 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.

    Science.gov (United States)

    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 (pphi 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.

  5. Recent progresses of neural network unsupervised learning: I. Independent component analyses generalizing PCA

    Science.gov (United States)

    Szu, Harold H.

    1999-03-01

    The early vision principle of redundancy reduction of 108 sensor excitations is understandable from computer vision viewpoint toward sparse edge maps. It is only recently derived using a truly unsupervised learning paradigm of artificial neural networks (ANN). In fact, the biological vision, Hubel- Wiesel edge maps, is reproduced seeking the underlying independent components analyses (ICA) among 102 image samples by maximizing the ANN output entropy (partial)H(V)/(partial)[W] equals (partial)[W]/(partial)t. When a pair of newborn eyes or ears meet the bustling and hustling world without supervision, they seek ICA by comparing 2 sensory measurements (x1(t), x2(t))T equalsV X(t). Assuming a linear and instantaneous mixture model of the external world X(t) equals [A] S(t), where both the mixing matrix ([A] equalsV [a1, a2] of ICA vectors and the source percentages (s1(t), s2(t))T equalsV S(t) are unknown, we seek the independent sources approximately equals [I] where the approximated sign indicates that higher order statistics (HOS) may not be trivial. Without a teacher, the ANN weight matrix [W] equalsV [w1, w2] adjusts the outputs V(t) equals tanh([W]X(t)) approximately equals [W]X(t) until no desired outputs except the (Gaussian) 'garbage' (neither YES '1' nor NO '-1' but at linear may-be range 'origin 0') defined by Gaussian covariance G equals [I] equals [W][A] the internal knowledge representation [W], as the inverse of the external world matrix [A]-1. To unify IC, PCA, ANN & HOS theories since 1991 (advanced by Jutten & Herault, Comon, Oja, Bell-Sejnowski, Amari-Cichocki, Cardoso), the LYAPONOV function L(v1,...,vn, w1,...wn,) equals E(v1,...,vn) - H(w1,...wn) is constructed as the HELMHOTZ free energy to prove both convergences of supervised energy E and unsupervised entropy H learning. Consequently, rather using the faithful but dumb computer: 'GARBAGE-IN, GARBAGE-OUT,' the smarter neurocomputer will be equipped with an unsupervised learning that extracts

  6. PCA-induced respiratory depression simulating stroke following endoluminal repair of abdominal aortic aneurysm: a case report

    Directory of Open Access Journals (Sweden)

    Ahmad Javed

    2007-07-01

    Full Text Available Abstract Aim To report a case of severe respiratory depression with PCA fentanyl use simulating stroke in a patient who underwent routine elective endoluminal graft repair for abdominal aortic aneurysm (AAA Case presentation A 78-year-old obese lady underwent routine endoluminal graft repair for AAA that was progressively increasing in size. Following an uneventful operation postoperative analgesia was managed with a patient-controlled analgesia (PCA device with fentanyl. On the morning following operation the patient was found to be unusually drowsy and unresponsive to stimuli. Her GCS level was 11 with plantars upgoing bilaterally. A provisional diagnosis of stroke was made. Urgent transfer to a high-dependency unit (HDU was arranged and she was given ventilatory support with a BiPap device. CT was performed and found to be normal. Arterial blood gas (ABG analysis showed respiratory acidosis with PaCO2 81 mmHg, PaO2 140 mmHg, pH 7.17 and base excess -2 mmol/l. A total dose of 600 mcg of fentanyl was self-administered in the 16 hours following emergence from general anaesthesia. Naloxone was given with good effect. There was an increase in the creatinine level from 90 μmol/L preoperatively to 167 μmol/L on the first postoperative day. The patient remained on BiPap for two days that resulted in marked improvement in gas exchange. Recovery was complete.

  7. Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models

    Science.gov (United States)

    2016-01-01

    The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, Singapore’s MSCI futures, South Korea’s KOSPI 200 futures, and Taiwan’s TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis. PMID:27248692

  8. Regionalization and classification of bioclimatic zones in the central-northeastern region of Mexico using principal component analysis (PCA)

    Energy Technology Data Exchange (ETDEWEB)

    Pineda-Martinez, L.F.; Carbajal, N.; Medina-Roldan, E. [Instituto Potosino de Investigacion Cientifica y Tecnologica, A. C., San Luis Potosi (Mexico)]. E-mail: lpineda@ipicyt.edu.mx

    2007-04-15

    Applying principal component analysis (PCA), we determined climate zones in a topographic gradient in the central-northeastern part of Mexico. We employed nearly 30 years of monthly temperature and precipitation data at 173 meteorological stations. The climate classification was carried out applying the Koeppen system modified for the conditions of Mexico. PCA indicates a regionalization in agreement with topographic characteristics and vegetation. We describe the different bioclimatic zones, associated with typical vegetation, for each climate using geographical information systems (GIS). [Spanish] Utilizando un analisis de componentes principales, determinamos zonas climaticas en un gradiente topografico en la zona centro-noreste de Mexico. Se emplearon datos de precipitacion y temperatura medias mensuales por un periodo de 30 anos de 173 estaciones meteorologicas. La clasificacion del clima fue llevada a cabo de acuerdo con el sistema de Koeppen modificado para las condiciones de Mexico. El analisis de componentes principales indico una regionalizacion que concuerda con caracteristicas de topografia y vegetacion. Se describen zonas bioclimaticas, asociadas a vegetacion tipica para cada clima, usando sistemas de informacion geografica (SIG).

  9. PCA determination of the radiometric noise of high spectral resolution infrared observations from spectral residuals: Application to IASI

    Science.gov (United States)

    Serio, C.; Masiello, G.; Camy-Peyret, C.; Jacquette, E.; Vandermarcq, O.; Bermudo, F.; Coppens, D.; Tobin, D.

    2018-02-01

    The problem of characterizing and estimating the instrumental or radiometric noise of satellite high spectral resolution infrared spectrometers directly from Earth observations is addressed in this paper. An approach has been developed, which relies on the Principal Component Analysis (PCA) with a suitable criterion to select the optimal number of PC scores. Different selection criteria have been set up and analysed, which is based on the estimation theory of Least Squares and/or Maximum Likelihood Principle. The approach is independent of any forward model and/or radiative transfer calculations. The PCA is used to define an orthogonal basis, which, in turn, is used to derive an optimal linear reconstruction of the observations. The residual vector that is the observation vector minus the calculated or reconstructed one is then used to estimate the instrumental noise. It will be shown that the use of the spectral residuals to assess the radiometric instrumental noise leads to efficient estimators, which are largely independent of possible departures of the true noise from that assumed a priori to model the observational covariance matrix. Application to the Infrared Atmospheric Sounder Interferometer (IASI) has been considered. A series of case studies has been set up, which make use of IASI observations. As a major result, the analysis confirms the high stability and radiometric performance of IASI. The approach also proved to be efficient in characterizing noise features due to mechanical micro-vibrations of the beam splitter of the IASI instrument.

  10. Detection of l-Cysteine in wheat flour by Raman microspectroscopy combined chemometrics of HCA and PCA.

    Science.gov (United States)

    Cebi, Nur; Dogan, Canan Ekinci; Develioglu, Ayşen; Yayla, Mediha Esra Altuntop; Sagdic, Osman

    2017-08-01

    l-Cysteine is deliberately added to various flour types since l-Cysteine has enabled favorable baking conditions such as low viscosity, increased elasticity and rise during baking. In Turkey, usage of l-Cysteine as a food additive isn't allowed in wheat flour according to the Turkish Food Codex Regulation on food additives. There is an urgent need for effective methods to detect l-Cysteine in wheat flour. In this study, for the first time, a new, rapid, effective, non-destructive and cost-effective method was developed for detection of l-Cysteine in wheat flour using Raman microscopy. Detection of l-Cysteine in wheat flour was accomplished successfully using Raman microscopy combined chemometrics of PCA (Principal Component Analysis) and HCA (Hierarchical Cluster Analysis). In this work, 500-2000cm -1 spectral range (fingerprint region) was determined to perform PCA and HCA analysis. l-Cysteine and l-Cystine were determined with detection limit of 0.125% (w/w) in different wheat flour samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Radar target classification method with high accuracy and decision speed performance using MUSIC spectrum vectors and PCA projection

    Science.gov (United States)

    Secmen, Mustafa

    2011-10-01

    This paper introduces the performance of an electromagnetic target recognition method in resonance scattering region, which includes pseudo spectrum Multiple Signal Classification (MUSIC) algorithm and principal component analysis (PCA) technique. The aim of this method is to classify an "unknown" target as one of the "known" targets in an aspect-independent manner. The suggested method initially collects the late-time portion of noise-free time-scattered signals obtained from different reference aspect angles of known targets. Afterward, these signals are used to obtain MUSIC spectrums in real frequency domain having super-resolution ability and noise resistant feature. In the final step, PCA technique is applied to these spectrums in order to reduce dimensionality and obtain only one feature vector per known target. In the decision stage, noise-free or noisy scattered signal of an unknown (test) target from an unknown aspect angle is initially obtained. Subsequently, MUSIC algorithm is processed for this test signal and resulting test vector is compared with feature vectors of known targets one by one. Finally, the highest correlation gives the type of test target. The method is applied to wire models of airplane targets, and it is shown that it can tolerate considerable noise levels although it has a few different reference aspect angles. Besides, the runtime of the method for a test target is sufficiently low, which makes the method suitable for real-time applications.

  12. Estimating the number of components and detecting outliers using Angle Distribution of Loading Subspaces (ADLS) in PCA analysis.

    Science.gov (United States)

    Liu, Y J; Tran, T; Postma, G; Buydens, L M C; Jansen, J

    2018-08-22

    Principal Component Analysis (PCA) is widely used in analytical chemistry, to reduce the dimensionality of a multivariate data set in a few Principal Components (PCs) that summarize the predominant patterns in the data. An accurate estimate of the number of PCs is indispensable to provide meaningful interpretations and extract useful information. We show how existing estimates for the number of PCs may fall short for datasets with considerable coherence, noise or outlier presence. We present here how Angle Distribution of the Loading Subspaces (ADLS) can be used to estimate the number of PCs based on the variability of loading subspace across bootstrap resamples. Based on comprehensive comparisons with other well-known methods applied on simulated dataset, we show that ADLS (1) may quantify the stability of a PCA model with several numbers of PCs simultaneously; (2) better estimate the appropriate number of PCs when compared with the cross-validation and scree plot methods, specifically for coherent data, and (3) facilitate integrated outlier detection, which we introduce in this manuscript. We, in addition, demonstrate how the analysis of different types of real-life spectroscopic datasets may benefit from these advantages of ADLS. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    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.

  14. Antiallergic effect of fisetin on IgE-mediated mast cell activation in vitro and on passive cutaneous anaphylaxis (PCA).

    Science.gov (United States)

    Jo, Woo-Ri; Park, Hye-Jin

    2017-10-01

    Fisetin (3,7,3',4'-tetrahydroxyflavone), a naturally occurring bioactive flavonoid, has been shown to inhibit inflammation. However, little is known about the effect of fisetin on immunoglobulin E (IgE)-mediated allergic responses. In this study, the effect of fisetin on rat basophilic leukemia (RBL-2H3) cell-mediated allergic reactions was investigated. Fisetin inhibited β-hexosaminidase release and decreased the level of interleukin-4 and tumor necrosis factor-α mRNA in IgE/antigen (IgE/Ag)-stimulated RBL-2H3 cells. To elucidate the antiallergic mechanism, we examined the levels of signaling molecules responsible for degranulation and release of inflammatory cytokines. Fisetin decreased the levels of activated spleen tyrosine kinase, Gab2 proteins, linker of activated T cells, extracellular signal-related kinase 1/2 in the IgE/Ag-stimulated RBL2H3 cells, and NFκB and STAT3 proteins activated in the ear tissue of mice with passive cutaneous anaphylaxis (PCA). In addition, fisetin significantly lowered of FcɛRI α-subunit mRNA expression. Consistent with the cellular data, fisetin markedly suppressed RBL-2H3 cell-dependent PCA in IgE/Ag-sensitized mice. These results suggest that fisetin may have potential as a therapeutic agent for the treatment of allergic diseases. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. A novel fusion method of improved adaptive LTP and two-directional two-dimensional PCA for face feature extraction

    Science.gov (United States)

    Luo, Yuan; Wang, Bo-yu; Zhang, Yi; Zhao, Li-ming

    2018-03-01

    In this paper, under different illuminations and random noises, focusing on the local texture feature's defects of a face image that cannot be completely described because the threshold of local ternary pattern (LTP) cannot be calculated adaptively, a local three-value model of improved adaptive local ternary pattern (IALTP) is proposed. Firstly, the difference function between the center pixel and the neighborhood pixel weight is established to obtain the statistical characteristics of the central pixel and the neighborhood pixel. Secondly, the adaptively gradient descent iterative function is established to calculate the difference coefficient which is defined to be the threshold of the IALTP operator. Finally, the mean and standard deviation of the pixel weight of the local region are used as the coding mode of IALTP. In order to reflect the overall properties of the face and reduce the dimension of features, the two-directional two-dimensional PCA ((2D)2PCA) is adopted. The IALTP is used to extract local texture features of eyes and mouth area. After combining the global features and local features, the fusion features (IALTP+) are obtained. The experimental results on the Extended Yale B and AR standard face databases indicate that under different illuminations and random noises, the algorithm proposed in this paper is more robust than others, and the feature's dimension is smaller. The shortest running time reaches 0.329 6 s, and the highest recognition rate reaches 97.39%.

  16. Prebiotic Low Sugar Chocolate Dairy Desserts: Physical and Optical Characteristics and Performance of PARAFAC and PCA Preference Map.

    Science.gov (United States)

    Morais, E C; Esmerino, E A; Monteiro, R A; Pinheiro, C M; Nunes, C A; Cruz, A G; Bolini, Helena M A

    2016-01-01

    The addition of prebiotic and sweeteners in chocolate dairy desserts opens up new opportunities to develop dairy desserts that besides having a lower calorie intake still has functional properties. In this study, prebiotic low sugar dairy desserts were evaluated by 120 consumers using a 9-point hedonic scale, in relation to the attributes of appearance, aroma, flavor, texture, and overall liking. Internal preference map using parallel factor analysis (PARAFAC) and principal component analysis (PCA) was performed using the consumer data. In addition, physical (texture profile) and optical (instrumental color) analyses were also performed. Prebiotic dairy desserts containing sucrose and sucralose were equally liked by the consumers. These samples were characterized by firmness and gumminess, which can be considered drivers of liking by the consumers. Optimization of the prebiotic low sugar dessert formulation should take in account the choice of ingredients that contribute in a positive manner for these parameters. PARAFAC allowed the extraction of more relevant information in relation to PCA, demonstrating that consumer acceptance analysis can be evaluated by simultaneously considering several attributes. Multiple factor analysis reported Rv value of 0.964, suggesting excellent concordance for both methods. © 2015 Institute of Food Technologists®

  17. An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada

    Directory of Open Access Journals (Sweden)

    Kamil Faisal

    2017-03-01

    Full Text Available The United Nations estimates that the global population is going to be double in the coming 40 years, which may cause a negative impact on the environment and human life. Such an impact may instigate increased water demand, overuse of power, anthropogenic noise, etc. Thus, modelling the Urban Environmental Quality (UEQ becomes indispensable for a better city planning and an efficient urban sprawl control. This study aims to investigate the ability of using remote sensing and Geographic Information System (GIS techniques to model the UEQ with a case study in the city of Toronto via deriving different environmental, urban and socio-economic parameters. Remote sensing, GIS and census data were first obtained to derive environmental, urban and socio-economic parameters. Two techniques, GIS overlay and Principal Component Analysis (PCA, were used to integrate all of these environmental, urban and socio-economic parameters. Socio-economic parameters including family income, higher education and land value were used as a reference to assess the outcomes derived from the two integration methods. The outcomes were assessed through evaluating the relationship between the extracted UEQ results and the reference layers. Preliminary findings showed that the GIS overlay represents a better precision and accuracy (71% and 65%, respectively, comparing to the PCA technique. The outcomes of the research can serve as a generic indicator to help the authority for better city planning with consideration of all possible social, environmental and urban requirements or constraints.

  18. Retrieval of spheroid particle size distribution from spectral extinction data in the independent mode using PCA approach

    International Nuclear Information System (INIS)

    Tang, Hong; Lin, Jian-Zhong

    2013-01-01

    An improved anomalous diffraction approximation (ADA) method is presented for calculating the extinction efficiency of spheroids firstly. In this approach, the extinction efficiency of spheroid particles can be calculated with good accuracy and high efficiency in a wider size range by combining the Latimer method and the ADA theory, and this method can present a more general expression for calculating the extinction efficiency of spheroid particles with various complex refractive indices and aspect ratios. Meanwhile, the visible spectral extinction with varied spheroid particle size distributions and complex refractive indices is surveyed. Furthermore, a selection principle about the spectral extinction data is developed based on PCA (principle component analysis) of first derivative spectral extinction. By calculating the contribution rate of first derivative spectral extinction, the spectral extinction with more significant features can be selected as the input data, and those with less features is removed from the inversion data. In addition, we propose an improved Tikhonov iteration method to retrieve the spheroid particle size distributions in the independent mode. Simulation experiments indicate that the spheroid particle size distributions obtained with the proposed method coincide fairly well with the given distributions, and this inversion method provides a simple, reliable and efficient method to retrieve the spheroid particle size distributions from the spectral extinction data. -- Highlights: ► Improved ADA is presented for calculating the extinction efficiency of spheroids. ► Selection principle about spectral extinction data is developed based on PCA. ► Improved Tikhonov iteration method is proposed to retrieve the spheroid PSD.

  19. Management of Localized Prostate Cancer by Focal Transurethral Resection of Prostate Cancer: An Application of Radical TUR-PCa to Focal Therapy

    Directory of Open Access Journals (Sweden)

    Masaru Morita

    2012-01-01

    Full Text Available Background. We analyzed radical TUR-PCa against localized prostate cancer. Patients and Methods. Seventy-nine out of 209 patients with prostate cancer in one lobe were studied. Patients’ age ranged from 58 to 91 years and preoperative PSA, 0.70 to 17.30 ng/mL. In other 16 additional patients we performed focal TUR-PCa. Patients’ age ranged from 51 to 87 years and preoperative PSA, 1.51 to 25.74 ng/mL. Results. PSA failure in radical TUR-PCa was 5.1% during the mean follow-up period of 58.9 months. The actuarial biochemical non-recurrence rate was 98.2% for pT2a and 90.5% for pT2b. Bladder neck contracture occurred in 28 patients (35.4%. In 209 patients, pathological study revealed prostate cancer of the peripheral zone near the neurovascular bundle bilaterally in 25%, unilaterally in 39% and no cancer bilaterally in 35%, suggesting the possibility of focal TUR-PCa. Postoperative PSA of 16 patients treated by focal TUR-PCa was stable between 0.007 and 0.406 ng/mL at 24.2 months’ follow-up. No patients suffered from urinary incontinence. Bladder neck contracture developed in only 1 patient and all 5 patients underwent nerve-preserving TUR-PCa did not show erectile dysfunction. Conclusion. Focal TUR-PCa was considered to be a promising option among focal therapies against localized prostate cancer.

  20. Management of Localized Prostate Cancer by Focal Transurethral Resection of Prostate Cancer: An Application of Radical TUR-PCa to Focal Therapy.

    Science.gov (United States)

    Morita, Masaru; Matsuura, Takeshi

    2012-01-01

    Background. We analyzed radical TUR-PCa against localized prostate cancer. Patients and Methods. Seventy-nine out of 209 patients with prostate cancer in one lobe were studied. Patients' age ranged from 58 to 91 years and preoperative PSA, 0.70 to 17.30 ng/mL. In other 16 additional patients we performed focal TUR-PCa. Patients' age ranged from 51 to 87 years and preoperative PSA, 1.51 to 25.74 ng/mL. Results. PSA failure in radical TUR-PCa was 5.1% during the mean follow-up period of 58.9 months. The actuarial biochemical non-recurrence rate was 98.2% for pT2a and 90.5% for pT2b. Bladder neck contracture occurred in 28 patients (35.4%). In 209 patients, pathological study revealed prostate cancer of the peripheral zone near the neurovascular bundle bilaterally in 25%, unilaterally in 39% and no cancer bilaterally in 35%, suggesting the possibility of focal TUR-PCa. Postoperative PSA of 16 patients treated by focal TUR-PCa was stable between 0.007 and 0.406 ng/mL at 24.2 months' follow-up. No patients suffered from urinary incontinence. Bladder neck contracture developed in only 1 patient and all 5 patients underwent nerve-preserving TUR-PCa did not show erectile dysfunction. Conclusion. Focal TUR-PCa was considered to be a promising option among focal therapies against localized prostate cancer.

  1. Tracking polychlorinated biphenyls (PCBs) congener patterns in Newark Bay surface sediment using principal component analysis (PCA) and positive matrix factorization (PMF).

    Science.gov (United States)

    Saba, Tarek; Su, Steave

    2013-09-15

    PCB congener data for Newark Bay surface sediments were analyzed using PCA and PMF, and relationships between the outcomes from these two techniques were explored. The PCA scores plot separated the Lower Passaic River Mouth samples from North Newark Bay, thus indicating dissimilarity. Although PCA was able to identify subareas in the Bay system with specific PCB congener patterns (e.g., higher chlorinated congeners in Elizabeth River), further conclusions reading potential PCB source profiles or potential upland source areas were not clear for the PCA scores plot. PMF identified five source factors, and explained the Bay sample congener profiles as a mix of these Factors. This PMF solution was equivalent to (1) defining an envelope that encompasses all samples on the PCA scores plot, (2) defining source factors that plot on that envelope, and (3) explaining the congener profile for each Bay sediment sample (inside the scores plot envelope) as a mix of factors. PMF analysis allowed identifying characteristic features in the source factor congener distributions that allowed tracking of source factors to shoreline areas where PCB inputs to the Bay may have originated. The combined analysis from PCA and PMF showed that direct discharges to the Bay are likely the dominant sources of PCBs to the sediment. Review of historical upland activities and regulatory files will be needed, in addition to the PCA and PMF analysis, to fully reconstruct the history of operations and PCB releases around the Newark Bay area that impacted the Bay sediment. Copyright © 2013 Elsevier B.V. All rights reserved.

  2. The Comparison of Intrathecal Morphine and IV Morphine PCA on Pain Control, Patient Satisfaction, Morphine Consumption, and Adverse Effects in Patients Undergoing Reduction Mammoplasty.

    Science.gov (United States)

    Karamese, Mehtap; Akdağ, Osman; Kara, İnci; Yıldıran, Gokce Unal; Tosun, Zekeriya

    2015-01-01

    Following breast reduction procedures, the level of postoperative pain can be severe, and sufficient pain control influences a patient's physiological, immunological, and psychological status. The aim of this study was to examine the use of intrathecal morphine (ITM) in breast reduction surgery with patient-controlled analgesia (PCA). Sixty-two female patients who underwent breast reductions with the same technique participated in this study. The study group (ITM + PCA) included 32 patients; a single shot (0.2 mg) of ITM and intravenous morphine with PCA were administered. In the control group, morphine PCA alone was intravenously administered to 30 patients. Comparisons between the groups of cumulative morphine consumption, visual analog scale scores, and patient satisfaction scores, which were the primary outcome measures, and adverse effects, which were the secondary outcome measures, were conducted. The patients in the 2 groups had similar degrees of pain and satisfaction scores. The study group had lower cumulative morphine consumption (P = .001) than the PCA-only control group; there was no statistically significant difference in adverse effects between the 2 groups. Intrathecal morphine may effectively control pain with lower total morphine consumption following breast reduction surgery.

  3. Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.

    Science.gov (United States)

    Ginsburg, Shoshana B; Viswanath, Satish E; Bloch, B Nicolas; Rofsky, Neil M; Genega, Elizabeth M; Lenkinski, Robert E; Madabhushi, Anant

    2015-05-01

    To identify computer-extracted features for central gland and peripheral zone prostate cancer localization on multiparametric magnetic resonance imaging (MRI). Preoperative T2-weighted (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI were acquired from 23 men with confirmed prostate cancer. Following radical prostatectomy, the cancer extent was delineated by a pathologist on ex vivo histology and mapped to MRI by nonlinear registration of histology and corresponding MRI slices. In all, 244 computer-extracted features were extracted from MRI, and principal component analysis (PCA) was employed to reduce the data dimensionality so that a generalizable classifier could be constructed. A novel variable importance on projection (VIP) measure for PCA (PCA-VIP) was leveraged to identify computer-extracted MRI features that discriminate between cancer and normal prostate, and these features were used to construct classifiers for cancer localization. Classifiers using features selected by PCA-VIP yielded an area under the curve (AUC) of 0.79 and 0.85 for peripheral zone and central gland tumors, respectively. For tumor localization in the central gland, T2w, DCE, and DWI MRI features contributed 71.6%, 18.1%, and 10.2%, respectively; for peripheral zone tumors T2w, DCE, and DWI MRI contributed 29.6%, 21.7%, and 48.7%, respectively. PCA-VIP identified relatively stable subsets of MRI features that performed well in localizing prostate cancer on MRI. © 2014 Wiley Periodicals, Inc.

  4. Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images.

    Science.gov (United States)

    Sanz-Requena, Roberto; Prats-Montalbán, José Manuel; Martí-Bonmatí, Luis; Alberich-Bayarri, Ángel; García-Martí, Gracián; Pérez, Rosario; Ferrer, Alberto

    2015-08-01

    To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on principal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters. The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results. Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61). The automatic PCA-based approach minimizes the variability associated to obtaining individual volume-based AIFs in DCE-MR studies of the prostate. © 2014 Wiley Periodicals, Inc.

  5. Serum crosslinked-N-terminal telopeptide of type I collagen (NTx) has prognostic implications for patients with initial prostate carcinoma (PCa): a pilot study.

    Science.gov (United States)

    Jablonka, Fernando; Alves, Beatriz da Costa Aguiar; de Oliveira, Claudia Giorgia Bronzati; Wroclawski, Marcelo L; Szwarc, Marcelo; Vitória, Webster de Oliveira; Fonseca, Fernando; Del Giglio, Auro

    2014-09-25

    NTx is a type I collagen metabolite previously shown to be increased in patients with bone metastasis. We evaluate NTx potential prognostic role in PCa at diagnosis, when most of the patients have no overt bone involvement. Men with histologic diagnosis of PCa were included at diagnosis. Serum NTx was measured serially every 3 months up to two years by ELISA. Fifty-five PCa patients with a median age of 67 y (51-83 y) were included. Most (86%) had stage I; 4% stage II; 2% stage III and 10% stage IV disease. At entry, median NTx was 14.65 nMBCE and it did not correlate with age, Gleason score or PSA, but we observed a significant direct correlation with stage (p=0.0094). With a median follow up of 23 months, serum NTx correlated significantly with biochemical recurrence (p=0.012), as did Gleason score (p=0.00056), stage (p=0.012) and PSA (pPCa at diagnosis. These results emphasize the importance of bone metabolism biomarkers in patients with PCa even without evident overt bone involvement. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Feature Selection pada Dataset Faktor Kesiapan Bencana pada Provinsi di Indonesia Menggunakan Metode PCA (Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Septa Firmansyah Putra

    2017-01-01

    Full Text Available Penelitian ini bertujuan untuk mengetahui atribut-atribut apa yang akan digunakan untuk klasterisasi provinsi di Indonesia berdasarkan faktor kesiapan dalam menghadapi bencana. Data yang digunakan terdiri dari tiga kelompok data yaitu data jumlah kejadian bencana yang terdiri dari 19 sub-atribut, data jumlah fasilitas kesehatan yang terdiri dari 14 sub-atribut dan data jumlah tenaga kesehatan yang terdiri dari 11 sub atribut. Penelitian ini dapat menjadi gambaran tentang bagaimana melakukan pembersihan dan pemilihan data sebelum digunakan dalam proses klasterisasi. Data-data ini akan dibersihkan dan dipilih sebelum nantinya digunakan pada proses klasterisasi. Proses pembersihan dan pemilihan data dilakukan dengan bantuan PCA (Principal Component Analysis namun sebelumnya dibersihkan telebih dahulu dengan cara manual. Penelitian dibagi menjadi 3 percobaan. Pada percobaan pertama didapatkan 31 sub-atribut yang siap digunakan, percobaan kedua didapatkan 29 sub-atribut yang siap digunakan dan pada percobaan ketiga didapatkan 24 sub-atribut yang siap digunakan.

  7. [Pattern recognition of decorative papers with different visual characteristics using visible spectroscopy coupled with principal component analysis (PCA)].

    Science.gov (United States)

    Zhang, Mao-mao; Yang, Zhong; Lu, Bin; Liu, Ya-na; Sun, Xue-dong

    2015-02-01

    As one of the most important decorative materials for the modern household products, decorative papers impregnated with melamine not only have better decorative performance, but also could greatly improve the surface properties of materials. However, the appearance quality (such as color-difference evaluation and control) of decorative papers, as an important index for the surface quality of decorative paper, has been a puzzle for manufacturers and consumers. Nowadays, human eye is used to discriminate whether there exist color difference in the factory, which is not only of low efficiency but also prone to bring subjective error. Thus, it is of great significance to find an effective method in order to realize the fast recognition and classification of the decorative papers. In the present study, the visible spectroscopy coupled with principal component analysis (PCA) was used for the pattern recognition of decorative papers with different visual characteristics to investigate the feasibility of visible spectroscopy to rapidly recognize the types of decorative papers. The results showed that the correlation between visible spectroscopy and visual characteristics (L*, a* and b*) was significant, and the correlation coefficients wereup to 0.85 and some was even more than 0. 99, which might suggest that the visible spectroscopy reflected some information about visual characteristics on the surface of decorative papers. When using the visible spectroscopy coupled with PCA to recognize the types of decorative papers, the accuracy reached 94%-100%, which might suggest that the visible spectroscopy was a very potential new method for the rapid, objective and accurate recognition of decorative papers with different visual characteristics.

  8. On application of kernel PCA for generating stimulus features for fMRI during continuous music listening.

    Science.gov (United States)

    Tsatsishvili, Valeri; Burunat, Iballa; Cong, Fengyu; Toiviainen, Petri; Alluri, Vinoo; Ristaniemi, Tapani

    2018-06-01

    There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. NEW METHOD: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined. The generated features captured musical percepts that were hidden from the linear PCA features, namely Rhythmic Complexity and Event Synchronicity. Neural correlates of the new features revealed activations associated to processing of complex rhythms, including auditory, motor, and frontal areas. Results were compared with the findings in the previously published study, which analyzed the same fMRI data but applied linear PCA for generating stimulus features. To enable comparison of the results, methodology for finding stimulus-driven functional maps was adopted from the previous study. Exploiting nonlinear relationships among acoustic descriptors can lead to the novel high-level stimulus features, which can in turn reveal new brain structures involved in music processing. Copyright © 2018 Elsevier B.V. All rights reserved.

  9. Cluster analysis of commercial samples of Bauhinia spp. using HPLC-UV/PDA and MCR-ALS/PCA without peak alignment procedure.

    Science.gov (United States)

    Ardila, Jorge Armando; Funari, Cristiano Soleo; Andrade, André Marques; Cavalheiro, Alberto José; Carneiro, Renato Lajarim

    2015-01-01

    Bauhinia forficata Link. is recognised by the Brazilian Health Ministry as a treatment of hypoglycemia and diabetes. Analytical methods are useful to assess the plant identity due the similarities found in plants from Bauhinia spp. HPLC-UV/PDA in combination with chemometric tools is an alternative widely used and suitable for authentication of plant material, however, the shifts of retention times for similar compounds in different samples is a problem. To perform comparisons between the authentic medicinal plant (Bauhinia forficata Link.) and samples commercially available in drugstores claiming to be "Bauhinia spp. to treat diabetes" and to evaluate the performance of multivariate curve resolution - alternating least squares (MCR-ALS) associated to principal component analysis (PCA) when compared to pure PCA. HPLC-UV/PDA data obtained from extracts of leaves were evaluated employing a combination of MCR-ALS and PCA, which allowed the use of the full chromatographic and spectrometric information without the need of peak alignment procedures. The use of MCR-ALS/PCA showed better results than the conventional PCA using only one wavelength. Only two of nine commercial samples presented characteristics similar to the authentic Bauhinia forficata spp., considering the full HPLC-UV/PDA data. The combination of MCR-ALS and PCA is very useful when applied to a group of samples where a general alignment procedure could not be applied due to the different chromatographic profiles. This work also demonstrates the need of more strict control from the health authorities regarding herbal products available on the market. Copyright © 2015 John Wiley & Sons, Ltd.

  10. Masked-Volume-Wise PCA and "reference Logan" illustrate similar regional differences in kinetic behavior in human brain PET study using [11C]-PIB

    Directory of Open Access Journals (Sweden)

    Engler Henry

    2009-01-01

    Full Text Available Abstract Background Kinetic modeling using reference Logan is commonly used to analyze data obtained from dynamic Positron Emission Tomography (PET studies on patients with Alzheimer's disease (AD and healthy volunteers (HVs using amyloid imaging agent N-methyl [11C]2-(4'-methylaminophenyl-6-hydroxy-benzothiazole, [11C]-PIB. The aim of the present study was to explore whether results obtained using the newly introduced method, Masked Volume Wise Principal Component Analysis, MVW-PCA, were similar to the results obtained using reference Logan. Methods MVW-PCA and reference Logan were performed on dynamic PET images obtained from four Alzheimer's disease (AD patients on two occasions (baseline and follow-up and on four healthy volunteers (HVs. Regions of interest (ROIs of similar sizes were positioned in different parts of the brain in both AD patients and HVs where the difference between AD patients and HVs is largest. Signal-to-noise ratio (SNR and discrimination power (DP were calculated for images generated by the different methods and the results were compared both qualitatively and quantitatively. Results MVW-PCA generated images that illustrated similar regional binding patterns compared to reference Logan images and with slightly higher quality, enhanced contrast, improved SNR and DP, without being based on modeling assumptions. MVW-PCA also generated additional MVW-PC images by using the whole dataset, which illustrated regions with different and uncorrelated kinetic behaviors of the administered tracer. This additional information might improve the understanding of kinetic behavior of the administered tracer. Conclusion MVW-PCA is a potential multivariate method that without modeling assumptions generates high quality images, which illustrated similar regional changes compared to modeling methods such as reference Logan. In addition, MVW-PCA could be used as a new technique, applicable not only on dynamic human brain studies but also on

  11. Predicting the effectiveness of virtual reality relaxation on pain and anxiety when added to PCA morphine in patients having burns dressings changes.

    Science.gov (United States)

    Konstantatos, A H; Angliss, M; Costello, V; Cleland, H; Stafrace, S

    2009-06-01

    Pain arising in burns sufferers is often severe and protracted. The prospect of a dressing change can heighten existing pain by impacting both physically and psychologically. In this trial we examined whether pre-procedural virtual reality guided relaxation added to patient controlled analgesia with morphine reduced pain severity during awake dressings changes in burns patients. We conducted a prospective randomized clinical trial in all patients with burns necessitating admission to a tertiary burns referral centre. Eligible patients requiring awake dressings changes were randomly allocated to single use virtual reality relaxation plus intravenous morphine patient controlled analgesia (PCA) infusion or to intravenous morphine patient controlled analgesia infusion alone. Patients rated their worst pain intensity during the dressing change using a visual analogue scale. The primary outcome measure was presence of 30% or greater difference in pain intensity ratings between the groups in estimation of worst pain during the dressing change. Of 88 eligible and consenting patients having awake dressings changes, 43 were assigned to virtual reality relaxation plus intravenous morphine PCA infusion and 43 to morphine PCA infusion alone. The group receiving virtual reality relaxation plus morphine PCA infusion reported significantly higher pain intensities during the dressing change (mean=7.3) compared with patients receiving morphine PCA alone (mean=5.3) (p=0.003) (95% CI 0.6-2.8). The addition of virtual reality guided relaxation to morphine PCA infusion in burns patients resulted in a significant increase in pain experienced during awake dressings changes. In the absence of a validated predictor for responsiveness to virtual reality relaxation such a therapy cannot be recommended for general use in burns patients having awake dressings changes.

  12. APLICACIÓN DEL SOFTWARE PCA 1.0: PARA REDUCIR EL DETERIORO DE LA CALIDAD DEL AIRE EN CALI-COLOMBIA (FASE I

    Directory of Open Access Journals (Sweden)

    Luis Granada

    2009-01-01

    Full Text Available El PCA 1.0 se diseñó como herramienta de soporte para un Procedimiento Organizacional que colecta y trata la información obtenida en el monitoreo y control de contaminantes atmosféricos en Cali - Colombia1. El PCA 1.0 es una aplicación hecha en java con la Interfaz de Desarrollo (IDE NetBeans2. El software PCA 1.0 está diseñado para recepcionar, depurar, validar y generar reportes de manera sistemática de los datos obtenidos en la Red de Monitoreo de Calidad del Aire, Meteorología y de la Inspección Técnico Mecánica y de Gases. El PCA 1.0 estima los Factores de Emisión y la Carga Ambiental diaria generada por las fuentes móviles en kilogramos, así como el valor promedio horario de la concentración de contaminantes criterio. La metodología general del Procedimiento Organizacional se fundamentó en lo establecido en la ISO 14040 (Análisis del Ciclo de Vida. El resultado más importante de la aplicación del PCA 1.0, se evidencia en facilitar a la autoridad ambiental, sanitaria, tránsito y transporte, la toma de decisiones con base en la selección de escenarios de contaminación atmosférica y actuaciones encaminadas hacia la publicación de normas que buscan reducir el deterioro de la calidad del aire en Cali ¿ Colombia. Finalmente, el PCA 1.0 se perfila como una herramienta ágil y adaptable en los sistemas de gestión ambiental municipal en su componente aire.

  13. The Northern Norway Mother-and-Child Contaminant Cohort (MISA) Study: PCA analyses of environmental contaminants in maternal sera and dietary intake in early pregnancy.

    Science.gov (United States)

    Veyhe, Anna Sofía; Hofoss, Dag; Hansen, Solrunn; Thomassen, Yngvar; Sandanger, Torkjel M; Odland, Jon Øyvind; Nieboer, Evert

    2015-03-01

    Although predictors of contaminants in serum or whole blood are usually examined by chemical groups (e.g., POPs, toxic and/or essential elements; dietary sources), principal component analysis (PCA) permits consideration of both individual substances and combined variables. Our study had two primary objectives: (i) Characterize the sources and predictors of a suite of eight PCBs, four organochlorine (OC) pesticides, five essential and five toxic elements in serum and/or whole blood of pregnant women recruited as part of the Mother-and-Child Contaminant Cohort Study conducted in Northern Norway (The MISA study); and (ii) determine the influence of personal and social characteristics on both dietary and contaminant factors. Recruitment and sampling started in May 2007 and continued for the next 31 months until December 2009. Blood/serum samples were collected during the 2nd trimester (mean: 18.2 weeks, range 9.0-36.0). A validated questionnaire was administered to obtain personal information. The samples were analysed by established laboratories employing verified methods and reference standards. PCA involved Varimax rotation, and significant predictors (p≤0.05) in linear regression models were included in the multivariable linear regression analysis. When considering all the contaminants, three prominent PCA axes stood out with prominent loadings of: all POPs; arsenic, selenium and mercury; and cadmium and lead. Respectively, in the multivariate models the following were predictors: maternal age, parity and consumption of freshwater fish and land-based wild animals; marine fish; cigarette smoking, dietary PCA axes reflecting consumption of grains and cereals, and food items involving hunting. PCA of only the POPs separated them into two axes that, in terms of recently published findings, could be understood to reflect longitudinal trends and their relative contributions to summed POPs. The linear combinations of variables generated by PCA identified prominent

  14. Real-time dynamic MR image reconstruction using compressed sensing and principal component analysis (CS-PCA): Demonstration in lung tumor tracking.

    Science.gov (United States)

    Dietz, Bryson; Yip, Eugene; Yun, Jihyun; Fallone, B Gino; Wachowicz, Keith

    2017-08-01

    This work presents a real-time dynamic image reconstruction technique, which combines compressed sensing and principal component analysis (CS-PCA), to achieve real-time adaptive radiotherapy with the use of a linac-magnetic resonance imaging system. Six retrospective fully sampled dynamic data sets of patients diagnosed with non-small-cell lung cancer were used to investigate the CS-PCA algorithm. Using a database of fully sampled k-space, principal components (PC's) were calculated to aid in the reconstruction of undersampled images. Missing k-space data were calculated by projecting the current undersampled k-space data onto the PC's to generate the corresponding PC weights. The weighted PC's were summed together, and the missing k-space was iteratively updated. To gain insight into how the reconstruction might proceed at lower fields, 6× noise was added to the 3T data to investigate how the algorithm handles noisy data. Acceleration factors ranging from 2 to 10× were investigated using CS-PCA and Split Bregman CS for comparison. Metrics to determine the reconstruction quality included the normalized mean square error (NMSE), as well as the dice coefficients (DC) and centroid displacement of the tumor segmentations. Our results demonstrate that CS-PCA performed superior than CS alone. The CS-PCA patient averaged DC for 3T and 6× noise added data remained above 0.9 for acceleration factors up to 10×. The patient averaged NMSE gradually increased with increasing acceleration; however, it remained below 0.06 up to an acceleration factor of 10× for both 3T and 6× noise added data. The CS-PCA reconstruction speed ranged from 5 to 20 ms (Intel i7-4710HQ CPU @ 2.5 GHz), depending on the chosen parameters. A real-time reconstruction technique was developed for adaptive radiotherapy using a Linac-MRI system. Our CS-PCA algorithm can achieve tumor contours with DC greater than 0.9 and NMSE less than 0.06 at acceleration factors of up to, and including, 10×. The

  15. pcaGoPromoter--an R package for biological and regulatory interpretation of principal components in genome-wide gene expression data

    DEFF Research Database (Denmark)

    Hansen, Morten; Gerds, Thomas Alexander; Nielsen, Ole Haagen

    2012-01-01

    Analyzing data obtained from genome-wide gene expression experiments is challenging due to the quantity of variables, the need for multivariate analyses, and the demands of managing large amounts of data. Here we present the R package pcaGoPromoter, which facilitates the interpretation of genome.......g., cell cycle progression and the predicted involvement of expected transcription factors, including E2F. In addition, unexpected results, e.g., cholesterol synthesis in serum-depleted cells and NF-¿B activation in inhibitor treated cells, were noted. In summary, the pcaGoPromoter R package provides...

  16. Evaluation of PCA3 and multiparametric MRI’s: collective benefits before deciding initial prostate biopsy for patients with PSA level between 3-10ng/mL

    Directory of Open Access Journals (Sweden)

    Sezgin Okcelik

    2016-06-01

    Full Text Available ABSTRACT Objective To analyze the contribution of multiparametric MRI and PCA3 assay, pre- decision of initial biopsy in PSA level between 3-10 ng/mL patients with normal digital rectal examination(DRE. Materials and Methods PSA level 3-10 ng/mL ,patients, with normal DRE results and no previous prostate biopsy history, were included in this study. Each patient underwent multiparametric MRI one week before biopsy. Urine sample taking for PCA3 examination preceded the biopsy. Systematic and targeted biopsies were conducted. Patients with high PSA levels were seperated into two groups as: high PCA3 scored and low PCA3 scored. Then each group was divided into two sub-groups as: MRI lesion positive and negative. Tumor incidence, positive predictive values(PPV and negative predictive values(NPV were calculated. Results 53 patients were included between February 2013 and March 2014.Mean age 61.22 ± 1.06. Mean PSA value 5.13 ± 0.19 ng / mL. Mean PCA3 score 98.01 ± 23.13 and mean prostate size was 48.96 ± 2.67 grams. Fourty nine patients had both PCA3 score and multiparametric MRI. The PCA3’s PPV value was 58.33%. If multiparametric MRI lesions are added to high PCA3 scores , the PPV appears to elevate to 91.66%. NPV of PCA3 was 96%. NPV was 95% when there was no lesion in the multiparametric MRI with low PCA3 scores. Sensitivity was 91.66% , specificity was 95% respectively. Conclusion Adding multimetric MRI can also support biopsy decision for patients with high PCA3 value. When PCA3 value is low, patients can be survailled without any need to take a MRI.

  17. Application of PCA-LDA method to determine the geographical origin of tea based on determination of stable isotopes and multi-elements

    International Nuclear Information System (INIS)

    Yuan Yuwei; Zhang Yongzhi; Yang Guiling; Zhang Zhiheng; Fu Haiyan; Han Wenyan; Li Shufang

    2013-01-01

    The ratio of stable isotope and concentration of multi-element in tea was determinated with isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition techniques with principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify the geographical origins of tea from Fujian, Shandong and Zhejiang province, and Yuyao, Jinhua and Xihu region of Zhejiang. The results showed the values of δ"1"5N, δ"1"3C, δD, δ"1"8O and the ratios of "2"0"6Pb/"2"0"7Pb, "2"0"8Pb/"2"0"6Pb and "8"7Sr/"8"6Sr in tea samples were different from different origins. There was also large variable for the concentrations of 27 mineral elements, such as Li, Be, Na and so on, with a specific character of origin. The method of PCA could be used to classify the geographical origin of tea from different origins but with a cross in the scatter plot. However, PCA combining with LDA could gave correct assignation percentages of 99% for the tea samples among Fujian, Shandong and Zhejiang provinces, and 87% for the tea samples among Yuyao, Jinhua and Xihu region of Zhejiang. These results revealed that it was possible and feasible to classify the geographical origin of tea by the method of PCA-LDA based on the determination of isotopes and multi-elements. (authors)

  18. Climate change adaptation: Uncovering constraints to the use of adaptation strategies among food crop farmers in South-west, Nigeria using principal component analysis (PCA

    Directory of Open Access Journals (Sweden)

    Moradeyo Adebanjo Otitoju

    2016-12-01

    Full Text Available This study focused on the constraints to the use of climate variability/change adaptation strategies in South-west Nigeria. Multistage random technique was employed to select the location and the respondents. Descriptive statistics and principal component analysis (PCA were the analytical tools engaged in this study. The constraints to climate variability and change examined before did not use PCA but generalized factor analysis. Hence, there is need to examine these constraints extensively using PCA. Uncovering the constraints to the use of climate variability/change adaptation strategies among crop framers is important to give a realistic direction in the development of farmer-inclusive climate policies in Nigeria. The PCA result showed that the principal constraints that the farmers faced in climate change adaptation were public, institutional and labour constraint; land, neighbourhood norms and religious beliefs constraint; high cost of inputs, technological and information constraint; farm distance, access to climate information, off-farm job and credit constraint; and poor agricultural programmes and service delivery constraint. These findings pointed out the need for both the government and non-government organizations to intensify efforts on institutional, technological and farmers’ friendly land tenure and information systems as effective measures to guide inclusive climate change adaptation policies and development in South-west Nigeria.

  19. An analytical approach based on ESI-MS, LC-MS and PCA for the quali-quantitative analysis of cycloartane derivatives in Astragalus spp.

    Science.gov (United States)

    Napolitano, Assunta; Akay, Seref; Mari, Angela; Bedir, Erdal; Pizza, Cosimo; Piacente, Sonia

    2013-11-01

    Astragalus species are widely used as health foods and dietary supplements, as well as drugs in traditional medicine. To rapidly evaluate metabolite similarities and differences among the EtOH extracts of the roots of eight commercial Astragalus spp., an approach based on direct analyses by ESI-MS followed by PCA of ESI-MS data, was carried out. Successively, quali-quantitative analyses of cycloartane derivatives in the eight Astragalus spp. by LC-ESI-MS(n) and PCA of LC-ESI-MS data were performed. This approach allowed to promptly highlighting metabolite similarities and differences among the various Astragalus spp. PCA results from LC-ESI-MS data of Astragalus samples were in reasonable agreement with both PCA results of ESI-MS data and quantitative results. This study affords an analytical method for the quali-quantitative determination of cycloartane derivatives in herbal preparations used as health and food supplements. Copyright © 2013 Elsevier B.V. All rights reserved.

  20. Phase I/II clinical trial of dendritic-cell based immunotherapy (DCVAC/PCa) combined with chemotherapy in patients with metastatic, castration-resistant prostate cancer.

    Science.gov (United States)

    Podrazil, Michal; Horvath, Rudolf; Becht, Etienne; Rozkova, Daniela; Bilkova, Pavla; Sochorova, Klara; Hromadkova, Hana; Kayserova, Jana; Vavrova, Katerina; Lastovicka, Jan; Vrabcova, Petra; Kubackova, Katerina; Gasova, Zdenka; Jarolim, Ladislav; Babjuk, Marek; Spisek, Radek; Bartunkova, Jirina; Fucikova, Jitka

    2015-07-20

    We conducted an open-label, single-arm Phase I/II clinical trial in metastatic CRPC (mCRPC) patients eligible for docetaxel combined with treatment with autologous mature dendritic cells (DCs) pulsed with killed LNCaP prostate cancer cells (DCVAC/PCa). The primary and secondary endpoints were safety and immune responses, respectively. Overall survival (OS), followed as a part of the safety evaluation, was compared to the predicted OS according to the Halabi and MSKCC nomograms. Twenty-five patients with progressive mCRPC were enrolled. Treatment comprised of initial 7 days administration of metronomic cyclophosphamide 50 mg p.o. DCVAC/PCa treatment consisted of a median twelve doses of 1 × 107 dendritic cells per dose injected s.c. (Aldara creme was applied at the site of injection) during a one-year period. The initial 2 doses of DCVAC/PCa were administered at a 2-week interval, followed by the administration of docetaxel (75 mg/m2) and prednisone (5 mg twice daily) given every 3 weeks until toxicity or intolerance was observed. The DCVAC/PCa was then injected every 6 weeks up to the maximum number of doses manufactured from one leukapheresis. No serious DCVAC/PCa-related adverse events have been reported. The median OS was 19 months, whereas the predicted median OS was 11.8 months with the Halabi nomogram and 13 months with the MSKCC nomogram. Kaplan-Meier analyses showed that patients had a lower risk of death compared with both MSKCC (Hazard Ratio 0.26, 95% CI: 0.13-0.51) and Halabi (Hazard Ratio 0.33, 95% CI: 0.17-0.63) predictions. We observed a significant decrease in Tregs in the peripheral blood. The long-term administration of DCVAC/PCa led to the induction and maintenance of PSA specific T cells. We did not identify any immunological parameter that significantly correlated with better OS. In patients with mCRPC, the combined chemoimmunotherapy with DCVAC/PCa and docetaxel was safe and resulted in longer than expected survival. Concomitant chemotherapy

  1. The Role of PCA 3 as a Prognostic Factor in Patients with Castration-resistant Prostate Cancer (CRPC) Treated with Docetaxel.

    Science.gov (United States)

    Bourdoumis, Andreas; Chrisofos, Michael; Stasinou, Theodora; Christopoulos, Panagiotis; Mourmouris, Panagiotis; Kostakopoulos, Athanasios; Deliveliotis, Charalambos

    2015-05-01

    To investigate potential fluctuations in prostate cancer antigen 3 (PCA 3) scores in castration-resistant prostate cancer (CRPC) patients treated with docetaxel and investigate the assay as a potential prognostic factor. This was a prospective observational cohort study. Inclusion criteria included patients on hormonal treatment who were recently diagnosed with CRPC. Exclusion criteria included patients previously having radical treatment (surgery or radiotherapy) and patients who have completed the first cycle of chemotherapy. All urine samples were collected and analyzed using the Progensa® assay. Samples were collected before starting chemotherapy and at 12 months. A prospective database was created including routine blood tests, prostate staging and prostate-specific antigen (PSA) levels throughout the study period. The effects of chemotherapy were also recorded. Between January 2010 and February 2013, 12 patients were included in the study out of an initial cohort of 23 patients with CRPC. Mean follow-up was 14.8 months. Mean age at CRPC diagnosis was 73.8 years (±3.6 SD). Mean Gleason score was 8, with PSA 84.23 ng/ml (±158 SD). Mean duration of androgen deprivation treatment (ADT) was 45.16 months (±34.9 SD). Mean time to castrate-resistant state was 46.58 months (±35.3 SD). All twelve (n=12, 100%) patients had non-assessable PCA 3 scores at baseline and at 12 months follow-up. As a direct consequence, statistical analysis was not performed as the anticipated change in PCA 3 scores was not identified and correlation between measurable differences was not possible. All patients tolerated chemotherapy and completed the scheduled cycles with no serious adverse effects. To our knowledge, this is the first prospective study to demonstrate lack of expression of PCA3 in CRPC, with the result apparently not influenced by chemotherapy. There appears to be a strong association between hormonal treatment and lack of PCA 3 expression. It is still unknown whether

  2. The PCA3 test for guiding repeat biopsy of prostate cancer and its cut-off score: a systematic review and meta-analysis

    Directory of Open Access Journals (Sweden)

    Yong Luo

    2014-06-01

    Full Text Available The specificity of prostate-specific antigen (PSA for early intervention in repeat biopsy is unsatisfactory. Prostate cancer antigen 3 (PCA3 may be more accurate in outcome prediction than other methods for the early detection of prostate cancer (PCa. However, the results were inconsistent in repeated biopsies. Therefore, we performed a systematic review and meta-analysis to evaluate the role of PCA3 in outcome prediction. A systematic bibliographic search was conducted for articles published before April 2013, using PubMed, Medline, Web of Science, Embase and other databases from health technology assessment agencies. The quality of the studies was assessed on the basis of QUADAS criteria. Eleven studies of diagnostic tests with moderate to high quality were selected. A meta-analysis was carried out to synthesize the results. The results of the meta-analyses were heterogeneous among studies. We performed a subgroup analysis (with or without inclusion of high-grade prostatic intraepithelial neoplasia (HGPIN and atypical small acinar proliferation (ASAP. Using a PCA3 cutoff of 20 or 35, in the two sub-groups, the global sensitivity values were 0.93 or 0.80 and 0.79 or 0.75, specificities were 0.65 or 0.44 and 0.78 or 0.70, positive likelihood ratios were 1.86 or 1.58 and 2.49 or 1.78, negative likelihood ratios were 0.81 or 0.43 and 0.91 or 0.82 and diagnostic odd ratios (ORs were 5.73 or 3.45 and 7.13 or 4.11, respectively. The areas under the curve (AUCs of the summary receiver operating characteristic curve were 0.85 or 0.72 and 0.81 or 0.69, respectively. PCA3 can be used for repeat biopsy of the prostate to improve accuracy of PCa detection. Unnecessary biopsies can be avoided by using a PCa cutoff score of 20.

  3. Preliminary study of the application of Principal Components Analysis (PCA) to the determination of the origin of brandy type vodka

    International Nuclear Information System (INIS)

    Villalobos Chaves, Alberto E.

    2006-01-01

    Principal Components Analysis (PCA) was applied to the determination of the origin of samples of vodkas. Analytical parameters used were: the alcoholic degree, the difference between the alcoholic experimental degree and declared in the etiquette, the dried extract, the relative intensities of calcium atomic emission (beak area at 422,67 nm), sodium (sum of beaks areas Ca, Na / 588,99 and 589,59 nm) and potassium (sum of beaks areas to K/766,49 nm and 769,89 nm) and finally the ultraviolet absorbency to 200 nm. The accumulation of K-averages was used. The hypothesis of item is that the sample was constituted, approximately, for two big natural groupings, this is, national vodkas and foreign vodkas. Of the application of the above mentioned procedure there was obtained that really the components of the sample were distinguishable according to the national or foreign origin in two groups, which ellipses of confidence to 95 % not achieving , even if there were eliminated the variables of alcoholic degree and difference of the alcoholic degree. (author) [es

  4. Evaluation of short-term physical weathering of a heavy fuel oil by use of time warping and PCA

    International Nuclear Information System (INIS)

    Malmquist, L.M.V.; Olsen, R.R.; Christensen, J.H.; Andersen, O.

    2005-01-01

    An estimated 1,140 billion tons of oil was accidentally spilled to the environment during the 1990s. These spills present an ecotoxicologic risk due to the presence of toxic and mutagenic compounds in the oil. Oil is affected by short term and long term weathering processes such as evaporation, dissolution, dispersion, emulsification, photodegradation and biodegradation. Physical weathering processes change the composition of the oil but they do not alter the oil components. Gas chromatography and mass spectrometry can characterize the compositional changes resulting from evaporation. However, the process depends on subjective analysis because it is based on manual interpretation of results and visual inspection. This paper presents a rapid and objective method to compare oil sample compositions. The method is based on automated data preprocessing involving baseline removal, alignment of chromatograms using correlation optimized warping (COW) and normalization. Preprocessed data is analyzed by principal component analysis (PCA) based on the total chromatograms. The method has successfully resolved the effects of evaporation and dissolution processes and showed clear dependence of time, but it did not completely resolve the effect of weathering from the analytical variability because better quality data is required. 21 refs., 3 figs

  5. Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network

    Directory of Open Access Journals (Sweden)

    Debesh Jha

    2017-01-01

    Full Text Available Background. Error-free diagnosis of Alzheimer’s disease (AD from healthy control (HC patients at an early stage of the disease is a major concern, because information about the condition’s severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI classification. However, distinguishing between Alzheimer’s brain data and healthy brain data in older adults (age > 60 is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA. The reduced feature vector is sent to feed-forward neural network (FNN to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.

  6. Evaluation of short-term physical weathering of a heavy fuel oil by use of time warping and PCA

    Energy Technology Data Exchange (ETDEWEB)

    Malmquist, L.M.V.; Olsen, R.R. [Roskilde Univ., Roskilde (Denmark). Dept. of Life Sciences and Chemistry]|[National Environmental Research Inst., Roskilde (Denmark). Dept. of Environmental Chemistry and Microbiology; Christensen, J.H. [Royal Veterinary and Agricultural Univ., Thorvaldsensvej (Denmark). Dept. of Natural Sciences; Andersen, O. [Roskilde Univ., Roskilde (Denmark). Dept. of Life Sciences and Chemistry

    2005-07-01

    An estimated 1,140 billion tons of oil was accidentally spilled to the environment during the 1990s. These spills present an ecotoxicologic risk due to the presence of toxic and mutagenic compounds in the oil. Oil is affected by short term and long term weathering processes such as evaporation, dissolution, dispersion, emulsification, photodegradation and biodegradation. Physical weathering processes change the composition of the oil but they do not alter the oil components. Gas chromatography and mass spectrometry can characterize the compositional changes resulting from evaporation. However, the process depends on subjective analysis because it is based on manual interpretation of results and visual inspection. This paper presents a rapid and objective method to compare oil sample compositions. The method is based on automated data preprocessing involving baseline removal, alignment of chromatograms using correlation optimized warping (COW) and normalization. Preprocessed data is analyzed by principal component analysis (PCA) based on the total chromatograms. The method has successfully resolved the effects of evaporation and dissolution processes and showed clear dependence of time, but it did not completely resolve the effect of weathering from the analytical variability because better quality data is required. 21 refs., 3 figs.

  7. Hidden cross-correlation patterns in stock markets based on permutation cross-sample entropy and PCA

    Science.gov (United States)

    Lin, Aijing; Shang, Pengjian; Zhong, Bo

    2014-12-01

    In this article, we investigate the hidden cross-correlation structures in Chinese stock markets and US stock markets by performing PCSE combined with PCA approach. It is suggested that PCSE can provide a more faithful and more interpretable description of the dynamic mechanism between time series than cross-correlation matrix. We show that this new technique can be adapted to observe stock markets especially during financial crisis. In order to identify and compare the interactions and structures of stock markets during financial crisis, as well as in normal periods, all the samples are divided into four sub-periods. The results imply that the cross-correlations between Chinese group are stronger than the US group in the most sub-periods. In particular, it is likely that the US stock markets are more integrated with each other during global financial crisis than during Asian financial crisis. However, our results illustrate that Chinese stock markets are not immune from the global financial crisis, although less integrated with other markets if they are compared with US stock markets.

  8. Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

    Science.gov (United States)

    Mudali, D; Teune, L K; Renken, R J; Leenders, K L; Roerdink, J B T M

    2015-01-01

    Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

  9. Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

    Directory of Open Access Journals (Sweden)

    Jie Wang

    2017-03-01

    Full Text Available Deep convolutional neural networks (CNNs have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks.

  10. Olive mill wastewater evaporation management using PCA method Case study of natural degradation in stabilization ponds (Sfax, Tunisia).

    Science.gov (United States)

    Jarboui, Raja; Sellami, Fatma; Azri, Chafai; Gharsallah, Néji; Ammar, Emna

    2010-04-15

    Olive mill wastewater (OMW) evaporation ponds management was investigated in five serial evaporation open-air multiponds of 50 ha located in Sfax (Tunisia). Physico-chemical parameters and microbial flora evolution were considered. Empirical models describing the OMW characteristic changes with the operation time were established and Principal Component Analysis (PCA) described the correlation between physico-chemical and biological parameters. COD, BOD, total solids, polyphenols and electrical conductivity exhibited first-order models. Four groups exhibited high correlations. The first included temperature, density, COD, TSS, TS, BOD, VS, TOC, TKN, polyphenols and minerals. The second group was made up of yeasts and moulds. The third group was established with phenolic compounds, total sugars, fats, total phosphorous, NH(4)(+) and pH. The fourth group was constituted by exclusively aerobic bacteria. Bacterial-growth toxic effect was exhibited by high organic load, ash content and polyphenols, whereas moulds and yeasts were more adapted to OMW. During the storage, all the third group parameter values decreased and were inversely related to the others. In the last pond, COD, BOD, TS and TSS rates were reduced by 40%, 50%, 50% and 75% respectively. The evaporation and the biological activity were the main processes acting, predicting the OMW behavior during evaporation in air-open ponds. 2009 Elsevier B.V. All rights reserved.

  11. 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

    Science.gov (United States)

    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

  12. 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.

    Directory of Open Access Journals (Sweden)

    Matteo Ferro

    Full Text Available 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.

  13. A novel-type phosphatidylinositol phosphate-interactive, Ca-binding protein PCaP1 in Arabidopsis thaliana: stable association with plasma membrane and partial involvement in stomata closure.

    Science.gov (United States)

    Nagata, Chisako; Miwa, Chika; Tanaka, Natsuki; Kato, Mariko; Suito, Momoe; Tsuchihira, Ayako; Sato, Yori; Segami, Shoji; Maeshima, Masayoshi

    2016-05-01

    The Ca(2+)-binding protein-1 (PCaP1) of Arabidopsis thaliana is a new type protein that binds to phosphatidylinositol phosphates and Ca(2+)-calmodulin complex as well as free Ca(2+). Although biochemical properties, such as binding to ligands and N-myristoylation, have been revealed, the intracellular localization, tissue and cell specificity, integrity of membrane association and physiological roles of PCaP1 are unknown. We investigated the tissue and intracellular distribution of PCaP1 by using transgenic lines expressing PCaP1 linked with a green fluorescence protein (GFP) at the carboxyl terminus of PCaP1. GFP fluorescence was obviously detected in most tissues including root, stem, leaf and flower. In these tissues, PCaP1-GFP signal was observed predominantly in the plasma membrane even under physiological stress conditions but not in other organelles. The fluorescence was detected in the cytosol when the 25-residue N-terminal sequence was deleted from PCaP1 indicating essential contribution of N-myristoylation to the plasma membrane anchoring. Fluorescence intensity of PCaP1-GFP in roots was slightly decreased in seedlings grown in medium supplemented with high concentrations of iron for 1 week and increased in those grown with copper. In stomatal guard cells, PCaP1-GFP was strictly, specifically localized to the plasma membrane at the epidermal-cell side but not at the pore side. A T-DNA insertion mutant line of PCaP1 did not show marked phenotype in a life cycle except for well growth under high CO2 conditions. However, stomata of the mutant line did not close entirely even in high osmolarity, which usually induces stomata closure. These results suggest that PCaP1 is involved in the stomatal movement, especially closure process, in leaves and response to excessive copper in root and leaf as a mineral nutrient as a physiological role.

  14. Bayes PCA Revisited

    DEFF Research Database (Denmark)

    Sporring, Jon

    Principle Component Analysis is a simple tool to obtain linear models for stochastic data and is used both for a data reduction or equivalently noise elim- ination and for data analysis. Principle Component Analysis ts a multivariate Gaussian distribution to the data, and the typical method is by...

  15. Sparx PCA Module

    Energy Technology Data Exchange (ETDEWEB)

    2017-04-25

    Sparx, a new environment for Cryo-EM image processing; Cryo-EM, Single particle reconstruction, principal component analysis; Hardware Req.: PC, MAC, Supercomputer, Mainframe, Multiplatform, Workstation. Software Req.: operating system is Unix; Compiler C++; type of files: source code, object library, executable modules, compilation instructions; sample problem input data. Location/transmission: http://sparx-em.org; User manual & paper: http://sparx-em.org;

  16. SU-E-J-257: A PCA Model to Predict Adaptive Changes for Head&neck Patients Based On Extraction of Geometric Features From Daily CBCT Datasets

    Energy Technology Data Exchange (ETDEWEB)

    Chetvertkov, M [Wayne State University, Detroit, MI (United States); Henry Ford Health System, Detroit, MI (United States); Siddiqui, F; Chetty, I; Kim, J; Kumarasiri, A; Liu, C; Gordon, J [Henry Ford Health System, Detroit, MI (United States)

    2015-06-15

    Purpose: Using daily cone beam CTs (CBCTs) to develop principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients and to assess the possibility of using these prospectively in adaptive radiation therapy (ART). Methods: Planning CT (pCT) images of 4 H&N patients were deformed to model several different systematic changes in patient anatomy during the course of the radiation therapy (RT). A Pinnacle plugin was used to linearly interpolate the systematic change in patient for the 35 fraction RT course and to generate a set of 35 synthetic CBCTs. Each synthetic CBCT represents the systematic change in patient anatomy for each fraction. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CBCTs with random fraction-to-fraction changes were superimposed on the DVFs. A patient-specific PCA model was built using these DVFs containing systematic plus random changes. It was hypothesized that resulting eigenDVFs (EDVFs) with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: For all 4 patients, the PCA model provided different results depending on the type and size of systematic change in patient’s body. PCA was more successful in capturing the systematic changes early in the treatment course when these were of a larger scale with respect to the random fraction-to-fraction changes in patient’s anatomy. For smaller scale systematic changes, random changes in patient could completely “hide” the systematic change. Conclusion: The leading EDVF from the patientspecific PCA models could tentatively be identified as a major systematic change during treatment if the systematic change is large enough with respect to random fraction-to-fraction changes. Otherwise, leading EDVF could not represent systematic changes reliably. This work is expected to facilitate development of population-based PCA models that can be used to prospectively identify significant

  17. Local Infiltration Analgesia Compared With Epidural and Intravenous PCA After Surgical Hip Dislocation for the Treatment of Femoroacetabular Impingement in Adolescents.

    Science.gov (United States)

    Novais, Eduardo N; Kestel, Lauryn; Carry, Patrick M; Sink, Ernest; Strupp, Kim

    2018-01-01

    Open treatment of femoroacetabular impingement (FAI) through a surgical hip dislocation (SHD) approach has been reported to allow for improvement in pain and function. However, the approach require a trochanteric osteotomy and may be associated with high level of pain after surgery. Currently, there is no systematic approach for pain management after SHD for treatment of FAI. A retrospective chart review was used to collect data from 121 subjects (12 to 21 y and below) who received periarticular local infiltration analgesia (LIA, n=20), epidural analgesia (n=72), or intravenous patient-controlled analgesia (PCA, n=29) after SHD from January 2003 to June 2014. Verbal pain scores, opioid consumption, incidence of side effects/complications, and length of hospital stay (LOS) were recorded. All nonopioid medications with analgesic potential were included in the statistical models as potential confounding variables RESULTS:: Twelve hours after surgery, the odds of moderate/severe pain were higher in the PCA group (odds ratio, 20.5; 95% confidence interval (CI), 1.7-243.8; P=0.0166] and epidural group (odds ratio, 5.2; 95% CI, 0.7-92.0; P=0.3218) compared with the LIA group. There was no difference in pain scores across all groups 1 hour (P=0.0675) or 24 hours (P=0.3473) postoperatively. Total opioid consumption in the LIA group was 59.8% (95% CI, 15.0%-81.0%; P=0.0175) lower than the total opioid consumption in the epidural group and 60.7% (95% CI, 17.3-81.3; P=0.0144) lower than the total opioid consumption in the PCA group. LOS was increased in the epidural (mean difference, 22.1; 95% CI, 6.8-37.4 h; P=0.0051) and PCA (mean difference, 16 h; 95% CI, 1-31.5 h; P=0.0367) groups relative to the LIA group. There was 0 (0%) complication in the LIA group compared with 11 (15.3%) in the epidural group. LIA was more effective at controlling pain 12 hours after surgery in comparison with PCA with similar pain control to epidural. LIA was associated with significantly lower

  18. SU-E-J-257: A PCA Model to Predict Adaptive Changes for Head&neck Patients Based On Extraction of Geometric Features From Daily CBCT Datasets

    International Nuclear Information System (INIS)

    Chetvertkov, M; Siddiqui, F; Chetty, I; Kim, J; Kumarasiri, A; Liu, C; Gordon, J

    2015-01-01

    Purpose: Using daily cone beam CTs (CBCTs) to develop principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients and to assess the possibility of using these prospectively in adaptive radiation therapy (ART). Methods: Planning CT (pCT) images of 4 H&N patients were deformed to model several different systematic changes in patient anatomy during the course of the radiation therapy (RT). A Pinnacle plugin was used to linearly interpolate the systematic change in patient for the 35 fraction RT course and to generate a set of 35 synthetic CBCTs. Each synthetic CBCT represents the systematic change in patient anatomy for each fraction. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CBCTs with random fraction-to-fraction changes were superimposed on the DVFs. A patient-specific PCA model was built using these DVFs containing systematic plus random changes. It was hypothesized that resulting eigenDVFs (EDVFs) with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: For all 4 patients, the PCA model provided different results depending on the type and size of systematic change in patient’s body. PCA was more successful in capturing the systematic changes early in the treatment course when these were of a larger scale with respect to the random fraction-to-fraction changes in patient’s anatomy. For smaller scale systematic changes, random changes in patient could completely “hide” the systematic change. Conclusion: The leading EDVF from the patientspecific PCA models could tentatively be identified as a major systematic change during treatment if the systematic change is large enough with respect to random fraction-to-fraction changes. Otherwise, leading EDVF could not represent systematic changes reliably. This work is expected to facilitate development of population-based PCA models that can be used to prospectively identify significant

  19. Meta-Analysis of the Ease of Care From the Nurses' Perspective Comparing Fentanyl Iontophoretic Transdermal System (ITS) Vs Morphine Intravenous Patient-Controlled Analgesia (IV PCA) in Postoperative Pain Management.

    Science.gov (United States)

    Pestano, Cecile R; Lindley, Pam; Ding, Li; Danesi, Hassan; Jones, James B

    2017-08-01

    The aim of this meta-analysis was to compare the ease of care (EOC) of fentanyl iontophoretic transdermal system (ITS) vs the morphine intravenous patient-controlled analgesia (IV PCA) as assessed by the nurse. Meta-analysis of three phase 3B randomized active-comparator trials. This meta-analysis according to Cochrane's approach assessed EOC using a validated nurse questionnaire (22 items grouped into three subscales, which include time efficiency, convenience, and satisfaction) in adult patients treated with fentanyl ITS or morphine IV PCA for postoperative pain management. The weighted mean difference (WMD) between treatments was calculated. EOC analyses were based on responses to questionnaires from 848 (fentanyl ITS) and 761 (morphine IV PCA) nurses. Fentanyl ITS was reported to provide significant advantages compared with morphine IV PCA in terms of nurses' overall EOC (WMD = -0.57, P PCA. Copyright © 2016 American Society of PeriAnesthesia Nurses. Published by Elsevier Inc. All rights reserved.

  20. Jendl-3.1 iron validation on the PCA-REPLICA (H2O/Fe) shielding benchmark experiment

    International Nuclear Information System (INIS)

    Pescarini, M.; Borgia, M. G.

    1997-03-01

    The PCA-REPLICA (H 2 O/Fe) neutron shielding benchmarks experiment is analysed using the SN 2-D DOT 3.5-E code and the 3-D-equivalent flux synthesis method. This engineering benchmark reproduces the ex-core radial geometry of a PWR, including a mild steel reactor pressure vessel (RPV) simulator, and is designed to test the accuracy of the calculation of the in-vessel neutron exposure parameters. This accuracy is strongly dependent on the quality of the iron neutron cross sections used to describe the nuclear reactions within the RPV simulator. In particular, in this report, the cross sections based on the JENDL-3.1 iron data files are tested, through a comparison of the calculated integral and spectral results with the corresponding experimental data. In addition, the present results are compared, on the same benchmark experiment, with those of a preceding ENEA-Bologna validation of the ENDF/B VI iron cross sections. The integral result comparison indicates that, for all the threshold detectors considered (Rh-103 (n, n') Rh-103m, In-115 (n, n') In-115m and S-32 (n, p) P-32), the JENDL-3.1 natural iron data produce satisfactory results similar to those obtained with the ENDF/B VI iron data. On the contrary, when the JENDL/3.1 Fe-56 data file is used, strongly underestimated results are obtained for the lower energy threshold detectors, Rh-103 and In-115. This fact, in particular, becomes more evident with increasing the neutron penetration depth in the RPV simulator

  1. A Taguchi PCA fuzzy-based approach for the multi-objective extended optimization of a miniature optical engine

    International Nuclear Information System (INIS)

    Fan Yichin; Tzeng Yihfong; Li Sixiang

    2008-01-01

    The paper proposes a hybrid approach, integrating a combination of Taguchi methods, principal component analysis (PCA) and fuzzy theory for the extended optimization of multiple quality characteristics in optimization experiments of non-image optics; a miniature light emitting diode pocket-sized projection display system is demonstrated in this research as an optimization sample. Traditionally, the performance of projector optics can be evaluated by modulation transfer function and its optimization method is DLS (damped least square). Comparatively, light efficiency and uniformity play a part in non-image optics where the optimized method is based on the concept of non-sequential rays; for example, in the optical engine of a projector, which demands better light efficiency and uniformity. The DLS method is occasionally employed in the optimization of non-image optics such as optical engines, but it is sometimes sensitive to the number of rays employed and some over-optimization problems. In this research we propose as an alternative method to optimize in an extended way the optical engine of a miniature projector. Control factors were checked and then repeatedly examined before the experiments started. In the experiment, optimization works through an L18 orthogonal array. Finally, this proposed optimization work shows good success for the optimization of non-image optical engines because this method is less sensitive to the number of non-sequential rays. Compared with the initial design, the optimized parameter design is able to improve the luminous flux by 11.46 dB, the illumination uniformity by 3.14 and the packing size by 1.125 dB

  2. Relationship between swelling and irradiation creep in cold-worked PCA stainless steel irradiated to ∼178 dpa at ∼400 degrees C

    International Nuclear Information System (INIS)

    Toloczko, M.B.

    1993-09-01

    The eighth and final irradiation segment for pressurized tubes constructed from the fusion Prime Candidate Alloy (PCA) has been completed in FFTF. At 178 dpa and ∼400 degrees C, the irradiation creep of 20% cold-worked PCA has become dominated by the open-quotes creep disappearanceclose quotes phenomenon. The total diametral deformation rate has reached the limiting value of 0.33%/dpa at the three highest stress levels employed in this test. The stress-enhancement of swelling tends to camouflage the onset of creep disappearance, however, requiring the use of several non-traditional techniques to extract the creep coefficients. No failures occurred in these tubes, even though the swelling ranged from ∼20 to ∼40%

  3. Relationship between swelling and irradiation creep in cold-worked PCA stainless steel irradiated to similar 178 dpa at similar 400 C

    International Nuclear Information System (INIS)

    Toloczko, M.B.; Garner, F.A.

    1994-01-01

    The eighth and final irradiation segment for pressurized tubes constructed from the fusion Prime Candidate Alloy (PCA) has been completed in FFTF. At 178 dpa and similar 400 C, the irradiation creep of 20% cold-worked PCA has become dominated by the ''creep disappearance'' phenomenon. The total diametral deformation rate has reached the limiting value of 0.33%/dpa at the three highest stress levels employed in this test. The stress-enhancement of swelling tends to camouflage the onset of creep disappearance, however, requiring the use of several non-traditional techniques to extract the creep coefficients. No failures occurred in these tubes, even though the swelling ranged from similar 20 to 40%. ((orig.))

  4. Relationship between swelling and irradiation creep in cold-worked PCA stainless steel irradiated to similar 178 dpa at similar 400 C

    Energy Technology Data Exchange (ETDEWEB)

    Toloczko, M.B. (Department of Chemical and Nuclear Engineering, University of California, Santa Barbara, CA 93106 (United States)); Garner, F.A. (Pacific Northwest Laboratory, Richland, WA 99352 (United States))

    1994-09-01

    The eighth and final irradiation segment for pressurized tubes constructed from the fusion Prime Candidate Alloy (PCA) has been completed in FFTF. At 178 dpa and similar 400 C, the irradiation creep of 20% cold-worked PCA has become dominated by the creep disappearance'' phenomenon. The total diametral deformation rate has reached the limiting value of 0.33%/dpa at the three highest stress levels employed in this test. The stress-enhancement of swelling tends to camouflage the onset of creep disappearance, however, requiring the use of several non-traditional techniques to extract the creep coefficients. No failures occurred in these tubes, even though the swelling ranged from similar 20 to 40%. ((orig.))

  5. The Comparison of Intrathecal Morphine and IV Morphine PCA on Pain Control, Patient Satisfaction, Morphine Consumption, and Adverse Effects in Patients Undergoing Reduction Mammoplasty

    OpenAIRE

    Karamese, Mehtap; Akda?, Osman; Kara, ?nci; Y?ld?ran, Gokce Unal; Tosun, Zekeriya

    2015-01-01

    Background: Following breast reduction procedures, the level of postoperative pain can be severe, and sufficient pain control influences a patient's physiological, immunological, and psychological status. Objective: The aim of this study was to examine the use of intrathecal morphine (ITM) in breast reduction surgery with patient-controlled analgesia (PCA). Methods: Sixty-two female patients who underwent breast reductions with the same technique participated in this study. The study group (I...

  6. Application of fractal modeling and PCA method for hydrothermal alteration mapping in the Saveh area (Central Iran) based on ASTER multispectral data

    OpenAIRE

    Mirko Ahmadfaraj; Mirsaleh Mirmohammadi; Peyman Afzal

    2016-01-01

    The aim of this study is determination and separation of alteration zones using Concentration-Area (C-A) fractal model based on remote sensing data which has been extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The studied area is on the SW part of Saveh, 1:250,000 geological map, which is located in Urumieh-Dokhtar magmatic belt, Central Iran. The pixel values were computed by Principal Component Analysis (PCA) method used to determine phyllic, a...

  7. Global Proteome Response to Deletion of Genes Related to Mercury Methylation and Dissimilatory Metal Reduction Reveals Changes in Respiratory Metabolism in Geobacter sulfurreducens PCA.

    Science.gov (United States)

    Qian, Chen; Johs, Alexander; Chen, Hongmei; Mann, Benjamin F; Lu, Xia; Abraham, Paul E; Hettich, Robert L; Gu, Baohua

    2016-10-07

    Geobacter sulfurreducens PCA can reduce, sorb, and methylate mercury (Hg); however, the underlying biochemical mechanisms of these processes and interdependent metabolic pathways remain unknown. In this study, shotgun proteomics was used to compare global proteome profiles between wild-type G. sulfurreducens PCA and two mutant strains: a ΔhgcAB mutant, which is deficient in two genes known to be essential for Hg methylation and a ΔomcBESTZ mutant, which is deficient in five outer membrane c-type cytochromes and thus impaired in its ability for dissimilatory metal ion reduction. We were able to delineate the global response of G. sulfurreducens PCA in both mutants and identify cellular networks and metabolic pathways that were affected by the loss of these genes. Deletion of hgcAB increased the relative abundances of proteins implicated in extracellular electron transfer, including most of the c-type cytochromes, PilA-C, and OmpB, and is consistent with a previously observed increase in Hg reduction in the ΔhgcAB mutant. Deletion of omcBESTZ was found to significantly increase relative abundances of various methyltransferases, suggesting that a loss of dissimilatory reduction capacity results in elevated activity among one-carbon (C1) metabolic pathways and thus increased methylation. We show that G. sulfurreducens PCA encodes only the folate branch of the acetyl-CoA pathway, and proteins associated with the folate branch were found at lower abundance in the ΔhgcAB mutant strain than the wild type. This observation supports the hypothesis that the function of HgcA and HgcB is linked to C1 metabolism through the folate branch of the acetyl-CoA pathway by providing methyl groups required for Hg methylation.

  8. Impact of PCA Strategies on Pain Intensity and Functional Assessment Measures in Adults with Sickle Cell Disease during Hospitalized Vaso-Occlusive Episodes

    OpenAIRE

    Dampier, Carlton D.; Wager, Carrie G.; Harrison, Ryan; Hsu, Lewis L.; Minniti, Caterina P.; Smith, Wally R.

    2012-01-01

    Clinical trials of sickle cell disease (SCD) pain treatment usually observe only small decrements in pain intensity during the course of hospitalization. Sub-optimal analgesic management and inadequate pain assessment methods are possible explanations for these findings. In a search for better methods for assessing inpatient SCD pain in adults, we examined several pain intensity and interference measures in both arms of a randomized controlled trial comparing two different opioid PCA therapie...

  9. The added value of percentage of free to total prostate-specific antigen, PCA3, and a kallikrein panel to the ERSPC risk calculator for prostate cancer in prescreened men.

    Science.gov (United States)

    Vedder, Moniek M; de Bekker-Grob, Esther W; Lilja, Hans G; Vickers, Andrew J; van Leenders, Geert J L H; Steyerberg, Ewout W; Roobol, Monique J

    2014-12-01

    Prostate-specific antigen (PSA) testing has limited accuracy for the early detection of prostate cancer (PCa). To assess the value added by percentage of free to total PSA (%fPSA), prostate cancer antigen 3 (PCA3), and a kallikrein panel (4k-panel) to the European Randomised Study of Screening for Prostate Cancer (ERSPC) multivariable prediction models: risk calculator (RC) 4, including transrectal ultrasound, and RC 4 plus digital rectal examination (4+DRE) for prescreened men. Participants were invited for rescreening between October 2007 and February 2009 within the Dutch part of the ERSPC study. Biopsies were taken in men with a PSA level ≥3.0 ng/ml or a PCA3 score ≥10. Additional analyses of the 4k-panel were done on serum samples. Outcome was defined as PCa detectable by sextant biopsy. Receiver operating characteristic curve and decision curve analyses were performed to compare the predictive capabilities of %fPSA, PCA3, 4k-panel, the ERSPC RCs, and their combinations in logistic regression models. PCa was detected in 119 of 708 men. The %fPSA did not perform better univariately or added to the RCs compared with the RCs alone. In 202 men with an elevated PSA, the 4k-panel discriminated better than PCA3 when modelled univariately (area under the curve [AUC]: 0.78 vs. 0.62; p=0.01). The multivariable models with PCA3 or the 4k-panel were equivalent (AUC: 0.80 for RC 4+DRE). In the total population, PCA3 discriminated better than the 4k-panel (univariate AUC: 0.63 vs. 0.56; p=0.05). There was no statistically significant difference between the multivariable model with PCA3 (AUC: 0.73) versus the model with the 4k-panel (AUC: 0.71; p=0.18). The multivariable model with PCA3 performed better than the reference model (0.73 vs. 0.70; p=0.02). Decision curves confirmed these patterns, although numbers were small. Both PCA3 and, to a lesser extent, a 4k-panel have added value to the DRE-based ERSPC RC in detecting PCa in prescreened men. We studied the added

  10. Assessment of PCDD/Fs levels in soil at a contaminated sawmill site in Sweden – A GIS and PCA approach to interpret the contamination pattern and distribution

    International Nuclear Information System (INIS)

    Henriksson, S.; Hagberg, J.; Bäckström, M.; Persson, I.; Lindström, G.

    2013-01-01

    Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzo-p-furans (PCDD/Fs) were analysed in soil from a Swedish sawmill site where chlorophenols (CPs) had been used more than 40 years ago. The most contaminated area at the site was the preservation subarea where the PCDD/F WHO 2005 -TEQ level was 3450 times higher than the current Swedish guideline value of 200 ng TEQ/kg soil for land for industrial use. It was also shown that a fire which destroyed the sawmill might have affected the congener distribution at the concerned areas. To get a broader picture of the contamination both GIS (spatial interpolation analysis) and multivariate data analysis (PCA) were applied to visualize and compare PCDD/F levels as well as congener distributions at different areas at the site. It is shown that GIS and PCA are powerful tools in decisions on future investigations, risk assessments and remediation of contaminated sites. -- Highlights: •GIS and PCA visualize and compare site levels and congener patterns of dioxins. •Subareas were separated by differences in contamination levels and congener patterns. •Fire had a significant effect on the congener distribution at the site. -- The use of geostatistical and multivariate statistical methods are powerful tools to visualize the contamination pattern and distribution at a highly PCDD/Fs-contaminated site

  11. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2014-01-01

    Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

  12. EPOXIDAÇÃO DE ÓLEOS DE SOJA, GIRASSOL E MAMONA E AVALIAÇÃO QUIMIOMÉTRICA POR PCA e HCA

    Directory of Open Access Journals (Sweden)

    Fernanda Carla Bock

    2014-11-01

    Full Text Available Este estudo teve por objetivo realizar a epoxidação de diferentes óleos vegetais (girassol, mamona e soja e realizar a identificação assim como o acompanhamento reacional através da espectroscopia no infravermelho médio aplicando métodos multivariados de análise (análise de componentes principais – PCA e análise de agrupamento hierárquico – HCA. Os resultados obtidos mostraram que é possível obter epóxidos a partir de ésteres dos óleos de mamona, soja e girassol e que a taxa de conversão está associada ao tempo, a quantidade de enzima e ao óleo vegetal empregado. O estudo também indica que é possível o acompanhamento reacional através da espectroscopia no infravermelho médio associado à análise de componentes principais (PCA e a identificação dos epóxidos obtidos a partir de óleos de girassol, mamona e soja através da espectroscopia no infravermelho médio associada à análise de componentes principais (PCA e análise por agrupamento hierárquico (HCA.

  13. Genetic ancestry, self-reported race and ethnicity in African Americans and European Americans in the PCaP cohort.

    Directory of Open Access Journals (Sweden)

    Lara E Sucheston

    Full Text Available Family history and African-American race are important risk factors for both prostate cancer (CaP incidence and aggressiveness. When studying complex diseases such as CaP that have a heritable component, chances of finding true disease susceptibility alleles can be increased by accounting for genetic ancestry within the population investigated. Race, ethnicity and ancestry were studied in a geographically diverse cohort of men with newly diagnosed CaP.Individual ancestry (IA was estimated in the population-based North Carolina and Louisiana Prostate Cancer Project (PCaP, a cohort of 2,106 incident CaP cases (2063 with complete ethnicity information comprising roughly equal numbers of research subjects reporting as Black/African American (AA or European American/Caucasian/Caucasian American/White (EA from North Carolina or Louisiana. Mean genome wide individual ancestry estimates of percent African, European and Asian were obtained and tested for differences by state and ethnicity (Cajun and/or Creole and Hispanic/Latino using multivariate analysis of variance models. Principal components (PC were compared to assess differences in genetic composition by self-reported race and ethnicity between and within states.Mean individual ancestries differed by state for self-reporting AA (p = 0.03 and EA (p = 0.001. This geographic difference attenuated for AAs who answered "no" to all ethnicity membership questions (non-ethnic research subjects; p = 0.78 but not EA research subjects, p = 0.002. Mean ancestry estimates of self-identified AA Louisiana research subjects for each ethnic group; Cajun only, Creole only and both Cajun and Creole differed significantly from self-identified non-ethnic AA Louisiana research subjects. These ethnicity differences were not seen in those who self-identified as EA.Mean IA differed by race between states, elucidating a potential contributing factor to these differences in AA research participants: self-reported ethnicity

  14. On applicability of PCA, voxel-wise variance normalization and dimensionality assumptions for sliding temporal window sICA in resting-state fMRI.

    Science.gov (United States)

    Remes, Jukka J; Abou Elseoud, Ahmed; Ollila, Esa; Haapea, Marianne; Starck, Tuomo; Nikkinen, Juha; Tervonen, Osmo; Silven, Olli

    2013-10-01

    Subject-level resting-state fMRI (RS-fMRI) spatial independent component analysis (sICA) may provide new ways to analyze the data when performed in the sliding time window. However, whether principal component analysis (PCA) and voxel-wise variance normalization (VN) are applicable pre-processing procedures in the sliding-window context, as they are for regular sICA, has not been addressed so far. Also model order selection requires further studies concerning sliding-window sICA. In this paper we have addressed these concerns. First, we compared PCA-retained subspaces concerning overlapping parts of consecutive temporal windows to answer whether in-window PCA and VN can confound comparisons between sICA analyses in consecutive windows. Second, we compared the PCA subspaces between windowed and full data to assess expected comparability between windowed and full-data sICA results. Third, temporal evolution of dimensionality estimates in RS-fMRI data sets was monitored to identify potential challenges in model order selection in a sliding-window sICA context. Our results illustrate that in-window VN can be safely used, in-window PCA is applicable with most window widths and that comparisons between windowed and full data should not be performed from a subspace similarity point of view. In addition, our studies on dimensionality estimates demonstrated that there are sustained, periodic and very case-specific changes in signal-to-noise ratio within RS-fMRI data sets. Consequently, dimensionality estimation is needed for well-founded model order determination in the sliding-window case. The observed periodic changes correspond to a frequency band of ≤0.1 Hz, which is commonly associated with brain activity in RS-fMRI and become on average most pronounced at window widths of 80 and 60 time points (144 and 108 s, respectively). Wider windows provided only slightly better comparability between consecutive windows, and 60 time point or shorter windows also provided the

  15. 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.

    Science.gov (United States)

    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, Pprostatitis 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.

  16. Redundancy or heterogeneity in the electric activity of the biceps brachii muscle? Added value of PCA-processed multi-channel EMG muscle activation estimates in a parallel-fibered muscle

    NARCIS (Netherlands)

    Staudenmann, D.; Stegeman, D.F.; van Dieen, J.H.

    2013-01-01

    Conventional bipolar EMG provides imprecise muscle activation estimates due to possibly heterogeneous activity within muscles and due to improper alignment of the electrodes with the muscle fibers. Principal component analysis (PCA), applied on multi-channel monopolar EMG yielded substantial

  17. A molecular and isotopic study of the macromolecular organic matter of the ungrouped C2 WIS 91600 and its relationship to Tagish Lake and PCA 91008

    Science.gov (United States)

    Yabuta, Hikaru; O'D. Alexander, Conel M.; Fogel, Marilyn L.; Kilcoyne, A. L. David; Cody, George D.

    2010-09-01

    Insight into the chemical history of an ungrouped type 2 carbonaceous chondrite meteorite, Wisconsin Range (WIS) 91600, is gained through molecular analyses of insoluble organic matter (IOM) using solid-state 13C nuclear magnetic resonance (NMR) spectroscopy, X-ray absorption near edge structure spectroscopy (XANES), and pyrolysis-gas chromatography coupled with mass spectrometry (pyr-GC/MS), and our previous bulk elemental and isotopic data. The IOM from WIS 91600 exhibits similarities in its abundance and bulk δ15N value with IOM from another ungrouped carbonaceous chondrite Tagish Lake, while it exhibits H/C, δ13C, and δD values that are more similar to IOM from the heated CM, Pecora Escarpment (PCA) 91008. The 13C NMR spectra of IOM of WIS 91600 and Tagish Lake are similar, except for a greater abundance of CHxO species in the latter and sharper carbonyl absorption in the former. Unusual cross-polarization (CP) dynamics is observed for WIS 91600 that indicate the presence of two physically distinct organic domains, in which the degrees of aromatic condensation are distinctly different. The presence of two different organic domains in WIS 91600 is consistent with its brecciated nature. The formation of more condensed aromatics is the likely result of short duration thermal excursions during impacts. The fact that both WIS 91600 and PCA 91008 were subjected to short duration heating that is distinct from the thermal history of type 3 chondrites is confirmed by Carbon-XANES. Finally, after being briefly heated (400 °C for 10 s), the pyrolysis behavior of Tagish Lake IOM is similar to that of WIS 91600 and PCA 91008. We conclude that WIS 91600 experienced very moderate, short duration heating at low temperatures (<500 °C) after an episode of aqueous alteration under conditions that were similar to those experienced by Tagish Lake.

  18. Source apportionment of ambient non-methane hydrocarbons in Hong Kong: application of a principal component analysis/absolute principal component scores (PCA/APCS) receptor model.

    Science.gov (United States)

    Guo, H; Wang, T; Louie, P K K

    2004-06-01

    Receptor-oriented source apportionment models are often used to identify sources of ambient air pollutants and to estimate source contributions to air pollutant concentrations. In this study, a PCA/APCS model was applied to the data on non-methane hydrocarbons (NMHCs) measured from January to December 2001 at two sampling sites: Tsuen Wan (TW) and Central & Western (CW) Toxic Air Pollutants Monitoring Stations in Hong Kong. This multivariate method enables the identification of major air pollution sources along with the quantitative apportionment of each source to pollutant species. The PCA analysis identified four major pollution sources at TW site and five major sources at CW site. The extracted pollution sources included vehicular internal engine combustion with unburned fuel emissions, use of solvent particularly paints, liquefied petroleum gas (LPG) or natural gas leakage, and industrial, commercial and domestic sources such as solvents, decoration, fuel combustion, chemical factories and power plants. The results of APCS receptor model indicated that 39% and 48% of the total NMHCs mass concentrations measured at CW and TW were originated from vehicle emissions, respectively. 32% and 36.4% of the total NMHCs were emitted from the use of solvent and 11% and 19.4% were apportioned to the LPG or natural gas leakage, respectively. 5.2% and 9% of the total NMHCs mass concentrations were attributed to other industrial, commercial and domestic sources, respectively. It was also found that vehicle emissions and LPG or natural gas leakage were the main sources of C(3)-C(5) alkanes and C(3)-C(5) alkenes while aromatics were predominantly released from paints. Comparison of source contributions to ambient NMHCs at the two sites indicated that the contribution of LPG or natural gas at CW site was almost twice that at TW site. High correlation coefficients (R(2) > 0.8) between the measured and predicted values suggested that the PCA/APCS model was applicable for estimation

  19. Intravenous sufentanil and morphine for post-cardiac surgery pain relief using patient-controlled analgesia (pca) device: a randomized double-blind clinical trial

    International Nuclear Information System (INIS)

    Alavi, S.M.; Kish, R.F.; Farsad, F.; Imani, F.; Sheikhvatan, M.

    2010-01-01

    Selection of the best analgesic technique in patients undergoing major surgeries can result in lower morbidity and satisfactory postoperative pain relief. In the present study, we tried to compare the effect of morphine and sufentanil on postoperative pain severity and hemodynamic changes by using patient-controlled analgesia (PCA) device in patients who were candidate for coronary artery bypass surgery (CABG). It was a randomized double-blinded clinical trial in which 120 patients aged 30-65 years, ASA physical status I-III, candidate for CABG in Shahid Rajaee hospital in Tehran were included. Before anesthesia, patients were randomly assigned to one of three groups to receive sufentanil (n=40), morphine (n=40) or normal saline (n=40). After tracheal extubation at intensive care unit, PCA was started by, sufentanil 4mg for the first group, morphine 2mg for the second group and normal saline, at same volume for the third group, intravenously with 10 minute lockout interval. Postoperative pain was evaluated by VAS scale, 1, 6, 12, 18 and 24 hours after extubation and systolic blood pressure, arterial oxygen saturation, PCO2 and PO2 were recorded 24 hours after extubation. VAS scores at rest revealed significantly less pain for patients in sufentanil and morphine groups than normal saline group, throughout the twenty-four hours after operation (P<0.001). However, there were no significant differences in the means of VAS scores between sufentanil and morphine groups. Among studied hemodynamic parameters, only systolic blood pressure was reduced more in morphine than sufentanil group (P<0.001). After CABG surgery, administration of intravenous sufentanil and morphine using PCA can lead to similar reduction of postoperative pain severity. (author)

  20. Principle component analysis (PCA) for investigation of relationship between population dynamics of microbial pathogenesis, chemical and sensory characteristics in beef slices containing Tarragon essential oil.

    Science.gov (United States)

    Alizadeh Behbahani, Behrooz; Tabatabaei Yazdi, Farideh; Shahidi, Fakhri; Mortazavi, Seyed Ali; Mohebbi, Mohebbat

    2017-04-01

    Principle component analysis (PCA) was employed to examine the effect of the exerted treatments on the beef shelf life as well as discovering the correlations between the studied responses. Considering the variability of the dimensions of the responses, correlation coefficients were applied to form the matrix and extract the eigenvalue. Antimicrobial effect was evaluated on 10 pathogenic microorganisms through the methods of hole-plate diffusion method, disk diffusion method, pour plate method, minimum inhibitory concentration and minimum bactericidal/fungicidal concentration. Antioxidant potential and total phenolic content were examined through the method of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and Folin-Ciocalteu method, respectively. The components were identified through gas chromatography and gas chromatography/mass spectrometry. Barhang seed mucilage (BSM) based edible coating containing 0, 0.5, 1 and 1.5% (w/w) Tarragon (T) essential oil mix were applied on beef slices to control the growth of pathogenic microorganisms. Microbiological (total viable count, psychrotrophic count, Escherichia coli, Staphylococcus aureus and fungi), chemical (thiobarbituric acid, peroxide value and pH) and sensory characteristics (odor, color and overall acceptability) analysis measurements were made during the storage periodically. PCA was employed to examine the effect of the exerted treatments on the beef shelf life as well as discovering the correlations between the studied responses. Considering the variability of the dimensions of the responses, correlation coefficients were applied to form the matrix and extract the eigenvalue. The PCA showed that the properties of the uncoated meat samples on the 9th, 12th, 15th and 18th days of storage are continuously changing independent of the exerted treatments on the other samples. This reveals the effect of the exerted treatments on the samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology.

    Science.gov (United States)

    Araki, Tadashi; Ikeda, Nobutaka; Shukla, Devarshi; Jain, Pankaj K; Londhe, Narendra D; Shrivastava, Vimal K; Banchhor, Sumit K; Saba, Luca; Nicolaides, Andrew; Shafique, Shoaib; Laird, John R; Suri, Jasjit S

    2016-05-01

    Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary

  2. Direct Involvement of ombB, omaB and omcB Genes in Extracellular Reduction of Fe(III by Geobacter sulfurreducens PCA

    Directory of Open Access Journals (Sweden)

    Yimo eLiu

    2015-10-01

    Full Text Available The tandem gene clusters orfR-ombB-omaB-omcB and orfS-ombC-omaC-omcC of the metal-reducing bacterium Geobacter sulfurreducens PCA are responsible for trans-outer membrane electron transfer during extracellular reduction of Fe(III-citrate and ferrihydrite [a poorly crystalline Fe(III oxide]. Each gene cluster encodes a putative transcriptional factor (OrfR/OrfS, a porin-like outer-membrane protein (OmbB/OmbC, a periplasmic c-type cytochrome (c-Cyt, OmaB/OmaC and an outer-membrane c-Cyt (OmcB/OmcC. The individual roles of OmbB, OmaB and OmcB in extracellular reduction of Fe(III, however, have remained either uninvestigated or controversial. Here, we showed that replacements of ombB, omaB, omcB and ombB-omaB with an antibiotic gene in the presence of ombC-omaC-omcC had no impact on reduction of Fe(III-citrate by G. sulfurreducens PCA. Disruption of ombB, omaB, omcB and ombB-omaB in the absence of ombC-omaC-omcC, however, severely impaired the bacterial ability to reduce Fe(III-citrate as well as ferrihydrite. These results unequivocally demonstrate an overlapping role of ombB-omaB-omcB and ombC-omaC-omcC in extracellular Fe(III reduction by G. sulfurreducens PCA. Involvement of both ombB-omaB-omcB and ombC-omaC-omcC in extracellular Fe(III reduction reflects the importance of these trans-outer membrane protein complexes in the physiology of this bacterium. Moreover, the kinetics of Fe(III-citrate and ferrihydrite reduction by these mutants in the absence of ombC-omaC-omcC were nearly identical, which suggests that absence of any protein subunit eliminates function of OmaB/OmbB/OmcB protein complex. Finally, orfS was found to have a negative impact on the extracellular reduction of Fe(III-citrate and ferrihydrite in G. sulfurreducens PCA probably by serving as a transcriptional repressor.

  3. Perfil Clínico de Apego (PCA): Elaboración de un sistema de categorías para la evaluación del apego

    OpenAIRE

    Lucena, Glòria; Cifre, Ignacio; Castillo Garayoa, José A.; Aragonés, Elena

    2015-01-01

    A partir de la teoría de Bowlby y de los estilos de apego propuestos por Bartholomew y Horowitz, se desarrolla el Perfil Clínico de Apego-narrativas (PCA-n), un sistema de observación para evaluar el apego a partir de las narrativas de los y las pacientes. En el Estudio 1, se construye una primera versión del instrumento (PCAv1). La consistencia interna resulta adecuada en la evaluación del apego seguro y evitativo, pero insuficiente en el caso del apego preocupado y temeroso. En el Estudio 2...

  4. The Effect of Temperature on Pressurised Hot Water Extraction of Pharmacologically Important Metabolites as Analysed by UPLC-qTOF-MS and PCA

    Directory of Open Access Journals (Sweden)

    B. S. Khoza

    2014-01-01

    Full Text Available Metabolite extraction methods have been shown to be a critical consideration for pharmacometabolomics studies and, as such, optimization and development of new extraction methods are crucial. In the current study, an organic solvent-free method, namely, pressurised hot water extraction (PHWE, was used to extract pharmacologically important metabolites from dried Moringa oleifera leaves. Here, the temperature of the extraction solvent (pure water was altered while keeping other factors constant using a homemade PHWE system. Samples extracted at different temperatures (50, 100, and 150°C were assayed for antioxidant activities and the effect of the temperature on the extraction process was evaluated. The samples were further analysed by mass spectrometry to elucidate their metabolite compositions. Principal component analysis (PCA evaluation of the UPLC-MS data showed distinctive differential metabolite patterns. Here, temperature changes during PHWE were shown to affect the levels of metabolites with known pharmacological activities, such as chlorogenic acids and flavonoids. Our overall findings suggest that, if not well optimised, the extraction temperature could compromise the “pharmacological potency” of the extracts. The use of MS in combination with PCA was furthermore shown to be an excellent approach to evaluate the quality and content of pharmacologically important extracts.

  5. Assessment of PCDD/Fs levels in soil at a contaminated sawmill site in Sweden--a GIS and PCA approach to interpret the contamination pattern and distribution.

    Science.gov (United States)

    Henriksson, S; Hagberg, J; Bäckström, M; Persson, I; Lindström, G

    2013-09-01

    Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzo-p-furans (PCDD/Fs) were analysed in soil from a Swedish sawmill site where chlorophenols (CPs) had been used more than 40 years ago. The most contaminated area at the site was the preservation subarea where the PCDD/F WHO2005-TEQ level was 3450 times higher than the current Swedish guideline value of 200 ng TEQ/kg soil for land for industrial use. It was also shown that a fire which destroyed the sawmill might have affected the congener distribution at the concerned areas. To get a broader picture of the contamination both GIS (spatial interpolation analysis) and multivariate data analysis (PCA) were applied to visualize and compare PCDD/F levels as well as congener distributions at different areas at the site. It is shown that GIS and PCA are powerful tools in decisions on future investigations, risk assessments and remediation of contaminated sites. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    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.

  7. Computation of mean and variance of the radiotherapy dose for PCA-modeled random shape and position variations of the target.

    Science.gov (United States)

    Budiarto, E; Keijzer, M; Storchi, P R M; Heemink, A W; Breedveld, S; Heijmen, B J M

    2014-01-20

    Radiotherapy dose delivery in the tumor and surrounding healthy tissues is affected by movements and deformations of the corresponding organs between fractions. The random variations may be characterized by non-rigid, anisotropic principal component analysis (PCA) modes. In this article new dynamic dose deposition matrices, based on established PCA modes, are introduced as a tool to evaluate the mean and the variance of the dose at each target point resulting from any given set of fluence profiles. The method is tested for a simple cubic geometry and for a prostate case. The movements spread out the distributions of the mean dose and cause the variance of the dose to be highest near the edges of the beams. The non-rigidity and anisotropy of the movements are reflected in both quantities. The dynamic dose deposition matrices facilitate the inclusion of the mean and the variance of the dose in the existing fluence-profile optimizer for radiotherapy planning, to ensure robust plans with respect to the movements.

  8. Computation of mean and variance of the radiotherapy dose for PCA-modeled random shape and position variations of the target

    International Nuclear Information System (INIS)

    Budiarto, E; Keijzer, M; Heemink, A W; Storchi, P R M; Breedveld, S; Heijmen, B J M

    2014-01-01

    Radiotherapy dose delivery in the tumor and surrounding healthy tissues is affected by movements and deformations of the corresponding organs between fractions. The random variations may be characterized by non-rigid, anisotropic principal component analysis (PCA) modes. In this article new dynamic dose deposition matrices, based on established PCA modes, are introduced as a tool to evaluate the mean and the variance of the dose at each target point resulting from any given set of fluence profiles. The method is tested for a simple cubic geometry and for a prostate case. The movements spread out the distributions of the mean dose and cause the variance of the dose to be highest near the edges of the beams. The non-rigidity and anisotropy of the movements are reflected in both quantities. The dynamic dose deposition matrices facilitate the inclusion of the mean and the variance of the dose in the existing fluence-profile optimizer for radiotherapy planning, to ensure robust plans with respect to the movements. (paper)

  9. Impact of PCA Strategies on Pain Intensity and Functional Assessment Measures in Adults with Sickle Cell Disease during Hospitalized Vaso-Occlusive Episodes

    Science.gov (United States)

    Dampier, Carlton D.; Wager, Carrie G.; Harrison, Ryan; Hsu, Lewis L.; Minniti, Caterina P.; Smith, Wally R.

    2012-01-01

    Clinical trials of sickle cell disease (SCD) pain treatment usually observe only small decrements in pain intensity during the course of hospitalization. Sub-optimal analgesic management and inadequate pain assessment methods are possible explanations for these findings. In a search for better methods for assessing inpatient SCD pain in adults, we examined several pain intensity and interference measures in both arms of a randomized controlled trial comparing two different opioid PCA therapies. Based upon longitudinal analysis of pain episodes, we found that scores from daily average Visual Analogue Scales (VAS) and several other measures, especially the Brief Pain Inventory (BPI), were sensitive to change in daily improvements in pain intensity associated with resolution of vaso-occlusive pain. In this preliminary trial, the low demand, high basal infusion (LDHI) strategy demonstrated faster, larger improvements in various measures of pain than the high demand, low basal infusion (HDLI) strategy for opioid PCA dosing, however, verification in larger studies is required. The measures and statistical approaches used in this analysis may facilitate design, reduce sample size, and improve analyses of treatment response in future SCD clinical trials of vaso-occlusive episodes. PMID:22886853

  10. PCA and multidimensional visualization techniques united to aid in the bioindication of elements from transplanted Sphagnum palustre moss exposed in the Gdańsk City area.

    Science.gov (United States)

    Astel, Aleksander; Astel, Karolina; Biziuk, Marek

    2008-01-01

    Neutron Activation Analysis in moss samples. Elimination of variables covered the elements whose concentrations in moss were lower than the reported detection limits for INAA for most observations or in cases where particular elements did not show any variation. Eighteen elements: a, Ca, Sc, Fe, Co, Zn, As, Br, Mo, Sb, Ba, La, Ce, Sm, Yb, Lu, Hf, Th, were selected for the research presented. Two runs of PCA were performed since, in the first-run a heavy polluted location (Stogi - 'Sto') understood as outlier in the term of PCA approach was detected and results in the form of block diagrams and surface maps were presented. As ensues from the first-run PCA analysis, the factor layout for both indicators is similar but not identical due to the differences in the elements accumulation mechanism. Three latent factors ('phosphatic fertilizer plant impact', 'urban impact' and 'marine impact') explain over 89% and 82% of the total variance for dry and living moss respectively. In the second-run PCA three latent factors are responsible for the data structure in both moss materials. However, in the case of dry moss analysis these factors explain 85% of the total variance but they are rather hard to interpret. On the other hand living moss shows the same pattern as in first-run PCA. Three latent factors explain over 84% of the total variance in this case. The pollution profiles extracted in PCA of dry moss data differ tremendously between both runs, while no deterioration was found after removal of Stogi from data set in case of living moss. Performance of the second-run PCA with exception of Stogi as a heavy polluted location has led to the conclusion that living moss shows better indication properties than dry one. While using moss as wet and dry deposition sampier it is not possible to calculate deposition values since the real volume of collected water and dust is hard to estimate due to a splash effect and irregular surface. Therefore, accumulation values seam to be reasonable

  11. The RESPITE trial: remifentanil intravenously administered patient-controlled analgesia (PCA) versus pethidine intramuscular injection for pain relief in labour: study protocol for a randomised controlled trial.

    Science.gov (United States)

    Wilson, Matthew; MacArthur, Christine; Gao Smith, Fang; Homer, Leanne; Handley, Kelly; Daniels, Jane

    2016-12-12

    The commonest opioid used for pain relief in labour is pethidine (meperidine); however, its effectiveness has long been challenged and the drug has known side effects including maternal sedation, nausea and potential transfer across the placenta to the foetus. Over a third of women receiving pethidine require an epidural due to inadequate pain relief. Epidural analgesia increases the risk of an instrumental vaginal delivery and its associated effects. Therefore, there is a clear need for a safe, effective, alternative analgesic to pethidine. Evidence suggests that remifentanil patient-controlled analgesia (PCA) reduces epidural conversion rates compared to pethidine; however, no trial has yet investigated this as a primary endpoint. We are, therefore, comparing pethidine intramuscular injection to remifentanil PCA in a randomised controlled trial. Women in established labour, requesting systemic opioid pain relief, will be randomised to either intravenously administered remifentanil PCA (intervention) or pethidine intramuscular injection (control) in an unblinded, 1:1 individual randomised trial. Following informed consent, 400 women in established labour, who request systemic opioid pain relief, from NHS Trusts across England will undergo a minimised randomisation by a computer or automated telephone system to either pethidine or remifentanil. In order to balance the groups this minimisation is based on four parameters; parity (nulliparous versus multiparous), maternal age (Asian (Pakistani/Indian/Bangladeshi) versus Other) and induced versus spontaneous labour. The effectiveness of pain relief provided by each technique will be recorded every 30 min after time zero, until epidural placement, delivery or transfer to theatre, quantified by Visual Analogue Scale. Incidence of maternal side effects including sedation, delivery mode, foetal distress requiring delivery, neonatal status at delivery and rate of initiation of breastfeeding within the first hour of birth

  12. MODEL APPLICATION MULTIVARIATE ANALYSIS OF STATISTICAL TECHNIQUES PCA AND HCA ASSESSMENT QUESTIONNAIRE ON CUSTOMER SATISFACTION: CASE STUDY IN A METALLURGICAL COMPANY OF METAL CONTAINERS

    Directory of Open Access Journals (Sweden)

    Cláudio Roberto Rosário

    2012-07-01

    Full Text Available The purpose of this research is to improve the practice on customer satisfaction analysis The article presents an analysis model to analyze the answers of a customer satisfaction evaluation in a systematic way with the aid of multivariate statistical techniques, specifically, exploratory analysis with PCA – Partial Components Analysis with HCA - Hierarchical Cluster Analysis. It was tried to evaluate the applicability of the model to be used by the issue company as a tool to assist itself on identifying the value chain perceived by the customer when applied the questionnaire of customer satisfaction. It was found with the assistance of multivariate statistical analysis that it was observed similar behavior among customers. It also allowed the company to conduct reviews on questions of the questionnaires, using analysis of the degree of correlation between the questions that was not a company’s practice before this research.

  13. Covariances of fission-integral measurements at the NBS 252Cf and ISNF facilities and at the ORNL-PCA facility

    International Nuclear Information System (INIS)

    Wagschal, J.J.; Maerker, R.E.; Gilliam, D.M.

    1979-01-01

    Integral measurements together with accompanying uncertainty estimates have been used for the past fifteen years in cross section adjustments. As the field of cross section adjustment came of age, the crude uncertainty estimates were replaced - only in principle, initially - by a quantitative cross section uncertainty covariance description and by uncertainty correlations of integral experiments. There is current interest in the fission reaction rate ratio measurements in the NBS standard neutron fields by people involved in fast reactor cross sections. Also those in the LWR pressure vessel surveillance dosimetry program are interested in these measurements and in similar measurements performed in the Oak Ridge Pool Critical Assembly (PCA). A careful re-examination of uncertainty analysis is presented

  14. DSC, FT-IR, NIR, NIR-PCA and NIR-ANOVA for determination of chemical stability of diuretic drugs: impact of excipients

    Directory of Open Access Journals (Sweden)

    Gumieniczek Anna

    2018-03-01

    Full Text Available It is well known that drugs can directly react with excipients. In addition, excipients can be a source of impurities that either directly react with drugs or catalyze their degradation. Thus, binary mixtures of three diuretics, torasemide, furosemide and amiloride with different excipients, i.e. citric acid anhydrous, povidone K25 (PVP, magnesium stearate (Mg stearate, lactose, D-mannitol, glycine, calcium hydrogen phosphate anhydrous (CaHPO4 and starch, were examined to detect interactions. High temperature and humidity or UV/VIS irradiation were applied as stressing conditions. Differential scanning calorimetry (DSC, FT-IR and NIR were used to adequately collect information. In addition, chemometric assessments of NIR signals with principal component analysis (PCA and ANOVA were applied.

  15. Principle component analysis (PCA) and second-order global hard-modelling for the complete resolution of transition metal ions complex formation with 1,10-phenantroline

    Energy Technology Data Exchange (ETDEWEB)

    Shariati-Rad, Masoud [Faculty of Chemistry, Bu-Ali Sina University, Hamedan 65174 (Iran, Islamic Republic of); Hasani, Masoumeh, E-mail: hasani@basu.ac.ir [Faculty of Chemistry, Bu-Ali Sina University, Hamedan 65174 (Iran, Islamic Republic of)

    2009-08-19

    Second-order global hard-modelling was applied to resolve the complex formation between Co{sup 2+}, Ni{sup 2+}, and Cd{sup 2+} cations and 1,10-phenantroline. The highly correlated spectral and concentration profiles of the species in these systems and low concentration of some species in the individual collected data matrices prevent the well-resolution of the profiles. Therefore, a collection of six equilibrium data matrices including series of absorption spectra taken with pH changes at different reactant ratios were analyzed. Firstly, a precise principle component analysis (PCA) of different augmented arrangements of the individual data matrices was used to distinguish the number of species involved in the equilibria. Based on the results of PCA, the equilibria included in the data were specified and second-order global hard-modelling of the appropriate arrangement of six collected equilibrium data matrices resulted in well-resolved profiles and equilibrium constants. The protonation constant of the ligand (1,10-phenantroline) and spectral profiles of its protonated and unprotonated forms are the additional information obtained by global analysis. For comparison, multivariate curve resolution-alternating least squares (MCR-ALS) was applied to the same data. The results showed that second-order global hard-modelling is more convenient compared with MCR-ALS especially for systems with completely known model. It can completely resolve the system and the concentration profiles which are closer to correct ones. Moreover, parameters showing the goodness of fit are better with second-order global hard-modelling.

  16. A new method for quantitative assessment of resilience engineering by PCA and NT approach: A case study in a process industry

    International Nuclear Information System (INIS)

    Shirali, Gh.A.; Mohammadfam, I.; Ebrahimipour, V.

    2013-01-01

    In recent years, resilience engineering (RE) has attracted widespread interest from industry as well as academia because it presents a new way of thinking about safety and accident. Although the concept of RE was defined scholarly in various areas, there are only few which specifically focus on how to measure RE. Therefore, there is a gap in assessing resilience by quantitative methods. This research aimed at presenting a new method for quantitative assessment of RE using questionnaire and based on principal component analysis. However, six resilience indicators, i.e., top management commitment, Just culture, learning culture, awareness and opacity, preparedness, and flexibility were chosen, and the data related to those in the 11 units of a process industry using a questionnaire was gathered. The data was analyzed based on principal component analysis (PCA) approach. The analysis also leads to determination of the score of resilience indicators and the process units. The process units were ranked using these scores. Consequently, the prescribed approach can determine the poor indicators and the process units. This is the first study that considers a quantitative assessment in RE area which is conducted through PCA. Implementation of the proposed methods would enable the managers to recognize the current weaknesses and challenges against the resilience of their system. -- Highlights: •We quantitatively measure the potential of resilience. •The results are more tangible to understand and interpret. •The method facilitates comparison of resilience state among various process units. •The method facilitates comparison of units' resilience state with the best practice

  17. A PCA-based hyperspectral approach to detect infections by mycophilic fungi on dried porcini mushrooms (boletus edulis and allied species).

    Science.gov (United States)

    Bagnasco, Lucia; Zotti, Mirca; Sitta, Nicola; Oliveri, Paolo

    2015-11-01

    Mycophilic fungi of anamorphic genus Sepedonium (telomorphs in Hypomyces, Hypocreales, Ascomycota) infect and parasitize sporomata of boletes. The obligated hosts such as Boletus edulis and allied species (known as "porcini mushrooms") are among the most valued and prized edible wild mushrooms in the world. Sepedonium infections have a great morphological variability: at the initial state, contaminated mushrooms present a white coating covering tubes and pores; at the final state, Sepedonium forms a deep and thick hyphal layer that eventually leads to the total necrosis of the host. Up to date, Sepedonium infections in porcini mushrooms have been evaluated only through macroscopic and microscopic visual analysis. In this study, in order to implement the infection evaluation as a routine methodology for industrial purposes, the potential application of Hyperspectral Imaging (HSI) and Principal Component Analysis (PCA) for detection of Sepedonium presence on sliced and dried B. edulis and allied species was investigated. Hyperspectral images were obtained using a pushbroom line-scanning HSI instrument, operating in the wavelength range between 400 and 1000 nm with 5 nm resolution. PCA was applied on normal and contaminated samples. To reduce the spectral variability caused by factors unrelated to Sepedonium infection, such as scattering effects and differences in sample height, different spectral pre-treatments were applied. A supervised rule was then developed to assign spectra recorded on new test samples to each of the two classes, based on the PC scores. This allowed to visualize directly - within false-color images of test samples - which points of the samples were contaminated. The results achieved may lead to the development of a non-destructive monitoring system for a rapid on-line screening of contaminated mushrooms. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Time-of-flight secondary ion mass spectrometry of a range of coal samples: a chemometrics (PCA, cluster, and PLS) analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lei Pei; Guilin Jiang; Bonnie J. Tyler; Larry L. Baxter; Matthew R. Linford [Brigham Young University, Provo, UT (United States). Department of Chemistry and Biochemistry

    2008-03-15

    This paper documents time-of-flight secondary ion mass spectrometry (ToF-SIMS) analyses of 34 different coal samples. In many cases, the inorganic Na{sup +}, Al{sup +}, Si{sup +}, and K{sup +} ions dominate the spectra, eclipsing the organic peaks. A scores plot of principal component 1 (PC1) versus principal component 2 (PC2) in a principal components analysis (PCA) effectively separates the coal spectra into a triangular pattern, where the different vertices of this pattern come from (I) spectra that have a strong inorganic signature that is dominated by Na{sup +}, (ii) spectra that have a strong inorganic signature that is dominated by Al{sup +}, Si{sup +}, and K{sup +}, and (iii) spectra that have a strong organic signature. Loadings plots of PC1 and PC2 confirm these observations. The spectra with the more prominent inorganic signatures come from samples with higher ash contents. Cluster analysis with the K-means algorithm was also applied to the data. The progressive clustering revealed in the dendrogram correlates extremely well with the clustering of the data points found in the scores plot of PC1 versus PC2 from the PCA. In addition, this clustering often correlates with properties of the coal samples, as measured by traditional analyses. Partial least-squares (PLS), which included the use of interval PLS and a genetic algorithm for variable selection, shows a good correlation between ToF-SIMS spectra and some of the properties measured by traditional means. Thus, ToF-SIMS appears to be a promising technique for the analysis of this important fuel. 33 refs., 9 figs., 5 tabs.

  19. Principle component analysis (PCA) and second-order global hard-modelling for the complete resolution of transition metal ions complex formation with 1,10-phenantroline

    International Nuclear Information System (INIS)

    Shariati-Rad, Masoud; Hasani, Masoumeh

    2009-01-01

    Second-order global hard-modelling was applied to resolve the complex formation between Co 2+ , Ni 2+ , and Cd 2+ cations and 1,10-phenantroline. The highly correlated spectral and concentration profiles of the species in these systems and low concentration of some species in the individual collected data matrices prevent the well-resolution of the profiles. Therefore, a collection of six equilibrium data matrices including series of absorption spectra taken with pH changes at different reactant ratios were analyzed. Firstly, a precise principle component analysis (PCA) of different augmented arrangements of the individual data matrices was used to distinguish the number of species involved in the equilibria. Based on the results of PCA, the equilibria included in the data were specified and second-order global hard-modelling of the appropriate arrangement of six collected equilibrium data matrices resulted in well-resolved profiles and equilibrium constants. The protonation constant of the ligand (1,10-phenantroline) and spectral profiles of its protonated and unprotonated forms are the additional information obtained by global analysis. For comparison, multivariate curve resolution-alternating least squares (MCR-ALS) was applied to the same data. The results showed that second-order global hard-modelling is more convenient compared with MCR-ALS especially for systems with completely known model. It can completely resolve the system and the concentration profiles which are closer to correct ones. Moreover, parameters showing the goodness of fit are better with second-order global hard-modelling.

  20. Evaluation of urinary prostate cancer antigen-3 (PCA3) and TMPRSS2-ERG score changes when starting androgen-deprivation therapy with triptorelin 6-month formulation in patients with locally advanced and metastatic prostate cancer

    DEFF Research Database (Denmark)

    Martínez-Piñeiro, Luis; Schalken, Jack A; Cabri, Patrick

    2014-01-01

    change at 6 months, according to baseline variables. Other outcome measures included urinary PCA3 and TMPRSS2-ERG scores and statuses, and serum testosterone and prostate-specific antigen (PSA) levels at baseline and at 1, 3 and 6 months after initiation of ADT. Safety was assessed by recording adverse......OBJECTIVE: To assess prostate cancer antigen-3 (PCA3) and TMPRSS2-ERG scores in patients with advanced and metastatic prostate cancer at baseline and after 6 months of treatment with triptorelin 22.5 mg, and analyse these scores in patient-groups defined by different disease characteristics....... PATIENTS AND METHODS: The Triptocare study was a prospective, open-label, multicentre, single-arm, Phase III study of triptorelin 22.5 mg in men with locally advanced or metastatic prostate cancer, who were naïve to androgen-deprivation therapy (ADT). The primary objective was to model the urinary PCA3...

  1. Three-Dimensional (X,Y,Z) Deterministic Analysis of the PCA-Replica Neutron Shielding Benchmark Experiment using the TORT-3.2 Code and Group Cross Section Libraries for LWR Shielding and Pressure Vessel Dosimetry

    OpenAIRE

    Pescarini Massimo; Orsi Roberto; Frisoni Manuela

    2016-01-01

    The PCA-Replica 12/13 (H2O/Fe) neutron shielding benchmark experiment was analysed using the ORNL TORT-3.2 3D SN code. PCA-Replica, specifically conceived to test the accuracy of nuclear data and transport codes employed in LWR shielding and radiation damage calculations, reproduces a PWR ex-core radial geometry with alternate layers of water and steel including a PWR pressure vessel simulator. Three broad-group coupled neutron/photon working cross section libraries in FIDO-ANISN format with ...

  2. Validation of the BUGJEFF311.BOLIB, BUGENDF70.BOLIB and BUGLE-B7 broad-group libraries on the PCA-Replica (H2O/Fe) neutron shielding benchmark experiment

    OpenAIRE

    Pescarini Massimo; Orsi Roberto; Frisoni Manuela

    2016-01-01

    The PCA-Replica 12/13 (H2O/Fe) neutron shielding benchmark experiment was analysed using the TORT-3.2 3D SN code. PCA-Replica reproduces a PWR ex-core radial geometry with alternate layers of water and steel including a pressure vessel simulator. Three broad-group coupled neutron/photon working cross section libraries in FIDO-ANISN format with the same energy group structure (47 n + 20 γ) and based on different nuclear data were alternatively used: the ENEA BUGJEFF311.BOLIB (JEFF-3.1.1) and U...

  3. Estimating 4D-CBCT from prior information and extremely limited angle projections using structural PCA and weighted free-form deformation for lung radiotherapy.

    Science.gov (United States)

    Harris, Wendy; Zhang, You; Yin, Fang-Fang; Ren, Lei

    2017-03-01

    To investigate the feasibility of using structural-based principal component analysis (PCA) motion-modeling and weighted free-form deformation to estimate on-board 4D-CBCT using prior information and extremely limited angle projections for potential 4D target verification of lung radiotherapy. A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In the previous method, each phase of the 4D-CBCT was generated by deforming a prior CT volume. The DFM was solved by a motion model extracted by a global PCA and free-form deformation (GMM-FD) technique, using a data fidelity constraint and deformation energy minimization. In this study, a new structural PCA method was developed to build a structural motion model (SMM) by accounting for potential relative motion pattern changes between different anatomical structures from simulation to treatment. The motion model extracted from planning 4DCT was divided into two structures: tumor and body excluding tumor, and the parameters of both structures were optimized together. Weighted free-form deformation (WFD) was employed afterwards to introduce flexibility in adjusting the weightings of different structures in the data fidelity constraint based on clinical interests. XCAT (computerized patient model) simulation with a 30 mm diameter lesion was simulated with various anatomical and respiratory changes from planning 4D-CT to on-board volume to evaluate the method. The estimation accuracy was evaluated by the volume percent difference (VPD)/center-of-mass-shift (COMS) between lesions in the estimated and "ground-truth" on-board 4D-CBCT. Different on-board projection acquisition scenarios and projection noise levels were simulated to investigate their effects on the estimation accuracy. The method was also evaluated against three lung patients. The SMM-WFD method achieved substantially better accuracy than the GMM-FD method for CBCT estimation using extremely

  4. PCA 2006[Protection, control and automation (PCA)]; VKA 2006

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2006-07-01

    The conference contains 16 presentations with topics on electric network functioning, optimization, operation, maintenance and reliability. There are papers on various aspects of generator technology and applications, wind power plant simulation and construction, small power plant systems, connecting large industrial customers to networks and official documentation requirements. (tk)

  5. Comparison of Multiple Bioactive Constituents in Different Parts of Eucommia ulmoides Based on UFLC-QTRAP-MS/MS Combined with PCA.

    Science.gov (United States)

    Yan, Ying; Zhao, Hui; Chen, Cuihua; Zou, Lisi; Liu, Xunhong; Chai, Chuan; Wang, Chengcheng; Shi, Jingjing; Chen, Shuyu

    2018-03-13

    Eucommia ulmoides Oilv. (EU), also called Du-zhong, is a classical traditional Chinese medicine. Its bark, leaf, and male flower are all used for medicinal purposes, called Eucommiae Cortex (EC), Eucommiae Folium (EF), and Eucommiae Flos Male (EFM). In order to study the difference in synthesis and the accumulation of metabolites in different parts of EU, a reliable method based on ultra-fast liquid chromatography tandem triple quadrupole mass spectrometry (UFLC-QTRAP-MS/MS) was developed for the simultaneous determination of a total of 21 constituents, including two lignans, 6 iridoids, 6 penylpropanoids, 6 flavonoids, and one phenol in the samples (EC, EF, and EFM). Furthermore, principal component analysis (PCA) was performed to evaluate and classify the samples according to the contents of these 21 constituents. All of the results demonstrated that the chemical compositions in EC, EF, and EFM were significantly different and the differential constituents (i.e., aucubin, geniposidic acid, chlorogenic acid, pinoresinol-di- O -β-d-glucopyranoside, geniposide, cryptochlorogenic acid, rutin, and quercetin) were remarkably associated with sample classifications. The research will provide the basic information for revealing the laws of metabolite accumulation in EC, EF, and EFM from the same origin.

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

    Science.gov (United States)

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

    2018-08-15

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

  7. Comparison of Multiple Bioactive Constituents in Different Parts of Eucommia ulmoides Based on UFLC-QTRAP-MS/MS Combined with PCA

    Directory of Open Access Journals (Sweden)

    Ying Yan

    2018-03-01

    Full Text Available Eucommia ulmoides Oilv. (EU, also called Du-zhong, is a classical traditional Chinese medicine. Its bark, leaf, and male flower are all used for medicinal purposes, called Eucommiae Cortex (EC, Eucommiae Folium (EF, and Eucommiae Flos Male (EFM. In order to study the difference in synthesis and the accumulation of metabolites in different parts of EU, a reliable method based on ultra-fast liquid chromatography tandem triple quadrupole mass spectrometry (UFLC-QTRAP-MS/MS was developed for the simultaneous determination of a total of 21 constituents, including two lignans, 6 iridoids, 6 penylpropanoids, 6 flavonoids, and one phenol in the samples (EC, EF, and EFM. Furthermore, principal component analysis (PCA was performed to evaluate and classify the samples according to the contents of these 21 constituents. All of the results demonstrated that the chemical compositions in EC, EF, and EFM were significantly different and the differential constituents (i.e., aucubin, geniposidic acid, chlorogenic acid, pinoresinol-di-O-β-d-glucopyranoside, geniposide, cryptochlorogenic acid, rutin, and quercetin were remarkably associated with sample classifications. The research will provide the basic information for revealing the laws of metabolite accumulation in EC, EF, and EFM from the same origin.

  8. Application of fractal modeling and PCA method for hydrothermal alteration mapping in the Saveh area (Central Iran based on ASTER multispectral data

    Directory of Open Access Journals (Sweden)

    Mirko Ahmadfaraj

    2016-06-01

    Full Text Available The aim of this study is determination and separation of alteration zones using Concentration-Area (C-A fractal model based on remote sensing data which has been extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER images. The studied area is on the SW part of Saveh, 1:250,000 geological map, which is located in Urumieh-Dokhtar magmatic belt, Central Iran. The pixel values were computed by Principal Component Analysis (PCA method used to determine phyllic, argillic, and propylitic alteration zones. The C-A fractal model is utilized for separation of different parts of alteration zones due to their intensity. The log-log C-A plots reveal multifractal nature for phyllic, argillic, and propylitic alteration zones. The obtained results based on fractal model show that the main trend of the alteration zones is in NW-SE direction. Compared to the geological map of the study area and copper mineralizations, the alteration zones have been detected properly and correlate with the mineral occurrences, intrusive rock, and faults.

  9. Analysis of differences between Western and East-Asian faces based on facial region segmentation and PCA for facial expression recognition

    Science.gov (United States)

    Benitez-Garcia, Gibran; Nakamura, Tomoaki; Kaneko, Masahide

    2017-01-01

    Darwin was the first one to assert that facial expressions are innate and universal, which are recognized across all cultures. However, recent some cross-cultural studies have questioned this assumed universality. Therefore, this paper presents an analysis of the differences between Western and East-Asian faces of the six basic expressions (anger, disgust, fear, happiness, sadness and surprise) focused on three individual facial regions of eyes-eyebrows, nose and mouth. The analysis is conducted by applying PCA for two feature extraction methods: appearance-based by using the pixel intensities of facial parts, and geometric-based by handling 125 feature points from the face. Both methods are evaluated using 4 standard databases for both racial groups and the results are compared with a cross-cultural human study applied to 20 participants. Our analysis reveals that differences between Westerns and East-Asians exist mainly on the regions of eyes-eyebrows and mouth for expressions of fear and disgust respectively. This work presents important findings for a better design of automatic facial expression recognition systems based on the difference between two racial groups.

  10. ERS-2 SAR and IRS-1C LISS III data fusion: A PCA approach to improve remote sensing based geological interpretation

    Science.gov (United States)

    Pal, S. K.; Majumdar, T. J.; Bhattacharya, Amit K.

    Fusion of optical and synthetic aperture radar data has been attempted in the present study for mapping of various lithologic units over a part of the Singhbhum Shear Zone (SSZ) and its surroundings. ERS-2 SAR data over the study area has been enhanced using Fast Fourier Transformation (FFT) based filtering approach, and also using Frost filtering technique. Both the enhanced SAR imagery have been then separately fused with histogram equalized IRS-1C LISS III image using Principal Component Analysis (PCA) technique. Later, Feature-oriented Principal Components Selection (FPCS) technique has been applied to generate False Color Composite (FCC) images, from which corresponding geological maps have been prepared. Finally, GIS techniques have been successfully used for change detection analysis in the lithological interpretation between the published geological map and the fusion based geological maps. In general, there is good agreement between these maps over a large portion of the study area. Based on the change detection studies, few areas could be identified which need attention for further detailed ground-based geological studies.

  11. Análise multivariada aplicada na identificação de fármacos antidepressivos. Parte II: Análise por componentes principais (PCA e o método de classificação SIMCA Multivariate analysis to applied in the identification of antidepressants. Part II: principal components analysis (PCA and soft independent modeling of class analogies (SIMCA

    Directory of Open Access Journals (Sweden)

    Janusa Goelzer Sabin

    2004-09-01

    Full Text Available Neste trabalho a identificação e a discriminação de dois diferentes fármacos utilizados como antidepressivos foi estudada, empregando os espectros de reflexão difusa no infravermelho médio com transformada de Fourier (DRIFTS, juntamente com a análise de componentes principais (PCA e o método de classificação SIMCA. Os espectros no infravermelho de amostras contendo diferentes concentrações dos princípios ativos cloridrato de amitriptilina e cloridrato de imipramina, foram coletados em um espectrofotômetro NICOLET Magna 550, sendo realizadas 2 réplicas para cada amostra, com resolução de 4 cm-1 e 32 varreduras. A análise de componentes principais confirmou a existência de dois grupos distintos, correspondendo aos dois diferentes princípios ativos utilizados, além de evidenciar a presença de amostras anômalas no conjunto de dados que, possivelmente, iriam interferir na modelagem. Já o método de classificação SIMCA possibilitou o reconhecimento de amostras dos princípios ativos cloridrato de imipramina e cloridrato de amitriptilina com resultados indicando 100% de classificação correta das classes modeladas.In this work the certification of two different drugs used as antidepressants was studied, using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS, together with the analysis of principal components (PCA and the method of soft independent modeling of class analogies (SIMCA. The DRIFT spectra of samples with different concentrations of the active principles amitriptiline and imipramine hydrochlorides had been collected in Magna 550 spectrofotometer, two spectra for each sample, with resolution of 4 cm-1 and 32 scans. The PCA confirmed the existence of two distinct groups, corresponding to the two different active principles used. Otherwise the method of classification SIMCA made possible the recognition of samples of the principles amitriptyline and imipramine hydrochlorides with results indicating

  12. Mapping the Wetland Vegetation Communities of the Australian Great Artesian Basin Springs Using SAM, Mtmf and Spectrally Segmented PCA Hyperspectral Analyses

    Science.gov (United States)

    White, D. C.; Lewis, M. M.

    2012-07-01

    The Australian Great Artesian Basin (GAB) supports a unique and diverse range of groundwater dependent wetland ecosystems termed GAB springs. In recent decades the ecological sustainability of the springs has become uncertain as demands on this iconic groundwater resource increase. The impacts of existing water extractions for mining and pastoral activities are unknown. This situation is compounded by the likelihood of future increasing demand for extractions. Hyperspectral remote sensing provides the necessary spectral and spatial detail to discriminate wetland vegetation communities. Therefore the objectives of this paper are to discriminate the spatial extent and distribution of key spring wetland vegetation communities associated with the GAB springs evaluating three hyperspectral techniques: Spectral Angle Mapper (SAM), Mixture Tuned Matched Filtering (MTMF) and Spectrally Segmented PCA. In addition, to determine if the hyperspectral techniques developed can be applied at a number of sites representative of the range of spring formations and geomorphic settings and at two temporal intervals. Two epochs of HyMap airborne hyperspectral imagery were captured for this research in March 2009 and April 2011 at a number of sites representative of the floristic and geomorphic diversity of GAB spring groups/complexes within South Australia. Colour digital aerial photography at 30 cm GSD was acquired concurrently with the HyMap imagery. The image acquisition coincided with a field campaign of spectroradiometry measurements and a botanical survey. To identify key wavebands which have the greatest capability to discriminate vegetation communities of the GAB springs and surrounding area three hyperspectral data reduction techniques were employed: (i) Spectrally Segmented PCA (SSPCA); (ii) the Minimum Noise Transform (MNF); and (iii) the Pixel Purity Index (PPI). SSPCA was applied to NDVI-masked vegetation portions of the HyMap imagery with wavelength regions spectrally

  13. Predicting prostate cancer-specific outcome after radical prostatectomy among men with very high-risk cT3b/4 PCa: a multi-institutional outcome study of 266 patients.

    Science.gov (United States)

    Moltzahn, F; Karnes, J; Gontero, P; Kneitz, B; Tombal, B; Bader, P; Briganti, A; Montorsi, F; Van Poppel, H; Joniau, S; Spahn, M

    2015-03-01

    The value of radical prostatectomy (RP) as an approach for very high-risk prostate cancer (PCa) patients is controversial. To examine the risk of 10-year cancer-specific mortality (CSM) and other-cause mortality (OCM) according to clinical and pathological characteristics of very high-risk cT3b/4 PCa patients treated with RP as the primary treatment option. In a multi-institutional cohort, 266 patients with very high-risk cT3b/4 PCa treated with RP were identified. All patients underwent RP and pelvic lymph-node dissection. Competing-risk analyses assessed 10-year CSM and OCM before and after stratification for age and Charlson comorbidity index (CCI). Overall, 34 (13%) patients died from PCa and 73 (28%) from OCM. Ten-year CSM and OCM rates ranged from 5.6% to 12.9% and from 10% to 38%, respectively. OCM was the leading cause of death in all subgroups. Age and comorbidities were the main determinants of OCM. In healthy men, CSM rate did not differ among age groups (10-year CSM rate for ⩽64, 65-69 and ⩾70 years: 16.2%, 11.5% and 17.1%, respectively). Men with a CCI ⩾1 showed a very low risk of CSM irrespective of age (10-year CSM: 5.6-6.1%), whereas the 10-year OCM rates increased with age up to 38% in men ⩾70 years. Very high-risk cT3b/4 PCa represents a heterogeneous group. We revealed overall low CSM rates despite the highly unfavorable clinical disease. For healthy men, CSM was independent of age, supporting RP even for older men. Conversely, less healthy patients had the highest risk of dying from OCM while sharing very low risk of CSM, indicating that this group might not benefit from an aggressive surgical treatment. Outcome after RP as the primary treatment option in cT3b/4 PCa patients is related to age and comorbidity status.

  14. Probing the mysteries of the X-ray binary 4U 1210-64 with ASM, PCA, MAXI, BAT, and Suzaku

    Energy Technology Data Exchange (ETDEWEB)

    Coley, Joel B.; Corbet, Robin H. D.; Mukai, Koji; Pottschmidt, Katja, E-mail: jcoley1@umbc.edu [University of Maryland Baltimore County, 1000 Hilltop Cir, Baltimore, MD 21250 (United States)

    2014-10-01

    4U 1210-64 has been postulated to be a high-mass X-ray binary powered by the Be mechanism. X-ray observations with Suzaku, the ISS Monitor of All-sky X-ray Image (MAXI), and the Rossi X-ray Timing Explorer Proportional Counter Array (PCA) and All Sky Monitor (ASM) provide detailed temporal and spectral information on this poorly understood source. Long-term ASM and MAXI observations show distinct high and low states and the presence of a 6.7101 ± 0.0005 day modulation, interpreted as the orbital period. Folded light curves reveal a sharp dip, interpreted as an eclipse. To determine the nature of the mass donor, the predicted eclipse half-angle was calculated as a function of inclination angle for several stellar spectral types. The eclipse half-angle is not consistent with a mass donor of spectral type B5 V; however, stars with spectral types B0 V or B0-5 III are possible. The best-fit spectral model consists of a power law with index Γ = 1.85{sub −0.05}{sup +0.04} and a high-energy cutoff at 5.5 ± 0.2 keV modified by an absorber that fully covers the source as well as partially covering absorption. Emission lines from S XVI Kα, Fe Kα, Fe XXV Kα, and Fe XXVI Kα were observed in the Suzaku spectra. Out of eclipse, the Fe Kα line flux was strongly correlated with unabsorbed continuum flux, indicating that the Fe I emission is the result of fluorescence of cold dense material near the compact object. The Fe I feature is not detected during eclipse, further supporting an origin close to the compact object.

  15. Jendl-3.1 iron validation on the PCA-REPLICA (H{sub 2}O/Fe) shielding benchmark experiment

    Energy Technology Data Exchange (ETDEWEB)

    Pescarini, M.; Borgia, M. G. [ENEA, Centro Ricerche ``Ezio Clementel``, Bologna (Italy). Dipt. Energia

    1997-03-01

    The PCA-REPLICA (H{sub 2}O/Fe) neutron shielding benchmarks experiment is analysed using the SN 2-D DOT 3.5-E code and the 3-D-equivalent flux synthesis method. This engineering benchmark reproduces the ex-core radial geometry of a PWR, including a mild steel reactor pressure vessel (RPV) simulator, and is designed to test the accuracy of the calculation of the in-vessel neutron exposure parameters. This accuracy is strongly dependent on the quality of the iron neutron cross sections used to describe the nuclear reactions within the RPV simulator. In particular, in this report, the cross sections based on the JENDL-3.1 iron data files are tested, through a comparison of the calculated integral and spectral results with the corresponding experimental data. In addition, the present results are compared, on the same benchmark experiment, with those of a preceding ENEA-Bologna validation of the ENDF/B VI iron cross sections. The integral result comparison indicates that, for all the threshold detectors considered (Rh-103 (n, n`) Rh-103m, In-115 (n, n`) In-115m and S-32 (n, p) P-32), the JENDL-3.1 natural iron data produce satisfactory results similar to those obtained with the ENDF/B VI iron data. On the contrary, when the JENDL/3.1 Fe-56 data file is used, strongly underestimated results are obtained for the lower energy threshold detectors, Rh-103 and In-115. This fact, in particular, becomes more evident with increasing the neutron penetration depth in the RPV simulator.

  16. Application of PCA and SIMCA statistical analysis of FT-IR spectra for the classification and identification of different slag types with environmental origin.

    Science.gov (United States)

    Stumpe, B; Engel, T; Steinweg, B; Marschner, B

    2012-04-03

    In the past, different slag materials were often used for landscaping and construction purposes or simply dumped. Nowadays German environmental laws strictly control the use of slags, but there is still a remaining part of 35% which is uncontrolled dumped in landfills. Since some slags have high heavy metal contents and different slag types have typical chemical and physical properties that will influence the risk potential and other characteristics of the deposits, an identification of the slag types is needed. We developed a FT-IR-based statistical method to identify different slags classes. Slags samples were collected at different sites throughout various cities within the industrial Ruhr area. Then, spectra of 35 samples from four different slags classes, ladle furnace (LF), blast furnace (BF), oxygen furnace steel (OF), and zinc furnace slags (ZF), were determined in the mid-infrared region (4000-400 cm(-1)). The spectra data sets were subject to statistical classification methods for the separation of separate spectral data of different slag classes. Principal component analysis (PCA) models for each slag class were developed and further used for soft independent modeling of class analogy (SIMCA). Precise classification of slag samples into four different slag classes were achieved using two different SIMCA models stepwise. At first, SIMCA 1 was used for classification of ZF as well as OF slags over the total spectral range. If no correct classification was found, then the spectrum was analyzed with SIMCA 2 at reduced wavenumbers for the classification of LF as well as BF spectra. As a result, we provide a time- and cost-efficient method based on FT-IR spectroscopy for processing and identifying large numbers of environmental slag samples.

  17. Association between Plasma 25-Hydroxyvitamin D, Ancestry and Aggressive Prostate Cancer among African Americans and European Americans in PCaP.

    Directory of Open Access Journals (Sweden)

    Susan E Steck

    Full Text Available African Americans (AAs have lower circulating 25-hydroxyvitamin D3 [25(OHD3] concentrations and higher prostate cancer (CaP aggressiveness than other racial/ethnic groups. The purpose of the current study was to examine the relationship between plasma 25(OHD3, African ancestry and CaP aggressiveness among AAs and European Americans (EAs.Plasma 25(OHD3 was measured using LC-MS/MS (Liquid Chromatography Tandem Mass Spectrometry in 537 AA and 663 EA newly-diagnosed CaP patients from the North Carolina-Louisiana Prostate Cancer Project (PCaP classified as having either 'high' or 'low' aggressive disease based on clinical stage, Gleason grade and prostate specific antigen at diagnosis. Mean plasma 25(OHD3 concentrations were compared by proportion of African ancestry. Logistic regression was used to calculate multivariable adjusted odds ratios (OR and 95% confidence intervals (95%CI for high aggressive CaP by tertile of plasma 25(OHD3.AAs with highest percent African ancestry (>95% had the lowest mean plasma 25(OHD3 concentrations. Overall, plasma 25(OHD3 was associated positively with aggressiveness among AA men, an association that was modified by calcium intake (ORT 3vs.T1: 2.23, 95%CI: 1.26-3.95 among men with low calcium intake, and ORT 3vs.T1: 0.19, 95%CI: 0.05-0.70 among men with high calcium intake. Among EAs, the point estimates of the ORs were <1.0 for the upper tertiles with CIs that included the null.Among AAs, plasma 25(OHD3 was associated positively with CaP aggressiveness among men with low calcium intake and inversely among men with high calcium intake. The clinical significance of circulating concentrations of 25(OHD3 and interactions with calcium intake in the AA population warrants further study.

  18. A perspective on two chemometrics tools: PCA and MCR, and introduction of a new one: Pattern recognition entropy (PRE), as applied to XPS and ToF-SIMS depth profiles of organic and inorganic materials

    Science.gov (United States)

    Chatterjee, Shiladitya; Singh, Bhupinder; Diwan, Anubhav; Lee, Zheng Rong; Engelhard, Mark H.; Terry, Jeff; Tolley, H. Dennis; Gallagher, Neal B.; Linford, Matthew R.

    2018-03-01

    X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) are much used analytical techniques that provide information about the outermost atomic and molecular layers of materials. In this work, we discuss the application of multivariate spectral techniques, including principal component analysis (PCA) and multivariate curve resolution (MCR), to the analysis of XPS and ToF-SIMS depth profiles. Multivariate analyses often provide insight into data sets that is not easily obtained in a univariate fashion. Pattern recognition entropy (PRE), which has its roots in Shannon's information theory, is also introduced. This approach is not the same as the mutual information/entropy approaches sometimes used in data processing. A discussion of the theory of each technique is presented. PCA, MCR, and PRE are applied to four different data sets obtained from: a ToF-SIMS depth profile through ca. 100 nm of plasma polymerized C3F6 on Si, a ToF-SIMS depth profile through ca. 100 nm of plasma polymerized PNIPAM (poly (N-isopropylacrylamide)) on Si, an XPS depth profile through a film of SiO2 on Si, and an XPS depth profile through a film of Ta2O5 on Ta. PCA, MCR, and PRE reveal the presence of interfaces in the films, and often indicate that the first few scans in the depth profiles are different from those that follow. PRE and backward difference PRE provide this information in a straightforward fashion. Rises in the PRE signals at interfaces suggest greater complexity to the corresponding spectra. Results from PCA, especially for the higher principal components, were sometimes difficult to understand. MCR analyses were generally more interpretable.

  19. Phase I/II trial of dendritic cell-based active cellular immunotherapy with DCVAC/PCa in patients with rising PSA after primary prostatectomy or salvage radiotherapy for the treatment of prostate cancer.

    Science.gov (United States)

    Fucikova, Jitka; Podrazil, Michal; Jarolim, Ladislav; Bilkova, Pavla; Hensler, Michal; Becht, Etienne; Gasova, Zdenka; Klouckova, Jana; Kayserova, Jana; Horvath, Rudolf; Fialova, Anna; Vavrova, Katerina; Sochorova, Klara; Rozkova, Daniela; Spisek, Radek; Bartunkova, Jirina

    2018-01-01

    Immunotherapy of cancer has the potential to be effective mostly in patients with a low tumour burden. Rising PSA (prostate-specific antigen) levels in patients with prostate cancer represents such a situation. We performed the present clinical study with dendritic cell (DC)-based immunotherapy in this patient population. The single-arm phase I/II trial registered as EudraCT 2009-017259-91 involved 27 patients with rising PSA levels. The study medication consisted of autologous DCs pulsed with the killed LNCaP cell line (DCVAC/PCa). Twelve patients with a favourable PSA response continued with the second cycle of immunotherapy. The primary and secondary objectives of the study were to assess the safety and determine the PSA doubling time (PSADT), respectively. No significant side effects were recorded. The median PSADT in all treated patients increased from 5.67 months prior to immunotherapy to 18.85 months after 12 doses (p PSA-reacting T lymphocytes were increased significantly already after the fourth dose, and a stable frequency was detected throughout the remainder of DCVAC/PCa treatment. Long-term immunotherapy of prostate cancer patients experiencing early signs of PSA recurrence using DCVAC/PCa was safe, induced an immune response and led to the significant prolongation of PSADT. Long-term follow-up may show whether the changes in PSADT might improve the clinical outcome in patients with biochemical recurrence of the prostate cancer.

  20. Association between Plasma 25-Hydroxyvitamin D, Ancestry and Aggressive Prostate Cancer among African Americans and European Americans in PCaP

    Science.gov (United States)

    Steck, Susan E.; Arab, Lenore; Zhang, Hongmei; Bensen, Jeannette T.; Fontham, Elizabeth T. H.; Johnson, Candace S.; Mohler, James L.; Smith, Gary J.; Su, Joseph L.; Trump, Donald L.; Woloszynska-Read, Anna

    2015-01-01

    Background African Americans (AAs) have lower circulating 25-hydroxyvitamin D3 [25(OH)D3] concentrations and higher prostate cancer (CaP) aggressiveness than other racial/ethnic groups. The purpose of the current study was to examine the relationship between plasma 25(OH)D3, African ancestry and CaP aggressiveness among AAs and European Americans (EAs). Methods Plasma 25(OH)D3 was measured using LC-MS/MS (Liquid Chromatography Tandem Mass Spectrometry) in 537 AA and 663 EA newly-diagnosed CaP patients from the North Carolina-Louisiana Prostate Cancer Project (PCaP) classified as having either ‘high’ or ‘low’ aggressive disease based on clinical stage, Gleason grade and prostate specific antigen at diagnosis. Mean plasma 25(OH)D3 concentrations were compared by proportion of African ancestry. Logistic regression was used to calculate multivariable adjusted odds ratios (OR) and 95% confidence intervals (95%CI) for high aggressive CaP by tertile of plasma 25(OH)D3. Results AAs with highest percent African ancestry (>95%) had the lowest mean plasma 25(OH)D3 concentrations. Overall, plasma 25(OH)D3 was associated positively with aggressiveness among AA men, an association that was modified by calcium intake (ORT3vs.T1: 2.23, 95%CI: 1.26–3.95 among men with low calcium intake, and ORT3vs.T1: 0.19, 95%CI: 0.05–0.70 among men with high calcium intake). Among EAs, the point estimates of the ORs were <1.0 for the upper tertiles with CIs that included the null. Conclusions Among AAs, plasma 25(OH)D3 was associated positively with CaP aggressiveness among men with low calcium intake and inversely among men with high calcium intake. The clinical significance of circulating concentrations of 25(OH)D3 and interactions with calcium intake in the AA population warrants further study. PMID:25919866

  1. Response Surface Methodology Modelling of an Aqueous Two-Phase System for Purification of Protease from Penicillium candidum (PCA 1/TT031) under Solid State Fermentation and Its Biochemical Characterization

    Science.gov (United States)

    Alhelli, Amaal M.; Abdul Manap, Mohd Yazid; Mohammed, Abdulkarim Sabo; Mirhosseini, Hamed; Suliman, Eilaf; Shad, Zahra; Mohammed, Nameer Khairulla; Meor Hussin, Anis Shobirin

    2016-01-01

    Penicillium candidum (PCA 1/TT031) synthesizes different types of extracellular proteases. The objective of this study is to optimize polyethylene glycol (PEG)/citrate based on an aqueous two-phase system (ATPS) and Response Surface Methodology (RSM) to purify protease from Penicillium candidum (PCA 1/TT031). The effects of different PEG molecular weights (1500–10,000 g/mol), PEG concentration (9%–20%), concentrations of NaCl (0%–10%) and the citrate buffer (8%–16%) on protease were also studied. The best protease purification could be achieved under the conditions of 9.0% (w/w) PEG 8000, 5.2% NaCl, and 15.9% sodium citrate concentration, which resulted in a one-sided protease partitioning for the bottom phase with a partition coefficient of 0.2, a 6.8-fold protease purification factor, and a yield of 93%. The response surface models displayed a significant (p ≤ 0.05) response which was fit for the variables that were studied as well as a high coefficient of determination (R2). Similarly, the predicted and observed values displayed no significant (p > 0.05) differences. In addition, our enzyme characterization study revealed that Penicillium candidum (PCA 1/TT031) produced a slight neutral protease with a molecular weight between 100 and 140 kDa. The optimal activity of the purified enzyme occurred at a pH of 6.0 and at a temperature of 50 °C. The stability between different pH and temperature ranges along with the effect of chemical metal ions and inhibitors were also studied. Our results reveal that the purified enzyme could be used in the dairy industry such as in accelerated cheese ripening. PMID:27845736

  2. Response Surface Methodology Modelling of an Aqueous Two-Phase System for Purification of Protease from Penicillium candidum (PCA 1/TT031 under Solid State Fermentation and Its Biochemical Characterization

    Directory of Open Access Journals (Sweden)

    Amaal M. Alhelli

    2016-11-01

    Full Text Available Penicillium candidum (PCA 1/TT031 synthesizes different types of extracellular proteases. The objective of this study is to optimize polyethylene glycol (PEG/citrate based on an aqueous two-phase system (ATPS and Response Surface Methodology (RSM to purify protease from Penicillium candidum (PCA 1/TT031. The effects of different PEG molecular weights (1500–10,000 g/mol, PEG concentration (9%–20%, concentrations of NaCl (0%–10% and the citrate buffer (8%–16% on protease were also studied. The best protease purification could be achieved under the conditions of 9.0% (w/w PEG 8000, 5.2% NaCl, and 15.9% sodium citrate concentration, which resulted in a one-sided protease partitioning for the bottom phase with a partition coefficient of 0.2, a 6.8-fold protease purification factor, and a yield of 93%. The response surface models displayed a significant (p ≤ 0.05 response which was fit for the variables that were studied as well as a high coefficient of determination (R2. Similarly, the predicted and observed values displayed no significant (p > 0.05 differences. In addition, our enzyme characterization study revealed that Penicillium candidum (PCA 1/TT031 produced a slight neutral protease with a molecular weight between 100 and 140 kDa. The optimal activity of the purified enzyme occurred at a pH of 6.0 and at a temperature of 50 °C. The stability between different pH and temperature ranges along with the effect of chemical metal ions and inhibitors were also studied. Our results reveal that the purified enzyme could be used in the dairy industry such as in accelerated cheese ripening.

  3. APLICAÇÃO DAS NORMAS DO PLANO DE CONTROLE AMBIENTAL (PCA EM PISCICULTURAS DA REGIÃO METROPOLITANA DE GOIÂNIA E SUAS IMPLICAÇÕES AMBIENTAIS

    Directory of Open Access Journals (Sweden)

    Afonso Pereira Fialho

    2006-10-01

    Full Text Available Apresenta-se, neste trabalho, uma análise com base em dados colhidos em trinta propriedades rurais da Região Metropolitana de Goiânia e entorno, das condições de pisciculturas já instaladas, cujos projetos encontram-se na Agência Ambiental do Estado de Goiás, órgão que regulamenta tal atividade. Para tanto, vale-se de estudo de casos,em que se procurou verificar se há ou não observância das normas emanadas do Plano de Controle Ambiental (PCA. Nos cinco itens referentes a impactos ambientais – respeito à distância da margem, preservação de nascentes, canal de derivação, tratamento dos efluentes e proteção da saída de escoamento de água dos viveiros –, verificou-se que:apenas uma propriedade respeitou as normas; três projetos respeitaram entre 20% e 40% dos itens; seis projetos acataram 60% dos itens; quatorze cumpriram 80% dos itens e três não cumpriram as normas do PCA. Concluiu-se assim que, a despeito da rica literatura existente e das normas claras e eficientes, o produtor rural envolvido nesse ramo de atividade não está comprometido com o respeito ao meio ambiente, por falta quer de uma fiscalização mais rigorosa, quer de uma educação continuada. PALAVRAS-CHAVE: Impactos ambientais, normas (PCA, piscicultura.

  4. DOT 3.5-E (DOT 3.5-E/JEF-1) analysis of the PCA-Replica (H2O/FE) shielding benchmark for the LWR-PV damage prediction

    International Nuclear Information System (INIS)

    Pescarini, M.

    1991-01-01

    The results of a DOT 3.5-E/JEF-1 validation on the (H2O/Fr) PCA-REPLICA (UKAEA-Winfith) low-flux shielding benchmark are presented. The PCA-REPLICA experiments reproduces the excore radial geometry of a PWR and is closely related to LWR safety since it is dedicated to test the accuracy of the calculated neutron exposure parameters (fast fluence and iron displacement rates) in a pressure vessel simulator. The NJOY/THEMIS data processing system is employed to obtain the neutron damage-energy cross sections for the JEF-1 iron file. The SN 1-D ANISN code is used to collapse cross sections from the VITAMIN-J (175 n) shielding library, based on the JEF-1 data, to a 28 group working library for 2-D calculations. A 3-D-equivalent synthesis (X,Y,Z) of 2-D and 1-D DOT 3.5-E SN calculations in a plane geometry, gives the integral and spectral results for comparison with the respective experimental data. The underprediction of the in-vessel dosimeter experimental activities depends probably on an overestimation of the iron inelastic scattering cross section of the JEF-1 file

  5. Validation of the BUGJEFF311.BOLIB, BUGENDF70.BOLIB and BUGLE-B7 broad-group libraries on the PCA-Replica (H2O/Fe neutron shielding benchmark experiment

    Directory of Open Access Journals (Sweden)

    Pescarini Massimo

    2016-01-01

    Full Text Available The PCA-Replica 12/13 (H2O/Fe neutron shielding benchmark experiment was analysed using the TORT-3.2 3D SN code. PCA-Replica reproduces a PWR ex-core radial geometry with alternate layers of water and steel including a pressure vessel simulator. Three broad-group coupled neutron/photon working cross section libraries in FIDO-ANISN format with the same energy group structure (47 n + 20 γ and based on different nuclear data were alternatively used: the ENEA BUGJEFF311.BOLIB (JEFF-3.1.1 and UGENDF70.BOLIB (ENDF/B-VII.0 libraries and the ORNL BUGLE-B7 (ENDF/B-VII.0 library. Dosimeter cross sections derived from the IAEA IRDF-2002 dosimetry file were employed. The calculated reaction rates for the Rh-103(n,n′Rh-103m, In-115(n,n′In-115m and S-32(n,pP-32 threshold activation dosimeters and the calculated neutron spectra are compared with the corresponding experimental results.

  6. Validation of the BUGJEFF311.BOLIB, BUGENDF70.BOLIB and BUGLE-B7 broad-group libraries on the PCA-Replica (H2O/Fe) neutron shielding benchmark experiment

    Science.gov (United States)

    Pescarini, Massimo; Orsi, Roberto; Frisoni, Manuela

    2016-03-01

    The PCA-Replica 12/13 (H2O/Fe) neutron shielding benchmark experiment was analysed using the TORT-3.2 3D SN code. PCA-Replica reproduces a PWR ex-core radial geometry with alternate layers of water and steel including a pressure vessel simulator. Three broad-group coupled neutron/photon working cross section libraries in FIDO-ANISN format with the same energy group structure (47 n + 20 γ) and based on different nuclear data were alternatively used: the ENEA BUGJEFF311.BOLIB (JEFF-3.1.1) and UGENDF70.BOLIB (ENDF/B-VII.0) libraries and the ORNL BUGLE-B7 (ENDF/B-VII.0) library. Dosimeter cross sections derived from the IAEA IRDF-2002 dosimetry file were employed. The calculated reaction rates for the Rh-103(n,n')Rh-103m, In-115(n,n')In-115m and S-32(n,p)P-32 threshold activation dosimeters and the calculated neutron spectra are compared with the corresponding experimental results.

  7. SU-G-JeP3-04: Estimating 4D CBCT from Prior Information and Extremely Limited Angle Projections Using Structural PCA and Weighted Free-Form Deformation

    International Nuclear Information System (INIS)

    Harris, W; Yin, F; Zhang, Y; Ren, L

    2016-01-01

    Purpose: To investigate the feasibility of using structure-based principal component analysis (PCA) motion-modeling and weighted free-form deformation to estimate on-board 4D-CBCT using prior information and extremely limited angle projections for potential 4D target verification of lung radiotherapy. Methods: A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In the previous method, each phase of the 4D-CBCT was generated by deforming a prior CT volume. The DFM was solved by a motion-model extracted by global PCA and a free-form deformation (GMM-FD) technique, using data fidelity constraint and the deformation energy minimization. In this study, a new structural-PCA method was developed to build a structural motion-model (SMM) by accounting for potential relative motion pattern changes between different anatomical structures from simulation to treatment. The motion model extracted from planning 4DCT was divided into two structures: tumor and body excluding tumor, and the parameters of both structures were optimized together. Weighted free-form deformation (WFD) was employed afterwards to introduce flexibility in adjusting the weightings of different structures in the data fidelity constraint based on clinical interests. XCAT (computerized patient model) simulation with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D-CT to onboard volume. The estimation accuracy was evaluated by the Volume-Percent-Difference (VPD)/Center-of-Mass-Shift (COMS) between lesions in the estimated and “ground-truth” on board 4D-CBCT. Results: Among 6 different XCAT scenarios corresponding to respirational and anatomical changes from planning CT to on-board using single 30° on-board projections, the VPD/COMS for SMM-WFD was reduced to 10.64±3.04%/1.20±0.45mm from 21.72±9.24%/1.80±0.53mm for GMM-FD. Using 15° orthogonal projections, the VPD/COMS was

  8. Non-isothermal decomposition kinetics, heat capacity and thermal safety of 37.2/44/16/2.2/0.2/0.4-GAP/CL-20/Al/N-100/PCA/auxiliaries mixture

    International Nuclear Information System (INIS)

    Zhang, Jiao-Qiang; Gao, Hong-Xu; Ji, Tie-Zheng; Xu, Kang-Zhen; Hu, Rong-Zu

    2011-01-01

    Highlights: → Non-isothermal decomposition kinetics, heat capacity and thermal safety on 37.2/44/16/2.2/0.2/0.4-GAP/CL-20/Al/N-100/PCA/auxiliaries mixture. → Apparent activation energy and pre-exponential constant obtained. → Thermal explosion temperature, adiabatic time-to-explosion, 50% drop height of impact sensitivity, and critical temperature of hot-spot initiation calculated. - Abstract: The specific heat capacity (C p ) of 37.2/44/16/2.2/0.2/0.4-GAP/CL-20/Al/N-100/PCA/auxiliaries mixture was determined with the continuous C p mode of microcalorimeter. The equation of C p with temperature was obtained. The standard molar heat capacity of GAP/CL-20/Al/N-100/PCA/auxiliaries mixture was 1.225 J mol -1 K -1 at 298.15 K. With the help of the peak temperature (T p ) from the non-isothermal DTG curves of the mixture at different heating rates (β), the apparent activation energy (E k and E o ) and pre-exponential constant (A K ) of thermal decomposition reaction obtained by Kissinger's method and Ozawa's method. Using density (ρ) and thermal conductivity (λ), the decomposition heat (Q d , taking half-explosion heat), Zhang-Hu-Xie-Li's formula, the values (T e0 and T p0 ) of T e and T p corresponding to β → 0, thermal explosion temperature (T be and T bp ), adiabatic time-to-explosion (t TIad ), 50% drop height (H 50 ) of impact sensitivity, and critical temperature of hot-spot initiation (T cr,hotspot ) of thermal explosion of the mixture were calculated. The following results of evaluating the thermal safety of the mixture were obtained: T be = 441.64 K, T bp = 461.66 K, t Tlad = 78.0 s (n = 2), t Tlad = 74.87s (n = 1), t Tlad = 71.85 s (n = 0), H 50 = 21.33 cm.

  9. SU-G-JeP3-04: Estimating 4D CBCT from Prior Information and Extremely Limited Angle Projections Using Structural PCA and Weighted Free-Form Deformation

    Energy Technology Data Exchange (ETDEWEB)

    Harris, W; Yin, F; Zhang, Y; Ren, L [Duke University Medical Center, Durham, NC (United States)

    2016-06-15

    Purpose: To investigate the feasibility of using structure-based principal component analysis (PCA) motion-modeling and weighted free-form deformation to estimate on-board 4D-CBCT using prior information and extremely limited angle projections for potential 4D target verification of lung radiotherapy. Methods: A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In the previous method, each phase of the 4D-CBCT was generated by deforming a prior CT volume. The DFM was solved by a motion-model extracted by global PCA and a free-form deformation (GMM-FD) technique, using data fidelity constraint and the deformation energy minimization. In this study, a new structural-PCA method was developed to build a structural motion-model (SMM) by accounting for potential relative motion pattern changes between different anatomical structures from simulation to treatment. The motion model extracted from planning 4DCT was divided into two structures: tumor and body excluding tumor, and the parameters of both structures were optimized together. Weighted free-form deformation (WFD) was employed afterwards to introduce flexibility in adjusting the weightings of different structures in the data fidelity constraint based on clinical interests. XCAT (computerized patient model) simulation with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D-CT to onboard volume. The estimation accuracy was evaluated by the Volume-Percent-Difference (VPD)/Center-of-Mass-Shift (COMS) between lesions in the estimated and “ground-truth” on board 4D-CBCT. Results: Among 6 different XCAT scenarios corresponding to respirational and anatomical changes from planning CT to on-board using single 30° on-board projections, the VPD/COMS for SMM-WFD was reduced to 10.64±3.04%/1.20±0.45mm from 21.72±9.24%/1.80±0.53mm for GMM-FD. Using 15° orthogonal projections, the VPD/COMS was

  10. Principal component analysis (PCA of volatile terpene compounds dataset emitted by genetically modified sweet orange fruits and juices in which a D-limonene synthase was either up- or down-regulated vs. empty vector controls

    Directory of Open Access Journals (Sweden)

    Ana Rodríguez

    2016-12-01

    Full Text Available We have categorized the dataset from content and emission of terpene volatiles of peel and juice in both Navelina and Pineapple sweet orange cultivars in which D-limonene was either up- (S, down-regulated (AS or non-altered (EV; control (“Impact of D-limonene synthase up- or down-regulation on sweet orange fruit and juice odor perception”(A. Rodríguez, J.E. Peris, A. Redondo, T. Shimada, E. Costell, I. Carbonell, C. Rojas, L. Peña, (2016 [1]. Data from volatile identification and quantification by HS-SPME and GC–MS were classified by Principal Component Analysis (PCA individually or as chemical groups. AS juice was characterized by the higher influence of the oxygen fraction, and S juice by the major influence of ethyl esters. S juices emitted less linalool compared to AS and EV juices.

  11. The lipid-reactive oxygen species phenotype of breast cancer. Raman spectroscopy and mapping, PCA and PLSDA for invasive ductal carcinoma and invasive lobular carcinoma. Molecular tumorigenic mechanisms beyond Warburg effect.

    Science.gov (United States)

    Surmacki, Jakub; Brozek-Pluska, Beata; Kordek, Radzislaw; Abramczyk, Halina

    2015-04-07

    Vibrational signatures of human breast tissue (invasive ductal carcinoma and invasive lobular carcinoma) were used to identify, characterize and discriminate structures in normal (noncancerous) and cancerous tissues by confocal Raman imaging, Raman spectroscopy and IR spectroscopy. The most important differences between normal and cancerous tissues were found in regions characteristic for vibrations of carotenoids, fatty acids, proteins, and interfacial water. Particular attention was paid to the role played by unsaturated fatty acids and their derivatives. K-means clustering and basis analysis followed by PCA and PLSDA is employed to analyze Raman spectroscopic maps of human breast tissue and for a statistical analysis of the samples (82 patients, 164 samples). Raman maps successfully identify regions of carotenoids, fatty acids, and proteins. The intensities, frequencies and profiles of the average Raman spectra differentiate the biochemical composition of normal and cancerous tissues. The paper demonstrates that Raman imaging has reached a clinically relevant level in regard to breast cancer diagnosis applications. The sensitivity and specificity obtained directly from PLSLD and cross validation are equal to 90.5% and 84.8% for calibration and 84.7% and 71.9% for cross-validation respectively.

  12. Privacy Preserving PCA on Distributed Bioinformatics Datasets

    Science.gov (United States)

    Li, Xin

    2011-01-01

    In recent years, new bioinformatics technologies, such as gene expression microarray, genome-wide association study, proteomics, and metabolomics, have been widely used to simultaneously identify a huge number of human genomic/genetic biomarkers, generate a tremendously large amount of data, and dramatically increase the knowledge on human…

  13. Reactor calculation benchmark PCA blind test results

    International Nuclear Information System (INIS)

    Kam, F.B.K.; Stallmann, F.W.

    1980-01-01

    Further improvement in calculational procedures or a combination of calculations and measurements is necessary to attain 10 to 15% (1 sigma) accuracy for neutron exposure parameters (flux greater than 0.1 MeV, flux greater than 1.0 MeV, and dpa). The calculational modeling of power reactors should be benchmarked in an actual LWR plant to provide final uncertainty estimates for end-of-life predictions and limitations for plant operations. 26 references, 14 figures, 6 tables

  14. Reactor calculation benchmark PCA blind test results

    Energy Technology Data Exchange (ETDEWEB)

    Kam, F.B.K.; Stallmann, F.W.

    1980-01-01

    Further improvement in calculational procedures or a combination of calculations and measurements is necessary to attain 10 to 15% (1 sigma) accuracy for neutron exposure parameters (flux greater than 0.1 MeV, flux greater than 1.0 MeV, and dpa). The calculational modeling of power reactors should be benchmarked in an actual LWR plant to provide final uncertainty estimates for end-of-life predictions and limitations for plant operations. 26 references, 14 figures, 6 tables.

  15. Streaming PCA with many missing entries.

    Science.gov (United States)

    2015-12-01

    This paper considers the problem of matrix completion when some number of the columns are : completely and arbitrarily corrupted, potentially by a malicious adversary. It is well-known that standard : algorithms for matrix completion can return arbit...

  16. Imaging Prostate Cancer (PCa) Phenotype and Evolution

    Science.gov (United States)

    2016-10-01

    were treated with DFP or saline chronically until tumors became too large and mice required sacrifice . Since tumors were implanted orthotopically... sacrifice so the study was terminated. Almost all the TRAMP C2 tumors treated with DFP failed to show any growth. While DFP was effective in both...Int J Nanomedicine. 2012;7:2729-37 12. Cavey T, Ropert M, de Tayrac M, Bardou-Jacquet E, Island ML, Leroyer P, Bendavid C, Brissot P, Loreal O

  17. How does health literacy affect quality of life among men with newly diagnosed clinically localized prostate cancer? Findings from the North Carolina-Louisiana Prostate Cancer Project (PCaP).

    Science.gov (United States)

    Song, Lixin; Mishel, Merle; Bensen, Jeannette T; Chen, Ronald C; Knafl, George J; Blackard, Bonny; Farnan, Laura; Fontham, Elizabeth; Su, L Joseph; Brennan, Christine S; Mohler, James L; Godley, Paul A

    2012-08-01

    Health literacy deficits affect half of the US overall patient population, especially the elderly, and are linked to poor health outcomes among noncancer patients. Yet little is known about how health literacy affects cancer populations. The authors examined the relation between health-related quality of life (HRQOL) and health literacy among men with prostate cancer. Data analysis included 1581 men with newly diagnosed clinically localized prostate cancer from a population-based study, the North Carolina-Louisiana Prostate Cancer Project (PCaP). Participants completed assessment of health literacy using Rapid Estimate of Adult Literacy in Medicine (REALM) and HRQOL using the Short Form-12 General Health Survey (SF12). Bivariate and multivariate regression was used to determine the potential association between REALM and HRQOL, while controlling for sociodemographic and illness-related variables. Higher health literacy level was significantly associated with better mental well-being (SF12-Mental Component Summary [MCS]; P < .001) and physical well-being (SF12-Physical Component Summary [PCS]; P < .001) in bivariate analyses. After controlling for sociodemographic (age, marital status, race, income, and education) and illness-related factors (types of cancer treatment, tumor aggressiveness, and comorbidities), health literacy remained significantly associated with SF12-MCS scores (P < .05) but not with SF12-PCS scores. Among patients with newly diagnosed localized prostate cancer, those with low health literacy levels were more vulnerable to mental distress than those with higher health literacy levels, but physical well-being was no different. These findings suggest that health literacy may be important in patients managing prostate cancer and the effects of treatment, and provide the hypothesis that supportive interventions targeting patients with lower health literacy may improve their HRQOL. Copyright © 2011 American Cancer Society.

  18. Clasificación de extractos etanólicos de especies de la familia Lauraceae por cromatografía en capa fina bidimensional y análisis estadísticos multivariado CCD-2D/ PCA-cluster

    OpenAIRE

    William A. Delgado; Luis E. Cuca S.

    2016-01-01

    Se caracterizaron los extractos etanólicos de hojas y cortezas de 13 especies de la familia Lauraceae mediante cromatografía en capa fina de dos dimensiones (2D-CCD).  Los datos posteriores se analizaron mediante técnicas de análisis estadístico multivariado (cluster y análisis de componentes principales (PCA)). Lo anterior permitió hacer una distinción entre los extractos obtenidos de diferentes partes de la planta (hojas y cortezas). Se observó, además, que la metodología usada es capaz de ...

  19. Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

    Science.gov (United States)

    Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

    2016-08-01

    The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data

  20. Modelo para aplicação de análise multivariada através de técnicas estatísticas PCA e HCA sobre questionário de avaliação da satisfação de clientes: estudo de caso em uma empresa metalúrgica de embalagens metálicas

    Directory of Open Access Journals (Sweden)

    Marcos Florês Ferrão

    2012-01-01

    Full Text Available O propósito da pesquisa consiste em aprimorar a prática de análise da satisfação dos clientes. O artigo apresenta um modelopara análise das respostas do questionário de avaliação da satisfação dos clientes de forma sistemática com auxílio de técnicasestatísticas multivariadas, especificamente, análise exploratória com PCA - Análise de Componentes Principais (Partial ComponentsAnalysis com HCA – Análise de Agrupamento Hierárquico (Hierarchical Cluster Analysis. Buscou-se avaliar a aplicabilidade domodelo ao uso, por parte da Empresa estudada, como ferramentas para auxílio na identificação da cadeia de valor percebida pelocliente quando da aplicação do questionário de satisfação do cliente. Verificou-se que, com o auxílio das ferramentas de análiseestatística multivariada, foi possível identificar comportamentos similares entre os clientes. Permitiu, também, a motivação da empresana realização de revisões sobre as perguntas dos questionários, possibilitando realizar análise do grau de correlação entre as perguntas,o que antes não ocorria.Abstract The purpose of this research is to improvethe practice on customer satisfaction analysis The article presentsan analysis model to analyze the answers of a customersatisfaction evaluation in a systematic way with the aid ofmultivariate statistical techniques, specifically, exploratoryanalysis with PCA – Partial Components Analysis with HCA -Hierarchical Cluster Analysis. It was tried to evaluate theapplicability of the model to be used by the issue company as atool to assist itself on identifying the value chain perceived by the customer when applied the questionnaire of customersatisfaction. It was found with the assistance of multivariatestatistical analysis that it was observed similar behavior amongcustomers. It also allowed the company to conduct reviews onquestions of the questionnaires, using analysis of the degree ofcorrelation between the questions

  1. 基于“AHP-IE-PCA”组合赋权法的战略性新兴产业选择模型研究%Research on Selection Model of Strategic Emerging Industries Based on Combination Weights: AHP-IE-PCA

    Institute of Scientific and Technical Information of China (English)

    胡振华; 黎春秋; 熊勇清

    2011-01-01

    The weaknesses of AHP,IE,PCA and other selection theories and methods were analyzed during the application on strategic emerging industries.According to the specificity of selection and evaluation in strategic emerging industries,the corresponding indicator system was built.And then based on the combination weights——″AHP-IE-PCA″,the selection model of regional strategic emerging industries was tentative proposed,which was applied to N county in the selection and evaluation of strategic emerging industries.%分析了层次分析法(AHP)、信息熵法(IE)和主成分分析法(PCA)等现有产业选择与评价方法应用到区域战略性新兴产业中的不足,根据区域战略性新兴产业的选择与评价的特殊性,构建了相应的指标体系,尝试性地提出了基于"AHP-IE-PCA"组合赋权的区域战略性新兴产业选择模型,并将这一方法应用于N县战略性新兴产业的选择与评价中。

  2. PCA-based groupwise image registration for quantitative MRI

    NARCIS (Netherlands)

    Huizinga, W.; Poot, D. H. J.; Guyader, J.-M.; Klaassen, R.; Coolen, B. F.; van Kranenburg, M.; van Geuns, R. J. M.; Uitterdijk, A.; Polfliet, M.; Vandemeulebroucke, J.; Leemans, A.; Niessen, W. J.; Klein, S.

    2016-01-01

    Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T5 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different

  3. Evaluation of oil biodegradation using time warping and PCA

    International Nuclear Information System (INIS)

    Christensen, J.H.; Hansen, A.B.; Andersen, O.

    2005-01-01

    The effects of biodegradation on the composition of stranded oil after the Baltic Carrier oil spill in March 2001 was evaluated using a newly developed multivariate statistical methodology. Gas chromatography and mass spectrometry provided data on the oil compounds and oil biodegradation was determined by applying weighted least square principal component analysis to the preprocessed chromatograms of methylphenanthrenes and methyldibenzothiophenes. One principal component explained 46 per cent of the variation in the complete data set. Samples collected immediately after the spill and 2.5 months after the spill did not exhibit changes in isomer composition. However, the isomer patterns changed in samples collected between 6.5 and 16.5 months after the spill. Samples collected after 8.5 months were the most greatly affected. An evaluation of the degradation patterns suggest that time warping and multivariate statistical methods can successfully identify links between spill samples and can determine how chemical composition will respond to biodegradation processes. 27 refs., 1 tab., 3 figs

  4. Evaluation of oil biodegradation using time warping and PCA

    Energy Technology Data Exchange (ETDEWEB)

    Christensen, J.H. [Royal Veterinary and Agricultural Univ., Thorvaldsensvej (Denmark). Dept. of Natural Sciences; Hansen, A.B. [National Environmental Research Inst., Roskilde (Denmark). Dept. of Environmental Chemistry and Microbiology; Andersen, O. [Roskilde Univ., Roskilde (Denmark). Dept. of Life Sciences and Chemistry

    2005-07-01

    The effects of biodegradation on the composition of stranded oil after the Baltic Carrier oil spill in March 2001 was evaluated using a newly developed multivariate statistical methodology. Gas chromatography and mass spectrometry provided data on the oil compounds and oil biodegradation was determined by applying weighted least square principal component analysis to the preprocessed chromatograms of methylphenanthrenes and methyldibenzothiophenes. One principal component explained 46 per cent of the variation in the complete data set. Samples collected immediately after the spill and 2.5 months after the spill did not exhibit changes in isomer composition. However, the isomer patterns changed in samples collected between 6.5 and 16.5 months after the spill. Samples collected after 8.5 months were the most greatly affected. An evaluation of the degradation patterns suggest that time warping and multivariate statistical methods can successfully identify links between spill samples and can determine how chemical composition will respond to biodegradation processes. 27 refs., 1 tab., 3 figs.

  5. PCA Fault Feature Extraction in Complex Electric Power Systems

    Directory of Open Access Journals (Sweden)

    ZHANG, J.

    2010-08-01

    Full Text Available Electric power system is one of the most complex artificial systems in the world. The complexity is determined by its characteristics about constitution, configuration, operation, organization, etc. The fault in electric power system cannot be completely avoided. When electric power system operates from normal state to failure or abnormal, its electric quantities (current, voltage and angles, etc. may change significantly. Our researches indicate that the variable with the biggest coefficient in principal component usually corresponds to the fault. Therefore, utilizing real-time measurements of phasor measurement unit, based on principal components analysis technology, we have extracted successfully the distinct features of fault component. Of course, because of the complexity of different types of faults in electric power system, there still exists enormous problems need a close and intensive study.

  6. Yielding physically-interpretable emulators - A Sparse PCA approach

    Science.gov (United States)

    Galelli, S.; Alsahaf, A.; Giuliani, M.; Castelletti, A.

    2015-12-01

    Projection-based techniques, such as Principal Orthogonal Decomposition (POD), are a common approach to surrogate high-fidelity process-based models by lower order dynamic emulators. With POD, the dimensionality reduction is achieved by using observations, or 'snapshots' - generated with the high-fidelity model -, to project the entire set of input and state variables of this model onto a smaller set of basis functions that account for most of the variability in the data. While reduction efficiency and variance control of POD techniques are usually very high, the resulting emulators are structurally highly complex and can hardly be given a physically meaningful interpretation as each basis is a projection of the entire set of inputs and states. In this work, we propose a novel approach based on Sparse Principal Component Analysis (SPCA) that combines the several assets of POD methods with the potential for ex-post interpretation of the emulator structure. SPCA reduces the number of non-zero coefficients in the basis functions by identifying a sparse matrix of coefficients. While the resulting set of basis functions may retain less variance of the snapshots, the presence of a few non-zero coefficients assists in the interpretation of the underlying physical processes. The SPCA approach is tested on the reduction of a 1D hydro-ecological model (DYRESM-CAEDYM) used to describe the main ecological and hydrodynamic processes in Tono Dam, Japan. An experimental comparison against a standard POD approach shows that SPCA achieves the same accuracy in emulating a given output variable - for the same level of dimensionality reduction - while yielding better insights of the main process dynamics.

  7. Efficient Stochastic Inversion Using Adjoint Models and Kernel-PCA

    Energy Technology Data Exchange (ETDEWEB)

    Thimmisetty, Charanraj A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; Zhao, Wenju [Florida State Univ., Tallahassee, FL (United States). Dept. of Scientific Computing; Chen, Xiao [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; Tong, Charles H. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing; White, Joshua A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Atmospheric, Earth and Energy Division

    2017-10-18

    Performing stochastic inversion on a computationally expensive forward simulation model with a high-dimensional uncertain parameter space (e.g. a spatial random field) is computationally prohibitive even when gradient information can be computed efficiently. Moreover, the ‘nonlinear’ mapping from parameters to observables generally gives rise to non-Gaussian posteriors even with Gaussian priors, thus hampering the use of efficient inversion algorithms designed for models with Gaussian assumptions. In this paper, we propose a novel Bayesian stochastic inversion methodology, which is characterized by a tight coupling between the gradient-based Langevin Markov Chain Monte Carlo (LMCMC) method and a kernel principal component analysis (KPCA). This approach addresses the ‘curse-of-dimensionality’ via KPCA to identify a low-dimensional feature space within the high-dimensional and nonlinearly correlated parameter space. In addition, non-Gaussian posterior distributions are estimated via an efficient LMCMC method on the projected low-dimensional feature space. We will demonstrate this computational framework by integrating and adapting our recent data-driven statistics-on-manifolds constructions and reduction-through-projection techniques to a linear elasticity model.

  8. PCA/HEXTE Observations of M82 and NGC253

    Science.gov (United States)

    Rephaeli, Yoel

    1999-01-01

    The nearby starburst galaxies M82 & NGC 253 were observed by the RXTE satellite during the period February - November 1997. Data reduction and analysis began in summer 1998, and continued through March 1999. The spectral analysis indicates that the main emission in these galaxies is from hot gas. We find a considerable degree of variability in the emission from M82, possibly due to M81 (a known AGN) in the FOV. The main spectral parameters were determined for both sources. The results of the analysis were reported in the March 1999 meeting of the AAS. Summary of the data analysis, theoretical modeling, and interpretation will be presented in a paper which is currently being prepared for publication in the Astrophysical Journal.

  9. PCA Infarction With Its Cognitive Findings: More Than Hemianopia

    Directory of Open Access Journals (Sweden)

    Zehra Bozdoğan

    2013-12-01

    Full Text Available Posterior cerebral artery infarctions constitute 5-10 % of all ischemic strokes.In this case report, a 62 year-old man presenting with right hemiparesia and dysarthria is going to be discussed. The patient had acute onset- demential symptoms and difficulty in reading, and acute infarction in the territory of left posterior cerebral artery was detected in cranial magnetic resonance imaging. The patient who had acute ischemic lesions in left parahippocampus, splenium of corpus callosum, left fusiform gyrus and left thalamus was evaluated with detailed neuropsychological tests. Cognitive deficits severe enough to affect daily living were detected. Pure alexia, anterograde amnesia and anomia, being often neglected manifestations of unilateral posterior cerebral artery infarction, are going to be reviewed with corresponding radiological findings

  10. A Streaming PCA VLSI Chip for Neural Data Compression.

    Science.gov (United States)

    Wu, Tong; Zhao, Wenfeng; Guo, Hongsun; Lim, Hubert H; Yang, Zhi

    2017-12-01

    Neural recording system miniaturization and integration with low-power wireless technologies require compressing neural data before transmission. Feature extraction is a procedure to represent data in a low-dimensional space; its integration into a recording chip can be an efficient approach to compress neural data. In this paper, we propose a streaming principal component analysis algorithm and its microchip implementation to compress multichannel local field potential (LFP) and spike data. The circuits have been designed in a 65-nm CMOS technology and occupy a silicon area of 0.06 mm. Throughout the experiments, the chip compresses LFPs by 10 at the expense of as low as 1% reconstruction errors and 144-nW/channel power consumption; for spikes, the achieved compression ratio is 25 with 8% reconstruction errors and 3.05-W/channel power consumption. In addition, the algorithm and its hardware architecture can swiftly adapt to nonstationary spiking activities, which enables efficient hardware sharing among multiple channels to support a high-channel count recorder.

  11. Preliminary Results Of PCA On MRO CRISM Multispectral Images

    Science.gov (United States)

    Klassen, David R.; Smith, M. D.

    2008-09-01

    Mars Reconnaissance Orbiter arrived at Mars in March 2006 and by September had achieved its science-phase orbit with the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) beginning its visible to near-infrared (VIS/NIR) spectral imaging shortly thereafter. One of the goals of CRISM is to fill in the spatial gaps between the various targeted observations, eventually mapping the entire surface. Due to the large volume of data this would create, the instrument works in a reduced spectral sampling mode creating "multispectral” images. From this data we can create image cubes using 70 wavelengths from 0.410 to 3.504 µm. We present here a preliminary analysis of these multispectral mode data products using the technique of Principal Components Analysis. Previous work with ground-based images has shown that over an entire visible hemisphere, there are only three to four meaningful components out of 32-105 wavelengths over 1.5-4.1 µm. The first two of these components are fairly consistent over all time intervals from day-to-day and season-to-season. [1-4] The preliminary work on the CRISM images cubes implies similar results_three to four significant principal components that are fairly consistent over time. We will show these components and a rough linear mixture modeling based on in-data spectral endmembers derived from the extrema of the principal components [5]. References: [1] Klassen, D. R. and Bell III, J. F. (2001) BAAS 33, 1069. [2] Klassen, D. R. and Bell III, J. F. (2003) BAAS, 35, 936. [3] Klassen, D. R., Wark, T. J., Cugliotta, C. G. (2005) BAAS, 37, 693. [4] Klassen, D. R. and Bell III, J. F. (2007) in preparation. [5] Klassen, D. R. and Bell III, J. F. (2000) BAAS, 32, 1105.

  12. Polymorphous Computing Architecture (PCA) Kernel-Level Benchmarks

    National Research Council Canada - National Science Library

    Lebak, J

    2004-01-01

    .... "Computation" aspects include floating-point and integer performance, as well as the memory hierarchy, while the "communication" aspects include the network, the memory hierarchy, and the 110 capabilities...

  13. Two-dimensional PCA-based human gait identification

    Science.gov (United States)

    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.

  14. Identification of spatially-localized initial conditions via sparse PCA

    Science.gov (United States)

    Dwivedi, Anubhav; Jovanovic, Mihailo

    2017-11-01

    Principal Component Analysis involves maximization of a quadratic form subject to a quadratic constraint on the initial flow perturbations and it is routinely used to identify the most energetic flow structures. For general flow configurations, principal components can be efficiently computed via power iteration of the forward and adjoint governing equations. However, the resulting flow structures typically have a large spatial support leading to a question of physical realizability. To obtain spatially-localized structures, we modify the quadratic constraint on the initial condition to include a convex combination with an additional regularization term which promotes sparsity in the physical domain. We formulate this constrained optimization problem as a nonlinear eigenvalue problem and employ an inverse power-iteration-based method to solve it. The resulting solution is guaranteed to converge to a nonlinear eigenvector which becomes increasingly localized as our emphasis on sparsity increases. We use several fluids examples to demonstrate that our method indeed identifies the most energetic initial perturbations that are spatially compact. This work was supported by Office of Naval Research through Grant Number N00014-15-1-2522.

  15. Retrieving the correlation matrix from a truncated PCA solution : The inverse principal component problem

    NARCIS (Netherlands)

    ten Berge, Jos M.F.; Kiers, Henk A.L.

    When r Principal Components are available for k variables, the correlation matrix is approximated in the least squares sense by the loading matrix times its transpose. The approximation is generally not perfect unless r = k. In the present paper it is shown that, when r is at or above the Ledermann

  16. Variational group-PCA for intrinsic dimensionality determination in fMRI data

    DEFF Research Database (Denmark)

    Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Madsen, Kristoffer Hougaard

    2016-01-01

    . Furthermore, in an fMRI-context it is not fully understood how information from multiple subjects should best be incorporated when applying dimensionality reduction. We propose a Bayesian group principal component analysis (Group-BPCA) model with an automatic relevance determination (ARD) prior to determine...... on the spatial maps are shared, leads to pruning components, but provide better generalization in two of three scenarios. We show that the right level of subject variability is highly dependent on the chosen validation scheme....

  17. An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods

    Directory of Open Access Journals (Sweden)

    Arjon Turnip

    2014-12-01

    Full Text Available In the modern world of automation, biological signals, especially Electroencephalogram (EEG is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained. 

  18. Viewing Spearman's Hypothesis from the perspective of multi-group PCA: A comment on Schonemann's criticism

    NARCIS (Netherlands)

    Dolan, C.V.; Lubke, G.H.

    2001-01-01

    Jensen’s test of Spearman’s hypothesis is meant to demonstrate the importance of general intelligence in Black-White (B-W) differences in psychometric intelligence test scores. Schönemann purports to demonstrate, through an analysis of real and simulated data, and the presentation of a theorem, that

  19. A coordinate descent MM algorithm for fast computation of sparse logistic PCA

    KAUST Repository

    Lee, Seokho

    2013-06-01

    Sparse logistic principal component analysis was proposed in Lee et al. (2010) for exploratory analysis of binary data. Relying on the joint estimation of multiple principal components, the algorithm therein is computationally too demanding to be useful when the data dimension is high. We develop a computationally fast algorithm using a combination of coordinate descent and majorization-minimization (MM) auxiliary optimization. Our new algorithm decouples the joint estimation of multiple components into separate estimations and consists of closed-form elementwise updating formulas for each sparse principal component. The performance of the proposed algorithm is tested using simulation and high-dimensional real-world datasets. © 2013 Elsevier B.V. All rights reserved.

  20. Discrimination of commercial cheeses from fatty acid profiles and phytosterol contents obtained by GC and PCA.

    Science.gov (United States)

    Kim, Nam Sook; Lee, Ji Hyun; Han, Kyoung Moon; Kim, Ji Won; Cho, Sooyeul; Kim, Jinho

    2014-01-15

    In this study, a method for discriminating natural mozzarella cheese from cheese substitutes, using fatty acid profiles, phytosterol contents, and statistical comparison, was developed. A total of 27 cheeses were evaluated: eight natural mozzarella cheeses (NMCs), four imitation mozzarella cheeses (IMCs), 12 processed cheeses (PCs) and three mixed cheeses (MCs) composed of NMCs and IMCs. The fatty acid composition of the NMC class was distinct from those of the IMC and MC classes, but statistically similar (pIMCs, but similar (pIMCs, but discrimination between the NMCs and the PCs could not be achieved. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Pre-Processing Data Using Wavelet Transform and PCA Based on ...

    Indian Academy of Sciences (India)

    5

    wavelet theory in the last century, using this tool in the hydrology has been an ... These models are based on Statistical Learning Theory, which is ...... Hutcheson G and Nick S 1999 The multivariate social scientist: Introductory statistics using.

  2. Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

    Directory of Open Access Journals (Sweden)

    Junbao Zheng

    2012-03-01

    Full Text Available Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor as well as its parallel channels (inner factor. The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

  3. Negative ions in the auroral mesosphere during a PCA event around sunset

    Directory of Open Access Journals (Sweden)

    C. F. del Pozo

    Full Text Available This is a study of the negative ion chemistry in the mesosphere above Tromsø using a number of EISCAT observations of high energy proton precipitation events during the last solar maximum, and in particular around sunset on 23 October, 1989. In these conditions it is possible to look at the relative importance of the various photodetachment and photodissociation processes controlling the concentration of negative ions. The data analysed are from several UHF GEN11 determinations of the ion-plasma ACF together with the pseudo zero-lag estimate of the `raw' electron density, at heights between 55 km and 85 km, at less than 1 km resolution. The power profiles from the UHF are combined with the 55-ion Sodankylä model to obtain consistent estimates of the electron density, the negative ion concentrations, and the average ion mass with height. The neutral concentrations and ion temperature are given by the MSIS90 model. These parameters are then used to compare the calculated widths of the ion-line with the GEN11 determinations. The ion-line spectrum gives information on the effects of negative ions below 70 km where they are dominant; the spectral width is almost a direct measure of the relative abundance of negative ions.

    Key words. Ionosphere (auroral ionosphere; ion chemistry and composition; particle precipitation.

  4. Sparse PCA, a new method for unsupervised analyses of fMRI data

    DEFF Research Database (Denmark)

    Sjöstrand, Karl; Lund, Torben E.; Madsen, Kristoffer Hougaard

    2006-01-01

    favorable circumstances, one of more of these signals describe activation patterns, while others model noise and other nuisance factors. This work introduces a competing method for fMRI analysis known as sparse principal component analysis (SPCA). We argue that SPCA is less committed than ICA and show...... that similar results, with better suppression of noise, are obtained....

  5. β-catenin as a prognostic factor for prostate cancer (PCa)

    Science.gov (United States)

    Nowicki, Andrzej; Duda-Szymańska, Joanna

    2012-01-01

    Introduction The prostate cancer is difficult to predict, and treatment failure is associated with local infiltration, as well as distant metastases. Adhesion and migration abilities to of cancer cells play a major role in formation of metastasis. The participation of β-catenin in pathogene-sis of many types of cancer and benign processes has been an important discovery of recent years. Material and methods The studied material was obtained by transrectal, sextant core biopsy from 102 patients hospitalized in Department of Urology, Regional Hospital in Kalisz (2001-2004). The aim of our study was to determine the predictive value of β-catenin immunoexpression in prostate cancer, to analyze the prognostic aspect of some histopathological features and finally to assess the relationship between β-catenin immunoreactivity and the microscopic image of the tumor. Relationships between the investigated variables were analyzed using the Chi2 test of compatibility. We used the Kaplan-Meier curves to assess survival differences between groups of patients. Finally we established which of the studied factors significantly affect the patient outcome, using the method of Cox proportional hazard regression. Results In prostate cancer in comparison with the normal epithelium, both the location and the strength of β-catenin immunoexpression are impaired. Conclusions Our results indicate that the presence of disorders in β-catenin immunoexpression in prostate cancer cells indicates a high risk of death due to tumor progression and makes it imperative for immediate treatment procedures. PMID:24578946

  6. A PCA-Based Change Detection Framework for Multidimensional Data Streams

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali; Alharbi, Basma Mohammed; Wang, Suojin; Zhang, Xiangliang

    2015-01-01

    the efforts required from users on the threshold parameter setting by using the Page-Hinkley test. The evaluation results on synthetic and real data show that our framework outperforms two baseline methods in terms of both detection accuracy and computational

  7. On Decomposing Object Appearance using PCA and Wavelet bases with Applications to Image Segmentation

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Forchhammer, Søren

    2002-01-01

    the complete object surface using principal component analysis. This typically involves matrices with a few thousands and up to 100.000+ rows. This paper demonstrates applications of such models applied on colour images of human faces and cardiac magnetic resonance images. Further, we devise methods...

  8. Regresion PLS y PCA como Solucion al Problema de Multicolinealidad en Regresion Multiple

    Directory of Open Access Journals (Sweden)

    José Carlos Vega Vilca

    2011-03-01

    Full Text Available We present and compare principal components regression and partial least squares regression, and their solution to the problem of multicollinearity. We illustrate the use of both techniques, and demonstrate the superiority of partial least squares.

  9. A coordinate descent MM algorithm for fast computation of sparse logistic PCA

    KAUST Repository

    Lee, Seokho; Huang, Jianhua Z.

    2013-01-01

    Sparse logistic principal component analysis was proposed in Lee et al. (2010) for exploratory analysis of binary data. Relying on the joint estimation of multiple principal components, the algorithm therein is computationally too demanding

  10. Integration of multimodal MRI data via PCA to explain language performance

    Directory of Open Access Journals (Sweden)

    N.E. Kucukboyaci

    2014-01-01

    Conclusions: Quantitative MRI measures from T1 and diffusion-weighted scans are unlikely to represent perfectly orthogonal vectors of disease in individuals with epilepsy. On the contrary, they exhibit highly intercorrelated PCs in their factor structures, which is consistent with an underlying pathological process that affects both the cortical and the subcortical structures simultaneously. In addition to hippocampal volume, the PCs of diffusion weighted measures (FA and MD increase the sensitivity and specificity for determining naming impairment in patients with TLE. These findings underline the importance of combining multimodal imaging measures to better predict language performance in TLE that could extend to other patients with prominent language impairments.

  11. Investigation of rotated PCA from the perspective of network communities applied to climate data

    Czech Academy of Sciences Publication Activity Database

    Hartman, David; Hlinka, Jaroslav; Vejmelka, Martin; Paluš, Milan

    2013-01-01

    Roč. 15, - (2013), s. 13124 ISSN 1607-7962. [European Geosciences Union General Assembly 2013. 07.04.2013-12.04.2013, Vienna] R&D Projects: GA ČR GCP103/11/J068 Institutional support: RVO:67985807 Keywords : complex networks * graph theory * climate dynamics Subject RIV: BB - Applied Statistics, Operational Research

  12. SVM and PCA Based Learning Feature Classification Approaches for E-Learning System

    Science.gov (United States)

    Khamparia, Aditya; Pandey, Babita

    2018-01-01

    E-learning and online education has made great improvements in the recent past. It has shifted the teaching paradigm from conventional classroom learning to dynamic web based learning. Due to this, a dynamic learning material has been delivered to learners, instead ofstatic content, according to their skills, needs and preferences. In this…

  13. Fault detection in wind turbines using PCA and statistical hypothesis testing

    OpenAIRE

    Serrahima de Cambra, Josep Mª

    2016-01-01

    L’augment de la mida dels aerogeneradors per la generació d’electricitat i la seva construcció en llocs remots per maximitzar la producció suposa un augment en costos de manteniment i operació. Per tal de reduir aquests costos, eliminar manteniments programats i millorar la seguretat, apareix la necessitat de sistemes de control a distància. Structural health monitoring és el procés d’implantació d’una estratègia de detecció de fallades a l’estructura. Aplicat als aerogeneradors, fins i tot e...

  14. Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference

    Directory of Open Access Journals (Sweden)

    Francesc Pozo

    2018-03-01

    Full Text Available This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA. Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, α ∈ [ 1 % , 13 % ] , the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.

  15. Bootstrap-based confidence estimation in PCA and multivariate statistical process control

    DEFF Research Database (Denmark)

    Babamoradi, Hamid

    be used to detect outliers in the data since the outliers can distort the bootstrap estimates. Bootstrap-based confidence limits were suggested as alternative to the asymptotic limits for control charts and contribution plots in MSPC (Paper II). The results showed that in case of the Q-statistic......Traditional/Asymptotic confidence estimation has limited applicability since it needs statistical theories to estimate the confidences, which are not available for all indicators/parameters. Furthermore, in case the theories are available for a specific indicator/parameter, the theories are based....... The goal was to improve process monitoring by improving the quality of MSPC charts and contribution plots. Bootstrapping algorithm to build confidence limits was illustrated in a case study format (Paper I). The main steps in the algorithm were discussed where a set of sensible choices (plus...

  16. Using Principal Component Analysis (PCA) to Speed up Radiative Transfer (RT) Computations

    Science.gov (United States)

    Natraj, Vijay

    2012-01-01

    Multiple scattering RT calculations time-consuming. Need a speed improvement of about 1000 (for OCO)! Solution: Make use of redundancies in spectra. Correlated-k (Lacis and Wang, Lacis and Oinas, Goody et al, Fu and Liou) Problem: Assume that spectral variation of atmospheric optical properties spatially correlated at all points along optical path. High accuracy (HI) and 2-stream (2S) calculations have high correlation. Single scattering (SS) computations highly scenario-dependent, but not time consuming. Perform SS and 2S calculations at every wavelength. Perform small number of HI computations. Need to compute correction factor B at every wavelength.

  17. No. 1 Recovery Furnace Maintenance, Repair and Replacement Project, PCA Pulp and Paper Mill, Valdosta, Georgia

    Science.gov (United States)

    This document may be of assistance in applying the New Source Review (NSR) air permitting regulations including the Prevention of Significant Deterioration (PSD) requirements. This document is part of the NSR Policy and Guidance Database. Some documents in the database are a scanned or retyped version of a paper photocopy of the original. Although we have taken considerable effort to quality assure the documents, some may contain typographical errors. Contact the office that issued the document if you need a copy of the original.

  18. Prostate Cancer Research Training in Health Disparities for Undergraduates (PCaRT)

    Science.gov (United States)

    2011-03-01

    PORTION SIZE EACH TIME NIL NO RARE OCCAS SIONS SOME TIMES OTHER DAY DAILY MANY TIMES Chicken , Hen Turkey / Roast Turkey Smoked Poultry Duck 1/16 1/8...pc Beef/Steak, Roast beef Lamb, Goat Pork, Pork chop, Ham Smoked pork Bush meat * # M1/M3/Pc, # Kp Pig feet, Cow leg, Skin...65 years Chicken Rarely Sometimes Frequently 3(2.8) 57 (52.3) 49 (45.0) 5 (6.7) 36 (48.0) 34 (45.3) 0 (0.0) 30 (55.6) 24 (44.4) Beef ** Rarely

  19. Face Hallucination with Linear Regression Model in Semi-Orthogonal Multilinear PCA Method

    Science.gov (United States)

    Asavaskulkiet, Krissada

    2018-04-01

    In this paper, we propose a new face hallucination technique, face images reconstruction in HSV color space with a semi-orthogonal multilinear principal component analysis method. This novel hallucination technique can perform directly from tensors via tensor-to-vector projection by imposing the orthogonality constraint in only one mode. In our experiments, we use facial images from FERET database to test our hallucination approach which is demonstrated by extensive experiments with high-quality hallucinated color faces. The experimental results assure clearly demonstrated that we can generate photorealistic color face images by using the SO-MPCA subspace with a linear regression model.

  20. Performances of the PCA method in electrical machines diagnosis using Matlab

    OpenAIRE

    Ramahaleomiarantsoa , J.F.; Sambatra , Eric Jean Roy; Heraud , Nicolas; Razafimahenina , Jean Marie

    2012-01-01

    Nowadays, faults diagnosis is almost an inevitable step to be maintained in the optimal safety operating of every physical system. Electrical machines, main elements of every electromechanical system, are among the research topics of many academic and industrial laboratories because of the importance of their roles in the industrial process. Lots of technologies of these machines are old and well controlled. However, they remain the seat of several electrical and mechanical faults [1-4]. Thus...

  1. pyPcazip: A PCA-based toolkit for compression and analysis of molecular simulation data

    Directory of Open Access Journals (Sweden)

    Ardita Shkurti

    2016-01-01

    Full Text Available The biomolecular simulation community is currently in need of novel and optimised software tools that can analyse and process, in reasonable timescales, the large generated amounts of molecular simulation data. In light of this, we have developed and present here pyPcazip: a suite of software tools for compression and analysis of molecular dynamics (MD simulation data. The software is compatible with trajectory file formats generated by most contemporary MD engines such as AMBER, CHARMM, GROMACS and NAMD, and is MPI parallelised to permit the efficient processing of very large datasets. pyPcazip is a Unix based open-source software (BSD licenced written in Python.

  2. Detection of Weak Spots in Benchmarks Memory Space by using PCA and CA

    Directory of Open Access Journals (Sweden)

    Abdul Kareem PARCHUR

    2010-12-01

    Full Text Available This paper describes the weak spots in SPEC CPU INT 2006 Benchmarks memory space by using Principal Component Analysis and Cluster Analysis. We used recently published SPEC CPU INT 2006 Benchmark scores of AMD Opteron 2000+ and AMD Opteron 8000+ series processors. The four most significant PCs, which are retained for 72.6% of the variance, PC2, PC3, and PC4 covers 26.5%, 2.9%, 0.91% and 0.019% variance respectively. The dendrogram is useful to identify the similarities and dissimilarities between the benchmarks in workload space. These results and analysis can be used by performance engineers, scientists and developers to better understand the benchmark behavior in workload space and to design a Benchmark Suite that covers the complete workload space.

  3. Autophagosomal Sequestration of Mitochondria as an Indicator of Antiandrogen Therapy Resistance of Prostate Cancer (PCa)

    Science.gov (United States)

    2017-11-01

    intensities using a Nikon A1 or a Leica Di8 fluorescence confocal microscope and Image J software . Results and Discussion: Our data show that the...intensities of the FRET signals from all optical sections have been quantified and integrated using Image J software with appropriate threshold to...journal when all the data are collected. Inventions, patent applications, and/or licenses Nothing to report Other products Nothing to report

  4. Analysis and Extension of the PCA Method, Estimating a Noise Curve from a Single Image

    Directory of Open Access Journals (Sweden)

    Miguel Colom

    2016-12-01

    Full Text Available In the article 'Image Noise Level Estimation by Principal Component Analysis', S. Pyatykh, J. Hesser, and L. Zheng propose a new method to estimate the variance of the noise in an image from the eigenvalues of the covariance matrix of the overlapping blocks of the noisy image. Instead of using all the patches of the noisy image, the authors propose an iterative strategy to adaptively choose the optimal set containing the patches with lowest variance. Although the method measures uniform Gaussian noise, it can be easily adapted to deal with signal-dependent noise, which is realistic with the Poisson noise model obtained by a CMOS or CCD device in a digital camera.

  5. 40 CFR 86.1111-87 - Test procedures for PCA testing.

    Science.gov (United States)

    2010-07-01

    ... supply to the chemiluminescent analyzers is placed in the standby position.] (B) The manufacturer shall... manufacturer shall inspect the fuel system to insure the absence of any leaks of liquid or vapor to the atmosphere by applying a pressure of 14.5 ±0.5 inches of water to the fuel system, allowing the pressure to...

  6. Semantic category interference in overt picture naming: sharpening current density localization by PCA.

    Science.gov (United States)

    Maess, Burkhard; Friederici, Angela D; Damian, Markus; Meyer, Antje S; Levelt, Willem J M

    2002-04-01

    The study investigated the neuronal basis of the retrieval of words from the mental lexicon. The semantic category interference effect was used to locate lexical retrieval processes in time and space. This effect reflects the finding that, for overt naming, volunteers are slower when naming pictures out of a sequence of items from the same semantic category than from different categories. Participants named pictures blockwise either in the context of same- or mixed-category items while the brain response was registered using magnetoencephalography (MEG). Fifteen out of 20 participants showed longer response latencies in the same-category compared to the mixed-category condition. Event-related MEG signals for the participants demonstrating the interference effect were submitted to a current source density (CSD) analysis. As a new approach, a principal component analysis was applied to decompose the grand average CSD distribution into spatial subcomponents (factors). The spatial factor indicating left temporal activity revealed significantly different activation for the same-category compared to the mixed-category condition in the time window between 150 and 225 msec post picture onset. These findings indicate a major involvement of the left temporal cortex in the semantic interference effect. As this effect has been shown to take place at the level of lexical selection, the data suggest that the left temporal cortex supports processes of lexical retrieval during production.

  7. Semantic category interference in overt picture naming: Sharpening current density localization by PCA

    NARCIS (Netherlands)

    Maess, B.; Friederici, A.D.; Damian, M.F.; Meyer, A.S.; Levelt, W.J.M.

    2002-01-01

    The study investigated the neuronal basis of the retrieval of words from the mental lexicon. The semantic category interference effect was used to locate lexical retrieval processes in time and space. This effect reflects the finding that, for overt naming, volunteers are slower when naming pictures

  8. VizieR Online Data Catalog: PCA-based inversion of stellar parameters (Gebran+, 2016)

    Science.gov (United States)

    Gebran, M.; Farah, W.; Paletou, F.; Monier, R.; Watson, V.

    2016-03-01

    Inverted effective temperatures, surface gravities, projected rotational velocities, metalicities, and radial velocities for the selected A stars. The "closest" are the values found in Vizier catalogues closest to our inverted parameters, while "median" are the median of the catalogue values. Outliers are marked as "1" in the "outliers" column (see sect. 6) (1 data file).

  9. A PCA-Based Change Detection Framework for Multidimensional Data Streams

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali

    2015-08-10

    Detecting changes in multidimensional data streams is an important and challenging task. In unsupervised change detection, changes are usually detected by comparing the distribution in a current (test) window with a reference window. It is thus essential to design divergence metrics and density estimators for comparing the data distributions, which are mostly done for univariate data. Detecting changes in multidimensional data streams brings difficulties to the density estimation and comparisons. In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations. The proposed framework also has advantages over existing approaches by reducing computational costs with an efficient density estimator, promoting the change-score calculation by introducing effective divergence metrics, and by minimizing the efforts required from users on the threshold parameter setting by using the Page-Hinkley test. The evaluation results on synthetic and real data show that our framework outperforms two baseline methods in terms of both detection accuracy and computational costs.

  10. A Novel Mitochondria-Dependent Apoptotic Pathway (MAP) in Prostate Cancer (Pca) Cells

    National Research Council Canada - National Science Library

    Chandra, Dhyan

    2004-01-01

    ...) are also up-regulated (Chandra et al., J. Biol. Chem., 277, 50842-54; 2002). Later, when the apoptotic machinery is activated, I notice that there is prominent localization of active caspase-9 and -3 in the mitochondria...

  11. Autophagosomal Sequestration of Mitochondria as an Indicator of Antiandrogen Therapy Resistance of Prostate Cancer (PCa)

    Science.gov (United States)

    2017-11-01

    degree of mitophagy is more in androgen-dependent LNCaP cells than in –independent C4-2 cells, both growing in androgen-depleted media . Enzalutamide...suspension containing ~4,000 cells are then seeded in F1C4 in each well of a 96 well plate except blanks, which received only media . A day after seeding...appreciable increase in FRET intensity has been observed in Enzalutamide treated cells as compared to that in the control vehicle treated cells for MDC

  12. A PCA and ELM Based Adaptive Method for Channel Equalization in MFL Inspection

    Directory of Open Access Journals (Sweden)

    Zhenning Wu

    2014-01-01

    Full Text Available Magnetic flux leakage (MFL as an efficient method for pipeline flaw detection plays important role in pipeline safety. This nondestructive test technique assesses the health of the buried pipeline. The signal is gathered by an array of hall-effect sensors disposed at the magnetic neutral plane of a pair of permanent magnet in the pipeline inspection gauge (PIG clinging to the inner surface of the pipe wall. The magnetic flux measured by the sensors reflects the health condition of the pipe. The signal is influenced by not only the condition of the pipe, but also by the lift-off value of the sensors and various properties of electronic component. The consistency of the position of the sensors is almost never satisfied and each sensor measures differently. In this paper, a new scheme of channel equalization is proposed for MFL signal in order to correct sensor misalignments, which eventually improves accuracy of defect characterization. The algorithm proposed in this paper is adaptive to the effects of error on the disposition of the sensor due to manufacturing imperfections and movements of the sensors. The algorithm is tested by data acquired from an experimental pipeline. The results show the effectiveness of the proposed algorithm.

  13. A New Dataset Size Reduction Approach for PCA-Based Classification in OCR Application

    Directory of Open Access Journals (Sweden)

    Mohammad Amin Shayegan

    2014-01-01

    Full Text Available A major problem of pattern recognition systems is due to the large volume of training datasets including duplicate and similar training samples. In order to overcome this problem, some dataset size reduction and also dimensionality reduction techniques have been introduced. The algorithms presently used for dataset size reduction usually remove samples near to the centers of classes or support vector samples between different classes. However, the samples near to a class center include valuable information about the class characteristics and the support vector is important for evaluating system efficiency. This paper reports on the use of Modified Frequency Diagram technique for dataset size reduction. In this new proposed technique, a training dataset is rearranged and then sieved. The sieved training dataset along with automatic feature extraction/selection operation using Principal Component Analysis is used in an OCR application. The experimental results obtained when using the proposed system on one of the biggest handwritten Farsi/Arabic numeral standard OCR datasets, Hoda, show about 97% accuracy in the recognition rate. The recognition speed increased by 2.28 times, while the accuracy decreased only by 0.7%, when a sieved version of the dataset, which is only as half as the size of the initial training dataset, was used.

  14. Input Space Regularization Stabilizes Pre-images for Kernel PCA De-noising

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2009-01-01

    Solution of the pre-image problem is key to efficient nonlinear de-noising using kernel Principal Component Analysis. Pre-image estimation is inherently ill-posed for typical kernels used in applications and consequently the most widely used estimation schemes lack stability. For de...

  15. OMPS/NPP PCA SO2 Total Column 1-Orbit L2 Swath 50x50km V1 (OMPS_NPP_NMSO2_PCA_L2) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — The goal of this dataset is to create and archive a Level 2 Sulfur Dioxide (SO2) Earth Science Data Record (ESDR) from backscatter Ultraviolet (BUV) measurements...

  16. Measurement of Scenic Spots Sustainable Capacity Based on PCA-Entropy TOPSIS: A Case Study from 30 Provinces, China

    Directory of Open Access Journals (Sweden)

    Xuedong Liang

    2017-12-01

    Full Text Available In connection with the sustainable development of scenic spots, this paper, with consideration of resource conditions, economic benefits, auxiliary industry scale and ecological environment, establishes a comprehensive measurement model of the sustainable capacity of scenic spots; optimizes the index system by principal components analysis to extract principal components; assigns the weight of principal components by entropy method; analyzes the sustainable capacity of scenic spots in each province of China comprehensively in combination with TOPSIS method and finally puts forward suggestions aid decision-making. According to the study, this method provides an effective reference for the study of the sustainable development of scenic spots and is very significant for considering the sustainable development of scenic spots and auxiliary industries to establish specific and scientific countermeasures for improvement.

  17. 1H NMR and PCA-based analysis revealed variety dependent changes in phenolic contents of apple fruit after drying.

    Science.gov (United States)

    Francini, Alessandra; Romeo, Stefania; Cifelli, Mario; Gori, Daniele; Domenici, Valentina; Sebastiani, Luca

    2017-04-15

    Dry and fresh apples have been studied monitoring their polyphenolic profiles through 1 H NMR, antioxidant capacity and total polyphenol content. Six ancient and underutilized apple varieties (Mantovana, Mora, Nesta, Cipolla, Ruggina, Sassola) and a commercial one (Golden Delicious) were dried with an air-drying system at 45°C for 19h. Although some of their polyphenol constituents were lost during drying, the antioxidant capacity of some apple varieties remained higher compared to Golden Delicious. This result is very important for ancient and underutilized varieties that are not consumed on large scale as fresh product since they have low attractiveness, due to their ugly appearance. Combining quantitative NMR spectroscopy with principal component analysis we have identified and quantified several polyphenols (such as catechin, epicathechin, and chlorogenic acid) that are important to establish the nutraceutical value of the different investigated apple varieties. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Fractionation, analysis, and PCA modeling of properties of four technical lignins for prediction of their application potential in binders

    NARCIS (Netherlands)

    Gosselink, R.J.A.; Dam, van J.E.G.; Jong, de E.; Scott, E.L.; Sanders, J.P.M.

    2010-01-01

    Functional properties of technical lignins need to be characterized in more detail to become a higher added value renewable raw material for the chemical industry. The suitability of a lignin from different plants or trees obtained by different technical processes can only be predicted for selected

  19. Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Debesh Jha

    2017-01-01

    Full Text Available Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach. In this study, we propose a new model that includes Wiener filtering for noise reduction, 2D-discrete wavelet transform (2D-DWT for feature extraction, probabilistic principal component analysis (PPCA for dimensionality reduction, and a random subspace ensemble (RSE classifier along with the K-nearest neighbors (KNN algorithm as a base classifier to classify brain images as pathological or normal ones. The proposed methods provide a significant improvement in classification results when compared to other studies. Based on 5×5 cross-validation (CV, the proposed method outperforms 21 state-of-the-art algorithms in terms of classification accuracy, sensitivity, and specificity for all four datasets used in the study.

  20. Association of Empirically Derived Dietary Patterns with Cardiovascular Risk Factors: A Comparison of PCA and RRR Methods.

    Directory of Open Access Journals (Sweden)

    Nicolas Sauvageot

    Full Text Available Principal component analysis is used to determine dietary behaviors of a population whereas reduced rank regression is used to construct disease-related dietary patterns. This study aimed to compare both types of DP and theirs associations with cardiovascular risk factors (CVRF.Data were derived from the cross sectional NESCAV (Nutrition, Environment and Cardiovascular Health study, aiming to describe the cardiovascular health of the Greater region's population (Grand duchy of Luxembourg, Wallonia (Belgium, Lorraine (France. 2298 individuals were included for this study and dietary intake was assessed using a 134-item food frequency questionnaire.We found that CVRF-related patterns also reflect eating behaviours of the population. Comparing concordant food groups between both dietary pattern methods, a diet high in fruits, oleaginous and dried fruits, vegetables, olive oil, fats rich in omega 6 and tea and low in fried foods, lean and fatty meat, processed meat, ready meal, soft drink and beer was associated with lower prevalence of CVRF. In the opposite, a pattern characterized by high intakes of fried foods, meat, offal, beer, wine and aperitifs and spirits, and low intakes of cereals, sugar and sweets and soft drinks was associated with higher prevalence of CVRF.In sum, we found that a "Prudent" and "Animal protein and alcohol" patterns were both associated with CVRF and behaviourally meaningful. Moreover, the relationships of those dietary patterns with lifestyle characteristics support the theory that food choices are part of a larger pattern of healthy lifestyle.

  1. Prediction of A CRS Frontier Function and A Transformation Function for A CCR DEA Using EMBEDED PCA

    Directory of Open Access Journals (Sweden)

    Subhadip Sarkar

    2013-08-01

    Full Text Available Data Envelopment Analysis is a nonparametric tool for measuring the performance of a number of homogenous Decision Making Units. In this paper, Principal Component Analysis is used as an alternative tool to estimate the frontier in a Data Envelopment Analysis under the assumption of Constant Return to Scale. Apart from this, in the context of a multiple inputs and single output, a transformation function, is developed here using the Most Productive Scale Size condition stated by Starrett. This function complies with all postulates of a frontier function and is very similar to the formula given by Aigner and Chu. Moreover, it is capable of defining the threshold value for any resource.

  2. Measurement of Scenic Spots Sustainable Capacity Based on PCA-Entropy TOPSIS: A Case Study from 30 Provinces, China.

    Science.gov (United States)

    Liang, Xuedong; Liu, Canmian; Li, Zhi

    2017-12-22

    In connection with the sustainable development of scenic spots, this paper, with consideration of resource conditions, economic benefits, auxiliary industry scale and ecological environment, establishes a comprehensive measurement model of the sustainable capacity of scenic spots; optimizes the index system by principal components analysis to extract principal components; assigns the weight of principal components by entropy method; analyzes the sustainable capacity of scenic spots in each province of China comprehensively in combination with TOPSIS method and finally puts forward suggestions aid decision-making. According to the study, this method provides an effective reference for the study of the sustainable development of scenic spots and is very significant for considering the sustainable development of scenic spots and auxiliary industries to establish specific and scientific countermeasures for improvement.

  3. Application of the principal component analysis (PCA) to HVSR data aimed at the seismic characterization of earthquake prone areas

    Science.gov (United States)

    Paolucci, Enrico; Lunedei, Enrico; Albarello, Dario

    2017-10-01

    In this work, we propose a procedure based on principal component analysis on data sets consisting of many horizontal to vertical spectral ratio (HVSR or H/V) curves obtained by single-station ambient vibration acquisitions. This kind of analysis aimed at the seismic characterization of the investigated area by identifying sites characterized by similar HVSR curves. It also allows to extract the typical HVSR patterns of the explored area and to establish their relative importance, providing an estimate of the level of heterogeneity under the seismic point of view. In this way, an automatic explorative seismic characterization of the area becomes possible by only considering ambient vibration data. This also implies that the relevant outcomes can be safely compared with other available information (geological data, borehole measurements, etc.) without any conceptual trade-off. The whole algorithm is remarkably fast: on a common personal computer, the processing time takes few seconds for a data set including 100-200 HVSR measurements. The procedure has been tested in three study areas in the Central-Northern Italy characterized by different geological settings. Outcomes demonstrate that this technique is effective and well correlates with most significant seismostratigraphical heterogeneities present in each of the study areas.

  4. Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

    Directory of Open Access Journals (Sweden)

    Ramesh Kumar Lama

    2017-01-01

    Full Text Available Alzheimer’s disease (AD is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR images to discriminate AD, mild cognitive impairment (MCI, and healthy control (HC subjects using a support vector machine (SVM, an import vector machine (IVM, and a regularized extreme learning machine (RELM. The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

  5. On the use of the observation-wise k-fold operation in PCA cross-validation

    NARCIS (Netherlands)

    Saccenti, E.; Camacho, J.

    2015-01-01

    Cross-validation (CV) is a common approach for determining the optimal number of components in a principal component analysis model. To guarantee the independence between model testing and calibration, the observationwise k-fold operation is commonly implemented in each cross-validation step. This

  6. Fault Detection and Diagnosis In Hall-Héroult Cells Based on Individual Anode Current Measurements Using Dynamic Kernel PCA

    Science.gov (United States)

    Yao, Yuchen; Bao, Jie; Skyllas-Kazacos, Maria; Welch, Barry J.; Akhmetov, Sergey

    2018-04-01

    Individual anode current signals in aluminum reduction cells provide localized cell conditions in the vicinity of each anode, which contain more information than the conventionally measured cell voltage and line current. One common use of this measurement is to identify process faults that can cause significant changes in the anode current signals. While this method is simple and direct, it ignores the interactions between anode currents and other important process variables. This paper presents an approach that applies multivariate statistical analysis techniques to individual anode currents and other process operating data, for the detection and diagnosis of local process abnormalities in aluminum reduction cells. Specifically, since the Hall-Héroult process is time-varying with its process variables dynamically and nonlinearly correlated, dynamic kernel principal component analysis with moving windows is used. The cell is discretized into a number of subsystems, with each subsystem representing one anode and cell conditions in its vicinity. The fault associated with each subsystem is identified based on multivariate statistical control charts. The results show that the proposed approach is able to not only effectively pinpoint the problematic areas in the cell, but also assess the effect of the fault on different parts of the cell.

  7. OMPS/NPP PCA SO2 Total Column 1-Orbit L2 Swath 50x50km NRT

    Data.gov (United States)

    National Aeronautics and Space Administration — The OMPS-NPP L2 NM Sulfur Dioxide (SO2) Total and Tropospheric Column swath orbital collection 2 version 2.0 product contains the retrieved sulfur dioxide (SO2)...

  8. Differentiation of live and dead salmonella cells using fourier transform infrared (FTIR) spectroscopy and principle component analysis (PCA) technique

    Science.gov (United States)

    Various technologies have been developed for pathogen detection using optical, electrochemical, biochemical and physical properties. Conventional microbiological methods need time from days to week to get the result. Though this method is very sensitive and accurate, a rapid detection of pathogens i...

  9. Genetic diversity and relationship of Hedychium from Northeast India as dissected using PCA analysis and hierarchical clustering.

    Science.gov (United States)

    Basak, Supriyo; Ramesh, Aadi Moolam; Kesari, Vigya; Parida, Ajay; Mitra, Sudip; Rangan, Latha

    2014-12-01

    Molecular genetic fingerprints of eleven Hedychium species from Northeast India were developed using PCR based markers. Fifteen inter-simple sequence repeats (ISSRs) and five amplified fragment length polymorphism (AFLP) primers produced 547 polymorphic fragments. Positive correlation (r = 0.46) was observed between the mean genetic similarity and genetic diversity parameters at the inter-species level. AFLP and ISSR markers were able to group the species according to its altitude and intensity of flower aroma. Cophenetic correlation coefficients between the dendrogram and the original similarity matrix were significant for ISSR (r = 0.89) compared to AFLP (r = 0.83) markers. This genetic characterization of Hedychium from Northeast India contributes to the knowledge of genetic structure of the species and can be used to define strategies for their conservation and management.

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

    Science.gov (United States)

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

    2012-09-26

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

  11. Contribution to a Taxonomic Revision of the Sicilian Helichrysum Taxa by PCA Analysis of Their Essential-Oil Compositions.

    Science.gov (United States)

    Maggio, Antonella; Bruno, Maurizio; Guarino, Riccardo; Senatore, Felice; Ilardi, Vincenzo

    2016-02-01

    The chemical profile of the essential oils in ten populations of the genus Helichrysum Mill. (Asteraceae), collected in the loci classici of the nomenclatural types of the taxa endemic to Sicily, were analyzed. Our results confirm that the analysis of secondary metabolites can be used to fingerprint wild populations of Helichrysum, the chemical profiles being coherent with the systematic arrangement of the investigated populations in three main clusters, referring to the aggregates of H. stoechas, H. rupestre, and H. italicum, all belonging to the section Stoechadina. The correct nomenclatural designation of the investigated populations is discussed and the following two new combinations are proposed: Helichrysum preslianum subsp. compactum (Guss.) Maggio, Bruno, Guarino, Senatore & Ilardi and Helichrysum panormitanum subsp. latifolium Maggio, Bruno, Guarino, Senatore & Ilardi. Copyright © 2016 Verlag Helvetica Chimica Acta AG, Zürich.

  12. Identification of Novel Inherited Genetic Markers for Aggressive PCa in European and African Americans Using Whole Genome Sequencing

    Science.gov (United States)

    2014-10-01

    genes were TEK ( Tyrosine Kinase, Endothelial), CDH2 (Cadherin 2), and BEST2 (Bestrophin 2); while in the CAPS population, the gene showed significant...patients with acute myeloid 20 leukemia after gene transfer of INPP5D. Gene Ther 16: 570 -3. doi: 10.1038/gt.2008.184 Miele A, Braastad CD

  13. Ethnicity-dependent influence of innate immune genetic markers on morphine PCA requirements and adverse effects in postoperative pain.

    Science.gov (United States)

    Somogyi, Andrew A; Sia, Alex T; Tan, Ene-Choo; Coller, Janet K; Hutchinson, Mark R; Barratt, Daniel T

    2016-11-01

    Although several genetic factors have been associated with postsurgical morphine requirements, those involving the innate immune system and cytokines have not been well investigated. The aim of this study was to investigate the contribution of genetic variability in innate immune signalling pathways to variability in morphine dosage after elective caesarean section under spinal anaesthesia in 133 Indian, 230 Malay, and 598 Han Chinese women previously studied. Twenty single nucleotide polymorphisms in 14 genes involved in glial activation (TLR2, TLR4, MYD88, MD2), inflammatory signalling (IL2, IL6, IL10, IL1B, IL6R, TNFA, TGFB1, CRP, CASP1), and neuronal regulation (BDNF) were newly investigated, in addition to OPRM1, COMT, and ABCB1 genetic variability identified previously. Postsurgical patient-controlled analgesia morphine use (mg/24 hours) was binned into 6 normally distributed groups and scored 0 to 5 to facilitate step-down multiple linear regression analysis of genetic predictors, controlling for ethnicity and nongenetic variables. Ethnicity, OPRM1 rs1799971 (increased), TLR2 rs3804100 (decreased), and an interaction between ethnicity and IL1B rs1143634 (increased), predicted 9.8% of variability in morphine use scores in the entire cohort. In the Indian cohort, 14.5% of the variance in morphine use score was explained by IL1B rs1143634 (increased) and TGFB1 rs1800469 (decreased). In Chinese patients, the incidence of postsurgical pain was significantly higher in variant COMT rs4680 genotypes (P = 0.0007) but not in the Malay or Indian cohorts. Innate immune genetics may contribute to variability in postsurgical opioid requirements in an ethnicity-dependent manner.

  14. Effect of process control agent (PCA) on the characteristics of mechanically alloyed Ti-Mg powders [Conference paper

    CSIR Research Space (South Africa)

    Machio, Christopher N

    2010-10-01

    Full Text Available of he A f e n ow fo P eld u to ca et tio R P ea ts id uti ern M als , H __ pu m ty as ec der 80 m am pow flo and d r ow p sin me nn erm n t es art n p are n on Af eta Co K ___ rity fo...

  15. Principal Components Analysis (PCA) Image used to characterize the complexity of the seafloor around St. John, USVI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Eight complexity surfaces (mean depth, standard deviation of depth, curvature, plan curvature, profile curvature, rugosity, slope, and slope of slope) were stacked...

  16. Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014

    Science.gov (United States)

    Rahayu, Sri; Sugiarto, Teguh; Madu, Ludiro; Holiawati; Subagyo, Ahmad

    2017-01-01

    This study aims to apply the model principal component analysis to reduce multicollinearity on variable currency exchange rate in eight countries in Asia against US Dollar including the Yen (Japan), Won (South Korea), Dollar (Hong Kong), Yuan (China), Bath (Thailand), Rupiah (Indonesia), Ringgit (Malaysia), Dollar (Singapore). It looks at yield…

  17. Patient and surgery related factors associated with fatigue type polyethylene wear on 49 PCA and DURACON retrievals at autopsy and revision

    Directory of Open Access Journals (Sweden)

    Ochsner Peter E

    2008-02-01

    Full Text Available Abstract Background Polyethylene wear is an important factor for longevity of total knee arthroplasty. Proven and suspicious factors causing wear can be grouped as material, patient and surgery related. There are more studies correlating design and/or biomaterial factors to in vivo wear than those to patient and surgery related factors. Many retrieval studies just include revision implants and therefore may not be representative. This study is aimed to correlate patient- and surgery- related factors to visual wear score by minimizing design influence and include both autopsy and revision implants. Comparison between the groups was expected to unmask patient and surgery-related factors responsible for wear. Methods The amount of joint side wear on polyethylene retrievals was measured using a modification of an established visual wear score. Fatigue type wear was defined as summation of the most severe wear modes of delamination, pitting and cracks. Analysis of patient and surgery related variables suspicious to cause wear included prospectively sampled patient activity which was measured by self reported walking capacity. Statistical analysis was done by univariate analysis of variance. Activity level and implantation time were merged to an index of use and correlated to the wear score. Results Wear score after comparable implantation time was significantly less in the autopsy group. Even so, fatigue type wear accounted for 84 and 93 % of total wear score on autopsy and revision implants respectively. A highly significant influence on wear score was found in time of implantation (p = 0.002, level of activity (p = 0.025 and inserts belonging to revision group (p = 0.006. No influence was found for the kind of patella replacement (p = 0.483. Body mass index and accuracy of component alignment had no significant influence on visual wear score. Fatigue-type wear in the medial compartment was closely correlated to the index of use in the autopsy (R2 = 0.383 and the revision group (R2 = 0.813. Conclusion The present study's finding of substantial fatigue type wear in both autopsy and revision retrievals supports the theory that polyethylene fatigue strength is generally exceeded in this type of prosthesis. Furthermore, this study correlated fatigue-type polyethylene wear to an index of use as calculated by activity over time. Future retrieval studies may use activity over time as an important patient related factor correlated to the visual wear score. When evaluating total knee arthroplasty routine follow up, the surgeon must think of substantial wear present even without major clinical signs.

  18. BURST FLUENCE DISTRIBUTIONS OF SOFT GAMMA REPEATERS 1806–20 AND 1900+14 IN THE ROSSI X-RAY TIMING EXPLORER PCA ERA

    International Nuclear Information System (INIS)

    Prieskorn, Zachary; Kaaret, Philip

    2012-01-01

    We study the fluence distributions of over 3040 bursts from SGR 1806–20 and over 1963 bursts from SGR 1900+14 using the complete set of observations available from the Rossi X-Ray Timing Explorer/Proportional Counter Array through 2011 March. Cumulative event distributions are presented for both sources and are fitted with single and broken power laws as well as an exponential cutoff. The distributions are best fitted by a broken power law with exponential cutoff; however the statistical significance of the cutoff is not high and the upper portion of the broken power law can be explained as the expected number of false bursts due to random noise fluctuations. Event distributions are also examined in high and low burst rate regimes and power-law indices are found to be consistent, independent of the burst rate. The contribution function of the event fluence is calculated. This distribution shows that the energy released in the soft gamma repeater (SGR) bursts is dominated by the most powerful events for both sources. The power-law nature of these distributions combined with the dominant energy dissipation of the system occurring in the large, less frequent bursts is indicative of a self-organized critical system, as suggested by Goğus et al. in 1999.

  19. Pre-Cancer Atlas (PCA) and Other Human Tumor Atlas Network (HTAN) Funding Opportunity Announcements (FOAs) Released | Division of Cancer Prevention

    Science.gov (United States)

    There are 3 new funding opportunity announcements about the Pre-Cancer Atlas associated with the Beau Biden Cancer MoonshotSM Initiative that are intended to accelerate cancer research. The purpose of the FOAs is to promote research that results in a comprehensive view of the dynamic, multidimensional tumor ecosystem and is a direct response to the Moonshot Blue Ribbon Panel recommendation to generate human tumor atlases. |

  20. Classification and structural analysis of live and dead salmonella cells using fourier transform infrared (FT-IR) spectroscopy and principle component analysis (PCA)

    Science.gov (United States)

    Fourier Transform Infrared Spectroscopy (FT-IR) was used to detect Salmonella typhimurium and Salmonella enteritidis foodborne bacteria and distinguish between live and dead cells of both serotypes. Bacteria were loaded individually on the ZnSe Attenuated Total Reflection (ATR) crystal surface and s...