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...

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.

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.

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

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

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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.

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.

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

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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.

Condition Monitoring of Sensors in a NPP Using Optimized PCA

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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.

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).

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

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.

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.

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.

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.

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

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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.

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

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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.

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.

Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

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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.

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.

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 ...

[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.

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...

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.

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.

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.

A urinary biomarker-based risk score correlates with multiparametric MRI for prostate cancer detection.

Science.gov (United States)

Hendriks, Rianne J; van der Leest, Marloes M G; Dijkstra, Siebren; Barentsz, Jelle O; Van Criekinge, Wim; Hulsbergen-van de Kaa, Christina A; Schalken, Jack A; Mulders, Peter F A; van Oort, Inge M

2017-10-01

Prostate cancer (PCa) diagnostics would greatly benefit from more accurate, non-invasive techniques for the detection of clinically significant disease, leading to a reduction of over-diagnosis and over-treatment. The aim of this study was to determine the association between a novel urinary biomarker-based risk score (SelectMDx), multiparametric MRI (mpMRI) outcomes, and biopsy results for PCa detection. This retrospective observational study used data from the validation study of the SelectMDx score, in which urine was collected after digital rectal examination from men undergoing prostate biopsies. A subset of these patients also underwent a mpMRI scan of the prostate. The indications for performing mpMRI were based on persistent clinical suspicion of PCa or local staging after PCa was found upon biopsy. All mpMRI images were centrally reviewed in 2016 by an experienced radiologist blinded for the urine test results and biopsy outcome. The PI-RADS version 2 was used. In total, 172 patients were included for analysis. Hundred (58%) patients had PCa detected upon prostate biopsy, of which 52 (52%) had high-grade disease correlated with a significantly higher SelectMDx score (P < 0.01). The median SelectMDx score was significantly higher in patients with a suspicious significant lesion on mpMRI compared to no suspicion of significant PCa (P < 0.01). For the prediction of mpMRI outcome, the area-under-the-curve of SelectMDx was 0.83 compared to 0.66 for PSA and 0.65 for PCA3. There was a positive association between SelectMDx score and the final PI-RADS grade. There was a statistically significant difference in SelectMDx score between PI-RADS 3 and 4 (P < 0.01) and between PI-RADS 4 and 5 (P < 0.01). The novel urinary biomarker-based SelectMDx score is a promising tool in PCa detection. This study showed promising results regarding the correlation between the SelectMDx score and mpMRI outcomes, outperforming PCA3. Our results suggest that this risk

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…

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.

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.

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.

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...

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.

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.

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.

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

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.

A measurement-based fault detection approach applied to monitor robots swarm

KAUST Repository

Khaldi, Belkacem

2017-07-10

Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.

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.

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.

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)

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

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.

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

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

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.

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.

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.

Amalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring

KAUST Repository

Harrou, Fouzi

2016-01-29

Accurate and effective anomaly detection and diagnosis of modern industrial systems are crucial for ensuring reliability and safety and for maintaining desired product quality. Anomaly detection based on principal component analysis (PCA) has been studied intensively and largely applied to multivariate processes with highly cross-correlated process variables; howver conventional PCA-based methods often fail to detect small or moderate anomalies. In this paper, the proposed approach integrates two popular process-monitoring detection tools, the conventional PCA-based monitoring indices Hotelling’s T2 and Q and the exponentially weighted moving average (EWMA). We develop two EWMA tools based on the Q and T2 statistics, T2-EWMA and Q-EWMA, to detect anomalies in the process mean. The performances of the proposed methods were compared with that of conventional PCA-based anomaly-detection methods by applying each method to two examples: a synthetic data set and experimental data collected from a flow heating system. The results clearly show the benefits and effectiveness of the proposed methods over conventional PCA-based methods.

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.

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.

Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department

KAUST Repository

Harrou, Fouzi

2015-07-03

Monitoring of production systems, such as those in hospitals, is primordial for ensuring the best management and maintenance desired product quality. Detection of emergent abnormalities allows preemptive actions that can prevent more serious consequences. Principal component analysis (PCA)-based anomaly-detection approach has been used successfully for monitoring systems with highly correlated variables. However, conventional PCA-based detection indices, such as the Hotelling’s T2T2 and the Q statistics, are ill suited to detect small abnormalities because they use only information from the most recent observations. Other multivariate statistical metrics, such as the multivariate cumulative sum (MCUSUM) control scheme, are more suitable for detection small anomalies. In this paper, a generic anomaly detection scheme based on PCA is proposed to monitor demands to an emergency department. In such a framework, the MCUSUM control chart is applied to the uncorrelated residuals obtained from the PCA model. The proposed PCA-based MCUSUM anomaly detection strategy is successfully applied to the practical data collected from the database of the pediatric emergency department in the Lille Regional Hospital Centre, France. The detection results evidence that the proposed method is more effective than the conventional PCA-based anomaly-detection methods.

Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department

KAUST Repository

Harrou, Fouzi; Kadri, Farid; Chaabane, Sondé s; Tahon, Christian; Sun, Ying

2015-01-01

Monitoring of production systems, such as those in hospitals, is primordial for ensuring the best management and maintenance desired product quality. Detection of emergent abnormalities allows preemptive actions that can prevent more serious consequences. Principal component analysis (PCA)-based anomaly-detection approach has been used successfully for monitoring systems with highly correlated variables. However, conventional PCA-based detection indices, such as the Hotelling’s T2T2 and the Q statistics, are ill suited to detect small abnormalities because they use only information from the most recent observations. Other multivariate statistical metrics, such as the multivariate cumulative sum (MCUSUM) control scheme, are more suitable for detection small anomalies. In this paper, a generic anomaly detection scheme based on PCA is proposed to monitor demands to an emergency department. In such a framework, the MCUSUM control chart is applied to the uncorrelated residuals obtained from the PCA model. The proposed PCA-based MCUSUM anomaly detection strategy is successfully applied to the practical data collected from the database of the pediatric emergency department in the Lille Regional Hospital Centre, France. The detection results evidence that the proposed method is more effective than the conventional PCA-based anomaly-detection methods.

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...

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

Memory Efficient PCA Methods for Large Group ICA.

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

2016-01-01

data analysis. Implications to other methods such as expectation maximization PCA (EM PCA) are also presented. Based on our results, general recommendations for efficient application of PCA methods are given according to problem size and available computational resources. MPOWIT and all other methods discussed here are implemented and readily available in the open source GIFT software.

Memory efficient PCA methods for large group ICA

Directory of Open Access Journals (Sweden)

Srinivas eRachakonda

2016-02-01

ideal for big data analysis. Implications to other methods such as expectation maximization PCA (EM PCA are also presented. Based on our results, general recommendations for efficient application of PCA methods are given according to problem size and available computational resources. MPOWIT and all other methods discussed here are implemented and readily available in the open source GIFT software.

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

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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.

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)

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

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

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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.

On the Link Between L1-PCA and ICA.

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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.

Nanotechnology-Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer

Science.gov (United States)

2017-08-01

AWARD NUMBER: W81XWH-15-1-0157 TITLE: Nanotechnology -Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer PRINCIPAL...TITLE AND SUBTITLE Nanotechnology -Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer 5a. CONTRACT NUMBER 5b. GRANT NUMBER...identify novel differentially expressed miRNAs in the body fluids (blood, urine, etc.) for an early detection of PCa. Advances in nanotechnology and

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

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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.

Hand-Eye LRF-Based Iterative Plane Detection Method for Autonomous Robotic Welding

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Sungmin Lee

2015-12-01

Full Text Available This paper proposes a hand-eye LRF-based (laser range finder welding plane-detection method for autonomous robotic welding in the field of shipbuilding. The hand-eye LRF system consists of a 6 DOF manipulator and an LRF attached to the wrist of the manipulator. The welding plane is detected by the LRF with only the wrist's rotation to minimize a mechanical error caused by the manipulator's motion. A position on the plane is determined as an average position of the detected points on the plane, and a normal vector to the plane is determined by applying PCA (principal component analysis to the detected points. In this case, the accuracy of the detected plane is analysed by simulations with respect to the wrist's angle interval and the plane angle. As a result of the analysis, an iterative plane-detection method with the manipulator's alignment motion is proposed to improve the performance of plane detection. For verifying the feasibility and effectiveness of the proposed plane-detection method, experiments are carried out with a prototype of the hand-eye LRF-based system, which consists of a 1 DOF wrist's joint, an LRF system and a rotatable plane. In addition, the experimental results of the PCA-based plane detection method are compared with those of the two representative plane-detection methods, based on RANSAC (RANdom SAmple Consensus and the 3D Hough transform in both accuracy and computation time's points of view.

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

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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.

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

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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

Testing a Modified PCA-Based Sharpening Approach for Image Fusion

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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

[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.

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

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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 .

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.

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.

Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network.

Science.gov (United States)

Wang, Zhiwei; Liu, Chaoyue; Cheng, Danpeng; Wang, Liang; Yang, Xin; Cheng, Kwang-Ting

2018-05-01

Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

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

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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%.

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.

An Android malware detection system based on machine learning

Science.gov (United States)

Wen, Long; Yu, Haiyang

2017-08-01

The Android smartphone, with its open source character and excellent performance, has attracted many users. However, the convenience of the Android platform also has motivated the development of malware. The traditional method which detects the malware based on the signature is unable to detect unknown applications. The article proposes a machine learning-based lightweight system that is capable of identifying malware on Android devices. In this system we extract features based on the static analysis and the dynamitic analysis, then a new feature selection approach based on principle component analysis (PCA) and relief are presented in the article to decrease the dimensions of the features. After that, a model will be constructed with support vector machine (SVM) for classification. Experimental results show that our system provides an effective method in Android malware detection.

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.

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.

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

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.

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.

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.

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

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.

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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.

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

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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.

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

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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.

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

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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.

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.

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.

Improved detection of incipient anomalies via multivariate memory monitoring charts: Application to an air flow heating system

KAUST Repository

Harrou, Fouzi

2016-08-11

Detecting anomalies is important for reliable operation of several engineering systems. Multivariate statistical monitoring charts are an efficient tool for checking the quality of a process by identifying abnormalities. Principal component analysis (PCA) was shown effective in monitoring processes with highly correlated data. Traditional PCA-based methods, nevertheless, often are relatively inefficient at detecting incipient anomalies. Here, we propose a statistical approach that exploits the advantages of PCA and those of multivariate memory monitoring schemes, like the multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) monitoring schemes to better detect incipient anomalies. Memory monitoring charts are sensitive to incipient anomalies in process mean, which significantly improve the performance of PCA method and enlarge its profitability, and to utilize these improvements in various applications. The performance of PCA-based MEWMA and MCUSUM control techniques are demonstrated and compared with traditional PCA-based monitoring methods. Using practical data gathered from a heating air-flow system, we demonstrate the greater sensitivity and efficiency of the developed method over the traditional PCA-based methods. Results indicate that the proposed techniques have potential for detecting incipient anomalies in multivariate data. © 2016 Elsevier Ltd

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.

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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

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.

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.

Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

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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.

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.

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.

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.

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.

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

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

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)

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

Science.gov (United States)

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

2014-11-01

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

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

A deviation based assessment methodology for multiple machine health patterns classification and fault detection

Science.gov (United States)

Jia, Xiaodong; Jin, Chao; Buzza, Matt; Di, Yuan; Siegel, David; Lee, Jay

2018-01-01

Successful applications of Diffusion Map (DM) in machine failure detection and diagnosis have been reported in several recent studies. DM provides an efficient way to visualize the high-dimensional, complex and nonlinear machine data, and thus suggests more knowledge about the machine under monitoring. In this paper, a DM based methodology named as DM-EVD is proposed for machine degradation assessment, abnormality detection and diagnosis in an online fashion. Several limitations and challenges of using DM for machine health monitoring have been analyzed and addressed. Based on the proposed DM-EVD, a deviation based methodology is then proposed to include more dimension reduction methods. In this work, the incorporation of Laplacian Eigen-map and Principal Component Analysis (PCA) are explored, and the latter algorithm is named as PCA-Dev and is validated in the case study. To show the successful application of the proposed methodology, case studies from diverse fields are presented and investigated in this work. Improved results are reported by benchmarking with other machine learning algorithms.

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

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.

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

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.

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.

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.

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.

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%].

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.

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.

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.)

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...

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.

Ozone Measurements Monitoring Using Data-Based Approach

KAUST Repository

Harrou, Fouzi; Kadri, Farid; Khadraoui, Sofiane; Sun, Ying

2016-01-01

The complexity of ozone (O3) formation mechanisms in the troposphere make the fast and accurate modeling of ozone very challenging. In the absence of a process model, principal component analysis (PCA) has been extensively used as a data-based monitoring technique for highly correlated process variables; however conventional PCA-based detection indices often fail to detect small or moderate anomalies. In this work, we propose an innovative method for detecting small anomalies in highly correlated multivariate data. The developed method combine the multivariate exponentially weighted moving average (MEWMA) monitoring scheme with PCA modelling in order to enhance anomaly detection performance. Such a choice is mainly motivated by the greater ability of the MEWMA monitoring scheme to detect small changes in the process mean. The proposed PCA-based MEWMA monitoring scheme is successfully applied to ozone measurements data collected from Upper Normandy region, France, via the network of air quality monitoring stations. The detection results of the proposed method are compared to that declared by Air Normand air monitoring association.

Ozone Measurements Monitoring Using Data-Based Approach

KAUST Repository

Harrou, Fouzi

2016-02-01

The complexity of ozone (O3) formation mechanisms in the troposphere make the fast and accurate modeling of ozone very challenging. In the absence of a process model, principal component analysis (PCA) has been extensively used as a data-based monitoring technique for highly correlated process variables; however conventional PCA-based detection indices often fail to detect small or moderate anomalies. In this work, we propose an innovative method for detecting small anomalies in highly correlated multivariate data. The developed method combine the multivariate exponentially weighted moving average (MEWMA) monitoring scheme with PCA modelling in order to enhance anomaly detection performance. Such a choice is mainly motivated by the greater ability of the MEWMA monitoring scheme to detect small changes in the process mean. The proposed PCA-based MEWMA monitoring scheme is successfully applied to ozone measurements data collected from Upper Normandy region, France, via the network of air quality monitoring stations. The detection results of the proposed method are compared to that declared by Air Normand air monitoring association.

Use of Sparse Principal Component Analysis (SPCA) for Fault Detection

DEFF Research Database (Denmark)

Gajjar, Shriram; Kulahci, Murat; Palazoglu, Ahmet

2016-01-01

Principal component analysis (PCA) has been widely used for data dimension reduction and process fault detection. However, interpreting the principal components and the outcomes of PCA-based monitoring techniques is a challenging task since each principal component is a linear combination of the ...

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...

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.

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. 

Enhanced dynamic data-driven fault detection approach: Application to a two-tank heater system

KAUST Repository

Harrou, Fouzi

2018-02-12

Principal components analysis (PCA) has been intensively studied and used in monitoring industrial systems. However, data generated from chemical processes are usually correlated in time due to process dynamics, which makes the fault detection based on PCA approach a challenging task. Accounting for the dynamic nature of data can also reflect the performance of the designed fault detection approaches. In PCA-based methods, this dynamic characteristic of the data can be accounted for by using dynamic PCA (DPCA), in which lagged variables are used in the PCA model to capture the time evolution of the process. This paper presents a new approach that combines the DPCA to account for autocorrelation in data and generalized likelihood ratio (GLR) test to detect faults. A DPCA model is applied to perform dimension reduction while appropriately considering the temporal relationships in the data. Specifically, the proposed approach uses the DPCA to generate residuals, and then apply GLR test to reveal any abnormality. The performances of the proposed method are evaluated through a continuous stirred tank heater system.

Detectability Factors for Earth-based Imaging of the LCROSS Ejecta Plume

Science.gov (United States)

Strycker, Paul D.; Schotte, Jonathan M.; Temme, Ruth L.; Chanover, Nancy J.

2017-10-01

NASA’s Lunar Crater Observation and Sensing Satellite (LCROSS) mission delivered a kinetic impactor into Cabeus Crater on 9 October 2009 [1, 2]. Observing campaigns from Earth-based telescopes at multiple facilities attempted to obtain temporally-resolved imaging of the ejecta plume [3], but no Earth-based imaging detections were reported until 2013 after processing images with Principal Component Analysis (PCA) filtering [4]. Subsequently, PCA filtering has revealed plume detections in two additional cameras and also confirmed a non-detection from one telescope [5, 6]. This combination of detection and non-detection data is useful in determining the criteria for detectability in future observations of transient events. The goal of this work is to identify factors contributing to detectability and to establish thresholds applicable to the LCROSS event. We take the data containing detections and then degrade a specific factor in them until the plume is no longer detectable. These derived thresholds for factors (e.g., scattered light, temporal resolution, spatial resolution, field of view, and signal-to-noise of the illuminated foreground of Cabeus) can be compared to the properties of the actual non-detection data to identify problems specific to its observing conditions or observational setup. The percent differences between the thresholds and both the detection data and non-detection data may also reveal the relative importance of these detectability factors. This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G. Observations reported here were obtained at the MMT Observatory, a joint facility of the Smithsonian Institution and the University of Arizona.References: [1] Colaprete, A. et al. (2010) Science, 330, 463-468. [2] Schultz, P. H. et al. (2010) Science, 330, 468-472. [3] Heldmann, J. L. et al. (2012) Space Sci. Rev., 167:93-140, doi:10.1007/s11214-011-9759-y. [4] Strycker, P. D. et al. (2013) Nat. Commun., 4

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 ...

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.

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

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

Directory of Open Access Journals (Sweden)

Yunlong Sun

2018-01-01

Full Text Available Gas sensors have been widely reported for industrial gas detection and monitoring. However, the rapid detection and identification of industrial gases are still a challenge. In this work, we measure four typical industrial gases including CO2, CH4, NH3, and volatile organic compounds (VOCs based on electronic nose (EN at different concentrations. To solve the problem of effective classification and identification of different industrial gases, we propose an algorithm based on the selective local linear embedding (SLLE to reduce the dimensionality and extract the features of high-dimensional data. Combining the Euclidean distance (ED formula with the proposed algorithm, we can achieve better classification and identification of four kinds of gases. We compared the classification and recognition results of classical principal component analysis (PCA, linear discriminate analysis (LDA, and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction. The experimental results show that the recognition accuracy rate of the SLLE reaches 91.36%, which is better than the other three algorithms. In addition, the SLLE algorithm provides more efficient and accurate responses to high-dimensional industrial gas data. It can be used in real-time industrial gas detection and monitoring combined with gas sensor networks.

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.

Tomato seeds maturity detection system based on chlorophyll fluorescence

Science.gov (United States)

Li, Cuiling; Wang, Xiu; Meng, Zhijun

2016-10-01

Chlorophyll fluorescence intensity can be used as seed maturity and quality evaluation indicator. Chlorophyll fluorescence intensity of seed coats is tested to judge the level of chlorophyll content in seeds, and further to judge the maturity and quality of seeds. This research developed a detection system of tomato seeds maturity based on chlorophyll fluorescence spectrum technology, the system included an excitation light source unit, a fluorescent signal acquisition unit and a data processing unit. The excitation light source unit consisted of two high power LEDs, two radiators and two constant current power supplies, and it was designed to excite chlorophyll fluorescence of tomato seeds. The fluorescent signal acquisition unit was made up of a fluorescence spectrometer, an optical fiber, an optical fiber scaffolds and a narrowband filter. The data processing unit mainly included a computer. Tomato fruits of green ripe stage, discoloration stage, firm ripe stage and full ripe stage were harvested, and their seeds were collected directly. In this research, the developed tomato seeds maturity testing system was used to collect fluorescence spectrums of tomato seeds of different maturities. Principal component analysis (PCA) method was utilized to reduce the dimension of spectral data and extract principal components, and PCA was combined with linear discriminant analysis (LDA) to establish discriminant model of tomato seeds maturity, the discriminant accuracy was greater than 90%. Research results show that using chlorophyll fluorescence spectrum technology is feasible for seeds maturity detection, and the developed tomato seeds maturity testing system has high detection accuracy.

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.

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

Love Wave Sensor for Prostate-Specific Membrane Antigen Detection Based on Hydrophilic Molecularly-Imprinted Polymer

Directory of Open Access Journals (Sweden)

Pingping Tang

2018-05-01

Full Text Available Prostate-specific membrane antigen (PSMA is a biomarker for prostate cancer (PCa, and a specific and reliable detection technique of PSMA is urgently required for PCa early diagnosis. A Love wave sensor has been widely studied for real-time sensing and highly sensitive applications, but the sensing unit needs special handling for selective detection purpose. In this study, we prepared a versatile Love wave sensor functionalized with molecularly-imprinted polymers (MIP, PSMA as the template molecule. To enhance the specific template bindings of MIP in pure aqueous solutions, facile reversible addition/fragmentation chain transfer (RAFT precipitation polymerization (RAFTPP was used to produce surface hydrophilic polymer brushes on MIP. The presence of hydrophilic polymer brushes on MIP improved its surface hydrophilicity and significantly reduced their hydrophobic interactions with template molecules in pure aqueous media. In detection process, the acoustic delay-line is confederative to a microfluidic chip and inserted in an oscillation loop. The real-time resonance frequency of the MIP-based Love wave sensor to different concentrations of PSMA was investigated. The limit of detection (LOD for this Love SAW sensor was 0.013 ng mL−1, which demonstrates that this sensor has outstanding performance in terms of the level of detection.

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...

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

Science.gov (United States)

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

2016-01-01

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

A robust physiology-based source separation method for QRS detection in low amplitude fetal ECG recordings

International Nuclear Information System (INIS)

Vullings, R; Bergmans, J W M; Peters, C H L; Hermans, M J M; Wijn, P F F; Oei, S G

2010-01-01

The use of the non-invasively obtained fetal electrocardiogram (ECG) in fetal monitoring is complicated by the low signal-to-noise ratio (SNR) of ECG signals. Even after removal of the predominant interference (i.e. the maternal ECG), the SNR is generally too low for medical diagnostics, and hence additional signal processing is still required. To this end, several methods for exploiting the spatial correlation of multi-channel fetal ECG recordings from the maternal abdomen have been proposed in the literature, of which principal component analysis (PCA) and independent component analysis (ICA) are the most prominent. Both PCA and ICA, however, suffer from the drawback that they are blind source separation (BSS) techniques and as such suboptimum in that they do not consider a priori knowledge on the abdominal electrode configuration and fetal heart activity. In this paper we propose a source separation technique that is based on the physiology of the fetal heart and on the knowledge of the electrode configuration. This technique operates by calculating the spatial fetal vectorcardiogram (VCG) and approximating the VCG for several overlayed heartbeats by an ellipse. By subsequently projecting the VCG onto the long axis of this ellipse, a source signal of the fetal ECG can be obtained. To evaluate the developed technique, its performance is compared to that of both PCA and ICA and to that of augmented versions of these techniques (aPCA and aICA; PCA and ICA applied on preprocessed signals) in generating a fetal ECG source signal with enhanced SNR that can be used to detect fetal QRS complexes. The evaluation shows that the developed source separation technique performs slightly better than aPCA and aICA and outperforms PCA and ICA and has the main advantage that, with respect to aPCA/PCA and aICA/ICA, it performs more robustly. This advantage renders it favorable for employment in automated, real-time fetal monitoring applications

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...

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

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...

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.

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

[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.

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.

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...

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.

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.

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)

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.

Enhanced dynamic data-driven fault detection approach: Application to a two-tank heater system

KAUST Repository

Harrou, Fouzi; Madakyaru, Muddu; Sun, Ying; Kammammettu, Sanjula

2018-01-01

on PCA approach a challenging task. Accounting for the dynamic nature of data can also reflect the performance of the designed fault detection approaches. In PCA-based methods, this dynamic characteristic of the data can be accounted for by using dynamic

Amalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring

KAUST Repository

Harrou, Fouzi; Sun, Ying; Khadraoui, Sofiane

2016-01-01

Accurate and effective anomaly detection and diagnosis of modern industrial systems are crucial for ensuring reliability and safety and for maintaining desired product quality. Anomaly detection based on principal component analysis (PCA) has been

A measurement-based fault detection approach applied to monitor robots swarm

KAUST Repository

Khaldi, Belkacem; Harrou, Fouzi; Sun, Ying; Cherif, Foudil

2017-01-01

present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart

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.

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.

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.

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

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.

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.

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.

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.

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

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

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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

Comparative study of PCA in classification of multichannel EMG signals.

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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).

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

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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.

Multisensor system based on bisphthalocyanine nanowires for the detection of antioxidants

International Nuclear Information System (INIS)

Gay Martín, Mónica; Saja, José Antonio de; Muñoz, Raquel; Rodríguez-Méndez, María Luz

2012-01-01

Highlights: ► Sensors based on LnPc 2 nanowires can be prepared by electrodeposition (EDP). ► An electronic tongue can be constructed by combining EDP sensors with a data treatment system. ► The e-tongue is able to discriminate antioxidants of interest in the food industry. ► The fast preparation and excellent performance of these nanostructured sensors is an advantage. - Abstract: Electrophoretic deposition has been used to prepare thin films based on nanowires of three lanthanoid bisphthalocyaninates (including dysprosium, gadolinium and lutetium). Nanowires of similar structural characteristics have been obtained for the three compounds by tuning the electrophoretic conditions according to the redox properties of each phthalocyanine. The three electrodes have been used to form an array of sensors that has been employed to discriminate phenolic antioxidants of interest in the food industry including caffeic, gallic, vanillic and ferulic acids. The Principal Component Analysis (PCA) and the Partial Least Squares Discriminant Analysis (PLS-DA) of the electrochemical signals has allowed a clear discrimination of the four phenols analyzed according to the number of phenolic groups attached to the structure (monophenol, diphenol or triphenol). The PCA loading plots indicate that the three electrodes bring complementary information facilitating the discrimination of the studied solutions. In addition, good correlations between the intensity of the redox processes observed in the electrodes and the concentration of phenolic compounds have been found with detection limits in the range of 10 −5 –10 −6 mol L −1 and good reproducibility. The fast preparation of these nanowires based films and their excellent performance offer a new sensing platform for the detection of antioxidants in a fast, reliable way.

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

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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.

Biometric identification based on feature fusion with PCA and SVM

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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.

Prostate Cancer Detection and Prognosis: From Prostate Specific Antigen (PSA) to Exosomal Biomarkers.

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Filella, Xavier; Foj, Laura

2016-10-26

Prostate specific antigen (PSA) remains the most used biomarker in the management of early prostate cancer (PCa), in spite of the problems related to false positive results and overdiagnosis. New biomarkers have been proposed in recent years with the aim of increasing specificity and distinguishing aggressive from non-aggressive PCa. The emerging role of the prostate health index and the 4Kscore is reviewed in this article. Both are blood-based tests related to the aggressiveness of the tumor, which provide the risk of suffering PCa and avoiding negative biopsies. Furthermore, the use of urine has emerged as a non-invasive way to identify new biomarkers in recent years, including the PCA3 and TMPRSS2:ERG fusion gene. Available results about the PCA3 score showed its usefulness to decide the repetition of biopsy in patients with a previous negative result, although its relationship with the aggressiveness of the tumor is controversial. More recently, aberrant microRNA expression in PCa has been reported by different authors. Preliminary results suggest the utility of circulating and urinary microRNAs in the detection and prognosis of PCa. Although several of these new biomarkers have been recommended by different guidelines, large prospective and comparative studies are necessary to establish their value in PCa detection and prognosis.

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

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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.

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

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Yang, Xin; Liu, Chaoyue; Wang, Zhiwei; Yang, Jun; Min, Hung Le; Wang, Liang; Cheng, Kwang-Ting Tim

2017-12-01

Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions' locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive

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

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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.

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

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

2007-11-01

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

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

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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

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

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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.

A data-based technique for monitoring of wound rotor induction machines: A simulation study

KAUST Repository

Harrou, Fouzi

2016-05-09

Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM). The proposed fault detection approach is based on the use of principal components analysis (PCA). However, conventional PCA-based detection indices, such as the T2T2 and the Q statistics, are not well suited to detect small faults because these indices only use information from the most recent available samples. Detection of small faults is one of the most crucial and challenging tasks in the area of fault detection and diagnosis. In this paper, a new statistical system monitoring strategy is proposed for detecting changes resulting from small shifts in several variables associated with WRIM. The proposed approach combines modeling using PCA modeling with the exponentially weighted moving average (EWMA) control scheme. In the proposed approach, EWMA control scheme is applied on the ignored principal components to detect the presence of faults. The performance of the proposed method is compared with those of the traditional PCA-based fault detection indices. The simulation results clearly show the effectiveness of the proposed method over the conventional ones, especially in the presence of faults with small magnitudes.

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

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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.

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

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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.

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.

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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

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

2016-10-01

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

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

Effects of PCA and DMAE on the namatode Caenorhabditis briggsae.

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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.

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

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

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...

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.

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

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.

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.

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.

Orthogonal transformations for change detection, Matlab code

DEFF Research Database (Denmark)

2005-01-01

Matlab code to do multivariate alteration detection (MAD) analysis, maximum autocorrelation factor (MAF) analysis, canonical correlation analysis (CCA) and principal component analysis (PCA) on image data.......Matlab code to do multivariate alteration detection (MAD) analysis, maximum autocorrelation factor (MAF) analysis, canonical correlation analysis (CCA) and principal component analysis (PCA) on image data....

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.

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.

Radar fall detection using principal component analysis

Science.gov (United States)

Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

2016-05-01

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

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.

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.

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

Support vector machine based fault detection approach for RFT-30 cyclotron

Energy Technology Data Exchange (ETDEWEB)

Kong, Young Bae, E-mail: ybkong@kaeri.re.kr; Lee, Eun Je; Hur, Min Goo; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

2016-10-21

An RFT-30 is a 30 MeV cyclotron used for radioisotope applications and radiopharmaceutical researches. The RFT-30 cyclotron is highly complex and includes many signals for control and monitoring of the system. It is quite difficult to detect and monitor the system failure in real time. Moreover, continuous monitoring of the system is hard and time-consuming work for human operators. In this paper, we propose a support vector machine (SVM) based fault detection approach for the RFT-30 cyclotron. The proposed approach performs SVM learning with training samples to construct the classification model. To compensate the system complexity due to the large-scale accelerator, we utilize the principal component analysis (PCA) for transformation of the original data. After training procedure, the proposed approach detects the system faults in real time. We analyzed the performance of the proposed approach utilizing the experimental data of the RFT-30 cyclotron. The performance results show that the proposed SVM approach can provide an efficient way to control the cyclotron system.

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....

Karhunen-Loève (PCA) based detection of multiple oscillations in multiple measurement signals from large-scale process plants

DEFF Research Database (Denmark)

Odgaard, Peter Fogh; Wickerhauser, M.V.

2007-01-01

 In the perspective of optimizing the control and operation of large scale process plants, it is important to detect and to locate oscillations in the plants. This paper presents a scheme for detecting and localizing multiple oscillations in multiple measurements from such a large-scale power plant....... The scheme is based on a Karhunen-Lo\\`{e}ve analysis of the data from the plant. The proposed scheme is subsequently tested on two sets of data: a set of synthetic data and a set of data from a coal-fired power plant. In both cases the scheme detects the beginning of the oscillation within only a few samples....... In addition the oscillation localization has also shown its potential by localizing the oscillations in both data sets....

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.

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...

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...

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

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.

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.

Performance of T2 Maps in the Detection of Prostate Cancer.

Science.gov (United States)

Chatterjee, Aritrick; Devaraj, Ajit; Mathew, Melvy; Szasz, Teodora; Antic, Tatjana; Karczmar, Gregory S; Oto, Aytekin

2018-05-03

This study compares the performance of T2 maps in the detection of prostate cancer (PCa) in comparison to T2-weighted (T2W) magnetic resonance images. The prospective study was institutional review board approved. Consenting patients (n = 45) with histologic confirmed PCa underwent preoperative 3-T magnetic resonance imaging with or without endorectal coil. Two radiologists, working independently, marked regions of interests (ROIs) on PCa lesions separately on T2W images and T2 maps. Each ROI was assigned a score of 1-5 based on the confidence in accurately detecting cancer, with 5 being the highest confidence. Subsequently, the histologically confirmed PCa lesions (n = 112) on whole-mount sections were matched with ROIs to calculate sensitivity, positive predictive value (PPV), and radiologist confidence score. Quantitative T2 values of PCa and benign tissue ROIs were measured. Sensitivity and confidence score for PCa detection were similar for T2W images (51%, 4.5 ± 0.8) and T2 maps (52%, 4.5 ± 0.6). However, PPV was significantly higher (P = .001) for T2 maps (88%) compared to T2W (72%) images. The use of endorectal coils nominally improved sensitivity (T2W: 55 vs 47%, T2 map: 54% vs 48%) compared to the use of no endorectal coils, but not the PPV and the confidence score. Quantitative T2 values for PCa (105 ± 28 milliseconds) were significantly (P = 9.3 × 10 -14 ) lower than benign peripheral zone tissue (211 ± 71 milliseconds), with moderate significant correlation with Gleason score (ρ = -0.284). Our study shows that review of T2 maps by radiologists has similar sensitivity but higher PPV compared to T2W images. Additional quantitative information obtained from T2 maps is helpful in differentiating cancer from normal prostate tissue and determining its aggressiveness. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

Science.gov (United States)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

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

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

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

2016-01-01

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

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

International Nuclear Information System (INIS)

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

2014-01-01

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

Novel RNA hybridization method for the in situ detection of ETV1, ETV4, and ETV5 gene fusions in prostate cancer.

Science.gov (United States)

Kunju, Lakshmi P; Carskadon, Shannon; Siddiqui, Javed; Tomlins, Scott A; Chinnaiyan, Arul M; Palanisamy, Nallasivam

2014-09-01

The genetic basis of 50% to 60% of prostate cancer (PCa) is attributable to rearrangements in E26 transformation-specific (ETS) (ERG, ETV1, ETV4, and ETV5), BRAF, and RAF1 genes and overexpression of SPINK1. The development and validation of reliable detection methods are warranted to classify various molecular subtypes of PCa for diagnostic and prognostic purposes. ETS gene rearrangements are typically detected by fluorescence in situ hybridization and reverse-transcription polymerase chain reaction methods. Recently, monoclonal antibodies against ERG have been developed that detect the truncated ERG protein in immunohistochemical assays where staining levels are strongly correlated with ERG rearrangement status by fluorescence in situ hybridization. However, specific antibodies for ETV1, ETV4, and ETV5 are unavailable, challenging their clinical use. We developed a novel RNA in situ hybridization-based assay for the in situ detection of ETV1, ETV4, and ETV5 in formalin-fixed paraffin-embedded tissues from prostate needle biopsies, prostatectomy, and metastatic PCa specimens using RNA probes. Further, with combined RNA in situ hybridization and immunohistochemistry we identified a rare subset of PCa with dual ETS gene rearrangements in collisions of independent tumor foci. The high specificity and sensitivity of RNA in situ hybridization provides an alternate method enabling bright-field in situ detection of ETS gene aberrations in routine clinically available PCa specimens.

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

Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

Science.gov (United States)

Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

2017-09-01

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to

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.

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.

Evaluation of Vitronectin Expression in Prostate Cancer and the Clinical Significance of the Association of Vitronectin Expression with Prostate Specific Antigen in Detecting Prostate Cancer.

Science.gov (United States)

Niu, Yue; Zhang, Ling; Bi, Xing; Yuan, Shuai; Chen, Peng

2016-03-05

To detect the expression of vitronectin (VTN) in the tissues and blood serum of prostate cancer (PCa) patients, and evaluate its clinical significance and to evaluate the significance of the combined assay of VTN and prostate specific antigens (PSA) in PCa diagnosis. To detect the expression of VTN as a potential marker for PCa diagnosis and prognosis, immunohistochemistry was performed on the tissues of 32 patients with metastatic PCa (PCaM), 34 patients with PCa without metastasis (PCa), and 41 patients with benign prostatic hyperplasia (BPH). The sera were then subjected to Western blot analysis. All cases were subsequently examined to determine the concentrations of PSA and VTN in the sera. The collected data were collated and analyzed. The positive expression rates of VTN in the tissues of the BPH and PCa groups (including PCa and PCaM groups) were 75.61% and 45.45%, respectively (P = .005). VTN was more highly expressed in the sera of the BPH patients (0.83 ± 0.07) than in the sera of the PCa patients (0.65 ± 0.06) (P < .05). It was also more highly expressed in the sera of the PCa patients than in the sera of the PCaM patients (0.35 ± 0.08) (P < .05). In the diagnosis of BPH and PCa, the Youden indexes of PSA detection, VTN detection, and combined detection were 0.2620, 0.3468, and 0.5635; the kappa values were 0.338, 0.304, and 0.448, respectively, and the areas under the receiver operating characteristic curve were 0.625, 0.673, and 0.703 (P < .05), respectively. VTN levels in sera may be used as a potential marker of PCa for the diagnosis and assessment of disease progression and metastasis. The combined detection of VTN and PSA in sera can be clinically applied in PCa diagnosis. .

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.

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...

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.

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.

Fault detection of flywheel system based on clustering and principal component analysis

Directory of Open Access Journals (Sweden)

Wang Rixin

2015-12-01

Full Text Available Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of “integrated power and attitude control” system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the relationship of parameters in each operation through the principal component analysis (PCA method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

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

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

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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.

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

Hyperspectral Based Skin Detection for Person of Interest Identification

Science.gov (United States)

2015-03-01

short-wave infrared VIS visible spectrum PCA principal component analysis FCBF Fast Correlation- Based Filter POI person of interest ANN artificial neural...an artificial neural network (ANN) that is created in MATLAB® using the Neural Network Toolbox to identify a POI based on their skin spectral data. A...identifying a POI based on skin spectral data. She identified an optimal feature subset to be used with the hyperspectral data she collected using a

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.

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

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.

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.

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

Feature extraction for ultrasonic sensor based defect detection in ceramic components

Science.gov (United States)

Kesharaju, Manasa; Nagarajah, Romesh

2014-02-01

High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

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.

GC-MS-Based Endometabolome Analysis Differentiates Prostate Cancer from Normal Prostate Cells

Directory of Open Access Journals (Sweden)

Ana Rita Lima

2018-03-01

Full Text Available Prostate cancer (PCa is an important health problem worldwide. Diagnosis and management of PCa is very complex because the detection of serum prostate specific antigen (PSA has several drawbacks. Metabolomics brings promise for cancer biomarker discovery and for better understanding PCa biochemistry. In this study, a gas chromatography–mass spectrometry (GC-MS based metabolomic profiling of PCa cell lines was performed. The cell lines include 22RV1 and LNCaP from PCa with androgen receptor (AR expression, DU145 and PC3 (which lack AR expression, and one normal prostate cell line (PNT2. Regarding the metastatic potential, PC3 is from an adenocarcinoma grade IV with high metastatic potential, DU145 has a moderate metastatic potential, and LNCaP has a low metastatic potential. Using multivariate analysis, alterations in levels of several intracellular metabolites were detected, disclosing the capability of the endometabolome to discriminate all PCa cell lines from the normal prostate cell line. Discriminant metabolites included amino acids, fatty acids, steroids, and sugars. Six stood out for the separation of all the studied PCa cell lines from the normal prostate cell line: ethanolamine, lactic acid, β-Alanine, L-valine, L-leucine, and L-tyrosine.

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.

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...

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.

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.

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....

Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

Science.gov (United States)

Sierra-Pérez, Julián; Torres-Arredondo, M.-A.; Alvarez-Montoya, Joham

2018-01-01

Structural health monitoring consists of using sensors integrated within structures together with algorithms to perform load monitoring, damage detection, damage location, damage size and severity, and prognosis. One possibility is to use strain sensors to infer structural integrity by comparing patterns in the strain field between the pristine and damaged conditions. In previous works, the authors have demonstrated that it is possible to detect small defects based on strain field pattern recognition by using robust machine learning techniques. They have focused on methodologies based on principal component analysis (PCA) and on the development of several unfolding and standardization techniques, which allow dealing with multiple load conditions. However, before a real implementation of this approach in engineering structures, changes in the strain field due to conditions different from damage occurrence need to be isolated. Since load conditions may vary in most engineering structures and promote significant changes in the strain field, it is necessary to implement novel techniques for uncoupling such changes from those produced by damage occurrence. A damage detection methodology based on optimal baseline selection (OBS) by means of clustering techniques is presented. The methodology includes the use of hierarchical nonlinear PCA as a nonlinear modeling technique in conjunction with Q and nonlinear-T 2 damage indices. The methodology is experimentally validated using strain measurements obtained by 32 fiber Bragg grating sensors bonded to an aluminum beam under dynamic bending loads and simultaneously submitted to variations in its pitch angle. The results demonstrated the capability of the methodology for clustering data according to 13 different load conditions (pitch angles), performing the OBS and detecting six different damages induced in a cumulative way. The proposed methodology showed a true positive rate of 100% and a false positive rate of 1.28% for a

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.

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

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.

Potentials and Limitations of Real-Time Elastography for Prostate Cancer Detection: A Whole-Mount Step Section Analysis

Directory of Open Access Journals (Sweden)

Daniel Junker

2012-01-01

Full Text Available Objectives. To evaluate prostate cancer (PCa detection rates of real-time elastography (RTE in dependence of tumor size, tumor volume, localization and histological type. Materials and Methods. Thirdy-nine patients with biopsy proven PCa underwent RTE before radical prostatectomy (RPE to assess prostate tissue elasticity, and hard lesions were considered suspicious for PCa. After RPE, the prostates were prepared as whole-mount step sections and were compared with imaging findings for analyzing PCa detection rates. Results. RTE detected 6/62 cancer lesions with a maximum diameter of 0–5 mm (9.7%, 10/37 with a maximum diameter of 6–10 mm (27%, 24/34 with a maximum diameter of 11–20 20 mm (70.6%, 14/14 with a maximum diameter of >20 mm (100% and 40/48 with a volume ≥0.2 cm3 (83.3%. Regarding cancer lesions with a volume ≥ 0.2 cm³ there was a significant difference in PCa detection rates between Gleason scores with predominant Gleason pattern 3 compared to those with predominant Gleason pattern 4 or 5 (75% versus 100%; P=0.028. Conclusions. RTE is able to detect PCa of significant tumor volume and of predominant Gleason pattern 4 or 5 with high confidence, but is of limited value in the detection of small cancer lesions.

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

Orthogonal transformations for change detection, Matlab code (ENVI-like headers)

DEFF Research Database (Denmark)

2007-01-01

Matlab code to do (iteratively reweighted) multivariate alteration detection (MAD) analysis, maximum autocorrelation factor (MAF) analysis, canonical correlation analysis (CCA) and principal component analysis (PCA) on image data; accommodates ENVI (like) header files.......Matlab code to do (iteratively reweighted) multivariate alteration detection (MAD) analysis, maximum autocorrelation factor (MAF) analysis, canonical correlation analysis (CCA) and principal component analysis (PCA) on image data; accommodates ENVI (like) header files....

A powerful score-based test statistic for detecting gene-gene co-association.

Science.gov (United States)

Xu, Jing; Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Li, Hongkai; Wu, Xuesen; Xue, Fuzhong; Liu, Yanxun

2016-01-29

The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the "missing heritability" problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association. Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ (2)) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice. SBS is a powerful and efficient gene-based method for detecting gene-gene co-association.

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.

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)

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.

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

A Novel Real-Time DDoS Attack Detection Mechanism Based on MDRA Algorithm in Big Data

Directory of Open Access Journals (Sweden)

Bin Jia

2016-01-01

Full Text Available In the wake of the rapid development and wide application of information technology and Internet, our society has come into the information explosion era. Meanwhile, it brings in new and severe challenges to the field of network attack behavior detection due to the explosive growth and high complexity of network traffic. Therefore, an effective and efficient detection mechanism that can detect attack behavior from large scale of network traffic plays an important role. In this paper, we focus on how to distinguish the attack traffic from normal data flows in Big Data and propose a novel real-time DDoS attack detection mechanism based on Multivariate Dimensionality Reduction Analysis (MDRA. In this mechanism, we first reduce the dimensionality of multiple characteristic variables in a network traffic record by Principal Component Analysis (PCA. Then, we analyze the correlation of the lower dimensional variables. Finally, the attack traffic can be differentiated from the normal traffic by MDRA and Mahalanobis distance (MD. Compared with previous research methods, our experimental results show that higher precision rate is achieved and it approximates to 100% in True Negative Rate (TNR for detection; CPU computing time is one-eightieth and memory resource consumption is one-third of the previous detection method based on Multivariate Correlation Analysis (MCA; computing complexity is constant.

Monitoring a robot swarm using a data-driven fault detection approach

KAUST Repository

Khaldi, Belkacem

2017-06-30

Using swarm robotics system, with one or more faulty robots, to accomplish specific tasks may lead to degradation in performances complying with the target requirements. In such circumstances, robot swarms require continuous monitoring to detect abnormal events and to sustain normal operations. In this paper, an innovative exogenous fault detection method for monitoring robots swarm is presented. The method merges the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average (EWMA) and cumulative sum (CUSUM) control charts to insidious changes. The method is tested and evaluated on a swarm of simulated foot-bot robots performing a circle formation task, via the viscoelastic control model. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed method where compared to the conventional PCA-based methods (i.e., T2 and Q).

Colorimetric sensor arrays based on pattern recognition for the detection of nitroaromatic molecules

Energy Technology Data Exchange (ETDEWEB)

Lu, Wei; Dong, Xiao [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Qiu, Lili, E-mail: qiulili@bit.edu.cn [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Yan, Zequn [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Meng, Zihui, E-mail: m_zihui@yahoo.com [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Xue, Min [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); He, Xuan; Liu, Xueyong [Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900 (China)

2017-03-15

Graphical abstract: A colorimetric sensor array based on four kinds molecularly imprinted photonic crystal (MIPC) was explored for the selective visual detection of TNT, 2,6-DNT, 2,4-DNT and 4-MNT. The color of individual sensor changed with the increasing concentration of the analytes, and a cross-responsive platform was evaluated by a “radar” pattern. With the assistance of principal component analysis (PCA), a separate response region contained 95.25% of significant characteristics for the detection of nitroaromatics was generated, which also promised high potential for the customized visual detection system of other harmful chemicals. - Highlights: • Nitroaromatics were visually detected by molecularly imprinted photonic crystal. • The adsorption capacity was calculated. • The cross responsive platform of sensor array was established and discussed. • The discrimination capability was promoted by principal component analysis. • This system had high potential to be used in other customed visual detection. - Abstract: This research demonstrated that, in a colorimetric sensor array, 2,4,6-trinitrotoluene (TNT), 2,6-dinitrotoluene (2,6-DNT), 2,4-dinitrotoluene (2,4-DNT) and 4-nitrotoluene (4-MNT) were identifiable through a unique pattern in a qualitative and semi-quantitative manner. The adsorption capacity of the molecularly imprinted colloidal particles (MICs) for their corresponding templates was 0.27 mmol TNT/g, 0.22 mmol 2,6-DNT/g, 0.31 mmol 2,4-DNT/g and 0.16 mmol 4-MNT/g, respectively. Every optical sensor utilized in the arrays contained three-dimensional molecularly imprinted photonic crystal (MIPC) sensor with different imprinted templates. The intelligent materials can display different colors from green to red to 20 mM corresponding nitroaromatics with varying diffraction red shifts of 84 nm (TNT), 46 nm (2,6-DNT), 54 nm (2,4-DNT) and 35 nm (4-MNT), respectively. With the assistance of principal component analysis (PCA) and rational design

Colorimetric sensor arrays based on pattern recognition for the detection of nitroaromatic molecules

International Nuclear Information System (INIS)

Lu, Wei; Dong, Xiao; Qiu, Lili; Yan, Zequn; Meng, Zihui; Xue, Min; He, Xuan; Liu, Xueyong

2017-01-01

Graphical abstract: A colorimetric sensor array based on four kinds molecularly imprinted photonic crystal (MIPC) was explored for the selective visual detection of TNT, 2,6-DNT, 2,4-DNT and 4-MNT. The color of individual sensor changed with the increasing concentration of the analytes, and a cross-responsive platform was evaluated by a “radar” pattern. With the assistance of principal component analysis (PCA), a separate response region contained 95.25% of significant characteristics for the detection of nitroaromatics was generated, which also promised high potential for the customized visual detection system of other harmful chemicals. - Highlights: • Nitroaromatics were visually detected by molecularly imprinted photonic crystal. • The adsorption capacity was calculated. • The cross responsive platform of sensor array was established and discussed. • The discrimination capability was promoted by principal component analysis. • This system had high potential to be used in other customed visual detection. - Abstract: This research demonstrated that, in a colorimetric sensor array, 2,4,6-trinitrotoluene (TNT), 2,6-dinitrotoluene (2,6-DNT), 2,4-dinitrotoluene (2,4-DNT) and 4-nitrotoluene (4-MNT) were identifiable through a unique pattern in a qualitative and semi-quantitative manner. The adsorption capacity of the molecularly imprinted colloidal particles (MICs) for their corresponding templates was 0.27 mmol TNT/g, 0.22 mmol 2,6-DNT/g, 0.31 mmol 2,4-DNT/g and 0.16 mmol 4-MNT/g, respectively. Every optical sensor utilized in the arrays contained three-dimensional molecularly imprinted photonic crystal (MIPC) sensor with different imprinted templates. The intelligent materials can display different colors from green to red to 20 mM corresponding nitroaromatics with varying diffraction red shifts of 84 nm (TNT), 46 nm (2,6-DNT), 54 nm (2,4-DNT) and 35 nm (4-MNT), respectively. With the assistance of principal component analysis (PCA) and rational design

Synthesis and biodistribution study of 3-iodo-4-hydroxyphenyl-cysteamine for detection of malignant melanoma based on specific enzyme of melanin formation

International Nuclear Information System (INIS)

Nishii, R.; Nagamachi, S.; Tamura, S.; Kawai, K.; Nishimura, K.; Kinuya, S.; Uehara, T.; Tonami, N.; Arano, Y.

2002-01-01

Purpose: The aim of our study is to develop a new radiopharmaceutical labeled with radioiodine for detection and therapy of tumors, which have affinity to a characteristic metabolism in tumor. 3-Iodo-4-hydroxyphenyl-L-cysteine (I- L-PC), which we have reported previously, was found to have an interaction for tyrosinase, an essential and rate-limiting enzyme to melanin biosynthesis. In this study, considering higher affinity for tyrosinase, we synthesized 3-iodo-4-hydroxyphenylcysteamine (I-PCA) that was an amine derivative of I-L-PC and examined biodistribution study in melanoma-bearing mice. Method/Materials: 4-Hydroxyphenylcysteamine (4-PCA) was synthesized and radioiodinated in our laboratory. Synthesis of 4-PCA was confirmed by 1 H-NMR, mass spectrometry and elemental analysis. 125 I-PCA was prepared by conventional chloramine-T method under a no-carrier added condition. 125 I-PCA was purified by Sep-Pak-C-18 cartridge and the labeling efficiency and radiochemical purity were examined by TLC analysis. Biodistribution study of I-PCA was performed using B16 melanoma-bearing C57BL6 mice. The radioactivities of each organ were measured and % injected dose / g wet tissue was determined. Moreover, the tumor-to-blood ratio (T/B ratio) and tumor-to-muscle ratios (T/M ratio) of 125 I-PCA were also evaluated and were compared with 125 I-L-PC, 67 Ga-citrate, 125 I-L-AMT and 123 I-MIBG. Results: Radiosynthesis of 125 I-PCA was carried out conveniently and efficiently within only 15 min. A labeling efficiency of more than 73 % resulted in the labeling of 4-PCA to 125 I-PCA. After the simple Sep-Pak purification, no-carrier added 125 I-PCA with radiochemical purity greater than 90 % was obtained. The biodistribution of 125 I-PCA showed rapid blood clearance, renal excretion and low accumulation in normal tissue, while increase of accumulation in the tumor for 30 min. As a consequence, T/B ratio reached approximately 1.6 ± 0.3 and T/M ratio increased up to 8.7 ± 3.2 at 60

Transrectal real-time tissue elastography targeted biopsy coupled with peak strain index improves the detection of clinically important prostate cancer.

Science.gov (United States)

Ma, Qi; Yang, Dong-Rong; Xue, Bo-Xin; Wang, Cheng; Chen, Han-Bin; Dong, Yun; Wang, Cai-Shan; Shan, Yu-Xi

2017-07-01

The focus of the present study was to evaluate transrectal real-time tissue elastography (RTE)-targeted two-core biopsy coupled with peak strain index for the detection of prostate cancer (PCa) and to compare this method with 10-core systematic biopsy. A total of 141 patients were enrolled for evaluation. The diagnostic value of peak strain index was assessed using a receiver operating characteristic curve. The cancer detection rates of the two approaches and corresponding positive cores and Gleason score were compared. The cancer detection rate per core in the RTE-targeted biopsy (44%) was higher compared with that in systematic biopsy (30%). The peak strain index value of PCa was higher compared with that of the benign lesion. PCa was detected with the highest sensitivity (87.5%) and specificity (85.5%) using the threshold value of a peak strain index of ≥5.97 with an area under the curve value of 0.95. When the Gleason score was ≥7, RTE-targeted biopsy coupled with peak strain index detected 95.6% of PCa cases, but 84.4% were detected using systematic biopsy. Peak strain index as a quantitative parameter may improve the differentiation of PCa from benign lesions in the prostate peripheral zone. Transrectal RTE-targeted biopsy coupled with peak strain index may enhance the detection of clinically significant PCa, particularly when combined with systematic biopsy.

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.

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

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)

Acoustic analysis assessment in speech pathology detection

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Panek Daria

2015-09-01

Full Text Available Automatic detection of voice pathologies enables non-invasive, low cost and objective assessments of the presence of disorders, as well as accelerating and improving the process of diagnosis and clinical treatment given to patients. In this work, a vector made up of 28 acoustic parameters is evaluated using principal component analysis (PCA, kernel principal component analysis (kPCA and an auto-associative neural network (NLPCA in four kinds of pathology detection (hyperfunctional dysphonia, functional dysphonia, laryngitis, vocal cord paralysis using the a, i and u vowels, spoken at a high, low and normal pitch. The results indicate that the kPCA and NLPCA methods can be considered a step towards pathology detection of the vocal folds. The results show that such an approach provides acceptable results for this purpose, with the best efficiency levels of around 100%. The study brings the most commonly used approaches to speech signal processing together and leads to a comparison of the machine learning methods determining the health status of the patient

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.

Metric Learning Method Aided Data-Driven Design of Fault Detection Systems

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Guoyang Yan

2014-01-01

Full Text Available Fault detection is fundamental to many industrial applications. With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency. Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances. In this paper, we firstly propose a metric learning-based fault detection framework in fault detection. Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature vector from detection signals. Experiments on Tennessee Eastman (TE chemical process datasets demonstrate that the proposed method has a better performance when comparing with existing methods, for example, principal component analysis (PCA and fisher discriminate analysis (FDA.

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.

Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

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Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

2018-03-01

Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

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...

Continuous dynamic monitoring of a lively footbridge for serviceability assessment and damage detection

Science.gov (United States)

Hu, Wei-Hua; Moutinho, Carlos; Caetano, Elsa; Magalhães, Filipe; Cunha, Álvaro

2012-11-01

This paper aims at analyzing the feasibility of applying a vibration based damage detection approach, based on Principal Components Analysis (PCA), to eliminate environmental effects using the large amount of high quality data continuously collected by the dynamic monitoring system of Pedro e Inês footbridge since 2007. Few works describe real data, regularly collected along several years by reliable continuous dynamic monitoring systems in bridge structures. One main contribution is to show a large difference between making academic research based on numerical simulations or limited experimental samples, and making validity tests of innovative procedures using large high quality databases collected in real structures. The monitoring system, installed with the only initial objective of checking the efficiency of vibration control devices used to mitigate lateral and vertical vibrations, was therefore further developed for research purposes by implementing LabVIEW based automated signal processing and output-only modal identification routines, that enabled the analysis of the correlation of modal estimates with the temperature and the vibration level, as well as the automatic tracking of modal parameters along several years. With the final purpose of detecting potential structural damage at an early stage, the Principal Components Analysis (PCA) was employed to effectively eliminate temperature effects, whereas Novelty Analysis on the residual errors of the PCA model was used to provide a statistical indication of damage. The efficiency of this vibration based damage detection approach was verified using 3 years of measurements at Pedro e Inês footbridge under operational conditions and simulating several realistic damage scenarios affecting the boundary conditions. It is demonstrated that such a dynamic monitoring system, apart from providing relevant instantaneous dynamic information, working as an alert system associated to the verification of vibration

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

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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.

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)

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)

[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.

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

[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.

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

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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.

Prediction of Protein-Protein Interactions by NanoLuc-Based Protein-Fragment Complementation Assay | Office of Cancer Genomics

Science.gov (United States)

The CTD2 Center at Emory has developed a new NanoLuc®-based protein-fragment complementation assay (NanoPCA) which allows the detection of novel protein-protein interactions (PPI). NanoPCA allows the study of PPI dynamics with reversible interactions.  Read the abstract. Experimental Approaches Read the detailed Experimetnal Approaches. 

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

Science.gov (United States)

Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

2017-06-26

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

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

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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.

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.

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)

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...

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....

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.

Identification of Sarcosine as a Target Molecule for the Canine Olfactory Detection of Prostate Carcinoma.

Science.gov (United States)

Pacik, Dalibor; Plevova, Mariana; Urbanova, Lucie; Lackova, Zuzana; Strmiska, Vladislav; Necas, Alois; Heger, Zbynek; Adam, Vojtech

2018-03-21

The hypothesis that dogs can detect malignant tumours through the identification of specific molecules is nearly 30 years old. To date, several reports have described the successful detection of distinct types of cancer. However, is still a lack of data regarding the specific molecules that can be recognized by a dog's olfactory apparatus. Hence, we performed a study with artificially prepared, well-characterized urinary specimens that were enriched with sarcosine, a widely reported urinary biomarker for prostate cancer (PCa). For the purposes of the study, a German shepherd dog was utilized for analyses of 60 positive and 120 negative samples. Our study provides the first evidence that a sniffer dog specially trained for the olfactory detection of PCa can recognize sarcosine in artificial urine with a performance [sensitivity of 90%, specificity of 95%, and precision of 90% for the highest amount of sarcosine (10 µmol/L)] that is comparable to the identification of PCa-diagnosed subjects (sensitivity of 93.5% and specificity of 91.6%). This study casts light on the unrevealed phenomenon of PCa olfactory detection and opens the door for further studies with canine olfactory detection and cancer diagnostics.

Comparative Study of Blood-Based Biomarkers, α2,3-Sialic Acid PSA and PHI, for High-Risk Prostate Cancer Detection.

Science.gov (United States)

Ferrer-Batallé, Montserrat; Llop, Esther; Ramírez, Manel; Aleixandre, Rosa Núria; Saez, Marc; Comet, Josep; de Llorens, Rafael; Peracaula, Rosa

2017-04-17

Prostate Specific Antigen (PSA) is the most commonly used serum marker for prostate cancer (PCa), although it is not specific and sensitive enough to allow the differential diagnosis of the more aggressive tumors. For that, new diagnostic methods are being developed, such as PCA-3, PSA isoforms that have resulted in the 4K score or the Prostate Health Index (PHI), and PSA glycoforms. In the present study, we have compared the PHI with our recently developed PSA glycoform assay, based on the determination of the α2,3-sialic acid percentage of serum PSA (% α2,3-SA), in a cohort of 79 patients, which include 50 PCa of different grades and 29 benign prostate hyperplasia (BPH) patients. The % α2,3-SA could distinguish high-risk PCa patients from the rest of patients better than the PHI (area under the curve (AUC) of 0.971 vs. 0.840), although the PHI correlated better with the Gleason score than the % α2,3-SA. The combination of both markers increased the AUC up to 0.985 resulting in 100% sensitivity and 94.7% specificity to differentiate high-risk PCa from the other low and intermediate-risk PCa and BPH patients. These results suggest that both serum markers complement each other and offer an improved diagnostic tool to identify high-risk PCa, which is an important requirement for guiding treatment decisions.

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.

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.

Abstracting audit data for lightweight intrusion detection

KAUST Repository

Wang, Wei; Zhang, Xiangliang; Pitsilis, Georgios

2010-01-01

are used to validate the two strategies of data abstraction. The extensive test results show that the process of exemplar extraction significantly improves the detection efficiency and has a better detection performance than PCA in data abstraction. © 2010

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...

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.

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

A PLSPM-Based Test Statistic for Detecting Gene-Gene Co-Association in Genome-Wide Association Study with Case-Control Design

Science.gov (United States)

Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong

2013-01-01

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods. PMID:23620809

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.

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

During the last decades, a technique for assessing atmospheric deposition of heavy elements was developed based on the principle that samples of moss are able to accumulate elements and airborne particles from rain, melting snow and dry deposition. Despite a broad interest in bioindication there are still ongoing works aimed at the preparation of a standard procedure allowing for a comparison of research carried out in various areas. This is why the comparison of living and dry moss of the same species and growth site seems to be interesting, logical and promising. A most reliable approach seems to be the application of bioindication connected with multivariate statistics and efficient visualization techniques in the interpretation of monitoring data. The aim of this study was: (i) to present cumulative properties of transplanted Sphagnum palustre moss with differentiation into dry and living biomaterial; (ii) to determine and geographically locate types of pollution sources responsible for a structure of the monitoring data set; (iii) to visualize geographical distribution of analytes in the Gdańsk metropolitan area and to identify the high-risk areas which can be targeted for environmental hazards and public health. A six month air pollution study based on Sphagnum palustre bioindication is presented and a simplified procedure of the experiment is given. The study area was located at the mouth of the Vistula River on the Baltic Sea, in Gdańsk City (Poland). Sphagnum palustre was selected for research because of its extraordinary morphological properties and its ease in being raised. The capability of dry and living moss to accumulate elements characteristic for anthropogenic and natural sources was shown by application of Principal Component Analysis. The high-risk areas and pollution profiles are detected and visualized using surface maps based on Kriging algorithm. The original selection of elements included all those that could be reliably determined by

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.

Abstracting audit data for lightweight intrusion detection

KAUST Repository

Wang, Wei

2010-01-01

High speed of processing massive audit data is crucial for an anomaly Intrusion Detection System (IDS) to achieve real-time performance during the detection. Abstracting audit data is a potential solution to improve the efficiency of data processing. In this work, we propose two strategies of data abstraction in order to build a lightweight detection model. The first strategy is exemplar extraction and the second is attribute abstraction. Two clustering algorithms, Affinity Propagation (AP) as well as traditional k-means, are employed to extract the exemplars, and Principal Component Analysis (PCA) is employed to abstract important attributes (a.k.a. features) from the audit data. Real HTTP traffic data collected in our institute as well as KDD 1999 data are used to validate the two strategies of data abstraction. The extensive test results show that the process of exemplar extraction significantly improves the detection efficiency and has a better detection performance than PCA in data abstraction. © 2010 Springer-Verlag.

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.

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

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

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

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...

Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data.

Science.gov (United States)

Duforet-Frebourg, Nicolas; Luu, Keurcien; Laval, Guillaume; Bazin, Eric; Blum, Michael G B

2016-04-01

To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Echo-Planar Imaging-Based, J-Resolved Spectroscopic Imaging for Improved Metabolite Detection in Prostate Cancer

Science.gov (United States)

2016-12-01

post-process the multi-dimensional MRS data from different prostate pathologies . Scope: Improved cancer detection (specificity) in differentiating...MATERIALS AND METHODS Patients Between March 2012 and May 2013, twenty-two patients with PCa with a mean age of 63.8 years (range, 46–79 years), who...tumor voxels, which was confirmed by the pathology report. After reconstruction, the EP-JRESI data were overlaid onto MRI images. MRI and MRSI A body

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

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

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.

A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

KAUST Repository

Zerrouki, Nabil

2016-07-26

Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures and not too computationally complex to allow a fast processing. The fall detection is addressed as a statistical anomaly detection problem. The proposed approach combines modeling using principal component analysis modeling with the exponentially weighted moving average (EWMA) monitoring chart. The EWMA scheme is applied on the ignored principal components to detect the presence of falls. Using two different fall detection datasets, URFD and FDD, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional PCA-based methods.

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...

Assessing the Clinical Role of Genetic Markers of Early-Onset Prostate Cancer Among High-Risk Men Enrolled in Prostate Cancer Early Detection

Science.gov (United States)

Hughes, Lucinda; Zhu, Fang; Ross, Eric; Gross, Laura; Uzzo, Robert G.; Chen, David Y. T.; Viterbo, Rosalia; Rebbeck, Timothy R.; Giri, Veda N.

2011-01-01

Background Men with familial prostate cancer (PCA) and African American men are at risk for developing PCA at younger ages. Genetic markers predicting early-onset PCA may provide clinically useful information to guide screening strategies for high-risk men. We evaluated clinical information from six polymorphisms associated with early-onset PCA in a longitudinal cohort of high-risk men enrolled in PCA early detection with significant African American participation. Methods Eligibility criteria include ages 35–69 with a family history of PCA or African American race. Participants undergo screening and biopsy per study criteria. Six markers associated with early-onset PCA (rs2171492 (7q32), rs6983561 (8q24), rs10993994 (10q11), rs4430796 (17q12), rs1799950 (17q21), and rs266849 (19q13)) were genotyped. Cox models were used to evaluate time to PCA diagnosis and PSA prediction for PCA by genotype. Harrell’s concordance index was used to evaluate predictive accuracy for PCA by PSA and genetic markers. Results 460 participants with complete data and ≥1 follow-up visit were included. 56% were African American. Among African American men, rs6983561 genotype was significantly associated with earlier time to PCA diagnosis (p=0.005) and influenced prediction for PCA by the PSA (p<0.001). When combined with PSA, rs6983561 improved predictive accuracy for PCA compared to PSA alone among African American men (PSA= 0.57 vs. PSA+rs6983561=0.75, p=0.03). Conclusions Early-onset marker rs6983561 adds potentially useful clinical information for African American men undergoing PCA risk assessment. Further study is warranted to validate these findings. Impact Genetic markers of early-onset PCA have potential to refine and personalize PCA early detection for high-risk men. PMID:22144497

Detecting spatial patterns with the cumulant function – Part 1: The theory

Directory of Open Access Journals (Sweden)

P. Naveau

2008-02-01

Full Text Available In climate studies, detecting spatial patterns that largely deviate from the sample mean still remains a statistical challenge. Although a Principal Component Analysis (PCA, or equivalently a Empirical Orthogonal Functions (EOF decomposition, is often applied for this purpose, it provides meaningful results only if the underlying multivariate distribution is Gaussian. Indeed, PCA is based on optimizing second order moments, and the covariance matrix captures the full dependence structure of multivariate Gaussian vectors. Whenever the application at hand can not satisfy this normality hypothesis (e.g. precipitation data, alternatives and/or improvements to PCA have to be developed and studied. To go beyond this second order statistics constraint, that limits the applicability of the PCA, we take advantage of the cumulant function that can produce higher order moments information. The cumulant function, well-known in the statistical literature, allows us to propose a new, simple and fast procedure to identify spatial patterns for non-Gaussian data. Our algorithm consists in maximizing the cumulant function. Three families of multivariate random vectors, for which explicit computations are obtained, are implemented to illustrate our approach. In addition, we show that our algorithm corresponds to selecting the directions along which projected data display the largest spread over the marginal probability density tails.

Detection of tumor markers in prostate cancer and comparison of sensitivity between real time and nested PCR.

Science.gov (United States)

Matsuoka, Takayuki; Shigemura, Katsumi; Yamamichi, Fukashi; Fujisawa, Masato; Kawabata, Masato; Shirakawa, Toshiro

2012-06-27

The objective of this study is to investigate and compare the sensitivity in conventional PCR, quantitative real time PCR, nested PCR and western blots for detection of prostate cancer tumor markers using prostate cancer (PCa) cells. We performed conventional PCR, quantitative real time PCR, nested PCR, and western blots using 5 kinds of PCa cells. Prostate specific antigen (PSA), prostate specific membrane antigen (PSMA), and androgen receptor (AR) were compared for their detection sensitivity by real time PCR and nested PCR. In real time PCR, there was a significant correlation between cell number and the RNA concentration obtained (R(2)=0.9944) for PSA, PSMA, and AR. We found it possible to detect these markers from a single LNCaP cell in both real time and nested PCR. By comparison, nested PCR reached a linear curve in fewer PCR cycles than real time PCR, suggesting that nested PCR may offer PCR results more quickly than real time PCR. In conclusion, nested PCR may offer tumor maker detection in PCa cells more quickly (with fewer PCR cycles) with the same high sensitivity as real time PCR. Further study is necessary to establish and evaluate the best tool for PCa tumor marker detection.

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.

Plasma-derived exosomal survivin, a plausible biomarker for early detection of prostate cancer.

Directory of Open Access Journals (Sweden)

Salma Khan

Full Text Available Survivin is expressed in prostate cancer (PCa, and its downregulation sensitizes PCa cells to chemotherapeutic agents in vitro and in vivo. Small membrane-bound vesicles called exosomes, secreted from the endosomal membrane compartment, contain RNA and protein that they readily transport via exosome internalization into recipient cells. Recent progress has shown that tumor-derived exosomes play multiple roles in tumor growth and metastasis and may produce these functions via immune escape, tumor invasion and angiogenesis. Furthermore, exosome analysis may provide novel biomarkers to diagnose or monitor PCa treatment.Exosomes were purified from the plasma and serum from 39 PCa patients, 20 BPH patients, 8 prostate cancer recurrent and 16 healthy controls using ultracentrifugation and their quantities and qualities were quantified and visualized from both the plasma and the purified exosomes using ELISA and Western blotting, respectively.Survivin was significantly increased in the tumor-derived samples, compared to those from BPH and controls with virtually no difference in the quantity of Survivin detected in exosomes collected from newly diagnosed patients exhibiting low (six or high (nine Gleason scores. Exosome Survivin levels were also higher in patients that had relapsed on chemotherapy compared to controls.These studies demonstrate that Survivin exists in plasma exosomes from both normal, BPH and PCa subjects. The relative amounts of exosomal Survivin in PCa plasma was significantly higher than in those with pre-inflammatory BPH and control plasma. This differential expression of exosomal Survivin was seen with both newly diagnosed and advanced PCa subjects with high or low-grade cancers. Analysis of plasma exosomal Survivin levels may offer a convenient tool for diagnosing or monitoring PCa and may, as it is elevated in low as well as high Gleason scored samples, be used for early detection.

Development of anomalous detection using movie prediction

International Nuclear Information System (INIS)

Sakakibara, Yoji; Demachi, Kazuyuki; Kawai, Masaki; Chhatluli, Ritu; Kamiaka, Kazuma

2012-01-01

In this research, the new method to predict the near-future of the movie images captured by video camera based on the combination of the Principle Component Analysis (PCA) and the Singular Spectral Analysis (SSA). In the normal condition of machines, the real-time captured movie is supposed to correspond to the predicted one. If the error between the both becomes significantly large, it may suggest some anomalous motion of the machines. So the movie prediction method has a possibility of the sensitive anomalous detection system. (author)

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

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

Clinical performance of serum [-2]proPSA derivatives, %p2PSA and PHI, in the detection and management of prostate cancer.

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Huang, Ya-Qiang; Sun, Tong; Zhong, Wei-De; Wu, Chin-Lee

2014-01-01

Prostate-specific antigen (PSA) has been widely used as a serum marker for prostate cancer (PCa) screening or progression monitoring, which dramatically increased rate of early detection while significantly reduced PCa-specific mortality. However, a number of limitations of PSA have been noticed. Low specificity of PSA may lead to overtreatment in men who presenting with a total PSA (tPSA) level of PHI) and %p2PSA, which were defined as [(p2PSA/fPSA) × √ tPSA] and [(p2PSA/fPSA) × 100] respectively, have been suggested to be increased in PCa and can better distinguish PCa from benign prostatic diseases than tPSA or fPSA. We performed a systematic review of the available scientific evidences to evaluate the potentials of %p2PSA and PHI in clinical application. Mounting evidences suggested that both %p2PSA and PHI possess higher area under the ROC curve (AUC) and better specificity at a high sensitivity for PCa detection when compare with tPSA and %fPSA. It indicated that measurements of %p2PSA and PHI significantly improved the accuracy of PCa detection and diminished unnecessary biopsies. Furthermore, elevations of %p2PSA and PHI are related to more aggressive diseases. %p2PSA and PHI might be helpful in reducing overtreatment on indolent cases or assessing the progression of PCa in men who undergo active surveillance. Further studies are needed before being applied in routine clinical practice.

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

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

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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

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...

The biometric-based module of smart grid system

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Engel, E.; Kovalev, I. V.; Ermoshkina, A.

2015-10-01

Within Smart Grid concept the flexible biometric-based module base on Principal Component Analysis (PCA) and selective Neural Network is developed. The formation of the selective Neural Network the biometric-based module uses the method which includes three main stages: preliminary processing of the image, face localization and face recognition. Experiments on the Yale face database show that (i) selective Neural Network exhibits promising classification capability for face detection, recognition problems; and (ii) the proposed biometric-based module achieves near real-time face detection, recognition speed and the competitive performance, as compared to some existing subspaces-based methods.

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

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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.

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.

Early detection of fungal growth in bakery products by use of an electronic nose based on mass spectrometry.

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Vinaixa, Maria; Marín, Sonia; Brezmes, Jesús; Llobet, Eduard; Vilanova, Xavier; Correig, Xavier; Ramos, Antonio; Sanchis, Vicent

2004-10-06

This paper presents the design, optimization, and evaluation of a mass spectrometry-based electronic nose (MS e-nose) for early detection of unwanted fungal growth in bakery products. Seven fungal species (Aspergillus flavus, Aspergillus niger, Eurotium amstelodami, Eurotium herbariorum, Eurotium rubrum, Eurotium repens, and Penicillium corylophillum) were isolated from bakery products and used for the study. Two sampling headspace techniques were tested: static headspace (SH) and solid-phase microextraction (SPME). Cross-validated models based on principal component analysis (PCA), coupled to discriminant function analysis (DFA) and fuzzy ARTMAP, were used as data treatment. When attempting to discriminate between inoculated and blank control vials or between genera or species of in vitro growing cultures, sampling based on SPME showed better results than those based on static headspace. The SPME-MS-based e-nose was able to predict fungal growth with 88% success after 24 h of inoculation and 98% success after 48 h when changes were monitored in the headspace of fungal cultures growing on bakery product analogues. Prediction of the right fungal genus reached 78% and 88% after 24 and 96 h, respectively.

Comparison of methods for detecting nondeterministic BOLD fluctuation in fMRI.

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Kiviniemi, Vesa; Kantola, Juha-Heikki; Jauhiainen, Jukka; Tervonen, Osmo

2004-02-01

Functional MR imaging (fMRI) has been used in detecting neuronal activation and intrinsic blood flow fluctuations in the brain cortex. This article is aimed for comparing the methods for analyzing the nondeterministic flow fluctuations. Fast Fourier Transformation (FFT), cross correlation (CC), spatial principal component analysis (sPCA), and independent component analysis (sICA) were compared. 15 subjects were imaged at 1.5 T. Three quantitative measures were compared: (1) The number of subjects with identifiable fluctuation, (2) the volume, and (3) mean correlation coefficient (MCC) of the detected voxels. The focusing on cortical structures and the overall usability were qualitatively assessed. sICA was spatially most accurate but time consuming, robust, and detected voxels with high temporal synchrony. The CC and FFT were fast suiting primary screening. The CC detected highest temporal synchrony but the subjective detection for reference vector produced excess variance of the detected volumes. The FFT and sPCA were not spatially accurate and did not detect adequate temporal synchrony of the voxels.

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...

A New Method to Detect Driver Fatigue Based on EMG and ECG Collected by Portable Non-Contact Sensors

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Lin Wang

2017-11-01

Full Text Available Recently, detection and prediction on driver fatigue have become interest of research worldwide. In the present work, a new method is built to effectively evaluate driver fatigue based on electromyography (EMG and electrocardiogram (ECG collected by portable real-time and non-contact sensors. First, under the non-disturbance condition for driver’s attention, mixed physiological signals (EMG, ECG and artefacts are collected by non-contact sensors located in a cushion on the driver’s seat. EMG and ECG are effectively separated by FastICA, and de-noised by empirical mode decomposition (EMD. Then, three physiological features, complexity of EMG, complexity of ECG, and sample entropy (SampEn of ECG, are extracted and analysed. Principal components are obtained by principal components analysis (PCA and are used as independent variables. Finally, a mathematical model of driver fatigue is built, and the accuracy of the model is up to 91%. Moreover, based on the questionnaire, the calculation results of model are consistent with real fatigue felt by the participants. Therefore, this model can effectively detect driver fatigue.

Potential usefulness of CTC detection in follow up of prostate cancer patients. A preliminary report obtained by using Adnagene platform

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Giuseppe Albino

2013-12-01

Full Text Available Objective: Prostate cancer (PCa represents one of the most important medical problems for males, being the second major cause of cancer death. Routinely, PCa patients are followed up with both periodic evaluation of serum PSA levels and imaging. Recently, alternative laboratory methods were proposed for PCa patients’ monitoring, with contrasting results. Aim of the present study was to evaluate the usefulness of a new commercially CE-IVD kit for detection of prostate circulating tumour cells. Our intention was to verify the Adnagene platform usefulness to identify patients with disease progression, whatever treatment ongoing, in order to modify the therapeutic process even before treatment failure is evident with imaging methods. Materials and Methods: Twenty-one patients were enrolled and subdivided into three groups: n = 10 high risk tumor PCa patients; n = 6 low risk PCa patients; n = 5 sbjects without any signs of PCa. AdnaTest Prostate Cancer kit was used for enrichment and molecular characterization of prostate circulating tumour cells. Results: Healthy subjects (with BPH and patients without metastases resulted as negative, while 3 out of 10 high risk PCa patients were positive at least for one molecular marker like PSA, while only two showed positivity for PSMA mRNA. Our results indicate that the test specificity is 100% and the sensitivity is 100%; of course the sample is too small to give it statistical validity. In detail we verified that only the “not responder” patients resulted positive for AdnaTest. Conclusions: The present preliminary report provides evidence that isolation and detection of circulating tumour cells (CTCs is feasible and it may be useful in the follow-up of patients with advanced prostate cancer. If the results of this preliminary study would be confirmed by a large prospective cohort study, it could be demonstrated that this test is a rapid diagnostic method, based on the analysis of a blood sample and

Comparison of sonoelastography guided biopsy with systematic biopsy: impact on prostate cancer detection

International Nuclear Information System (INIS)

Pallwein, Leo; Struve, Peter; Aigner, Friedrich; Gradl, Johann; Schurich, Matthias; Frauscher, Ferdinand; Mitterberger, Michael; Horninger, Wolfgang; Bartsch, Georg; Pedross, Florian

2007-01-01

A prospective study was performed to determine the value of sonoelastography (SE) targeted biopsy for prostate cancer (PCa) detection. A series of 230 male screening volunteers was examined. Two independent examiners evaluated each subject. One single investigator performed ≤5 SE targeted biopsies into suspicious regions in the peripheral zone only. The stiffness of the lesion was displayed by SE and color-coded from red (soft) to blue (hard). Hard lesions were considered as malignant and targeted by biopsy. Subsequently, another examiner performed ten systematic biopsies. Cancer detection rates of the two techniques were compared. Cancer was detected in 81 of the 230 patients (35%), including 68 (30%) by SE targeted biopsy and in 58 (25%) by systematic biopsy. Cancer was detected by targeted biopsy alone in 23 patients (10%) and by systematic biopsy alone in 13 patients (6%). The detection rate for SE targeted biopsy cores (12.7% or 135 of 1,109 cores) was significantly better than for systematic biopsy cores (5.6% or 130 of 2,300 cores, P < 0.001). SE targeted biopsy in a patient with cancer was 2.9-fold more likely to detect PCa than systematic biopsy. SE targeted biopsy detected more cases of PCa than systematic biopsy, with fewer than half the number of biopsy cores in this prostate-specific antigen screening population. (orig.)

Kernel based orthogonalization for change detection in hyperspectral images

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MNF analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via...... analysis all 126 spectral bands of the HyMap are included. Changes on the ground are most likely due to harvest having taken place between the two acquisitions and solar effects (both solar elevation and azimuth have changed). Both types of kernel analysis emphasize change and unlike kernel PCA, kernel MNF...

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

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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.

Damages detection in cylindrical metallic specimens by means of statistical baseline models and updated daily temperature profiles

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Villamizar-Mejia, Rodolfo; Mujica-Delgado, Luis-Eduardo; Ruiz-Ordóñez, Magda-Liliana; Camacho-Navarro, Jhonatan; Moreno-Beltrán, Gustavo

2017-05-01

In previous works, damage detection of metallic specimens exposed to temperature changes has been achieved by using a statistical baseline model based on Principal Component Analysis (PCA), piezodiagnostics principle and taking into account temperature effect by augmenting the baseline model or by using several baseline models according to the current temperature. In this paper a new approach is presented, where damage detection is based in a new index that combine Q and T2 statistical indices with current temperature measurements. Experimental tests were achieved in a carbon-steel pipe of 1m length and 1.5 inches diameter, instrumented with piezodevices acting as actuators or sensors. A PCA baseline model was obtained to a temperature of 21º and then T2 and Q statistical indices were obtained for a 24h temperature profile. Also, mass adding at different points of pipe between sensor and actuator was used as damage. By using the combined index the temperature contribution can be separated and a better differentiation of damages respect to undamaged cases can be graphically obtained.

Detection of Tumor Markers in Prostate Cancer and Comparison of Sensitivity between Real Time and Nested PCR

OpenAIRE

Matsuoka, Takayuki; Shigemura, Katsumi; Yamamichi, Fukashi; Fujisawa, Masato; Kawabata, Masato; Shirakawa, Toshiro

2012-01-01

The objective of this study is to investigate and compare the sensitivity in conventional PCR, quantitative real time PCR, nested PCR and western blots for detection of prostate cancer tumor markers using prostate cancer (PCa) cells. We performed conventional PCR, quantitative real time PCR, nested PCR, and western blots using 5 kinds of PCa cells. Prostate specific antigen (PSA), prostate specific membrane antigen (PSMA), and androgen receptor (AR) were compared for their detection sensitivi...

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

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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.

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

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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.

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

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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

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

Microfluidic-integrated patterned ITO immunosensor for rapid detection of prostate-specific membrane antigen biomarker in prostate cancer.

Science.gov (United States)

Seenivasan, Rajesh; Singh, Chandra K; Warrick, Jay W; Ahmad, Nihal; Gunasekaran, Sundaram

2017-09-15

An optically transparent patterned indium tin oxide (ITO) three-electrode sensor integrated with a microfluidic channel was designed for label-free immunosensing of prostate-specific membrane antigen (PSMA), a prostate cancer (PCa) biomarker, expressed on prostate tissue and circulating tumor cells but also found in serum. The sensor relies on cysteamine capped gold nanoparticles (N-AuNPs) covalently linked with anti-PSMA antibody (Ab) for target specificity. A polydimethylsiloxane (PDMS) microfluidic channel is used to efficiently and reproducibly introduce sample containing soluble proteins/cells to the sensor. The PSMA is detected and quantified by measuring the change in differential pulse voltammetry signal of a redox probe ([Fe(CN) 6 ] 3- /[Fe(CN) 6 ] 4- ) that is altered upon binding of PSMA with PSMA-Ab immobilized on N-AuNPs/ITO. Detection of PSMA expressing cells and soluble PSMA was tested. The limit of detection (LOD) of the sensor for PSMA-based PCa cells is 6/40µL (i.e., 150 cells/mL) (n=3) with a linear range of 15-400 cells/40µL (i.e., 375-10,000 cells/mL), and for the soluble PSMA is 0.499ng/40µL (i.e., 12.5ng/mL) (n=3) with the linear range of 0.75-250ng/40µL (i.e., 19-6250ng/mL), both with an incubation time of 10min. The results indicate that the sensor has a suitable sensitivity and dynamic range for routine detection of PCa circulating tumor cells and can be adapted to detect other biomarkers/cancer cells. Copyright © 2017 Elsevier B.V. All rights reserved.

Detecting Adulteration in Lotus Honey Using a Machine Olfactory System

Directory of Open Access Journals (Sweden)

M Hajinezhad

2017-10-01

classification of samples. It is based on the variance of the data-set. For PCA analysis, overall PC1 and PC2 explained 91% of the total variance among Lotus honey samples and the adulterations (PC1=80% and PC2=11%. The results indicate that it is clearly possible to recognize Lotus honey with adulterant using electronic nose systems. LDA Results The LDA method for the detection of adulterated honey samples using leave-one-out validation was estimated. The method is most widely used as a method of classification that maximizes variance between the clusters and minimizes variance of within classes. By applying LDA on the collected data, 100% accurate classification for detecting of honey and their adulterations was obtained. It can also be concluded that this method could recognize adulterated honey samples properly. ANN Results The back propagation multilayer perceptron algorithm was used to classify and to detect honey and adulterated types. Performance evaluations of each designed networks were compared by mean square error (MSE and correlation coefficient (r.The data were divided to three subsets: 60% was used for training, 20% for testing and the remaining 20% were kept for cross validation.After network training and validation using optimized ANN model, i.e. 6-8-4 structure, success rate for 4 outputs (0, 20, 35 and 50% adulterated levelswere found to be 100%.After detecting adulteration, e-nose system accompanied with ANN can accurately classify honey from honey mixtures with fraud materials. Conclusions An electronic nose based on six metal oxide semiconductor sensors was used to detect adulterated honey samples. Electronic nose system can successfully classify between original honey and the adulterated one by pattern recognition method. The PCA, LDA and ANN techniques and analyzes of the electronic nose were very useful for evaluating the quality of the lotus honey. The results of these methods were used to classify the fraud in Lotus honey. However, there is a need to

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.

On-line early fault detection and diagnosis of municipal solid waste incinerators

International Nuclear Information System (INIS)

Zhao Jinsong; Huang Jianchao; Sun Wei

2008-01-01

A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the fault diagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows that automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI

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

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.

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.

Ultra-sensitive high performance liquid chromatography-laser-induced fluorescence based proteomics for clinical applications.

Science.gov (United States)

Patil, Ajeetkumar; Bhat, Sujatha; Pai, Keerthilatha M; Rai, Lavanya; Kartha, V B; Chidangil, Santhosh

2015-09-08

An ultra-sensitive high performance liquid chromatography-laser induced fluorescence (HPLC-LIF) based technique has been developed by our group at Manipal, for screening, early detection, and staging for various cancers, using protein profiling of clinical samples like, body fluids, cellular specimens, and biopsy-tissue. More than 300 protein profiles of different clinical samples (serum, saliva, cellular samples and tissue homogenates) from volunteers (normal, and different pre-malignant/malignant conditions) were recorded using this set-up. The protein profiles were analyzed using principal component analysis (PCA) to achieve objective detection and classification of malignant, premalignant and healthy conditions with high sensitivity and specificity. The HPLC-LIF protein profiling combined with PCA, as a routine method for screening, diagnosis, and staging of cervical cancer and oral cancer, is discussed in this paper. In recent years, proteomics techniques have advanced tremendously in life sciences and medical sciences for the detection and identification of proteins in body fluids, tissue homogenates and cellular samples to understand biochemical mechanisms leading to different diseases. Some of the methods include techniques like high performance liquid chromatography, 2D-gel electrophoresis, MALDI-TOF-MS, SELDI-TOF-MS, CE-MS and LC-MS techniques. We have developed an ultra-sensitive high performance liquid chromatography-laser induced fluorescence (HPLC-LIF) based technique, for screening, early detection, and staging for various cancers, using protein profiling of clinical samples like, body fluids, cellular specimens, and biopsy-tissue. More than 300 protein profiles of different clinical samples (serum, saliva, cellular samples and tissue homogenates) from healthy and volunteers with different malignant conditions were recorded by using this set-up. The protein profile data were analyzed using principal component analysis (PCA) for objective

Targeted histology sampling from atypical small acinar proliferation area detected by repeat transrectal prostate biopsy

Directory of Open Access Journals (Sweden)

A. V. Karman

2017-01-01

Full Text Available Оbjective: to define the approach to the management of patients with the detected ASAP area.Materials and methods. In the time period from 2012 through 2015, 494 patients with previously negative biopsy and remaining suspicion of prostate cancer (PCa were examined. The patients underwent repeat 24-core multifocal prostate biopsy with taking additional tissue samples from suspicious areas detected by multiparametric magnetic resonance imaging and transrectal ultrasound. An isolated ASAP area was found in 127 (25. 7 % of the 494 examined men. All of them were offered to perform repeat target transrectal biopsy of this area. Targeted transrectal ultrasound guided biopsy of the ASAP area was performed in 56 (44.1 % of the 127 patients, 53 of them being included in the final analysis.Results. PCa was diagnosed in 14 (26.4 % of the 53 patients, their mean age being 64.4 ± 6.9 years. The average level of prostate-specific antigen (PSA in PCa patients was 6.8 ± 3.0 ng/ml, in those with benign lesions – 9.3 ± 6.5 ng/ml; the percentage ratio of free/total PSA with PCa was 16.2 ± 7,8 %, with benign lesions – 23.3 ± 7.7 %; PSA density in PCa patients was 0.14 ± 0.07 ng/ml/cm3, in those with benign lesions – 0.15 ± 0.12 ng/ml/cm3. Therefore, with ASAP area being detected in repeat prostate biopsy samples, it is advisable that targeted extended biopsy of this area be performed. 

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.

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.

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.

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.

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.

Detection of bacterial endotoxin in food: New planar interdigital sensors based approach

KAUST Repository

Abdul Rahman, Mohd Syaifudin

2013-02-01

Food poisoning caused by endotoxins or Lipopolysaccharide (LPS) are associated with Gram-negative bacteria. Two major food-borne pathogens, Escherichia coli and Salmonella are examples of Gram-negative bacteria which cause a large number of outbreaks of food poisoning. New types of planar interdigital sensors have been fabricated with different coating materials to assess their response to endotoxins. A carboxyl-functional polymer, APTES (3-Aminopropyltriethoxysilane) and Thionine were chosen to be coated onto FR4 interdigital sensors. The chosen coating materials have carboxylic or amine functional groups, which were optimized to be stable in water. All coated sensors were immobilized with PmB (Polymyxin B) which has specific binding properties to LPS. The sensors were tested with different concentrations of LPS O111:B4, ranging from 0.1 to 1000 μg/ml. Analyses of sensors\\' performance were based on the impedance spectroscopy method. The impedance spectra were modeled using a constant phase-element (CPE) equivalent circuit, and a principal component analysis (PCA) was used for data classification. Sensor coated with APTES has shown better selectivity for LPS detection. The experiments were repeated by coating APTES and immobilizing PmB to a new improve designed of novel interdigital sensors (thin film silicon based sensors). These sensors were observed to have better sensitivity and selectivity to the target biomolecules of LPS. Further experiments were conducted to study the effect of different coating thickness on sensor sensitivity, selectivity and stability. Different food samples contaminated with endotoxin were also tested to verify that the interdigital sensing approach is able to be used for endotoxin detection. © 2012 Elsevier Ltd. All rights reserved.

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.

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...

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

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...

An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1

Directory of Open Access Journals (Sweden)

Hai Guo

2015-01-01

Full Text Available An automatic detection model adopting pattern recognition technology is proposed in this paper; it can realize the measurement to the element of nanocomposite film. The features of gray level cooccurrence matrix (GLCM can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA. So it is possible to identify the element of film according to the Adaboost M1 algorithm of a strong classifier with ten decision tree classifiers. The experimental result shows that this model is superior to the ones of SVM (support vector machine, NN and BayesNet. The method proposed can be widely applied to the automatic detection of not only nanocomposite film element but also other nanocomposite material elements.

The discovery of putative urine markers for the specific detection of prostate tumor by integrative mining of public genomic profiles.

Directory of Open Access Journals (Sweden)

Min Chen

Full Text Available Urine has emerged as an attractive biofluid for the noninvasive detection of prostate cancer (PCa. There is a strong imperative to discover candidate urinary markers for the clinical diagnosis and prognosis of PCa. The rising flood of various omics profiles presents immense opportunities for the identification of prospective biomarkers. Here we present a simple and efficient strategy to derive candidate urine markers for prostate tumor by mining cancer genomic profiles from public databases. Prostate, bladder and kidney are three major tissues from which cellular matters could be released into urine. To identify urinary markers specific for PCa, upregulated entities that might be shed in exosomes of bladder cancer and kidney cancer are first excluded. Through the ontology-based filtering and further assessment, a reduced list of 19 entities encoding urinary proteins was derived as putative PCa markers. Among them, we have found 10 entities closely associated with the process of tumor cell growth and development by pathway enrichment analysis. Further, using the 10 entities as seeds, we have constructed a protein-protein interaction (PPI subnetwork and suggested a few urine markers as preferred prognostic markers to monitor the invasion and progression of PCa. Our approach is amenable to discover and prioritize potential markers present in a variety of body fluids for a spectrum of human diseases.

Rapid thyroid dysfunction screening based on serum surface-enhanced Raman scattering and multivariate statistical analysis

Science.gov (United States)

Tian, Dayong; Lü, Guodong; Zhai, Zhengang; Du, Guoli; Mo, Jiaqing; Lü, Xiaoyi

2018-01-01

In this paper, serum surface-enhanced Raman scattering and multivariate statistical analysis are used to investigate a rapid screening technique for thyroid function diseases. At present, the detection of thyroid function has become increasingly important, and it is urgently necessary to develop a rapid and portable method for the detection of thyroid function. Our experimental results show that, by using the Silmeco-based enhanced Raman signal, the signal strength greatly increases and the characteristic peak appears obviously. It is also observed that the Raman spectra of normal and anomalous thyroid function human serum are significantly different. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose thyroid dysfunction, and the diagnostic accuracy was 87.4%. The use of serum surface-enhanced Raman scattering technology combined with PCA-LDA shows good diagnostic performance for the rapid detection of thyroid function. By means of Raman technology, it is expected that a portable device for the rapid detection of thyroid function will be developed.

Novel algorithm for simultaneous component detection and pseudo-molecular ion characterization in liquid chromatography–mass spectrometry

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yufeng; Wang, Xiaoan; Wo, Siukwan [School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong (China); Ho, Hingman; Han, Quanbin [School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Kowloon Tong, Hong Kong (China); Fan, Xiaohui [College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 (China); Zuo, Zhong, E-mail: joanzuo@cuhk.edu.hk [School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong (China)

2015-01-01

Highlights: • Novel stepwise component detection algorithm (SCDA) for LC–MS datasets. • New isotopic distribution and adduct-ion models for mass spectra. • Automatic component classification based on adduct-ion and isotopic distributions. - Abstract: Resolving components and determining their pseudo-molecular ions (PMIs) are crucial steps in identifying complex herbal mixtures by liquid chromatography–mass spectrometry. To tackle such labor-intensive steps, we present here a novel algorithm for simultaneous detection of components and their PMIs. Our method consists of three steps: (1) obtaining a simplified dataset containing only mono-isotopic masses by removal of background noise and isotopic cluster ions based on the isotopic distribution model derived from all the reported natural compounds in dictionary of natural products; (2) stepwise resolving and removing all features of the highest abundant component from current simplified dataset and calculating PMI of each component according to an adduct-ion model, in which all non-fragment ions in a mass spectrum are considered as PMI plus one or several neutral species; (3) visual classification of detected components by principal component analysis (PCA) to exclude possible non-natural compounds (such as pharmaceutical excipients). This algorithm has been successfully applied to a standard mixture and three herbal extract/preparations. It indicated that our algorithm could detect components’ features as a whole and report their PMI with an accuracy of more than 98%. Furthermore, components originated from excipients/contaminants could be easily separated from those natural components in the bi-plots of PCA.

Novel algorithm for simultaneous component detection and pseudo-molecular ion characterization in liquid chromatography–mass spectrometry

International Nuclear Information System (INIS)

Zhang, Yufeng; Wang, Xiaoan; Wo, Siukwan; Ho, Hingman; Han, Quanbin; Fan, Xiaohui; Zuo, Zhong

2015-01-01

Highlights: • Novel stepwise component detection algorithm (SCDA) for LC–MS datasets. • New isotopic distribution and adduct-ion models for mass spectra. • Automatic component classification based on adduct-ion and isotopic distributions. - Abstract: Resolving components and determining their pseudo-molecular ions (PMIs) are crucial steps in identifying complex herbal mixtures by liquid chromatography–mass spectrometry. To tackle such labor-intensive steps, we present here a novel algorithm for simultaneous detection of components and their PMIs. Our method consists of three steps: (1) obtaining a simplified dataset containing only mono-isotopic masses by removal of background noise and isotopic cluster ions based on the isotopic distribution model derived from all the reported natural compounds in dictionary of natural products; (2) stepwise resolving and removing all features of the highest abundant component from current simplified dataset and calculating PMI of each component according to an adduct-ion model, in which all non-fragment ions in a mass spectrum are considered as PMI plus one or several neutral species; (3) visual classification of detected components by principal component analysis (PCA) to exclude possible non-natural compounds (such as pharmaceutical excipients). This algorithm has been successfully applied to a standard mixture and three herbal extract/preparations. It indicated that our algorithm could detect components’ features as a whole and report their PMI with an accuracy of more than 98%. Furthermore, components originated from excipients/contaminants could be easily separated from those natural components in the bi-plots of PCA

Vision-based Vehicle Detection Survey

Directory of Open Access Journals (Sweden)

Alex David S

2016-03-01

Full Text Available Nowadays thousands of drivers and passengers were losing their lives every year on road accident, due to deadly crashes between more than one vehicle. There are number of many research focuses were dedicated to the development of intellectual driver assistance systems and autonomous vehicles over the past decade, which reduces the danger by monitoring the on-road environment. In particular, researchers attracted towards the on-road detection of vehicles in recent years. Different parameters have been analyzed in this paper which includes camera placement and the various applications of monocular vehicle detection, common features and common classification methods, motion- based approaches and nighttime vehicle detection and monocular pose estimation. Previous works on the vehicle detection listed based on camera poisons, feature based detection and motion based detection works and night time detection.

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.

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)

Fast Detection of Striped Stem-Borer (Chilo suppressalis Walker Infested Rice Seedling Based on Visible/Near-Infrared Hyperspectral Imaging System

Directory of Open Access Journals (Sweden)

Yangyang Fan

2017-10-01

Full Text Available Striped stem-borer (SSB infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early SSB infestation in rice and identify the degree of infestation (DI. Visible/near-infrared hyperspectral images (in the spectral range of 380 nm to 1030 nm were taken of the healthy rice plants and infested rice plants by SSB for 2, 4, 6, 8 and 10 days. A total of 17 characteristic wavelengths were selected from the spectral data extracted from the hyperspectral images by the successive projection algorithm (SPA. Principal component analysis (PCA was applied to the hyperspectral images, and 16 textural features based on the gray-level co-occurrence matrix (GLCM were extracted from the first two principal component (PC images. A back-propagation neural network (BPNN was used to establish infestation degree evaluation models based on full spectra, characteristic wavelengths, textural features and features fusion, respectively. BPNN models based on a fusion of characteristic wavelengths and textural features achieved the best performance, with classification accuracy of calibration and prediction sets over 95%. The accuracy of each infestation degree was satisfactory, and the accuracy of rice samples infested for 2 days was slightly low. In all, this study indicated the feasibility of hyperspectral imaging techniques to detect early SSB infestation and identify degrees of infestation.

Alteration zone mapping for detecting potential mineralized areas in Kaladawan of north altyn tagh using ASTER data

International Nuclear Information System (INIS)

Yong-gui, Zhou; Bai-lin, Chen; Xing-tong, Chen; Zheng-le, Chen

2014-01-01

The Kaladawan area has been found developing intense hydrothermal altered rocks associated with mineralized area such as Kaladaban Pb-Zn deposit, A-bei Ag-Pb depositduring earlier geological investigations.Yet the sparse vegetation cover and excellent bedrock exposure make it a suitable place for the use of remote sensing methods for lithological mapping. ASTER data has been used in this study to identify alteration zones, and then to detect potential mineralized areas. Band ratio and PCA procedures were applied based on the analysis of spectral properties of typical alteration minerals. Band 4/2 and mineralogic indices proposed by Ninomiya were designed to map the distribution of Fe-oxides and alteration zones. Selected bands combinations were transformed in a PCA procedure to map the Al-OH, Mg-OH, CO 3 2− and Fe-oxides altered minerals. The analysis focused on the spatial distribution of hydrothermal altered minerals. Band ratio result images including both Fe-oxides and mineralogic indices show high-level similarity with the PCA transform procedure. They both show intense hydrothermal alteration zone in Kaladaban,west Kaladawan and A-bei area. Hence, these areas are considered to have potential for further mineralogic exploration. The results were validated by field work in the Kaladaban and west Kaladawan area,indicating that this method can be a useful tool for detecting potential mineralization area in Kaladawan and similar areas elsewhere

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

A Research on Fast Face Feature Points Detection on Smart Mobile Devices

Directory of Open Access Journals (Sweden)

Xiaohe Li

2018-01-01

Full Text Available We explore how to leverage the performance of face feature points detection on mobile terminals from 3 aspects. First, we optimize the models used in SDM algorithms via PCA and Spectrum Clustering. Second, we propose an evaluation criterion using Linear Discriminative Analysis to choose the best local feature descriptions which plays a critical role in feature points detection. Third, we take advantage of multicore architecture of mobile terminal and parallelize the optimized SDM algorithm to improve the efficiency further. The experiment observations show that our final accomplished GPC-SDM (improved Supervised Descent Method using spectrum clustering, PCA, and GPU acceleration suppresses the memory usage, which is beneficial and efficient to meet the real-time requirements.

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.

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...

Prostatitis, other genitourinary infections and prostate cancer: results from a population-based case-control study.

Science.gov (United States)

Boehm, Katharina; Valdivieso, Roger; Meskawi, Malek; Larcher, Alessandro; Schiffmann, Jonas; Sun, Maxine; Graefen, Markus; Saad, Fred; Parent, Marie-Élise; Karakiewicz, Pierre I

2016-03-01

We relied on a population-based case-control study (PROtEuS) to examine a potential association between the presence of histologically confirmed prostate cancer (PCa) and history of genitourinary infections, e.g., prostatitis, urethritis, orchitis and epididymitis. Cases were 1933 men with incident PCa, diagnosed across Montreal hospitals between 2005 and 2009. Population controls were 1994 men from the same residential area and age distribution. In-person interviews collected information about socio-demographic characteristics, lifestyle and medical history, e.g., self-reported history of several genitourinary infections, as well as on PCa screening. Logistic regression analyses tested overall and grade-specific associations, including subgroup analyses with frequent PSA testing. After multivariable adjustment, prostatitis was associated with an increased risk of any PCa (OR 1.81 [1.44-2.27]), but not urethritis (OR 1.05 [0.84-1.30]), orchitis (OR 1.28 [0.92-1.78]) or epididymitis (OR 0.98 [0.57-1.68]). The association between prostatitis and PCa was more pronounced for low-grade PCa (Gleason ≤ 6: OR 2.11 [1.61-2.77]; Gleason ≥ 7: OR 1.59 [1.22-2.07]). Adjusting for frequency of physician visits, PSA testing frequency or restricting analyses to frequently screened subjects did not affect these results. Prostatitis was associated with an increased probability for detecting PCa even after adjustment for frequency of PSA testing and physician visits, but not urethritis, orchitis or epididymitis. These considerations may be helpful in clinical risk stratification of individuals in whom the risk of PCa is pertinent.

Use of Standing Gold Nanorods for Detection of Malachite Green and Crystal Violet in Fish by SERS.

Science.gov (United States)

Chen, Xiaowei; Nguyen, Trang H D; Gu, Liqun; Lin, Mengshi

2017-07-01

With growing consumption of aquaculture products, there is increasing demand on rapid and sensitive techniques that can detect prohibited substances in the seafood products. This study aimed to develop a novel surface-enhanced Raman spectroscopy (SERS) method coupled with simplified extraction protocol and novel gold nanorod (AuNR) substrates to detect banned aquaculture substances (malachite green [MG] and crystal violet [CV]) and their mixture (1:1) in aqueous solution and fish samples. Multivariate statistical tools such as principal component analysis (PCA) and partial least squares regression (PLSR) were used in data analysis. PCA results demonstrate that SERS can distinguish MG, CV and their mixture (1:1) in aqueous solution and in fish samples. The detection limit of SERS coupled with standing AuNR substrates is 1 ppb for both MG and CV in fish samples. A good linear relationship between the actual concentration and predicted concentration of analytes based on PLSR models with R 2 values from 0.87 to 0.99 were obtained, indicating satisfactory quantification results of this method. These results demonstrate that the SERS method coupled with AuNR substrates can be used for rapid and accurate detection of MG and CV in fish samples. © 2017 Institute of Food Technologists®.

Damage detection of engine bladed-disks using multivariate statistical analysis

Science.gov (United States)

Fang, X.; Tang, J.

2006-03-01

The timely detection of damage in aero-engine bladed-disks is an extremely important and challenging research topic. Bladed-disks have high modal density and, particularly, their vibration responses are subject to significant uncertainties due to manufacturing tolerance (blade-to-blade difference or mistuning), operating condition change and sensor noise. In this study, we present a new methodology for the on-line damage detection of engine bladed-disks using their vibratory responses during spin-up or spin-down operations which can be measured by blade-tip-timing sensing technique. We apply a principle component analysis (PCA)-based approach for data compression, feature extraction, and denoising. The non-model based damage detection is achieved by analyzing the change between response features of the healthy structure and of the damaged one. We facilitate such comparison by incorporating the Hotelling's statistic T2 analysis, which yields damage declaration with a given confidence level. The effectiveness of the method is demonstrated by case studies.

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.

Multiparametric MRI in detection and staging of prostate cancer

DEFF Research Database (Denmark)

Boesen, Lars

2017-01-01

progression, the diagnosis and subsequent treatment planning can be challenging. The current diagnostic approach with PSA testing and digital rectal examination followed by transrectal ultrasound biopsies (TRUS-bx) lack in both sensitivity and specificity in PCa detection and offers limited information about......-MRI-targeted biopsies (mp-MRI-bx) under TRUS-guidance of any mp-MRI-suspicious lesion not hit on systematic TRUS-bx. Study II included patients with clinically localised PCa (cT1-T2) determined by digital rectal examination and/or TRUS and scheduled for radical prostatectomy (RP). Mp-MRI was performed prior to RP...... divided by ADCbenign). The ADC measurements (ADCtumour and ADCratio values) were correlated with the Gleason score (GS) from each tumour foci. RESULTS: Eighty-three patients were included in study I. PCa was found in 39/83 (47%) and both the PIRADS summation score and the over-all Likert classification...

Simultaneous Treatment with Statins and Aspirin Reduces the Risk of Prostate Cancer Detection and Tumorigenic Properties in Prostate Cancer Cell Lines

Science.gov (United States)

Olivan, M.; Rigau, M.; Colás, E.; Garcia, M.; Montes, M.; Sequeiros, T.; Regis, L.; Celma, A.; Planas, J.; Placer, J.; Reventós, J.; de Torres, I.; Doll, A.; Morote, J.

2015-01-01

Nowadays prostate cancer is the most common solid tumor in men from industrialized countries and the second leading cause of death. At the ages when PCa is usually diagnosed, mortality related to cardiovascular morbidity is high; therefore, men at risk for PCa frequently receive chronic lipid-lowering and antiplatelet treatment. The aim of this study was to analyze how chronic treatment with statins, aspirin, and their combination influenced the risk of PCa detection. The tumorigenic properties of these treatments were evaluated by proliferation, colony formation, invasion, and migration assays using different PCa cell lines, in order to assess how these treatments act at molecular level. The results showed that a combination of statins and aspirin enhances the effect of individual treatments and seems to reduce the risk of PCa detection (OR: 0.616 (95% CI: 0.467–0.812), P < 0.001). However, if treatments are maintained, aspirin (OR: 1.835 (95% CI: 1.068–3.155), P = 0.028) or the combination of both drugs (OR: 3.059 (95% CI: 1.894–4.939), P < 0.001) represents an increased risk of HGPCa. As observed at clinical level, these beneficial effects in vitro are enhanced when both treatments are administered simultaneously, suggesting that chronic, concomitant treatment with statins and aspirin has a protective effect on PCa incidence. PMID:25649906

Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.

Science.gov (United States)

Ginsburg, Shoshana B; Algohary, Ahmad; Pahwa, Shivani; Gulani, Vikas; Ponsky, Lee; Aronen, Hannu J; Boström, Peter J; Böhm, Maret; Haynes, Anne-Maree; Brenner, Phillip; Delprado, Warick; Thompson, James; Pulbrock, Marley; Taimen, Pekka; Villani, Robert; Stricker, Phillip; Rastinehad, Ardeshir R; Jambor, Ivan; Madabhushi, Anant

2017-07-01

To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ). 3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First-order statistical, co-occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE-MRI. Feature selection was performed to identify 10 features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver-operating characteristic curve (AUC). Classifier performance was compared with a zone-ignorant classifier. Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per-voxel basis, a PZ-specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC = 0.61-0.71) than a zone-ignorant classifier trained to detect cancer throughout the entire prostate (P  0.14) were obtained for all institutions. A zone-aware classifier significantly improves the accuracy of cancer detection in the PZ. 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:184-193. © 2016 International Society for Magnetic Resonance in Medicine.

Sensor Failure Detection of FASSIP System using Principal Component Analysis

Science.gov (United States)

Sudarno; Juarsa, Mulya; Santosa, Kussigit; Deswandri; Sunaryo, Geni Rina

2018-02-01

In the nuclear reactor accident of Fukushima Daiichi in Japan, the damages of core and pressure vessel were caused by the failure of its active cooling system (diesel generator was inundated by tsunami). Thus researches on passive cooling system for Nuclear Power Plant are performed to improve the safety aspects of nuclear reactors. The FASSIP system (Passive System Simulation Facility) is an installation used to study the characteristics of passive cooling systems at nuclear power plants. The accuracy of sensor measurement of FASSIP system is essential, because as the basis for determining the characteristics of a passive cooling system. In this research, a sensor failure detection method for FASSIP system is developed, so the indication of sensor failures can be detected early. The method used is Principal Component Analysis (PCA) to reduce the dimension of the sensor, with the Squarred Prediction Error (SPE) and statistic Hotteling criteria for detecting sensor failure indication. The results shows that PCA method is capable to detect the occurrence of a failure at any sensor.

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.

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.

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.

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- ...

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.

Swift-XRT detects X-ray burst from Circinus X-1

NARCIS (Netherlands)

Linares, M.; Soleri, P.; Altamirano, D.; Armas-Padilla, M.; Cavecchi, Y.; Degenaar, N.; Kalamkar, M.; Kaur, R.; van der Klis, M.; Patruno, A.; Watts, A.; Wijnands, R.; Yang, Y.; Casella, P.; Rea, N.; Chakrabarty, D.; Homan, J.

Following the recent re-brightening (ATel #2608) and RXTE-PCA detection of X-ray bursts from the peculiar X-ray binary Cir X-1 between May 15 and 25 (ATel #2643), we obtained a series of Swift-XRT observations of the field (see also ATel #2650). Swift-XRT detected an X-ray burst on 2010-05-28 at

A DIMENSION REDUCTION-BASED METHOD FOR CLASSIFICATION OF HYPERSPECTRAL AND LIDAR DATA

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B. Abbasi

2015-12-01

Full Text Available The existence of various natural objects such as grass, trees, and rivers along with artificial manmade features such as buildings and roads, make it difficult to classify ground objects. Consequently using single data or simple classification approach cannot improve classification results in object identification. Also, using of a variety of data from different sensors; increase the accuracy of spatial and spectral information. In this paper, we proposed a classification algorithm on joint use of hyperspectral and Lidar (Light Detection and Ranging data based on dimension reduction. First, some feature extraction techniques are applied to achieve more information from Lidar and hyperspectral data. Also Principal component analysis (PCA and Minimum Noise Fraction (MNF have been utilized to reduce the dimension of spectral features. The number of 30 features containing the most information of the hyperspectral images is considered for both PCA and MNF. In addition, Normalized Difference Vegetation Index (NDVI has been measured to highlight the vegetation. Furthermore, the extracted features from Lidar data calculated based on relation between every pixel of data and surrounding pixels in local neighbourhood windows. The extracted features are based on the Grey Level Co-occurrence Matrix (GLCM matrix. In second step, classification is operated in all features which obtained by MNF, PCA, NDVI and GLCM and trained by class samples. After this step, two classification maps are obtained by SVM classifier with MNF+NDVI+GLCM features and PCA+NDVI+GLCM features, respectively. Finally, the classified images are fused together to create final classification map by decision fusion based majority voting strategy.

Anterior prostate biopsy at initial and repeat evaluation: is it useful to detect significant prostate cancer?

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Pietro Pepe

2015-10-01

Full Text Available ABSTRACT Purpose: Detection rate for anterior prostate cancer (PCa in men who underwent initial and repeat biopsy has been prospectively evaluated. Materials and Methods: From January 2013 to March 2014, 400 patients all of Caucasian origin (median age 63.5 years underwent initial (285 cases and repeat (115 cases prostate biopsy; all the men had negative digital rectal examination and the indications to biopsy were: PSA values > 10 ng/mL, PSA between 4.1-10 or 2.6-4 ng/mL with free/total PSA≤25% and ≤20%, respectively. A median of 22 (initial biopsy and 31 cores (repeat biopsy were transperineally performed including 4 cores of the anterior zone (AZ and 4 cores of the AZ plus 2 cores of the transition zone (TZ, respectively. Results: Median PSA was 7.9 ng/mL; overall, a PCa was found in 180 (45% patients: in 135 (47.4% and 45 (36% of the men who underwent initial and repeat biopsy, respectively. An exclusive PCa of the anterior zone was found in the 8.9 (initial biopsy vs 13.3% (repeat biopsy of the men: a single microfocus of cancer was found in the 61.2% of the cases; moreover, in 7 out 18 AZ PCa the biopsy histology was predictive of significant cancer in 2 (28.5% and 5 (71.5% men who underwent initial and repeat biopsy, respectively. Conclusions: However AZ biopsies increased detection rate for PCa (10% of the cases, the majority of AZ PCa with histological findings predictive of clinically significant cancer were found at repeat biopsy (about 70% of the cases.

Prostate-based biofluids for the detection of prostate cancer: A comparative study of the diagnostic performance of cell-sourced RNA biomarkers

Directory of Open Access Journals (Sweden)

Matthew J. Roberts

2016-09-01

Conclusion: PEUW contains prostatic material, but has limited diagnostic accuracy when considering cell-derived DNA analysis. PCA3-based markers in ejaculate are comparable to serum PSA and digital rectal examination–urine markers.

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

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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.

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

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Alexandridis, Thomas K; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L; Kashefi, Javid; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios

2017-09-01

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

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.

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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.

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

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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.

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

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...

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

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

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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.

[Research on fast classification based on LIBS technology and principle component analyses].

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Yu, Qi; Ma, Xiao-Hong; Wang, Rui; Zhao, Hua-Feng

2014-11-01

Laser-induced breakdown spectroscopy (LIBS) and the principle component analysis (PCA) were combined to study aluminum alloy classification in the present article. Classification experiments were done on thirteen different kinds of standard samples of aluminum alloy which belong to 4 different types, and the results suggested that the LIBS-PCA method can be used to aluminum alloy fast classification. PCA was used to analyze the spectrum data from LIBS experiments, three principle components were figured out that contribute the most, the principle component scores of the spectrums were calculated, and the scores of the spectrums data in three-dimensional coordinates were plotted. It was found that the spectrum sample points show clear convergence phenomenon according to the type of aluminum alloy they belong to. This result ensured the three principle components and the preliminary aluminum alloy type zoning. In order to verify its accuracy, 20 different aluminum alloy samples were used to do the same experiments to verify the aluminum alloy type zoning. The experimental result showed that the spectrum sample points all located in their corresponding area of the aluminum alloy type, and this proved the correctness of the earlier aluminum alloy standard sample type zoning method. Based on this, the identification of unknown type of aluminum alloy can be done. All the experimental results showed that the accuracy of principle component analyses method based on laser-induced breakdown spectroscopy is more than 97.14%, and it can classify the different type effectively. Compared to commonly used chemical methods, laser-induced breakdown spectroscopy can do the detection of the sample in situ and fast with little sample preparation, therefore, using the method of the combination of LIBS and PCA in the areas such as quality testing and on-line industrial controlling can save a lot of time and cost, and improve the efficiency of detection greatly.

A Review of Imaging Methods for Prostate Cancer Detection

Directory of Open Access Journals (Sweden)

Saradwata Sarkar

2016-01-01

Full Text Available Imaging is playing an increasingly important role in the detection of prostate cancer (PCa. This review summarizes the key imaging modalities–multiparametric ultrasound (US, multiparametric magnetic resonance imaging (MRI, MRI-US fusion imaging, and positron emission tomography (PET imaging–-used in the diagnosis and localization of PCa. Emphasis is laid on the biological and functional characteristics of tumors that rationalize the use of a specific imaging technique. Changes to anatomical architecture of tissue can be detected by anatomical grayscale US and T2-weighted MRI. Tumors are known to progress through angiogenesis–-a fact exploited by Doppler and contrast-enhanced US and dynamic contrast-enhanced MRI. The increased cellular density of tumors is targeted by elastography and diffusion-weighted MRI. PET imaging employs several different radionuclides to target the metabolic and cellular activities during tumor growth. Results from studies using these various imaging techniques are discussed and compared.

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

Detection of compatibility between baclofen and excipients with aid of infrared spectroscopy and chemometry.

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Rojek, Barbara; Wesolowski, Marek; Suchacz, Bogdan

2013-12-01

In the paper infrared (IR) spectroscopy and multivariate exploration techniques: principal component analysis (PCA) and cluster analysis (CA) were applied as supportive methods for the detection of physicochemical incompatibilities between baclofen and excipients. In the course of research, the most useful rotational strategy in PCA proved to be varimax normalized, while in CA Ward's hierarchical agglomeration with Euclidean distance measure enabled to yield the most interpretable results. Chemometrical calculations confirmed the suitability of PCA and CA as the auxiliary methods for interpretation of infrared spectra in order to recognize whether compatibilities or incompatibilities between active substance and excipients occur. On the basis of IR spectra and the results of PCA and CA it was possible to demonstrate that the presence of lactose, β-cyclodextrin and meglumine in binary mixtures produce interactions with baclofen. The results were verified using differential scanning calorimetry, differential thermal analysis, thermogravimetry/differential thermogravimetry and X-ray powder diffraction analyses. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

MRI Fusion-Targeted Transrectal Prostate Biopsy and the Role of Prostate-Specific Antigen Density and Prostate Health Index for the Detection of Clinically Significant Prostate Cancer in Southeast Asian Men.

Science.gov (United States)

Tan, Teck Wei; Png, Keng Siang; Lee, Chau Hung; Yuwono, Arianto; Yeow, Yuyi; Chong, Kian Tai; Lee, Yee Mun; Tan, Cher Heng; Tan, Yung Khan

2017-11-01

To test the hypothesis that targeted biopsy has a higher detection rate for clinically significant prostate cancer (csPCa) than systematic biopsy. We defined csPCa as any Gleason sum ≥7 cancer. In patients with Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, to determine if factors, such as prostate-specific antigen density (PSAD) and prostate health index (PHI), can predict csPCa and help select patients for biopsy. We report the first series of targeted biopsies in Southeast Asian men, with comparison against systematic biopsy. Consecutive patients were registered into a prospective institutional review board-approved database in our institution. We reviewed patients who underwent biopsy from May 2016 to June 2017. Inclusion criteria for our study were patients with at least one PI-RADS ≥3, and who underwent both targeted and systematic biopsies in the same sitting. There were 115 patients in the study, of whom 74 (64.3%) had a previous negative systematic biopsy. Targeted biopsies detected significantly less Gleason 6 cancers than systematic biopsies (p < 0.01), and demonstrated significantly higher sensitivity, specificity, positive predictive value, and negative predictive value (NPV) for the detection of csPCa. For patients with PI-RADS 3 lesions, PHI and PSAD were found to be the best predictors for csPCa. PSAD <0.10 ng/mL/mL had an NPV of 93% and sensitivity of 92%, while allowing 20% of patients to avoid biopsy. PHI cutoff of <27 would allow 34% of patients to avoid biopsy, with both sensitivity and NPV of 100%. Targeted prostate biopsies were found to be significantly superior to systematic biopsies for the detection of csPCa, while detecting less Gleason 6 cancer. Usage of PSAD and PHI cutoff levels in patients with PI-RADS 3 lesions may enable a number of patients to avoid unnecessary biopsy.

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

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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.

Health status monitoring for ICU patients based on locally weighted principal component analysis.

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Ding, Yangyang; Ma, Xin; Wang, Youqing

2018-03-01

Intelligent status monitoring for critically ill patients can help medical stuff quickly discover and assess the changes of disease and then make appropriate treatment strategy. However, general-type monitoring model now widely used is difficult to adapt the changes of intensive care unit (ICU) patients' status due to its fixed pattern, and a more robust, efficient and fast monitoring model should be developed to the individual. A data-driven learning approach combining locally weighted projection regression (LWPR) and principal component analysis (PCA) is firstly proposed and applied to monitor the nonlinear process of patients' health status in ICU. LWPR is used to approximate the complex nonlinear process with local linear models, in which PCA could be further applied to status monitoring, and finally a global weighted statistic will be acquired for detecting the possible abnormalities. Moreover, some improved versions are developed, such as LWPR-MPCA and LWPR-JPCA, which also have superior performance. Eighteen subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and two vital signs of each subject were chosen for online monitoring. The proposed method was compared with several existing methods including traditional PCA, Partial least squares (PLS), just in time learning combined with modified PCA (L-PCA), and Kernel PCA (KPCA). The experimental results demonstrated that the mean fault detection rate (FDR) of PCA can be improved by 41.7% after adding LWPR. The mean FDR of LWPR-MPCA was increased by 8.3%, compared with the latest reported method L-PCA. Meanwhile, LWPR spent less training time than others, especially KPCA. LWPR is first introduced into ICU patients monitoring and achieves the best monitoring performance including adaptability to changes in patient status, sensitivity for abnormality detection as well as its fast learning speed and low computational complexity. The algorithm

Two-dimensional PCA-based human gait identification