Identification and suppression of the p-coumaroyl CoA:hydroxycinnamyl alcohol transferase in Zea mays L.

PubMed Central

Marita, Jane M; Hatfield, Ronald D; Rancour, David M; Frost, Kenneth E

2014-01-01

Grasses, such as Zea mays L. (maize), contain relatively high levels of p-coumarates (pCA) within their cell walls. Incorporation of pCA into cell walls is believed to be due to a hydroxycinnamyl transferase that couples pCA to monolignols. To understand the role of pCA in maize development, the p-coumaroyl CoA:hydroxycinnamyl alcohol transferase (pCAT) was isolated and purified from maize stems. Purified pCAT was subjected to partial trypsin digestion, and peptides were sequenced by tandem mass spectrometry. TBLASTN analysis of the acquired peptide sequences identified a single full-length maize cDNA clone encoding all the peptide sequences obtained from the purified enzyme. The cDNA clone was obtained and used to generate an RNAi construct for suppressing pCAT expression in maize. Here we describe the effects of suppression of pCAT in maize. Primary screening of transgenic maize seedling leaves using a new rapid analytical platform was used to identify plants with decreased amounts of pCA. Using this screening method, mature leaves from fully developed plants were analyzed, confirming reduced pCA levels throughout plant development. Complete analysis of isolated cell walls from mature transgenic stems and leaves revealed that lignin levels did not change, but pCA levels decreased and the lignin composition was altered. Transgenic plants with the lowest levels of pCA had decreased levels of syringyl units in the lignin. Thus, altering the levels of pCAT expression in maize leads to altered lignin composition, but does not appear to alter the total amount of lignin present in the cell walls. PMID:24654730

Identification and suppression of the p-coumaroyl CoA:hydroxycinnamyl alcohol transferase in Zea mays L.

PubMed

Marita, Jane M; Hatfield, Ronald D; Rancour, David M; Frost, Kenneth E

2014-06-01

Grasses, such as Zea mays L. (maize), contain relatively high levels of p-coumarates (pCA) within their cell walls. Incorporation of pCA into cell walls is believed to be due to a hydroxycinnamyl transferase that couples pCA to monolignols. To understand the role of pCA in maize development, the p-coumaroyl CoA:hydroxycinnamyl alcohol transferase (pCAT) was isolated and purified from maize stems. Purified pCAT was subjected to partial trypsin digestion, and peptides were sequenced by tandem mass spectrometry. TBLASTN analysis of the acquired peptide sequences identified a single full-length maize cDNA clone encoding all the peptide sequences obtained from the purified enzyme. The cDNA clone was obtained and used to generate an RNAi construct for suppressing pCAT expression in maize. Here we describe the effects of suppression of pCAT in maize. Primary screening of transgenic maize seedling leaves using a new rapid analytical platform was used to identify plants with decreased amounts of pCA. Using this screening method, mature leaves from fully developed plants were analyzed, confirming reduced pCA levels throughout plant development. Complete analysis of isolated cell walls from mature transgenic stems and leaves revealed that lignin levels did not change, but pCA levels decreased and the lignin composition was altered. Transgenic plants with the lowest levels of pCA had decreased levels of syringyl units in the lignin. Thus, altering the levels of pCAT expression in maize leads to altered lignin composition, but does not appear to alter the total amount of lignin present in the cell walls. © 2014 The Authors The Plant Journal © 2014 John Wiley & Sons Ltd.

Characterization of Leaf Extracts of Schinus terebinthifolius Raddi by GC-MS and Chemometric Analysis

PubMed Central

Carneiro, Fabíola B.; Lopes, Pablo Q.; Ramalho, Ricardo C.; Scotti, Marcus T.; Santos, Sócrates G.; Soares, Luiz A. L.

2017-01-01

Background: Schinus terebinthifolius Raddi belongs to Anacardiacea family and is widely known as “aroeira.” This species originates from South America, and its extracts are used in folk medicine due to its therapeutic properties, which include antimicrobial, anti-inflammatory, and antipyretic effects. The complexity and variability of the chemical constitution of the herbal raw material establishes the quality of the respective herbal medicine products. Objective: Thus, the purpose of this study was to investigate the variability of the volatile compounds from leaves of S. terebinthifolius. Materials and Methods: The samples were collected from different states of the Northeast region of Brazil and analyzed with a gas chromatograph coupled to a mass spectrometer (GC-MS). The collected data were analyzed using multivariate data analysis. Results: The samples’ chromatograms, obtained by GC-MS, showed similar chemical profiles in a number of peaks, but some differences were observed in the intensity of these analytical markers. The chromatographic fingerprints obtained by GC-MS were suitable for discrimination of the samples; these results along with a statistical treatment (principal component analysis [PCA]) were used as a tool for comparative analysis between the different samples of S. terebinthifolius. Conclusion: The experimental data show that the PCA used in this study clustered the samples into groups with similar chemical profiles, which builds an appropriate approach to evaluate the similarity in the phytochemical pattern found in the different leaf samples. SUMMARY The leave extracts of Schinus terebinthifolius were obtained by turbo-extractionThe extracts were partitioned with hexane and analyzed by GC-MSThe chromatographic data were analyzed using the principal component analysis (PCA)The PCA plots showed the main compounds (phellandrene, limonene, and carene), which were used to group the samples from a different geographical location in accordance to their chemical similarity. Abbreviations used: AL: Alagoas, BA: Bahia, CE: Ceará, CPETEC: Center for Weather Forecasting and Climate Studies, GC-MS: Gas chromatograph coupled to a mass spectrometer, MA: Maranhão, MVA: Multivariate data analysis, PB: Paraíba, PC1: Direction that describes the maximum variance of the original data, PC2: Maximum direction variance of the data in the subspace orthogonal to PC1, PCA: Principal component analysis, PE: Pernambuco, PI: Piauí, RN: Rio Grande do Norte, SE: Sergipe. PMID:29142431

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

PubMed

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

2015-10-01

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

A principal component analysis of the dynamics of subdomains and binding sites in human serum albumin.

PubMed

Paris, Guillaume; Ramseyer, Christophe; Enescu, Mironel

2014-05-01

The conformational dynamics of human serum albumin (HSA) was investigated by principal component analysis (PCA) applied to three molecular dynamics trajectories of 200 ns each. The overlap of the essential subspaces spanned by the first 10 principal components (PC) of different trajectories was about 0.3 showing that the PCA based on a trajectory length of 200 ns is not completely convergent for this protein. The contributions of the relative motion of subdomains and of the subdomains (internal) distortion to the first 10 PCs were found to be comparable. Based on the distribution of the first 3 PC, 10 protein conformers are identified showing relative root mean square deviations (RMSD) between 2.3 and 4.6 Å. The main PCs are found to be delocalized over the whole protein structure indicating that the motions of different protein subdomains are coupled. This coupling is considered as being related to the allosteric effects observed upon ligand binding to HSA. On the other hand, the first PC of one of the three trajectories describes a conformational transition of the protein domain I that is close to that experimentally observed upon myristate binding. This is a theoretical support for the older hypothesis stating that changes of the protein onformation favorable to binding can precede the ligand complexation. A detailed all atoms PCA performed on the primary Sites 1 and 2 confirms the multiconformational character of the HSA binding sites as well as the significant coupling of their motions. Copyright © 2013 Wiley Periodicals, Inc.

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

PubMed

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

2015-02-01

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

Geobacter sulfurreducens sp. nov., a hydrogen- and acetate-oxidizing dissimilatory metal-reducing microorganism.

PubMed Central

Caccavo, F; Lonergan, D J; Lovley, D R; Davis, M; Stolz, J F; McInerney, M J

1994-01-01

A dissimilatory metal- and sulfur-reducing microorganism was isolated from surface sediments of a hydrocarbon-contaminated ditch in Norman, Okla. The isolate, which was designated strain PCA, was an obligately anaerobic, nonfermentative nonmotile, gram-negative rod. PCA grew in a defined medium with acetate as an electron donor and ferric PPi, ferric oxyhydroxide, ferric citrate, elemental sulfur, Co(III)-EDTA, fumarate, or malate as the sole electron acceptor. PCA also coupled the oxidation of hydrogen to the reduction of Fe(III) but did not reduce Fe(III) with sulfur, glucose, lactate, fumarate, propionate, butyrate, isobutyrate, isovalerate, succinate, yeast extract, phenol, benzoate, ethanol, propanol, or butanol as an electron donor. PCA did not reduce oxygen, Mn(IV), U(VI), nitrate, sulfate, sulfite, or thiosulfate with acetate as the electron donor. Cell suspensions of PCA exhibited dithionite-reduced minus air-oxidized difference spectra which were characteristic of c-type cytochromes. Phylogenetic analysis of the 16S rRNA sequence placed PCA in the delta subgroup of the proteobacteria. Its closest known relative is Geobacter metallireducens. The ability to utilize either hydrogen or acetate as the sole electron donor for Fe(III) reduction makes strain PCA a unique addition to the relatively small group of respiratory metal-reducing microorganisms available in pure culture. A new species name, Geobacter sulfurreducens, is proposed. Images PMID:7527204

Improving couples' quality of life through a Web-based prostate cancer education intervention.

PubMed

Song, Lixin; Rini, Christine; Deal, Allison M; Nielsen, Matthew E; Chang, Hao; Kinneer, Patty; Teal, Randall; Johnson, David C; Dunn, Mary W; Mark, Barbara; Palmer, Mary H

2015-03-01

To evaluate the feasibility and acceptability of a newly developed web-based, couple-oriented intervention called Prostate Cancer Education and Resources for Couples (PERC). Quantitative, qualitative, mixed-methods approach. Oncology outpatient clinics at the University of North Carolina (UNC) Lineberger Comprehensive Cancer Center at UNC–Chapel Hill. 26 patients with localized prostate cancer (PCa) and their partners. Pre- and postpilot quantitative assessments and a postpilot qualitative interview were conducted. General and PCa-specific symptoms, quality of life, psychosocial factors, PERC’s ease of use, and web activities. Improvement was shown in some PCa-specific and general symptoms (small effect sizes for patients and small-to-medium effect sizes for partners), overall quality of life, and physical and social domains of quality of life for patients (small effect sizes). Web activity data indicated high PERC use. Qualitative and quantitative analyses indicated that participants found PERC easy to use and understand,as well as engaging, of high quality, and relevant. Overall, participants were satisfied with PERC and reported that PERC improved their knowledge about symptom management and communication as a couple. PERC was a feasible, acceptable method of reducing the side effects of PCa treatment–related symptoms and improving quality of life. PERC has the potential to reduce the negative impacts of symptoms and enhance quality of life for patients with localized PCa and their partners, particularly for those who live in rural areas and have limited access to post-treatment supportive care.

Characterization of phenolic amides from cortex lycii by ultra high-performance liquid chromatography coupled with LTQ-Orbitrap mass spectrometry

USDA-ARS?s Scientific Manuscript database

High performance liquid chromatography (UPLC) and flow injection electrospray ionization with ion trap mass spectrometry (FIMS) fingerprints combined with the principal component analysis (PCA) were examined for their potential in differentiating commercial organic and conventional sage samples. The...

A Novel Weighted Kernel PCA-Based Method for Optimization and Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Thimmisetty, C.; Talbot, C.; Chen, X.; Tong, C. H.

2016-12-01

It has been demonstrated that machine learning methods can be successfully applied to uncertainty quantification for geophysical systems through the use of the adjoint method coupled with kernel PCA-based optimization. In addition, it has been shown through weighted linear PCA how optimization with respect to both observation weights and feature space control variables can accelerate convergence of such methods. Linear machine learning methods, however, are inherently limited in their ability to represent features of non-Gaussian stochastic random fields, as they are based on only the first two statistical moments of the original data. Nonlinear spatial relationships and multipoint statistics leading to the tortuosity characteristic of channelized media, for example, are captured only to a limited extent by linear PCA. With the aim of coupling the kernel-based and weighted methods discussed, we present a novel mathematical formulation of kernel PCA, Weighted Kernel Principal Component Analysis (WKPCA), that both captures nonlinear relationships and incorporates the attribution of significance levels to different realizations of the stochastic random field of interest. We also demonstrate how new instantiations retaining defining characteristics of the random field can be generated using Bayesian methods. In particular, we present a novel WKPCA-based optimization method that minimizes a given objective function with respect to both feature space random variables and observation weights through which optimal snapshot significance levels and optimal features are learned. We showcase how WKPCA can be applied to nonlinear optimal control problems involving channelized media, and in particular demonstrate an application of the method to learning the spatial distribution of material parameter values in the context of linear elasticity, and discuss further extensions of the method to stochastic inversion.

G Protein-Coupled Estrogen Receptor-1 Is Involved in the Protective Effect of Protocatechuic Aldehyde against Endothelial Dysfunction

PubMed Central

Kong, Byung Soo; Cho, Yoon Hee; Lee, Eun Jig

2014-01-01

Protocatechuic aldehyde (PCA), a phenolic aldehyde, has therapeutic potency against atherosclerosis. Although PCA is known to inhibit the migration and proliferation of vascular smooth muscle cells and intravascular thrombosis, the underlying mechanism remains unclear. In this study, we investigated the protective effect of PCA on endothelial cells and injured vessels in vivo in association with G protein-coupled estrogen receptor-1 (GPER-1). With PCA treatment, cAMP production was increased in HUVECs, while GPER-1 expression was increased in both HUVECs and a rat aortic explant. PCA and G1, a GPER-1 agonist, reduced H2O2 stimulated ROS production in HUVECs, whereas, G15, a GPER-1 antagonist, increased ROS production further. These elevations were inhibited by co-treatment with PCA or G1. TNFα stimulated the expression of inflammatory markers (VCAM-1, ICAM-1 and CD40), phospho-NF-κB, phospho-p38 and HIF-1α; however, co-treatment with PCA or G1 down-regulated this expression significantly. Likewise, increased expression of inflammatory markers by treatment with G15 was inhibited by co-treatment with PCA. In re-endothelization, aortic ring sprouting and neointima formation assay, rat aortas treated with PCA or G1 showed accelerated re-endothelization of the endothelium and reduced sprouting and neointima formation. However, aortas from G15-treated rats showed decelerated re-endothelization and increased sprouting and neointima formation. The effects of G15 were restored by co-treatment with PCA or G1. Also, in the endothelia of these aortas, PCA and G1 increased CD31 and GPER-1 and decreased VCAM-1 and CD40 expression. In contrast, the opposite effect was observed in G15-treated endothelium. These results suggest that GPER-1 might mediate the protective effect of PCA on the endothelium. PMID:25411835

G protein-coupled estrogen receptor-1 is involved in the protective effect of protocatechuic aldehyde against endothelial dysfunction.

PubMed

Kong, Byung Soo; Cho, Yoon Hee; Lee, Eun Jig

2014-01-01

Protocatechuic aldehyde (PCA), a phenolic aldehyde, has therapeutic potency against atherosclerosis. Although PCA is known to inhibit the migration and proliferation of vascular smooth muscle cells and intravascular thrombosis, the underlying mechanism remains unclear. In this study, we investigated the protective effect of PCA on endothelial cells and injured vessels in vivo in association with G protein-coupled estrogen receptor-1 (GPER-1). With PCA treatment, cAMP production was increased in HUVECs, while GPER-1 expression was increased in both HUVECs and a rat aortic explant. PCA and G1, a GPER-1 agonist, reduced H2O2 stimulated ROS production in HUVECs, whereas, G15, a GPER-1 antagonist, increased ROS production further. These elevations were inhibited by co-treatment with PCA or G1. TNFα stimulated the expression of inflammatory markers (VCAM-1, ICAM-1 and CD40), phospho-NF-κB, phospho-p38 and HIF-1α; however, co-treatment with PCA or G1 down-regulated this expression significantly. Likewise, increased expression of inflammatory markers by treatment with G15 was inhibited by co-treatment with PCA. In re-endothelization, aortic ring sprouting and neointima formation assay, rat aortas treated with PCA or G1 showed accelerated re-endothelization of the endothelium and reduced sprouting and neointima formation. However, aortas from G15-treated rats showed decelerated re-endothelization and increased sprouting and neointima formation. The effects of G15 were restored by co-treatment with PCA or G1. Also, in the endothelia of these aortas, PCA and G1 increased CD31 and GPER-1 and decreased VCAM-1 and CD40 expression. In contrast, the opposite effect was observed in G15-treated endothelium. These results suggest that GPER-1 might mediate the protective effect of PCA on the endothelium.

Application of FT-IR spectroscopy on breast cancer serum analysis

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Deep brain stimulation does not change neurovascular coupling in non-motor visual cortex: an autonomic and visual evoked blood flow velocity response study.

PubMed

Azevedo, Elsa; Santos, Rosa; Freitas, João; Rosas, Maria-José; Gago, Miguel; Garrett, Carolina; Rosengarten, Bernhard

2010-11-01

In Parkinson's disease (PD) subthalamic nucleus deep brain stimulation (STN-DBS) improves motor function. Also an effect on the neurovascular coupling in motor cortex was reported due to a parallel activation of a subthalamic vasodilator area (SVA). To address this issue further we analysed neurovascular coupling in a non-motor area. Twenty PD patients selected for bilateral STN-DBS were investigated with functional transcranial Doppler (f-TCD) before and after surgery. Hemodynamic responses to visual stimulation were registered in left posterior cerebral artery (PCA) and analysed with a control-system approach (parameters gain, rate time, attenuation and natural frequency). To exclude autonomic effects of STN-DBS, we also addressed spectrum analysis of heart rate and of systolic arterial blood pressure variability, and baroreceptor gain. Findings in the PD group were compared with healthy age-matched controls. PD patients showed no neurovascular coupling changes in PCA territory, compared to controls, and STN-DBS changed neither blood flow regulatory parameters nor autonomic function. Improvement of vasoregulation in some motor cortical areas after STN-DBS might be related to an improved neuronal functional rather than indicating an effect on the neurovascular coupling or autonomic function. Copyright © 2010 Elsevier Ltd. All rights reserved.

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

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

Chetvertkov, M; Henry Ford Health System, Detroit, MI; Siddiqui, F

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-regularizedmore » 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 established early in the treatment course. This work is supported in part by a grant from Varian Medical Systems, Palo Alto, CA.« less

Dataset of Fourier transform-infrared coupled with chemometric analysis used to distinguish accessions of Garcinia mangostana L. in Peninsular Malaysia.

PubMed

Samsir, Sri A'jilah; Bunawan, Hamidun; Yen, Choong Chee; Noor, Normah Mohd

2016-09-01

In this dataset, we distinguish 15 accessions of Garcinia mangostana from Peninsular Malaysia using Fourier transform-infrared spectroscopy coupled with chemometric analysis. We found that the position and intensity of characteristic peaks at 3600-3100 cm(-) (1) in IR spectra allowed discrimination of G. mangostana from different locations. Further principal component analysis (PCA) of all the accessions suggests the two main clusters were formed: samples from Johor, Melaka, and Negeri Sembilan (South) were clustered together in one group while samples from Perak, Kedah, Penang, Selangor, Kelantan, and Terengganu (North and East Coast) were in another clustered group.

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

PubMed

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®.

Multi-ingredients determination and fingerprint analysis of leaves from Ilex latifolia using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry.

PubMed

Fan, Chunlin; Deng, Jiewei; Yang, Yunyun; Liu, Junshan; Wang, Ying; Zhang, Xiaoqi; Fai, Kuokchiu; Zhang, Qingwen; Ye, Wencai

2013-10-01

An ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) method integrating multi-ingredients determination and fingerprint analysis has been established for quality assessment and control of leaves from Ilex latifolia. The method possesses the advantages of speediness, efficiency, accuracy, and allows the multi-ingredients determination and fingerprint analysis in one chromatographic run within 13min. Multi-ingredients determination was performed based on the extracted ion chromatograms of the exact pseudo-molecular ions (with a 0.01Da window), and fingerprint analysis was performed based on the base peak chromatograms, obtained by negative-ion electrospray ionization QTOF-MS. The method validation results demonstrated our developed method possessing desirable specificity, linearity, precision and accuracy. The method was utilized to analyze 22 I. latifolia samples from different origins. The quality assessment was achieved by using both similarity analysis (SA) and principal component analysis (PCA), and the results from SA were consistent with those from PCA. Our experimental results demonstrate that the strategy integrated multi-ingredients determination and fingerprint analysis using UPLC-QTOF-MS technique is a useful approach for rapid pharmaceutical analysis, with promising prospects for the differentiation of origin, the determination of authenticity, and the overall quality assessment of herbal medicines. Copyright © 2013 Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2014-11-01

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

Targeted and non-targeted detection of lemon juice adulteration by LC-MS and chemometrics.

PubMed

Wang, Zhengfang; Jablonski, Joseph E

2016-01-01

Economically motivated adulteration (EMA) of lemon juice was detected by LC-MS and principal component analysis (PCA). Twenty-two batches of freshly squeezed lemon juice were adulterated by adding an aqueous solution containing 5% citric acid and 6% sucrose to pure lemon juice to obtain 30%, 60% and 100% lemon juice samples. Their total titratable acidities, °Brix and pH values were measured, and then all the lemon juice samples were subject to LC-MS analysis. Concentrations of hesperidin and eriocitrin, major phenolic components of lemon juice, were quantified. The PCA score plots for LC-MS datasets were used to preview the classification of pure and adulterated lemon juice samples. Results showed a large inherent variability in the chemical properties among 22 batches of 100% lemon juice samples. Measurement or quantitation of one or several chemical properties (targeted detection) was not effective in detecting lemon juice adulteration. However, by using the LC-MS datasets, including both chromatographic and mass spectrometric information, 100% lemon juice samples were successfully differentiated from adulterated samples containing 30% lemon juice in the PCA score plot. LC-MS coupled with chemometric analysis can be a complement to existing methods for detecting juice adulteration.

Probing long-range interactions by extracting free energies from genome-wide chromosome conformation capture data.

PubMed

Saberi, Saeed; Farré, Pau; Cuvier, Olivier; Emberly, Eldon

2015-05-23

A variety of DNA binding proteins are involved in regulating and shaping the packing of chromatin. They aid the formation of loops in the DNA that function to isolate different structural domains. A recent experimental technique, Hi-C, provides a method for determining the frequency of such looping between all distant parts of the genome. Given that the binding locations of many chromatin associated proteins have also been measured, it has been possible to make estimates for their influence on the long-range interactions as measured by Hi-C. However, a challenge in this analysis is the predominance of non-specific contacts that mask out the specific interactions of interest. We show that transforming the Hi-C contact frequencies into free energies gives a natural method for separating out the distance dependent non-specific interactions. In particular we apply Principal Component Analysis (PCA) to the transformed free energy matrix to identify the dominant modes of interaction. PCA identifies systematic effects as well as high frequency spatial noise in the Hi-C data which can be filtered out. Thus it can be used as a data driven approach for normalizing Hi-C data. We assess this PCA based normalization approach, along with several other normalization schemes, by fitting the transformed Hi-C data using a pairwise interaction model that takes as input the known locations of bound chromatin factors. The result of fitting is a set of predictions for the coupling energies between the various chromatin factors and their effect on the energetics of looping. We show that the quality of the fit can be used as a means to determine how much PCA filtering should be applied to the Hi-C data. We find that the different normalizations of the Hi-C data vary in the quality of fit to the pairwise interaction model. PCA filtering can improve the fit, and the predicted coupling energies lead to biologically meaningful insights for how various chromatin bound factors influence the stability of DNA loops in chromatin.

An inter-comparison of PM10 source apportionment using PCA and PMF receptor models in three European sites.

PubMed

Cesari, Daniela; Amato, F; Pandolfi, M; Alastuey, A; Querol, X; Contini, D

2016-08-01

Source apportionment of aerosol is an important approach to investigate aerosol formation and transformation processes as well as to assess appropriate mitigation strategies and to investigate causes of non-compliance with air quality standards (Directive 2008/50/CE). Receptor models (RMs) based on chemical composition of aerosol measured at specific sites are a useful, and widely used, tool to perform source apportionment. However, an analysis of available studies in the scientific literature reveals heterogeneities in the approaches used, in terms of "working variables" such as the number of samples in the dataset and the number of chemical species used as well as in the modeling tools used. In this work, an inter-comparison of PM10 source apportionment results obtained at three European measurement sites is presented, using two receptor models: principal component analysis coupled with multi-linear regression analysis (PCA-MLRA) and positive matrix factorization (PMF). The inter-comparison focuses on source identification, quantification of source contribution to PM10, robustness of the results, and how these are influenced by the number of chemical species available in the datasets. Results show very similar component/factor profiles identified by PCA and PMF, with some discrepancies in the number of factors. The PMF model appears to be more suitable to separate secondary sulfate and secondary nitrate with respect to PCA at least in the datasets analyzed. Further, some difficulties have been observed with PCA in separating industrial and heavy oil combustion contributions. Commonly at all sites, the crustal contributions found with PCA were larger than those found with PMF, and the secondary inorganic aerosol contributions found by PCA were lower than those found by PMF. Site-dependent differences were also observed for traffic and marine contributions. The inter-comparison of source apportionment performed on complete datasets (using the full range of available chemical species) and incomplete datasets (with reduced number of chemical species) allowed to investigate the sensitivity of source apportionment (SA) results to the working variables used in the RMs. Results show that, at both sites, the profiles and the contributions of the different sources calculated with PMF are comparable within the estimated uncertainties indicating a good stability and robustness of PMF results. In contrast, PCA outputs are more sensitive to the chemical species present in the datasets. In PCA, the crustal contributions are higher in the incomplete datasets and the traffic contributions are significantly lower for incomplete datasets.

Characterization of Hatay honeys according to their multi-element analysis using ICP-OES combined with chemometrics.

PubMed

Yücel, Yasin; Sultanoğlu, Pınar

2013-09-01

Chemical characterisation has been carried out on 45 honey samples collected from Hatay region of Turkey. The concentrations of 17 elements were determined by inductively coupled plasma optical emission spectrometry (ICP-OES). Ca, K, Mg and Na were the most abundant elements, with mean contents of 219.38, 446.93, 49.06 and 95.91 mg kg(-1) respectively. The trace element mean contents ranged between 0.03 and 15.07 mg kg(-1). Chemometric methods such as principal component analysis (PCA) and cluster analysis (CA) techniques were applied to classify honey according to mineral content. The first most important principal component (PC) was strongly associated with the value of Al, B, Cd and Co. CA showed eight clusters corresponding to the eight botanical origins of honey. PCA explained 75.69% of the variance with the first six PC variables. Chemometric analysis of the analytical data allowed the accurate classification of the honey samples according to origin. Copyright © 2013 Elsevier Ltd. All rights reserved.

The discrimination of honey origin using melissopalynology and Raman spectroscopy techniques coupled with multivariate analysis.

PubMed

Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria

2015-02-15

Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed Central

Cocco, Simona; Monasson, Remi; Weigt, Martin

2013-01-01

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

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

NASA Astrophysics Data System (ADS)

Sicard, François; Senet, Patrick

2013-06-01

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

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

PubMed

Sicard, François; Senet, Patrick

2013-06-21

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

[Quality evaluation of American ginseng using UPLC coupled with multivariate analysis].

PubMed

Tang, Yan; Yan, Shu-Mo; Wang, Jing-Jing; Yuan, Yuan; Yang, Bin

2016-05-01

An ultra performance liquid chromatography (UPLC)method combined with multivariate data analysis was developed to evaluate the quality of American ginseng by simultaneously determining the concentrations of six ginsenosides (Rg₁, Re, Rb₁, Rc, Ro and Rd)in the samples. For UPLC, acetonitrile with 0.01% formic acid and water with 0.01% formic acid were used as the mobile phase with gradient elution. Under the established chromatographic conditions, the six ginsenosides could be well separated and the results of linearity, stability, precision, repeatability, and recovery rate all reached the requirement of quantification analysis, respectively. The total contents of Rg₁, Re, and Rb₁ in 57 samples all reached the requirement of the 2015 edition of Chinese Pharmacopoeia. At the same time, the experimental data were analyzed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The crude drugs and the decoction pieces can be discriminated by a PCA method and the samples with different age can be distinguished by a PLS-DA method. Copyright© by the Chinese Pharmaceutical Association.

Homogeneity study of a corn flour laboratory reference material candidate for inorganic analysis.

PubMed

Dos Santos, Ana Maria Pinto; Dos Santos, Liz Oliveira; Brandao, Geovani Cardoso; Leao, Danilo Junqueira; Bernedo, Alfredo Victor Bellido; Lopes, Ricardo Tadeu; Lemos, Valfredo Azevedo

2015-07-01

In this work, a homogeneity study of a corn flour reference material candidate for inorganic analysis is presented. Seven kilograms of corn flour were used to prepare the material, which was distributed among 100 bottles. The elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo were quantified by inductively coupled plasma optical emission spectrometry (ICP OES) after acid digestion procedure. The method accuracy was confirmed by analyzing the rice flour certified reference material, NIST 1568a. All results were evaluated by analysis of variance (ANOVA) and principal component analysis (PCA). In the study, a sample mass of 400mg was established as the minimum mass required for analysis, according to the PCA. The between-bottle test was performed by analyzing 9 bottles of the material. Subsamples of a single bottle were analyzed for the within-bottle test. No significant differences were observed for the results obtained through the application of both statistical methods. This fact demonstrates that the material is homogeneous for use as a laboratory reference material. Copyright © 2015 Elsevier Ltd. All rights reserved.

The function of oxytocin: a potential biomarker for prostate cancer diagnosis and promoter of prostate cancer.

PubMed

Xu, Huan; Fu, Shi; Chen, Qi; Gu, Meng; Zhou, Juan; Liu, Chong; Chen, Yanbo; Wang, Zhong

2017-05-09

To measure the level of oxytocin in serum and prostate cancer (PCa) tissue and study its effect on the proliferation of PCa cells. Oxytocin level in serum was significantly increased in PCa patients compared with the no-carcinoma individuals. Additionally, the levels of oxytocin and its receptor were also elevated in the PCa tissue. However, no significant difference existed among the PCa of various Gleason grades. Western blot analysis confirmed the previous results and revealed an increased expression level of APPL1. The level of oxytocin in serum was measured by ELISA analysis. The expression of oxytocin and its receptor in prostate was analyzed by immunohistochemistry. The proliferation and apoptosis of PCa cells were assessed by the Cell Counting Kit 8 (CCK8) assay, cell cycle analysis and caspase3 activity analysis, respectively. Western blot analysis was used for the detection of PCNA, Caspase3 and APPL1 protein levels. Serum and prostatic oxytocin levels are increased in the PCa subjects. Serum oxytocin level may be a biomarker for PCa in the future. Oxytocin increases PCa growth and APPL1 expression.

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

NASA Technical Reports Server (NTRS)

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

1996-01-01

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

Near-infrared-excited confocal Raman spectroscopy advances in vivo diagnosis of cervical precancer.

PubMed

Duraipandian, Shiyamala; Zheng, Wei; Ng, Joseph; Low, Jeffrey J H; Ilancheran, Arunachalam; Huang, Zhiwei

2013-06-01

Raman spectroscopy is a unique optical technique that can probe the changes of vibrational modes of biomolecules associated with tissue premalignant transformation. This study evaluates the clinical utility of confocal Raman spectroscopy over near-infrared (NIR) autofluorescence (AF) spectroscopy and composite NIR AF/Raman spectroscopy for improving early diagnosis of cervical precancer in vivo at colposcopy. A rapid NIR Raman system coupled with a ball-lens fiber-optic confocal Raman probe was utilized for in vivo NIR AF/Raman spectral measurements of the cervix. A total of 1240 in vivo Raman spectra [normal (n=993), dysplasia (n=247)] were acquired from 84 cervical patients. Principal components analysis (PCA) and linear discriminant analysis (LDA) together with a leave-one-patient-out, cross-validation method were used to extract the diagnostic information associated with distinctive spectroscopic modalities. The diagnostic ability of confocal Raman spectroscopy was evaluated using the PCA-LDA model developed from the significant principal components (PCs) [i.e., PC4, 0.0023%; PC5, 0.00095%; PC8, 0.00022%, (p<0.05)], representing the primary tissue Raman features (e.g., 854, 937, 1095, 1253, 1311, 1445, and 1654 cm(-1)). Confocal Raman spectroscopy coupled with PCA-LDA modeling yielded the diagnostic accuracy of 84.1% (a sensitivity of 81.0% and a specificity of 87.1%) for in vivo discrimination of dysplastic cervix. The receiver operating characteristic curves further confirmed that the best classification was achieved using confocal Raman spectroscopy compared to the composite NIR AF/Raman spectroscopy or NIR AF spectroscopy alone. This study illustrates that confocal Raman spectroscopy has great potential to improve early diagnosis of cervical precancer in vivo during clinical colposcopy.

Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Mishra, D.; Goyal, P.

2014-12-01

Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

Autonomic dysfunction affects cerebral neurovascular coupling.

PubMed

Azevedo, Elsa; Castro, Pedro; Santos, Rosa; Freitas, João; Coelho, Teresa; Rosengarten, Bernhard; Panerai, Ronney

2011-12-01

Autonomic failure (AF) affects the peripheral vascular system, but little is known about its influence on cerebrovascular regulation. Patients with familial amyloidotic polyneuropathy (FAP) were studied as a model for AF. Ten mild (FAPm), 10 severe (FAPs) autonomic dysfunction FAP patients, and 15 healthy controls were monitored in supine and sitting positions for arterial blood pressure (ABP) and heart rate (HR) with arterial volume clamping, and for blood flow velocity (BFV) in posterior (PCA) and contralateral middle cerebral arteries (MCA) with transcranial Doppler. Analysis included resting BFV, cerebrovascular resistance parameters (cerebrovascular resistance index, CVRi; resistance area product, RAP; and critical closing pressure, CrCP), and neurovascular coupling through visually evoked BFV responses in PCA (gain, rate time, attenuation, and natural frequency). In non-stimulation conditions, in each position, there were no significant differences between the groups, regarding HR, BP, resting BFV, and vascular resistance parameters. Sitting ABP was higher than in supine in the three groups, although only significantly in controls. Mean BFV was lower in sitting in all the groups, lacking statistical significance only in FAPs PCA. CVRi and CrCP increased with sitting in all the groups, while RAP increased in controls but decreased in FAPm and FAPs. In visual stimulation conditions, FAPs comparing to controls had a significant decrease of natural frequency, in supine and sitting, and of rate time and gain in sitting position. These results demonstrate that cerebrovascular regulation is affected in FAP subjects with AF, and that it worsens with orthostasis.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

¹H and ¹³C NMR-based sugar profiling with chemometric analysis and antioxidant activity of herbhoneys and honeys.

PubMed

Jamróz, Marta K; Paradowska, Katarzyna; Zawada, Katarzyna; Makarova, Katerina; Kaźmierski, Sławomir; Wawer, Iwona

2014-01-30

Herbhoneys, relatively new bee products, are expected to have interesting medicinal properties. However, there is still a lack of data concerning their composition and antioxidant properties. ¹H and ¹³C NMR spectroscopy coupled with chemometric analysis (PCA and PLS-DA) and antioxidant assays (DPPH-ESR and ORAC-FL) were used to study 25 samples of Polish herbhoneys and honeys. Antioxidant activity varied among the samples. The best properties were exhibited by cocoa and instant coffee herbhoneys. The contents of total polyphenols and total carotenoids in the studied samples were found to be 70-1340 mg GAE kg⁻¹ and 0-28.05 mg kg⁻¹ respectively. No significant differences between herbhoney and honey samples were found in their sugar profiles. The PCA of ¹³C NMR spectra of the samples in DMSO-d6 resulted in sample clustering due to sucrose content. Herbhoneys have similar antioxidant properties to traditional honeys, being therefore of equal nutritional value. There was a noticeable influence of the extract concentration on the observed antioxidant effect. For samples with high antioxidant activity, polyphenols were responsible for the observed effect. Sample clustering due to sucrose content in the NMR-PCA study allowed effortless detection of adulteration. © 2013 Society of Chemical Industry.

SU-F-J-138: An Extension of PCA-Based Respiratory Deformation Modeling Via Multi-Linear Decomposition

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

Iliopoulos, AS; Sun, X; Pitsianis, N

Purpose: To address and lift the limited degree of freedom (DoF) of globally bilinear motion components such as those based on principal components analysis (PCA), for encoding and modeling volumetric deformation motion. Methods: We provide a systematic approach to obtaining a multi-linear decomposition (MLD) and associated motion model from deformation vector field (DVF) data. We had previously introduced MLD for capturing multi-way relationships between DVF variables, without being restricted by the bilinear component format of PCA-based models. PCA-based modeling is commonly used for encoding patient-specific deformation as per planning 4D-CT images, and aiding on-board motion estimation during radiotherapy. However, themore » bilinear space-time decomposition inherently limits the DoF of such models by the small number of respiratory phases. While this limit is not reached in model studies using analytical or digital phantoms with low-rank motion, it compromises modeling power in the presence of relative motion, asymmetries and hysteresis, etc, which are often observed in patient data. Specifically, a low-DoF model will spuriously couple incoherent motion components, compromising its adaptability to on-board deformation changes. By the multi-linear format of extracted motion components, MLD-based models can encode higher-DoF deformation structure. Results: We conduct mathematical and experimental comparisons between PCA- and MLD-based models. A set of temporally-sampled analytical trajectories provides a synthetic, high-rank DVF; trajectories correspond to respiratory and cardiac motion factors, including different relative frequencies and spatial variations. Additionally, a digital XCAT phantom is used to simulate a lung lesion deforming incoherently with respect to the body, which adheres to a simple respiratory trend. In both cases, coupling of incoherent motion components due to a low model DoF is clearly demonstrated. Conclusion: Multi-linear decomposition can enable decoupling of distinct motion factors in high-rank DVF measurements. This may improve motion model expressiveness and adaptability to on-board deformation, aiding model-based image reconstruction for target verification. NIH Grant No. R01-184173.« less

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

PubMed

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

2013-11-07

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

Exploring patterns enriched in a dataset with contrastive principal component analysis.

PubMed

Abid, Abubakar; Zhang, Martin J; Bagaria, Vivek K; Zou, James

2018-05-30

Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis (cPCA), which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns missed by PCA and other standard methods. We further provide a geometric interpretation of cPCA and strong mathematical guarantees. An implementation of cPCA is publicly available, and can be used for exploratory data analysis in many applications where PCA is currently used.

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

NASA Technical Reports Server (NTRS)

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

1997-01-01

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

A Molecular Dynamic Modeling of Hemoglobin-Hemoglobin Interactions

NASA Astrophysics Data System (ADS)

Wu, Tao; Yang, Ye; Sheldon Wang, X.; Cohen, Barry; Ge, Hongya

2010-05-01

In this paper, we present a study of hemoglobin-hemoglobin interaction with model reduction methods. We begin with a simple spring-mass system with given parameters (mass and stiffness). With this known system, we compare the mode superposition method with Singular Value Decomposition (SVD) based Principal Component Analysis (PCA). Through PCA we are able to recover the principal direction of this system, namely the model direction. This model direction will be matched with the eigenvector derived from mode superposition analysis. The same technique will be implemented in a much more complicated hemoglobin-hemoglobin molecule interaction model, in which thousands of atoms in hemoglobin molecules are coupled with tens of thousands of T3 water molecule models. In this model, complex inter-atomic and inter-molecular potentials are replaced by nonlinear springs. We employ the same method to get the most significant modes and their frequencies of this complex dynamical system. More complex physical phenomena can then be further studied by these coarse grained models.

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

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

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

PubMed

Jesse, Stephen; Kalinin, Sergei V

2009-02-25

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

Priority of VHS Development Based in Potential Area using Principal Component Analysis

NASA Astrophysics Data System (ADS)

Meirawan, D.; Ana, A.; Saripudin, S.

2018-02-01

The current condition of VHS is still inadequate in quality, quantity and relevance. The purpose of this research is to analyse the development of VHS based on the development of regional potential by using principal component analysis (PCA) in Bandung, Indonesia. This study used descriptive qualitative data analysis using the principle of secondary data reduction component. The method used is Principal Component Analysis (PCA) analysis with Minitab Statistics Software tool. The results of this study indicate the value of the lowest requirement is a priority of the construction of development VHS with a program of majors in accordance with the development of regional potential. Based on the PCA score found that the main priority in the development of VHS in Bandung is in Saguling, which has the lowest PCA value of 416.92 in area 1, Cihampelas with the lowest PCA value in region 2 and Padalarang with the lowest PCA value.

[Urine metabonomic study on hypertension patients of ascendant hyperactivity of gan yang syndrome by high performance liquid chromatography coupled with time of flight mass spectrometry].

PubMed

Jiang, Hai-Qiang; Li, Yun-Lun; Xie, Jun

2012-03-01

To study the changes of urine metabolites in hypertension patients of ascendant hyperactivity of Gan yang syndrome (AHGYS), and to explore its essence in hypertension patients. Ten typical hypertension patients of AHGYS were recruited as the patient group, and the other twelve healthy volunteers were recruited as the normal group. The metabolite profiling in the urine were collected using by high performance liquid chromatography coupled with time of flight mass spectrometry (HPLC-TOFMS). The principal component analysis (PCA) and partial least-square discriminant analysis (PLS-DA) were analyzed using SIMCA-P Software. The differential metabolites in the urine were found out and identified. The possible relevant metabolic pathways were explained. The data from the analysis by PCA in the urine samples of the patient group and the normal group showed, two sets of data could be obviously classified in the score plot. Compared with the normal group, significant changes happened to the body metabolism in the patient group. The metabolites relevant to hypertension patients of AHGYS were determined using the PLS-DA. Fifteen compounds of the structure and metabolic pathways had been confirmed through inquiring KEGG Database, mainly including amino acids, free fatty acids, sphingosine, and so on. The hypertension patients of AHGYS were studied using HPLC-TOFMS combined with pattern recognition, thus finding out small molecular metabolic markers from the microscopic field, which was advantageous in probing the biological nature of Chinese medicine syndromes.

Characterization of edible seaweed harvested on the Galician coast (northwestern Spain) using pattern recognition techniques and major and trace element data.

PubMed

Romarís-Hortas, Vanessa; García-Sartal, Cristina; Barciela-Alonso, María Carmen; Moreda-Piñeiro, Antonio; Bermejo-Barrera, Pilar

2010-02-10

Major and trace elements in North Atlantic seaweed originating from Galicia (northwestern Spain) were determined by using inductively coupled plasma-optical emission spectrometry (ICP-OES) (Ba, Ca, Cu, K, Mg, Mn, Na, Sr, and Zn), inductively coupled plasma-mass spectrometry (ICP-MS) (Br and I) and hydride generation-atomic fluorescence spectrometry (HG-AFS) (As). Pattern recognition techniques were then used to classify the edible seaweed according to their type (red, brown, and green seaweed) and also their variety (Wakame, Fucus, Sea Spaghetti, Kombu, Dulse, Nori, and Sea Lettuce). Principal component analysis (PCA) and cluster analysis (CA) were used as exploratory techniques, and linear discriminant analysis (LDA) and soft independent modeling of class analogy (SIMCA) were used as classification procedures. In total, t12 elements were determined in a range of 35 edible seaweed samples (20 brown seaweed, 10 red seaweed, 4 green seaweed, and 1 canned seaweed). Natural groupings of the samples (brown, red, and green types) were observed using PCA and CA (squared Euclidean distance between objects and Ward method as clustering procedure). The application of LDA gave correct assignation percentages of 100% for brown, red, and green types at a significance level of 5%. However, a satisfactory classification (recognition and prediction) using SIMCA was obtained only for red seaweed (100% of cases correctly classified), whereas percentages of 89 and 80% were obtained for brown seaweed for recognition (training set) and prediction (testing set), respectively.

The psychosocial impact of prostate cancer on patients and their partners.

PubMed

Couper, Jeremy W; Bloch, Sidney; Love, Anthony; Duchesne, Gillian; Macvean, Michelle; Kissane, David W

2006-10-16

To assess the psychosocial impact of the diagnosis of either localised or metastatic prostate cancer (PCA) on patients and their female partners. Observational, prospective study at Time 1 and 6 months later at Time 2 of two groups of couples facing PCA. Time 1 was when patients were first diagnosed with histologically confirmed localised (potentially curable) PCA or metastatic (incurable) PCA. Depression and anxiety disorders according to the Diagnostic and statistical manual of mental disorders 4th edition (DSM-IV); psychological distress; marital satisfaction. At Time 1, partners had rates of DSM-IV major depression and generalised anxiety disorder twice those of women in the Australian community, and considerably higher than the patients' rates. At Time 2, psychological distress in partners had lessened but that in patients had increased. On the other hand, at Time 2, partners' marital satisfaction had deteriorated. To be fully effective, interventions aimed at reducing the psychosocial morbidity of PCA must involve both patient and partner, rather than the patient alone.

Cathepsin B expression in prostate cancer of native Japanese and Japanese-American patients: an immunohistochemical study.

PubMed

Sinha, Akhouri A; Morgan, Jenifer L; Betre, Konjit; Wilson, Michael J; Le, Chap; Marks, Leonard S

2008-01-01

Japanese-American (J-A) men who have immigrated to the U.S.A. and acquired the Western lifestyle usually have more invasive prostate cancer (PCa) than native Japanese (NJ) living in Japan. The specific reasons for these differences remain unknown. The objective of this study was to examine immunostainings of cathepsin B (CB) and its endogenous inhibitor stefin A (SA) in tissue microarray (TMA) and radical prostatectomy (RP) tissue sections in the hope of obtaining insights into the invasiveness of PCa in Japanese patients. TMA and RP sections were evaluated in 50 men (25 NJ and 25 J-A) for CB and SA reaction products. The CB and SA immunostainings were imaged directly from microscope slides to a computer using a high performance charge coupled device (CCD) digital camera, quantified using Metamorph software, analyzed using the two-sample t-test, and confirmed by multiple regression analysis. The CB and SA proteins were localized in the carcinomatous glands and isolated cancer cells in the TMA and RP sections. The Gleason scores and pre-surgery serum total prostate-specific antigen (PSA) levels did not differ significantly in the NJ and J-A patients (p = 0.14, p = 0.16, respectively). The Chi-square analysis of clinical stage versus place of birth showed that the NJ patients had significantly more T2a and T2b clinical stages than the J-A patients who had more advanced T2c and T3a stages (p = 0.003). The CB and SA immunostainings and their ratios in Gleason score 6 tumors did not show any difference, but the CB:SA ratios in score > or = 7 tumors approached significance levels. The overall matching of specimens according to the Gleason grade/score, pre-RP serum total PSA levels, clinical stage and age prior to evaluation of immunostainings greatly minimizes subjectivity associated with the evaluation of markers in this ethnic sub-population of PCa patients. CB and SA immunostaining is similar in Japanese patients who have organ-confined and moderately-differentiated PCa. Analysis of the reaction product data provides indirect evidence that invasiveness of PCa is similar in the two Japanese patient populations.

Synthesis of a Stereochemically Diverse Library of Medium-Sized Lactams and Sultams via SNAr Cycloetherification

PubMed Central

Gerard, Baudouin; Duvall, Jeremy R.; Lowe, Jason T.; Murillo, Tiffanie; Wei, Jingqiang; Akella, Lakshmi B.; Marcaurelle, Lisa A.

2011-01-01

We have implemented an aldol-based ‘build/couple/pair’ (B/C/P) strategy for the synthesis of stereochemically diverse 8-membered lactam and sultam scaffolds via SNAr cycloetherification. Each scaffold contains two handles, an amine and aryl bromide, for solid-phase diversification via N-capping and Pd-mediated cross coupling. A sparse matrix design strategy that achieves the dual objective of controlling physicochemical properties and selecting diverse library members was implemented. The production of two 8000-membered libraries is discussed including a full analysis of library purity and property distribution. Library diversity was evaluated in comparison to the Molecular Library Small Molecule Repository (MLSMR) through the use of a multi-fusion similarity (MFS) map and principal component analysis (PCA). PMID:21526820

Volatile changes in Hawaiian noni fruit, Morinda citrifolia L., during ripening and fermentation.

PubMed

Wall, Marisa M; Miller, Samuel; Siderhurst, Matthew S

2018-07-01

Noni fruit (Morinda citrifolia L., Rubiaceae) has been used in traditional medicine throughout the tropics and subtropics and is now attracting interest in western medicine. Fermented noni juice is of particular interest for its promising antitumor activity. The present study collected and analyzed volatiles released at nine time intervals by noni fruit during ripening and fermentation using headspace autosampling coupled to gas chromatography-mass spectrometry. Twenty-three noni volatiles were identified and relatively quantified. In addition to volatiles previously identified in noni, four novel volatile 3-methyl-2/3-butenyl esters were identified via the synthesis of reference compounds. Principle component analysis (PCA) and canonical discriminant analysis (CDA) were used to facilitate multidimensional pattern recognition. PCA showed that ripening noni fruit cluster into three groups, pre-ripe, fully ripe (translucent) and fermented, based on released volatiles. CDA could 83.8% correctly classify noni samples when all ripeness stages were analyzed and 100% when samples were classified into the three PCA groupings. The results of the present study confirm the identities of 3-methyl-2/3-butenyl esters, both novel and previously identified, through the synthesis of reference compounds. These esters constitute a large percentage of the volatiles released by fully ripe and fermented noni and likely produced from the decomposition of noniosides, a group of unique glucosides present in the fruit. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Untargeted Metabolomic Analysis of Capsicum spp. by GC-MS.

PubMed

Aranha, Bianca Camargo; Hoffmann, Jessica Fernanda; Barbieri, Rosa Lia; Rombaldi, Cesar Valmor; Chaves, Fábio Clasen

2017-09-01

In order to conserve the biodiversity of Capsicum species and find genotypes with potential to be utilised commercially, Embrapa Clima Temperado maintains an active germplasm collection (AGC) that requires characterisation, enabling genotype selection and support for breeding programmes. The objective of this study was to characterise pepper accessions from the Embrapa Clima Temperado AGC and differentiate species based on their metabolic profile using an untargeted metabolomics approach. Cold (-20°C) methanol extraction residue of freeze-dried fruit samples was partitioned into water/methanol (A) and chloroform (B) fractions. The polar fraction (A) was derivatised and both fractions (A and B) were analysed by gas chromatography coupled to mass spectrometry (GC-MS). Data from each fraction was analysed using a multivariate principal component analysis (PCA) with XCMS software. Amino acids, sugars, organic acids, capsaicinoids, and hydrocarbons were identified. Outlying accessions including P116 (C. chinense), P46, and P76 (C. annuum) were observed in a PCA plot mainly due to their high sucrose and fructose contents. PCA also indicated a separation of P221 (C. annuum) and P200 (C. chinense), because of their high dihydrocapsaicin content. Although the metabolic profiling did not allow for grouping by species, it permitted the simultaneous identification and quantification of several compounds complementing and expanding the metabolic database of the studied Capsicum spp. in the AGC. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Dynamic Responses in Brain Networks to Social Feedback: A Dual EEG Acquisition Study in Adolescent Couples

PubMed Central

Kuo, Ching-Chang; Ha, Thao; Ebbert, Ashley M.; Tucker, Don M.; Dishion, Thomas J.

2017-01-01

Adolescence is a sensitive period for the development of romantic relationships. During this period the maturation of frontolimbic networks is particularly important for the capacity to regulate emotional experiences. In previous research, both functional magnetic resonance imaging (fMRI) and dense array electroencephalography (dEEG) measures have suggested that responses in limbic regions are enhanced in adolescents experiencing social rejection. In the present research, we examined social acceptance and rejection from romantic partners as they engaged in a Chatroom Interact Task. Dual 128-channel dEEG systems were used to record neural responses to acceptance and rejection from both adolescent romantic partners and unfamiliar peers (N = 75). We employed a two-step temporal principal component analysis (PCA) and spatial independent component analysis (ICA) approach to statistically identify the neural components related to social feedback. Results revealed that the early (288 ms) discrimination between acceptance and rejection reflected by the P3a component was significant for the romantic partner but not the unfamiliar peer. In contrast, the later (364 ms) P3b component discriminated between acceptance and rejection for both partners and peers. The two-step approach (PCA then ICA) was better able than either PCA or ICA alone in separating these components of the brain's electrical activity that reflected both temporal and spatial phases of the brain's processing of social feedback. PMID:28620292

Traceability of Opuntia ficus-indica L. Miller by ICP-MS multi-element profile and chemometric approach.

PubMed

Mottese, Antonio Francesco; Naccari, Clara; Vadalà, Rossella; Bua, Giuseppe Daniel; Bartolomeo, Giovanni; Rando, Rossana; Cicero, Nicola; Dugo, Giacomo

2018-01-01

Opuntia ficus-indica L. Miller fruits, particularly 'Ficodindia dell'Etna' of Biancavilla (POD), 'Fico d'india tradizionale di Roccapalumba' with protected brand and samples from an experimental field in Pezzolo (Sicily) were analyzed by inductively coupled plasma mass spectrometry in order to determine the multi-element profile. A multivariate chemometric approach, specifically principal component analysis (PCA), was applied to individuate how mineral elements may represent a marker of geographic origin, which would be useful for traceability. PCA has allowed us to verify that the geographical origin of prickly pear fruits is significantly influenced by trace element content, and the results found in Biancavilla PDO samples were linked to the geological composition of this volcanic areas. It was observed that two principal components accounted for 72.03% of the total variance in the data and, in more detail, PC1 explains 45.51% and PC2 26.52%, respectively. This study demonstrated that PCA is an integrated tool for the traceability of food products and, at the same time, a useful method of authentication of typical local fruits such as prickly pear. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Elemental fingerprinting of Hypericum perforatum (St John's Wort) herb and preparations using ICP-OES and chemometrics.

PubMed

Owen, Jade D; Kirton, Stewart B; Evans, Sara J; Stair, Jacqueline L

2016-06-05

St. John's wort (SJW) (Hypericum perforatum) is a herbal remedy commonly used to treat mild depression. The elemental profiles of 54 samples (i.e., dry herbs, tablets and capsules) were evaluated by monitoring 25 elements using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). The major elemental constituents in the SJW samples were Ca (300-199,000μg/g), Mg (410-3,530μg/g), Al (4.4-900μg/g), Fe (1.154-760μg/g), Mn (2.4-261μg/g), Sr (0.88-83.6μg/g), and Zn (7-64μg/g). For the sixteen elements that could be reliably quantified, principal component analysis (PCA) was used to investigate underlying patterns in the data. PCA models identified 7 key elements (i.e., Ba, Ca, Cd, Mg, Mo, Ni and Y), which described 85% of the variance in the dataset in the first three principal components. The PCA approach resulted in a general delineation between the three different formulations and provides a basis for monitoring product quality in this manner. Copyright © 2016 Elsevier B.V. All rights reserved.

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

PubMed

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

2010-12-01

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

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

PubMed

Kim, Hee-Ju

2008-03-01

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

Analysis of the principal component algorithm in phase-shifting interferometry.

PubMed

Vargas, J; Quiroga, J Antonio; Belenguer, T

2011-06-15

We recently presented a new asynchronous demodulation method for phase-sampling interferometry. The method is based in the principal component analysis (PCA) technique. In the former work, the PCA method was derived heuristically. In this work, we present an in-depth analysis of the PCA demodulation method.

A reversible Renilla luciferase protein complementation assay for rapid identification of protein–protein interactions reveals the existence of an interaction network involved in xyloglucan biosynthesis in the plant Golgi apparatus

PubMed Central

Lund, Christian H.; Bromley, Jennifer R.; Stenbæk, Anne; Rasmussen, Randi E.; Scheller, Henrik V.; Sakuragi, Yumiko

2015-01-01

A growing body of evidence suggests that protein–protein interactions (PPIs) occur amongst glycosyltransferases (GTs) required for plant glycan biosynthesis (e.g. cell wall polysaccharides and N-glycans) in the Golgi apparatus, and may control the functions of these enzymes. However, identification of PPIs in the endomembrane system in a relatively fast and simple fashion is technically challenging, hampering the progress in understanding the functional coordination of the enzymes in Golgi glycan biosynthesis. To solve the challenges, we adapted and streamlined a reversible Renilla luciferase protein complementation assay (Rluc-PCA), originally reported for use in human cells, for transient expression in Nicotiana benthamiana. We tested Rluc-PCA and successfully identified luminescence complementation amongst Golgi-localizing GTs known to form a heterodimer (GAUT1 and GAUT7) and those which homooligomerize (ARAD1). In contrast, no interaction was shown between negative controls (e.g. GAUT7, ARAD1, IRX9). Rluc-PCA was used to investigate PPIs amongst Golgi-localizing GTs involved in biosynthesis of hemicelluloses. Although no PPI was identified among six GTs involved in xylan biosynthesis, Rluc-PCA confirmed three previously proposed interactions and identified seven novel PPIs amongst GTs involved in xyloglucan biosynthesis. Notably, three of the novel PPIs were confirmed by a yeast-based split-ubiquitin assay. Finally, Gateway-enabled expression vectors were generated, allowing rapid construction of fusion proteins to the Rluc reporters and epitope tags. Our results show that Rluc-PCA coupled with transient expression in N. benthamiana is a fast and versatile method suitable for analysis of PPIs between Golgi resident proteins in an easy and mid-throughput fashion in planta. PMID:25326916

A reversible Renilla luciferase protein complementation assay for rapid identification of protein-protein interactions reveals the existence of an interaction network involved in xyloglucan biosynthesis in the plant Golgi apparatus

DOE PAGES

Lund, C. H.; Bromley, J. R.; Stenbaek, A.; ...

2014-10-18

A growing body of evidence suggests that protein–protein interactions (PPIs) occur amongst glycosyltransferases (GTs) required for plant glycan biosynthesis (e.g. cell wall polysaccharides and N-glycans) in the Golgi apparatus, and may control the functions of these enzymes. However, identification of PPIs in the endomembrane system in a relatively fast and simple fashion is technically challenging, hampering the progress in understanding the functional coordination of the enzymes in Golgi glycan biosynthesis. To solve the challenges, we adapted and streamlined a reversible Renilla luciferase protein complementation assay (Rluc-PCA), originally reported for use in human cells, for transient expression in Nicotiana benthamiana. Wemore » tested Rluc-PCA and successfully identified luminescence complementation amongst Golgi-localizing GTs known to form a heterodimer (GAUT1 and GAUT7) and those which homooligomerize (ARAD1). In contrast, no interaction was shown between negative controls (e.g. GAUT7, ARAD1, IRX9). Rluc-PCA was used to investigate PPIs amongst Golgi-localizing GTs involved in biosynthesis of hemicelluloses. Although no PPI was identified among six GTs involved in xylan biosynthesis, Rluc-PCA confirmed three previously proposed interactions and identified seven novel PPIs amongst GTs involved in xyloglucan biosynthesis. Notably, three of the novel PPIs were confirmed by a yeast-based split-ubiquitin assay. Finally, Gateway-enabled expression vectors were generated, allowing rapid construction of fusion proteins to the Rluc reporters and epitope tags. In conclusion, our results show that Rluc-PCA coupled with transient expression in N. benthamiana is a fast and versatile method suitable for analysis of PPIs between Golgi resident proteins in an easy and mid-throughput fashion in planta.« less

Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves

NASA Astrophysics Data System (ADS)

Haq, Quazi M. I.; Mabood, Fazal; Naureen, Zakira; Al-Harrasi, Ahmed; Gilani, Sayed A.; Hussain, Javid; Jabeen, Farah; Khan, Ajmal; Al-Sabari, Ruqaya S. M.; Al-khanbashi, Fatema H. S.; Al-Fahdi, Amira A. M.; Al-Zaabi, Ahoud K. A.; Al-Shuraiqi, Fatma A. M.; Al-Bahaisi, Iman M.

2018-06-01

Nucleic acid & serology based methods have revolutionized plant disease detection, however, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic infection, in addition, they need at least 1-2 days for sample harvesting, processing, and analysis. In this study, two reflectance spectroscopies i.e. Near Infrared reflectance spectroscopy (NIR) and Fourier-Transform-Infrared spectroscopy with Attenuated Total Reflection (FT-IR, ATR) coupled with multivariate exploratory methods like Principle Component Analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been deployed to detect begomovirus infection in papaya leaves. The application of those techniques demonstrates that they are very useful for robust in vivo detection of plant begomovirus infection. These methods are simple, sensitive, reproducible, precise, and do not require any lengthy samples preparation procedures.

Strain Transient Detection Techniques: A Comparison of Source Parameter Inversions of Signals Isolated through Principal Component Analysis (PCA), Non-Linear PCA, and Rotated PCA

NASA Astrophysics Data System (ADS)

Lipovsky, B.; Funning, G. J.

2009-12-01

We compare several techniques for the analysis of geodetic time series with the ultimate aim to characterize the physical processes which are represented therein. We compare three methods for the analysis of these data: Principal Component Analysis (PCA), Non-Linear PCA (NLPCA), and Rotated PCA (RPCA). We evaluate each method by its ability to isolate signals which may be any combination of low amplitude (near noise level), temporally transient, unaccompanied by seismic emissions, and small scale with respect to the spatial domain. PCA is a powerful tool for extracting structure from large datasets which is traditionally realized through either the solution of an eigenvalue problem or through iterative methods. PCA is an transformation of the coordinate system of our data such that the new "principal" data axes retain maximal variance and minimal reconstruction error (Pearson, 1901; Hotelling, 1933). RPCA is achieved by an orthogonal transformation of the principal axes determined in PCA. In the analysis of meteorological data sets, RPCA has been seen to overcome domain shape dependencies, correct for sampling errors, and to determine principal axes which more closely represent physical processes (e.g., Richman, 1986). NLPCA generalizes PCA such that principal axes are replaced by principal curves (e.g., Hsieh 2004). We achieve NLPCA through an auto-associative feed-forward neural network (Scholz, 2005). We show the geophysical relevance of these techniques by application of each to a synthetic data set. Results are compared by inverting principal axes to determine deformation source parameters. Temporal variability in source parameters, estimated by each method, are also compared.

Phytochemical Profiles and Antimicrobial Activities of Allium cepa Red cv. and A. sativum Subjected to Different Drying Methods: A Comparative MS-Based Metabolomics.

PubMed

Farag, Mohamed A; Ali, Sara E; Hodaya, Rashad H; El-Seedi, Hesham R; Sultani, Haider N; Laub, Annegret; Eissa, Tarek F; Abou-Zaid, Fouad O F; Wessjohann, Ludger A

2017-05-08

Plants of the Allium genus produce sulphur compounds that give them a characteristic (alliaceous) flavour and mediate for their medicinal use. In this study, the chemical composition and antimicrobial properties of Allium cepa red cv. and A. sativum in the context of three different drying processes were assessed using metabolomics. Bulbs were dried using either microwave, air drying, or freeze drying and further subjected to chemical analysis of their composition of volatile and non-volatile metabolites. Volatiles were collected using solid phase micro-extraction (SPME) coupled to gas chromatography-mass spectrometry (GC/MS) with 42 identified volatiles including 30 sulphur compounds, four nitriles, three aromatics, and three esters. Profiling of the polar non-volatile metabolites via ultra-performance liquid chromatography coupled to high resolution MS (UPLC/MS) annotated 51 metabolites including dipeptides, flavonoids, phenolic acids, and fatty acids. Major peaks in GC/MS or UPLC/MS contributing to the discrimination between A. sativum and A. cepa red cv. were assigned to sulphur compounds and flavonoids. Whereas sulphur conjugates amounted to the major forms in A. sativum , flavonoids predominated in the chemical composition of A. cepa red cv. With regard to drying impact on Allium metabolites, notable and clear separations among specimens were revealed using principal component analysis (PCA). The PCA scores plot of the UPLC/MS dataset showed closer metabolite composition of microwave dried specimens to freeze dried ones, and distant from air dried bulbs, observed in both A. cepa and A. sativum . Compared to GC/MS, the UPLC/MS derived PCA model was more consistent and better in assessing the impact of drying on Allium metabolism. A phthalate derivative was found exclusively in a commercial garlic preparation via GC/MS, of yet unknown origin. The freeze dried samples of both Allium species exhibited stronger antimicrobial activities compared to dried specimens with A. sativum being in general more active than A. cepa red cv.

Development of a solar receiver for an organic Rankine cycle engine

NASA Astrophysics Data System (ADS)

Haskins, H. J.; Taylor, R. M.; Osborn, D. B.

A prototype power conversion assembly (PCA) developed by an American aerospace company is considered. The PCA will be mounted at the focal point of a 12 meter parabolic dish and will produce approximately 20 kW of 3 kHz ac power to a ground-mounted rectifier. The PCA includes a cavity receiver coupled to an organic Rankine cycle engine. The engine working fluid is toluene. The performance goals of the receiver design are to maximize both the thermal efficiency and the heat capacity of the core. The latter goal is desired for stabilizing the PCA operation during intermittent cloud cover. The receiver design is based upon the utilization of a direct-heated copper shell. It was necessary to develop a feasible manufacturing process for assuring a good braze joint between the stainless steel, containing the toluene, and the copper shell.

Development of a solar receiver for an organic Rankine cycle engine

NASA Technical Reports Server (NTRS)

Haskins, H. J.; Taylor, R. M.; Osborn, D. B.

1981-01-01

A prototype power conversion assembly (PCA) developed by an American aerospace company is considered. The PCA will be mounted at the focal point of a 12 meter parabolic dish and will produce approximately 20 kW of 3 kHz ac power to a ground-mounted rectifier. The PCA includes a cavity receiver coupled to an organic Rankine cycle engine. The engine working fluid is toluene. The performance goals of the receiver design are to maximize both the thermal efficiency and the heat capacity of the core. The latter goal is desired for stabilizing the PCA operation during intermittent cloud cover. The receiver design is based upon the utilization of a direct-heated copper shell. It was necessary to develop a feasible manufacturing process for assuring a good braze joint between the stainless steel, containing the toluene, and the copper shell.

Quantification of dynamic protein complexes using Renilla luciferase fragment complementation applied to protein kinase A activities in vivo.

PubMed

Stefan, E; Aquin, S; Berger, N; Landry, C R; Nyfeler, B; Bouvier, M; Michnick, S W

2007-10-23

The G protein-coupled receptor (GPCR) superfamily represents the most important class of pharmaceutical targets. Therefore, the characterization of receptor cascades and their ligands is a prerequisite to discovering novel drugs. Quantification of agonist-induced second messengers and downstream-coupled kinase activities is central to characterization of GPCRs or other pathways that converge on GPCR-mediated signaling. Furthermore, there is a need for simple, cell-based assays that would report on direct or indirect actions on GPCR-mediated effectors of signaling. More generally, there is a demand for sensitive assays to quantify alterations of protein complexes in vivo. We describe the development of a Renilla luciferase (Rluc)-based protein fragment complementation assay (PCA) that was designed specifically to investigate dynamic protein complexes. We demonstrate these features for GPCR-induced disassembly of protein kinase A (PKA) regulatory and catalytic subunits, a key effector of GPCR signaling. Taken together, our observations show that the PCA allows for direct and accurate measurements of live changes of absolute values of protein complex assembly and disassembly as well as cellular imaging and dynamic localization of protein complexes. Moreover, the Rluc-PCA has a sufficiently high signal-to-background ratio to identify endogenously expressed Galpha(s) protein-coupled receptors. We provide pharmacological evidence that the phosphodiesterase-4 family selectively down-regulates constitutive beta-2 adrenergic- but not vasopressin-2 receptor-mediated PKA activities. Our results show that the sensitivity of the Rluc-PCA simplifies the recording of pharmacological profiles of GPCR-based candidate drugs and could be extended to high-throughput screens to identify novel direct modulators of PKA or upstream components of GPCR signaling cascades.

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

NASA Technical Reports Server (NTRS)

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

2005-01-01

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

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

PubMed

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

2012-02-01

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

Soy Consumption and the Risk of Prostate Cancer: An Updated Systematic Review and Meta-Analysis

PubMed Central

Ranard, Katherine M.; Jeon, Sookyoung; Erdman, John W.

2018-01-01

Prostate cancer (PCa) is the second most commonly diagnosed cancer in men, accounting for 15% of all cancers in men worldwide. Asian populations consume soy foods as part of a regular diet, which may contribute to the lower PCa incidence observed in these countries. This meta-analysis provides a comprehensive updated analysis that builds on previously published meta-analyses, demonstrating that soy foods and their isoflavones (genistein and daidzein) are associated with a lower risk of prostate carcinogenesis. Thirty articles were included for analysis of the potential impacts of soy food intake, isoflavone intake, and circulating isoflavone levels, on both primary and advanced PCa. Total soy food (p < 0.001), genistein (p = 0.008), daidzein (p = 0.018), and unfermented soy food (p < 0.001) intakes were significantly associated with a reduced risk of PCa. Fermented soy food intake, total isoflavone intake, and circulating isoflavones were not associated with PCa risk. Neither soy food intake nor circulating isoflavones were associated with advanced PCa risk, although very few studies currently exist to examine potential associations. Combined, this evidence from observational studies shows a statistically significant association between soy consumption and decreased PCa risk. Further studies are required to support soy consumption as a prophylactic dietary approach to reduce PCa carcinogenesis. PMID:29300347

Discrimination of cherry wines based on their sensory properties and aromatic fingerprinting using HS-SPME-GC-MS and multivariate analysis.

PubMed

Xiao, Zuobing; Liu, Shengjiang; Gu, Yongbo; Xu, Na; Shang, Yi; Zhu, Jiancai

2014-03-01

Volatiles of cherry wines were extracted by headspace solid phase microextraction (HS-SPME) and analyzed by gas chromatography mass spectrometry (GC-MS), multivariate statistical techniques (such as principal component analysis (PCA) and cluster analysis (CA) and correlation analysis) to differentiate sensory attributes of 3 groups of the wines through characterization of volatiles of cherry wine. Seventy-five volatiles were identified in 9 samples, including 29 esters, 22 alcohols, 8 acids, 3 ketones, 5 aldehydes, and 8 miscellaneous compounds. The PCA results showed that the cherry wines were mainly differentiated by 8 sensory attributes. The samples W2, W4, and W7 were grouped around sweet aromatic and the samples W1, W5, and W9 were highly associated with the sweet, esters, green, bitter, and fermented. Nevertheless, the samples W3, W6, and W8 were located close to the sour, alcoholic, and fruity. The final result of correlation analysis was in conformity with the conclusion of PCA. The CA results showed that the group of W2, W4, and W7, and the group of W1, W5, and W9 had less difference than the group of W3, W6, and W8. The reason should be that esterification reactions and fermentation process during the ageing period was more extended. The results of analyzing revealed that HS-SPME-GC-MS coupled with chemometrics could give an appropriate way of characterizing and classifying the cherry wines. Attributes that represent and discriminate among cherry wines might be made use of a better comprehending of the wines and for being utilized in future work. In addition, several chemometrics were used to classify the type of wines and try to install the relationship between volatiles and sensory property. Especially, PCA clearly revealed that the most contributing compounds for sensory attributes of cherry wines, CA was a more applicable way to distinguish types of cherry wines. Therefore, a feasible method that would be helpful to promote the quality of the wines by improving the winemaking process and analyzing aromatic characteristics of wines. © 2014 Institute of Food Technologists®

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

PubMed

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.

The Application of Principal Component Analysis Using Fixed Eigenvectors to the Infrared Thermographic Inspection of the Space Shuttle Thermal Protection System

NASA Technical Reports Server (NTRS)

Cramer, K. Elliott; Winfree, William P.

2006-01-01

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

Multilevel principal component analysis (mPCA) in shape analysis: A feasibility study in medical and dental imaging.

PubMed

Farnell, D J J; Popat, H; Richmond, S

2016-06-01

Methods used in image processing should reflect any multilevel structures inherent in the image dataset or they run the risk of functioning inadequately. We wish to test the feasibility of multilevel principal components analysis (PCA) to build active shape models (ASMs) for cases relevant to medical and dental imaging. Multilevel PCA was used to carry out model fitting to sets of landmark points and it was compared to the results of "standard" (single-level) PCA. Proof of principle was tested by applying mPCA to model basic peri-oral expressions (happy, neutral, sad) approximated to the junction between the mouth/lips. Monte Carlo simulations were used to create this data which allowed exploration of practical implementation issues such as the number of landmark points, number of images, and number of groups (i.e., "expressions" for this example). To further test the robustness of the method, mPCA was subsequently applied to a dental imaging dataset utilising landmark points (placed by different clinicians) along the boundary of mandibular cortical bone in panoramic radiographs of the face. Changes of expression that varied between groups were modelled correctly at one level of the model and changes in lip width that varied within groups at another for the Monte Carlo dataset. Extreme cases in the test dataset were modelled adequately by mPCA but not by standard PCA. Similarly, variations in the shape of the cortical bone were modelled by one level of mPCA and variations between the experts at another for the panoramic radiographs dataset. Results for mPCA were found to be comparable to those of standard PCA for point-to-point errors via miss-one-out testing for this dataset. These errors reduce with increasing number of eigenvectors/values retained, as expected. We have shown that mPCA can be used in shape models for dental and medical image processing. mPCA was found to provide more control and flexibility when compared to standard "single-level" PCA. Specifically, mPCA is preferable to "standard" PCA when multiple levels occur naturally in the dataset. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Multielemental composition of suet oil based on quantification by ultrawave/ICP-MS coupled with chemometric analysis.

PubMed

Jiang, Jun; Feng, Liang; Li, Jie; Sun, E; Ding, Shu-Min; Jia, Xiao-Bin

2014-04-10

Suet oil (SO) has been used commonly for food and medicine preparation. The determination of its elemental composition has became an important challenge for human safety and health owing to its possible contents of heavy metals or other elements. In this study, ultrawave single reaction chamber microwave digestion (Ultrawave) and inductively coupled plasma-mass spectrometry (ICP-MS) analysis was performed to determine 14 elements (Pb, As, Hg, Cd, Fe, Cu, Mn, Ti, Ni, V, Sr, Na, Ka and Ca) in SO samples. Furthermore, the multielemental content of 18 SO samples, which represented three different sources in China: Qinghai, Anhui and Jiangsu, were evaluated and compared. The optimal ultrawave digestion conditions, namely, the optimal time (35 min), temperature (210 °C) and pressure (90 bar), were screened by Box-Behnken design (BBD). Eighteen samples were successfully classified into three groups by principal component analysis (PCA) according to the contents of 14 elements. The results showed that all SO samples were rich in elements, but with significant differences corresponding to different origins. The outliers and majority of SO could be discriminated by PCA according to the multielemental content profile. The results highlighted that the element distribution was associated with the origins of SO samples. The proposed ultrawave digestion system was quite efficient and convenient, which could be mainly attributed to its high pressure and special high-throughput for the sample digestion procedure. Our established method could be useful for the quality control and standardization of elements in SO samples and products.

Characterization of dissolved organic matter in Dongjianghu Lake by UV-visible absorption spectroscopy with multivariate analysis.

PubMed

Zhu, Yanzhong; Song, Yonghui; Yu, Huibin; Liu, Ruixia; Liu, Lusan; Lv, Chunjian

2017-08-08

UV-visible absorption spectroscopy coupled with principal component analysis (PCA) and hierarchical cluster analysis (HCA) was applied to characterize spectroscopic components, detect latent factors, and investigate spatial variations of dissolved organic matter (DOM) in a large-scale lake. Twelve surface water samples were collected from Dongjianghu Lake in China. DOM contained lignin and quinine moieties, carboxylic acid, microbial products, and aromatic and alkyl groups, which in the northern part of the lake was largely different from the southern part. Fifteen spectroscopic indices were deduced from the absorption spectra to indicate molecular weight or humification degree of DOM. The northern part of the lake presented the smaller molecular weight or the lower humification degree of DOM than the southern part. E 2/4 , E 3/4 , E 2/3 , and S 2 were latent factors of characterizing the molecular weight of DOM, while E 2/5 , E 3/5 , E 2/6 , E 4/5 , E 3/6 , and A 2/1 were latent factors of evaluating the humification degree of DOM. The UV-visible absorption spectroscopy combined with PCA and HCA may not only characterize DOM fractions of lakes, but may be transferred to other types of waterscape.

Nonlinear Principal Components Analysis: Introduction and Application

ERIC Educational Resources Information Center

Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Koojj, Anita J.

2007-01-01

The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal…

The fractal characteristic of facial anthropometric data for developing PCA fit test panels for youth born in central China.

PubMed

Yang, Lei; Wei, Ran; Shen, Henggen

2017-01-01

New principal component analysis (PCA) respirator fit test panels had been developed for current American and Chinese civilian workers based on anthropometric surveys. The PCA panels used the first two principal components (PCs) obtained from a set of 10 facial dimensions. Although the PCA panels for American and Chinese subjects adopted the bivairate framework with two PCs, the number of the PCs retained in the PCA analysis was different between Chinese subjects and Americans. For the Chinese youth group, the third PC should be retained in the PCA analysis for developing new fit test panels. In this article, an additional number label (ANL) is used to explain the third PC in PCA analysis when the first two PCs are used to construct the PCA half-facepiece respirator fit test panel for Chinese group. The three-dimensional box-counting method is proposed to estimate the ANLs by calculating fractal dimensions of the facial anthropometric data of the Chinese youth. The linear regression coefficients of scale-free range R 2 are all over 0.960, which demonstrates that the facial anthropometric data of the Chinese youth has fractal characteristic. The youth subjects born in Henan province has an ANL of 2.002, which is lower than the composite facial anthropometric data of Chinese subjects born in many provinces. Hence, Henan youth subjects have the self-similar facial anthropometric characteristic and should use the particular ANL (2.002) as the important tool along with using the PCA panel. The ANL method proposed in this article not only provides a new methodology in quantifying the characteristics of facial anthropometric dimensions for any ethnic/racial group, but also extends the scope of PCA panel studies to higher dimensions.

Hetero-bivalent Imaging Agents for Simultaneous Targeting Prostate-Specific Membrane Antigen (PSMA) and Hepsin

DTIC Science & Technology

2013-09-01

Suzuki cross-coupling reaction. The effects of solvent and base on the synthesis of 3 were studied using Pd(PPh3)4 as catalyst . DMF, ethanol, and DMSO...PSMA/hepsin for in vitro cell uptake and in vivo imaging studies . Compound 13 showed a low but detectable increased cell uptake into the developed...have been comprehensive clinical studies whether PSA testing is an efficient biomarker in diagnosing PCa and reducing PCa deaths. Two European studies

Time-dependent analysis of dosage delivery information for patient-controlled analgesia services.

PubMed

Kuo, I-Ting; Chang, Kuang-Yi; Juan, De-Fong; Hsu, Steen J; Chan, Chia-Tai; Tsou, Mei-Yung

2018-01-01

Pain relief always plays the essential part of perioperative care and an important role of medical quality improvement. Patient-controlled analgesia (PCA) is a method that allows a patient to self-administer small boluses of analgesic to relieve the subjective pain. PCA logs from the infusion pump consisted of a lot of text messages which record all events during the therapies. The dosage information can be extracted from PCA logs to provide easily understanding features. The analysis of dosage information with time has great help to figure out the variance of a patient's pain relief condition. To explore the trend of pain relief requirement, we developed a PCA dosage information generator (PCA DIG) to extract meaningful messages from PCA logs during the first 48 hours of therapies. PCA dosage information including consumption, delivery, infusion rate, and the ratio between demand and delivery is presented with corresponding values in 4 successive time frames. Time-dependent statistical analysis demonstrated the trends of analgesia requirements decreased gradually along with time. These findings are compatible with clinical observations and further provide valuable information about the strategy to customize postoperative pain management.

Synergistic Hepatoprotective and Antioxidant Effect of Artichoke, Fig, Blackberry Herbal Mixture on HepG2 Cells and Their Metabolic Profiling Using NMR Coupled with Chemometrics.

PubMed

Youssef, Fadia S; Labib, Rola M; Eldahshan, Omayma A; Singab, Abdel Nasser B

2017-12-01

The edible plants have long been reported to possess a lot of biological activities. Herein, the hepatoprotective and the antioxidant activities of the aqueous infusion of the edible parts of Cynara cardunculus, Ficus carica, and Morus nigra and their herbal mixture (CFM) was investigated in vitro using CCl 4 induced damage in HepG2 cells. The highest amelioration was observed via the consumption of CFM at 1 mg/ml showing 47.00% and 37.09% decline in aspartate transaminase and alanine transaminase and 77.32% and 101.02% increase in reduced glutathione and superoxide dismutase comparable to CCl 4 treated cells. Metabolic profiling of their aqueous infusions was done using nuclear magnetic resonance spectroscopic experiments coupled with chemometrics particularly hierarchical cluster analysis (HCA) and principal component analysis (PCA). The structural closeness of the various metabolites existing in black berry and the mixture as reflected in the PCA score plot and HCA processed from the 1 H-NMR spectral data could eventually explained the close values in their biological behavior. For fig and artichoke, the existence of different phenolic metabolites that act synergistically could greatly interpret their potent biological behavior. Thus, it can be concluded that a herbal mixture composed of black berry, artichoke, and fig could afford an excellent natural candidate to combat oxidative stress and counteract hepatic toxins owing to its phenolic compounds. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model.

PubMed

Lin, Yuxin; Chen, Feifei; Shen, Li; Tang, Xiaoyu; Du, Cui; Sun, Zhandong; Ding, Huijie; Chen, Jiajia; Shen, Bairong

2018-05-21

Prostate cancer (PCa) is a fatal malignant tumor among males in the world and the metastasis is a leading cause for PCa death. Biomarkers are therefore urgently needed to detect PCa metastatic signature at the early time. MicroRNAs are small non-coding RNAs with the potential to be biomarkers for disease prediction. In addition, computer-aided biomarker discovery is now becoming an attractive paradigm for precision diagnosis and prognosis of complex diseases. In this study, we identified key microRNAs as biomarkers for predicting PCa metastasis based on network vulnerability analysis. We first extracted microRNAs and mRNAs that were differentially expressed between primary PCa and metastatic PCa (MPCa) samples. Then we constructed the MPCa-specific microRNA-mRNA network and screened microRNA biomarkers by a novel bioinformatics model. The model emphasized the characterization of systems stability changes and the network vulnerability with three measurements, i.e. the structurally single-line regulation, the functional importance of microRNA targets and the percentage of transcription factor genes in microRNA unique targets. With this model, we identified five microRNAs as putative biomarkers for PCa metastasis. Among them, miR-101-3p and miR-145-5p have been previously reported as biomarkers for PCa metastasis and the remaining three, i.e. miR-204-5p, miR-198 and miR-152, were screened as novel biomarkers for PCa metastasis. The results were further confirmed by the assessment of their predictive power and biological function analysis. Five microRNAs were identified as candidate biomarkers for predicting PCa metastasis based on our network vulnerability analysis model. The prediction performance, literature exploration and functional enrichment analysis convinced our findings. This novel bioinformatics model could be applied to biomarker discovery for other complex diseases.

Immunoseroproteomic Profiling in African American Men with Prostate Cancer: Evidence for an Autoantibody Response to Glycolysis and Plasminogen-Associated Proteins*

PubMed Central

Sanchez, Tino W.; Zhang, Guangyu; Li, Jitian; Dai, Liping; Mirshahidi, Saied; Wall, Nathan R.; Yates, Clayton; Wilson, Colwick; Montgomery, Susanne; Zhang, Jian-Ying; Casiano, Carlos A.

2016-01-01

African American (AA) men suffer from a disproportionately high incidence and mortality of prostate cancer (PCa) compared with other racial/ethnic groups. Despite these disparities, African American men are underrepresented in clinical trials and in studies on PCa biology and biomarker discovery. We used immunoseroproteomics to profile antitumor autoantibody responses in AA and European American (EA) men with PCa, and explored differences in these responses. This minimally invasive approach detects autoantibodies to tumor-associated antigens that could serve as clinical biomarkers and immunotherapeutic agents. Sera from AA and EA men with PCa were probed by immunoblotting against PC3 cell proteins, with AA sera showing stronger immunoreactivity. Mass spectrometry analysis of immunoreactive protein spots revealed that several AA sera contained autoantibodies to a number of proteins associated with both the glycolysis and plasminogen pathways, particularly to alpha-enolase (ENO1). The proteomic data is deposited in ProteomeXchange with identifier PXD003968. Analysis of sera from 340 racially diverse men by enzyme-linked immunosorbent assays (ELISA) showed higher frequency of anti-ENO1 autoantibodies in PCa sera compared with control sera. We observed differences between AA-PCa and EA-PCa patients in their immunoreactivity against ENO1. Although EA-PCa sera reacted with higher frequency against purified ENO1 in ELISA and recognized by immunoblotting the endogenous cellular ENO1 across a panel of prostate cell lines, AA-PCa sera reacted weakly against this protein by ELISA but recognized it by immunoblotting preferentially in metastatic cell lines. These race-related differences in immunoreactivity to ENO1 could not be accounted by differential autoantibody recognition of phosphoepitopes within this antigen. Proteomic analysis revealed differences in the posttranslational modification profiles of ENO1 variants differentially recognized by AA-PCa and EA-PCa sera. These intriguing results suggest the possibility of race-related differences in the antitumor autoantibody response in PCa, and have implications for defining novel biological determinants of PCa health disparities. PMID:27742740

Lycopene and Risk of Prostate Cancer

PubMed Central

Chen, Ping; Zhang, Wenhao; Wang, Xiao; Zhao, Keke; Negi, Devendra Singh; Zhuo, Li; Qi, Mao; Wang, Xinghuan; Zhang, Xinhua

2015-01-01

Abstract Prostate cancer (PCa) is a common illness for aging males. Lycopene has been identified as an antioxidant agent with potential anticancer properties. Studies investigating the relation between lycopene and PCa risk have produced inconsistent results. This study aims to determine dietary lycopene consumption/circulating concentration and any potential dose–response associations with the risk of PCa. Eligible studies published in English up to April 10, 2014, were searched and identified from Pubmed, Sciencedirect Online, Wiley online library databases and hand searching. The STATA (version 12.0) was applied to process the dose–response meta-analysis. Random effects models were used to calculate pooled relative risks (RRs) and 95% confidence intervals (CIs) and to incorporate variation between studies. The linear and nonlinear dose–response relations were evaluated with data from categories of lycopene consumption/circulating concentrations. Twenty-six studies were included with 17,517 cases of PCa reported from 563,299 participants. Although inverse association between lycopene consumption and PCa risk was not found in all studies, there was a trend that with higher lycopene intake, there was reduced incidence of PCa (P = 0.078). Removal of one Chinese study in sensitivity analysis, or recalculation using data from only high-quality studies for subgroup analysis, indicated that higher lycopene consumption significantly lowered PCa risk. Furthermore, our dose–response meta-analysis demonstrated that higher lycopene consumption was linearly associated with a reduced risk of PCa with a threshold between 9 and 21 mg/day. Consistently, higher circulating lycopene levels significantly reduced the risk of PCa. Interestingly, the concentration of circulating lycopene between 2.17 and 85 μg/dL was linearly inversed with PCa risk whereas there was no linear association >85 μg/dL. In addition, greater efficacy for the circulating lycopene concentration on preventing PCa was found for studies with high quality, follow-up >10 years and where results were adjusted by the age or the body mass index. In conclusion, our novel data demonstrates that higher lycopene consumption/circulating concentration is associated with a lower risk of PCa. However, further studies are required to determine the mechanism by which lycopene reduces the risk of PCa and if there are other factors in tomato products that might potentially decrease PCa risk and progression. PMID:26287411

Short communication: Suitability of fluorescence spectroscopy for characterization of commercial milk of different composition and origin.

PubMed

Ntakatsane, M P; Yang, X Q; Lin, M; Liu, X M; Zhou, P

2011-11-01

Thirteen milk brands comprising 76 pasteurized and UHT milk samples of various compositions (whole, reduced fat, skimmed, low lactose, and high protein) were obtained from local supermarkets, and milk samples manufactured in various countries were discriminated using front-face fluorescence spectroscopy (FFFS) coupled with chemometric tools. The emission spectra of Maillard reaction products and riboflavin (MRP/RF; 400 to 600 nm) and tryptophan (300 to 400 nm) were recorded using FFFS, and the excitation wavelengths were set at 360 nm for MRP/RF and 290 nm for tryptophan. Principal component analysis (PCA) was applied to analyze the normalized spectra. The PCA of spectral information from MRP/RF discriminated the milk samples originating in different countries, and PCA of spectral information from tryptophan discriminated the samples according to composition. The fluorescence spectral data were compared with liquid chromatography-mass spectrometry results for the glycation extent of the milk samples, and a positive association (R(2)=0.84) was found between the degree of glycation of α-lactalbumin and the MRP/RF spectral data. This study demonstrates the ability and sensitivity of FFFS to rapidly discriminate and classify commercial milk with various compositions and processing conditions. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Stability of Nonlinear Principal Components Analysis: An Empirical Study Using the Balanced Bootstrap

ERIC Educational Resources Information Center

Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Kooij, Anita J.

2007-01-01

Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate…

Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and artificial neural networks.

PubMed

Özbalci, Beril; Boyaci, İsmail Hakkı; Topcu, Ali; Kadılar, Cem; Tamer, Uğur

2013-02-15

The aim of this study was to quantify glucose, fructose, sucrose and maltose contents of honey samples using Raman spectroscopy as a rapid method. By performing a single measurement, quantifications of sugar contents have been said to be unaffordable according to the molecular similarities between sugar molecules in honey matrix. This bottleneck was overcome by coupling Raman spectroscopy with chemometric methods (principal component analysis (PCA) and partial least squares (PLS)) and an artificial neural network (ANN). Model solutions of four sugars were processed with PCA and significant separation was observed. This operation, done with the spectral features by using PLS and ANN methods, led to the discriminant analysis of sugar contents. Models/trained networks were created using a calibration data set and evaluated using a validation data set. The correlation coefficient values between actual and predicted values of glucose, fructose, sucrose and maltose were determined as 0.964, 0.965, 0.968 and 0.949 for PLS and 0.965, 0.965, 0.978 and 0.956 for ANN, respectively. The requirement of rapid analysis of sugar contents of commercial honeys has been met by the data processed within this article. Copyright © 2012 Elsevier Ltd. All rights reserved.

Raman spectral analysis for rapid screening of dengue infection

NASA Astrophysics Data System (ADS)

Mahmood, T.; Nawaz, H.; Ditta, A.; Majeed, M. I.; Hanif, M. A.; Rashid, N.; Bhatti, H. N.; Nargis, H. F.; Saleem, M.; Bonnier, F.; Byrne, H. J.

2018-07-01

Infection with the dengue virus is currently clinically detected according to different biomarkers in human blood plasma, commonly measured by enzyme linked immunosorbent assays, including non-structural proteins (Ns1), immunoglobulin M (IgM) and immunoglobulin G (IgG). However, there is little or no mutual correlation between the biomarkers, as demonstrated in this study by a comparison of their levels in samples from 17 patients. As an alternative, the label free, rapid screening technique, Raman spectroscopy has been used for the characterisation/diagnosis of healthy and dengue infected human blood plasma samples. In dengue positive samples, changes in specific Raman spectral bands associated with lipidic and amino acid/protein content are observed and assigned based on literature and these features can be considered as markers associated with dengue development. Based on the spectroscopic analysis of the current, albeit limited, cohort of samples, Principal Components Analysis (PCA) coupled Factorial Discriminant Analysis, yielded values of 97.95% sensitivity and 95.40% specificity for identification of dengue infection. Furthermore, in a comparison of the normal samples to the patient samples which scored low for only one of the biomarker tests, but high or medium for either or both of the other two, PCA-FDA demonstrated a sensitivity of 97.38% and specificity of 86.18%, thus providing an unambiguous screening technology.

Statistical analysis of fragmentation patterns of electron ionization mass spectra of enolized-trimethylsilylated anabolic androgenic steroids

NASA Astrophysics Data System (ADS)

Fragkaki, A. G.; Angelis, Y. S.; Tsantili-Kakoulidou, A.; Koupparis, M.; Georgakopoulos, C.

2009-08-01

Anabolic androgenic steroids (AAS) are included in the List of prohibited substances of the World Anti-Doping Agency (WADA) as substances abused to enhance athletic performance. Gas chromatography coupled to mass spectrometry (GC-MS) plays an important role in doping control analyses identifying AAS as their enolized-trimethylsilyl (TMS)-derivatives using the electron ionization (EI) mode. This paper explores the suitability of complementary GC-MS mass spectra and statistical analysis (principal component analysis, PCA and partial least squares-discriminant analysis, PLS-DA) to differentiate AAS as a function of their structural and conformational features expressed by their fragment ions. The results obtained showed that the application of PCA yielded a classification among the AAS molecules which became more apparent after applying PLS-DA to the dataset. The application of PLS-DA yielded a clear separation among the AAS molecules which were, thus, classified as: 1-ene-3-keto, 3-hydroxyl with saturated A-ring, 1-ene-3-hydroxyl, 4-ene-3-keto, 1,4-diene-3-keto and 3-keto with saturated A-ring anabolic steroids. The study of this paper also presents structurally diagnostic fragment ions and dissociation routes providing evidence for the presence of unknown AAS or chemically modified molecules known as designer steroids.

Metabolomics driven analysis of artichoke leaf and its commercial products via UHPLC-q-TOF-MS and chemometrics.

PubMed

Farag, Mohamed A; El-Ahmady, Sherweit H; Elian, Fatma S; Wessjohann, Ludger A

2013-11-01

The demand to develop efficient and reliable analytical methods for the quality control of herbal medicines and nutraceuticals is on the rise, together with an increase in the legal requirements for safe and consistent levels of active principles. Here, we describe an ultra-high performance liquid chromatography method (UHPLC) coupled with quadrupole high resolution time of flight mass spectrometry (qTOF-MS) analysis for the comprehensive measurement of metabolites from three Cynara scolymus (artichoke) cultivars: American Green Globe, French Hyrious, and Egyptian Baladi. Under optimized conditions, 50 metabolites were simultaneously quantified and identified including: eight caffeic acid derivatives, six saponins, 12 flavonoids and 10 fatty acids. Principal component analysis (PCA) was used to define both similarities and differences among the three artichoke leaf cultivars. In addition, batches from seven commercially available artichoke market products were analysed and showed variable quality, particularly in caffeic acid derivatives, flavonoid and fatty acid contents. PCA analysis was able to discriminate between various preparations, including differentiation between various batches from the same supplier. To the best of our knowledge, this study provides the first approach utilizing UHPLC-MS based metabolite fingerprinting to reveal secondary metabolite compositional differences in artichoke leaf extracts. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

PubMed

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

2015-10-01

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

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

NASA Astrophysics Data System (ADS)

Tsai, Jinn-Tsong; Chou, Ping-Yi; Chou, Jyh-Horng

2015-11-01

The aim of this study is to generate vector quantisation (VQ) codebooks by integrating principle component analysis (PCA) algorithm, Linde-Buzo-Gray (LBG) algorithm, and evolutionary algorithms (EAs). The EAs include genetic algorithm (GA), particle swarm optimisation (PSO), honey bee mating optimisation (HBMO), and firefly algorithm (FF). The study is to provide performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches. The PCA-EA-LBG approaches contain PCA-GA-LBG, PCA-PSO-LBG, PCA-HBMO-LBG, and PCA-FF-LBG, while the PCA-LBG-EA approaches contain PCA-LBG, PCA-LBG-GA, PCA-LBG-PSO, PCA-LBG-HBMO, and PCA-LBG-FF. All training vectors of test images are grouped according to PCA. The PCA-EA-LBG used the vectors grouped by PCA as initial individuals, and the best solution gained by the EAs was given for LBG to discover a codebook. The PCA-LBG approach is to use the PCA to select vectors as initial individuals for LBG to find a codebook. The PCA-LBG-EA used the final result of PCA-LBG as an initial individual for EAs to find a codebook. The search schemes in PCA-EA-LBG first used global search and then applied local search skill, while in PCA-LBG-EA first used local search and then employed global search skill. The results verify that the PCA-EA-LBG indeed gain superior results compared to the PCA-LBG-EA, because the PCA-EA-LBG explores a global area to find a solution, and then exploits a better one from the local area of the solution. Furthermore the proposed PCA-EA-LBG approaches in designing VQ codebooks outperform existing approaches shown in the literature.

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.

PubMed

Wang, Jing; Wu, Chen-Jiang; Bao, Mei-Ling; Zhang, Jing; Wang, Xiao-Ning; Zhang, Yu-Dong

2017-10-01

To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. • Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.

Applying robust variant of Principal Component Analysis as a damage detector in the presence of outliers

NASA Astrophysics Data System (ADS)

Gharibnezhad, Fahit; Mujica, Luis E.; Rodellar, José

2015-01-01

Using Principal Component Analysis (PCA) for Structural Health Monitoring (SHM) has received considerable attention over the past few years. PCA has been used not only as a direct method to identify, classify and localize damages but also as a significant primary step for other methods. Despite several positive specifications that PCA conveys, it is very sensitive to outliers. Outliers are anomalous observations that can affect the variance and the covariance as vital parts of PCA method. Therefore, the results based on PCA in the presence of outliers are not fully satisfactory. As a main contribution, this work suggests the use of robust variant of PCA not sensitive to outliers, as an effective way to deal with this problem in SHM field. In addition, the robust PCA is compared with the classical PCA in the sense of detecting probable damages. The comparison between the results shows that robust PCA can distinguish the damages much better than using classical one, and even in many cases allows the detection where classic PCA is not able to discern between damaged and non-damaged structures. Moreover, different types of robust PCA are compared with each other as well as with classical counterpart in the term of damage detection. All the results are obtained through experiments with an aircraft turbine blade using piezoelectric transducers as sensors and actuators and adding simulated damages.

[Analysis on component difference in Citrus reticulata before and after being processed with salt by UPLC-Q-TOF/MS].

PubMed

Zeng, Rui; Fu, Juan; Wu, La-Bin; Huang, Lin-Fang

2013-07-01

To analyze components of Citrus reticulata and salt-processed C. reticulata by ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS), and compared the changes in components before and after being processed with salt. Principal component analysis (PCA) and partial least squares discriminant analysis (OPLS-DA) were adopted to analyze the difference in fingerprint between crude and processed C. reticulata, showing increased content of eriocitrin, limonin, nomilin and obacunone increase in salt-processed C. reticulata. Potential chemical markers were identified as limonin, obacunone and nomilin, which could be used for distinguishing index components of crude and processed C. reticulata.

EFEMP1 as a novel DNA methylation marker for prostate cancer: array-based DNA methylation and expression profiling.

PubMed

Kim, Yong-June; Yoon, Hyung-Yoon; Kim, Seon-Kyu; Kim, Young-Won; Kim, Eun-Jung; Kim, Isaac Yi; Kim, Wun-Jae

2011-07-01

Abnormal DNA methylation is associated with many human cancers. The aim of the present study was to identify novel methylation markers in prostate cancer (PCa) by microarray analysis and to test whether these markers could discriminate normal and PCa cells. Microarray-based DNA methylation and gene expression profiling was carried out using a panel of PCa cell lines and a control normal prostate cell line. The methylation status of candidate genes in prostate cell lines was confirmed by real-time reverse transcriptase-PCR, bisulfite sequencing analysis, and treatment with a demethylation agent. DNA methylation and gene expression analysis in 203 human prostate specimens, including 106 PCa and 97 benign prostate hyperplasia (BPH), were carried out. Further validation using microarray gene expression data from the Gene Expression Omnibus (GEO) was carried out. Epidermal growth factor-containing fibulin-like extracellular matrix protein 1 (EFEMP1) was identified as a lead candidate methylation marker for PCa. The gene expression level of EFEMP1 was significantly higher in tissue samples from patients with BPH than in those with PCa (P < 0.001). The sensitivity and specificity of EFEMP1 methylation status in discriminating between PCa and BPH reached 95.3% (101 of 106) and 86.6% (84 of 97), respectively. From the GEO data set, we confirmed that the expression level of EFEMP1 was significantly different between PCa and BPH. Genome-wide characterization of DNA methylation profiles enabled the identification of EFEMP1 aberrant methylation patterns in PCa. EFEMP1 might be a useful indicator for the detection of PCa.

Using Serological Proteome Analysis to Identify Serum Anti-Nucleophosmin 1 Autoantibody as a Potential Biomarker in European-American and African-American Patients With Prostate Cancer.

PubMed

Dai, Liping; Li, Jitian; Xing, Mengtao; Sanchez, Tino W; Casiano, Carlos A; Zhang, Jian-Ying

2016-11-01

The prostate-specific antigen (PSA) testing has been widely implemented for the early detection and management of prostate cancer (PCa). However, the lack of specificity has led to overdiagnosis, resulting in many possibly unnecessary biopsies and overtreatment. Therefore, novel serological biomarkers with high sensitivity and specificity are of vital importance needed to complement PSA testing in the early diagnosis and effective management of PCa. This is particularly critical in the context of PCa health disparities, where early detection and management could help reduce the disproportionately high PCa mortality observed in African-American men. Previous studies have demonstrated that sera from patients with PCa contain autoantibodies that react with tumor-associated antigens (TAAs). The serological proteome analysis (SERPA) approach was used to identify tumor-associated antigens (TAAs) of PCa. In evaluation study, the level of anti-NPM1 antibody was examined in sera from test cohort, validation cohort, as well as European-American (EA) and African-American (AA) men with PCa by using immunoassay. Nucleophosmin 1 (NPM1) as a 33 kDa TAA in PCa was identified and characterized by SERPA approach. Anti-NPM1 antibody level in PCa was higher than in benign prostatic hyperplasia (BPH) patients and healthy individuals. Receiver operating characteristic (ROC) curve analysis showed similar high diagnostic value for PCa in the test cohort (area under the curve (AUC):0.860) and validation cohort (AUC: 0.822) to differentiate from normal individuals and BPH. Interestingly, AUC values were significantly higher for AA PCa patients. When considering concurrent serum measurements of anti-NPM1 antibody and PSA, 97.1% PCa patients at early stage were identified correctly, while 69.2% BPH patients who had elevated PSA levels were found to be anti-NPM1 negative. Additionally, anti-NPM1 antibody levels in PCa patients at early stage significantly increased after surgery treatment. This intriguing data suggested that NPM1 can elicit autoantibody response in PCa and might be a potential biomarker for the immunodiagnosis and prognosis of PCa, and for supplementing PSA testing in distinguishing PCa from BPH. Prostate 76:1375-1386, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

p-Coumaroyl-CoA:monolignol transferase (PMT) acts specifically in the lignin biosynthetic pathway in Brachypodium distachyon

PubMed Central

Petrik, Deborah L; Karlen, Steven D; Cass, Cynthia L; Padmakshan, Dharshana; Lu, Fachuang; Liu, Sarah; Le Bris, Philippe; Antelme, Sébastien; Santoro, Nicholas; Wilkerson, Curtis G; Sibout, Richard; Lapierre, Catherine; Ralph, John; Sedbrook, John C

2014-01-01

Grass lignins contain substantial amounts of p-coumarate (pCA) that acylate the side-chains of the phenylpropanoid polymer backbone. An acyltransferase, named p-coumaroyl-CoA:monolignol transferase (OsPMT), that could acylate monolignols with pCA in vitro was recently identified from rice. In planta, such monolignol-pCA conjugates become incorporated into lignin via oxidative radical coupling, thereby generating the observed pCA appendages; however p-coumarates also acylate arabinoxylans in grasses. To test the authenticity of PMT as a lignin biosynthetic pathway enzyme, we examined Brachypodium distachyon plants with altered BdPMT gene function. Using newly developed cell wall analytical methods, we determined that the transferase was involved specifically in monolignol acylation. A sodium azide-generated Bdpmt-1 missense mutant had no (<0.5%) residual pCA on lignin, and BdPMT RNAi plants had levels as low as 10% of wild-type, whereas the amounts of pCA acylating arabinosyl units on arabinoxylans in these PMT mutant plants remained unchanged. pCA acylation of lignin from BdPMT-overexpressing plants was found to be more than three-fold higher than that of wild-type, but again the level on arabinosyl units remained unchanged. Taken together, these data are consistent with a defined role for grass PMT genes in encoding BAHD (BEAT, AHCT, HCBT, and DAT) acyltransferases that specifically acylate monolignols with pCA and produce monolignol p-coumarate conjugates that are used for lignification in planta. PMID:24372757

Mini-DIAL system measurements coupled with multivariate data analysis to identify TIC and TIM simulants: preliminary absorption database analysis.

NASA Astrophysics Data System (ADS)

Gaudio, P.; Malizia, A.; Gelfusa, M.; Martinelli, E.; Di Natale, C.; Poggi, L. A.; Bellecci, C.

2017-01-01

Nowadays Toxic Industrial Components (TICs) and Toxic Industrial Materials (TIMs) are one of the most dangerous and diffuse vehicle of contamination in urban and industrial areas. The academic world together with the industrial and military one are working on innovative solutions to monitor the diffusion in atmosphere of such pollutants. In this phase the most common commercial sensors are based on “point detection” technology but it is clear that such instruments cannot satisfy the needs of the smart cities. The new challenge is developing stand-off systems to continuously monitor the atmosphere. Quantum Electronics and Plasma Physics (QEP) research group has a long experience in laser system development and has built two demonstrators based on DIAL (Differential Absorption of Light) technology could be able to identify chemical agents in atmosphere. In this work the authors will present one of those DIAL system, the miniaturized one, together with the preliminary results of an experimental campaign conducted on TICs and TIMs simulants in cell with aim of use the absorption database for the further atmospheric an analysis using the same DIAL system. The experimental results are analysed with standard multivariate data analysis technique as Principal Component Analysis (PCA) to develop a classification model aimed at identifying organic chemical compound in atmosphere. The preliminary results of absorption coefficients of some chemical compound are shown together pre PCA analysis.

Prostate Cancer Predictive Simulation Modelling, Assessing the Risk Technique (PCP-SMART): Introduction and Initial Clinical Efficacy Evaluation Data Presentation of a Simple Novel Mathematical Simulation Modelling Method, Devised to Predict the Outcome of Prostate Biopsy on an Individual Basis.

PubMed

Spyropoulos, Evangelos; Kotsiris, Dimitrios; Spyropoulos, Katherine; Panagopoulos, Aggelos; Galanakis, Ioannis; Mavrikos, Stamatios

2017-02-01

We developed a mathematical "prostate cancer (PCa) conditions simulating" predictive model (PCP-SMART), from which we derived a novel PCa predictor (prostate cancer risk determinator [PCRD] index) and a PCa risk equation. We used these to estimate the probability of finding PCa on prostate biopsy, on an individual basis. A total of 371 men who had undergone transrectal ultrasound-guided prostate biopsy were enrolled in the present study. Given that PCa risk relates to the total prostate-specific antigen (tPSA) level, age, prostate volume, free PSA (fPSA), fPSA/tPSA ratio, and PSA density and that tPSA ≥ 50 ng/mL has a 98.5% positive predictive value for a PCa diagnosis, we hypothesized that correlating 2 variables composed of 3 ratios (1, tPSA/age; 2, tPSA/prostate volume; and 3, fPSA/tPSA; 1 variable including the patient's tPSA and the other, a tPSA value of 50 ng/mL) could operate as a PCa conditions imitating/simulating model. Linear regression analysis was used to derive the coefficient of determination (R 2 ), termed the PCRD index. To estimate the PCRD index's predictive validity, we used the χ 2 test, multiple logistic regression analysis with PCa risk equation formation, calculation of test performance characteristics, and area under the receiver operating characteristic curve analysis using SPSS, version 22 (P < .05). The biopsy findings were positive for PCa in 167 patients (45.1%) and negative in 164 (44.2%). The PCRD index was positively signed in 89.82% positive PCa cases and negative in 91.46% negative PCa cases (χ 2 test; P < .001; relative risk, 8.98). The sensitivity was 89.8%, specificity was 91.5%, positive predictive value was 91.5%, negative predictive value was 89.8%, positive likelihood ratio was 10.5, negative likelihood ratio was 0.11, and accuracy was 90.6%. Multiple logistic regression revealed the PCRD index as an independent PCa predictor, and the formulated risk equation was 91% accurate in predicting the probability of finding PCa. On the receiver operating characteristic analysis, the PCRD index (area under the curve, 0.926) significantly (P < .001) outperformed other, established PCa predictors. The PCRD index effectively predicted the prostate biopsy outcome, correctly identifying 9 of 10 men who were eventually diagnosed with PCa and correctly ruling out PCa for 9 of 10 men who did not have PCa. Its predictive power significantly outperformed established PCa predictors, and the formulated risk equation accurately calculated the probability of finding cancer on biopsy, on an individual patient basis. Copyright © 2016 Elsevier Inc. All rights reserved.

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

PubMed Central

Wagner, Florian

2015-01-01

Method 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. Results 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. PMID:26575370

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

PubMed

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.

1H NMR spectra dataset and solid-state NMR data of cowpea (Vigna unguiculata).

PubMed

Alves Filho, Elenilson G; Silva, Lorena M A; Teofilo, Elizita M; Larsen, Flemming H; de Brito, Edy S

2017-04-01

In this article the NMR data from chemical shifts, coupling constants, and structures of all the characterized compounds were provided, beyond a complementary PCA evaluation for the corresponding manuscript (E.G. Alves Filho, L.M.A. Silva, E.M. Teofilo, F.H. Larsen, E.S. de Brito, 2017) [3]. In addition, a complementary assessment from solid-state NMR data was provided. For further chemometric analysis, numerical matrices from the raw 1 H NMR data were made available in Microsoft Excel workbook format (.xls).

Incorporating biological information in sparse principal component analysis with application to genomic data.

PubMed

Li, Ziyi; Safo, Sandra E; Long, Qi

2017-07-11

Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection. Our simulation studies suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensitivity and specificity when the graph structure is correctly specified, and are fairly robust to misspecified graph structures. Application to a glioblastoma gene expression dataset identified pathways that are suggested in the literature to be related with glioblastoma. The proposed sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological information in variable selection, leading to improved feature selection and more interpretable principal component loadings and potentially providing insights on molecular underpinnings of complex diseases.

Profiling contents of water-soluble metabolites and mineral nutrients to evaluate the effects of pesticides and organic and chemical fertilizers on tomato fruit quality.

PubMed

Watanabe, Masami; Ohta, Yuko; Licang, Sun; Motoyama, Naoki; Kikuchi, Jun

2015-02-15

In this study, the contents of water-soluble metabolites and mineral nutrients were measured in tomatoes cultured using organic and chemical fertilizers, with or without pesticides. Mineral nutrients and water-soluble metabolites were determined by inductively coupled plasma-atomic emission spectrometry and (1)H nuclear magnetic resonance spectrometry, respectively, and results were analysed by principal components analysis (PCA). The mineral nutrient and water-soluble metabolite profiles differed between organic and chemical fertilizer applications, which accounted for 88.0% and 55.4%, respectively, of the variation. (1)H-(13)C-hetero-nuclear single quantum coherence experiments identified aliphatic protons that contributed to the discrimination of PCA. Pesticide application had little effect on mineral nutrient content (except Fe and P), but affected the correlation between mineral nutrients and metabolites. Differences in the content of mineral nutrients and water-soluble metabolites resulting from different fertilizer and pesticide applications probably affect tomato quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

A feasibility study on age-related factors of wrist pulse using principal component analysis.

PubMed

Jang-Han Bae; Young Ju Jeon; Sanghun Lee; Jaeuk U Kim

2016-08-01

Various analysis methods for examining wrist pulse characteristics are needed for accurate pulse diagnosis. In this feasibility study, principal component analysis (PCA) was performed to observe age-related factors of wrist pulse from various analysis parameters. Forty subjects in the age group of 20s and 40s were participated, and their wrist pulse signal and respiration signal were acquired with the pulse tonometric device. After pre-processing of the signals, twenty analysis parameters which have been regarded as values reflecting pulse characteristics were calculated and PCA was performed. As a results, we could reduce complex parameters to lower dimension and age-related factors of wrist pulse were observed by combining-new analysis parameter derived from PCA. These results demonstrate that PCA can be useful tool for analyzing wrist pulse signal.

Quantitative comparison of caffeoylquinic acids and flavonoids in Chrysanthemum morifolium flowers and their sulfur-fumigated products by three-channel liquid chromatography with electrochemical detection.

PubMed

Chen, Liangmian; Kotani, Akira; Kusu, Fumiyo; Wang, Zhimin; Zhu, Jingjing; Hakamata, Hideki

2015-01-01

For the determination of seven caffeoylquinic acids [neochlorogenic acid (NcA), cryptochlorogenic acid (CcA), chlorogenic acid (CA), caffeic acid (CfA), isochlorogenic acid A (Ic A), isochlorogenic acid B (Ic B), isochlorogenic acid C (Ic C)] and two flavonoids [luteolin 7-O-glucoside (LtG) and luteolin (Lt)], a three-channel liquid chromatography with electrochemical detection (LC-3ECD) method was established. Chromatographic peak heights were proportional to each concentration, ranging from 2.5 to 100 ng/mL for NcA, CA, CcA, and CfA, and ranging from 2.5 to 250 ng/mL for LtG, Ic B, Ic A, Ic C, and Lt, respectively. The present LC-3ECD method was applied to the quantitative analysis of caffeoylquinic acids and flavonoids in four cultivars of Chrysanthemum morifolium flowers and their sulfur-fumigated products. It was found that 60% of LtG and more than 47% of caffeoylquinic acids were lost during the sulfur fumigation processing. Sulfur fumigation showed a destructive effect on the C. morifolium flowers. In addition, principle component analyses (PCA) were performed using the results of the quantitative analysis of caffeoylquinic acids and flavonoids to compare the "sameness" and "differences" of these analytes in C. morifolium flowers and the sulfur-fumigated products. PCA score plots showed that the four cultivars of C. morifolium flowers were clearly classified into four groups, and that significant differences were also found between the non-fumigated C. morifolium flowers and the sulfur-fumigated products. Therefore, it was demonstrated that the present LC-3ECD method coupled with PCA is applicable to the variation analysis of different C. morifolium flower samples.

Proton Nuclear Magnetic Resonance-Spectroscopic Discrimination of Wines Reflects Genetic Homology of Several Different Grape (V. vinifera L.) Cultivars.

PubMed

Hu, Boran; Yue, Yaqing; Zhu, Yong; Wen, Wen; Zhang, Fengmin; Hardie, Jim W

2015-01-01

Proton nuclear magnetic resonance spectroscopy coupled multivariate analysis (1H NMR-PCA/PLS-DA) is an important tool for the discrimination of wine products. Although 1H NMR has been shown to discriminate wines of different cultivars, a grape genetic component of the discrimination has been inferred only from discrimination of cultivars of undefined genetic homology and in the presence of many confounding environmental factors. We aimed to confirm the influence of grape genotypes in the absence of those factors. We applied 1H NMR-PCA/PLS-DA and hierarchical cluster analysis (HCA) to wines from five, variously genetically-related grapevine (V. vinifera) cultivars; all grown similarly on the same site and vinified similarly. We also compared the semi-quantitative profiles of the discriminant metabolites of each cultivar with previously reported chemical analyses. The cultivars were clearly distinguishable and there was a general correlation between their grouping and their genetic homology as revealed by recent genomic studies. Between cultivars, the relative amounts of several of the cultivar-related discriminant metabolites conformed closely with reported chemical analyses. Differences in grape-derived metabolites associated with genetic differences alone are a major source of 1H NMR-based discrimination of wines and 1H NMR has the capacity to discriminate between very closely related cultivars. The study confirms that genetic variation among grape cultivars alone can account for the discrimination of wine by 1H NMR-PCA/PLS and indicates that 1H NMR spectra of wine of single grape cultivars may in future be used in tandem with hierarchical cluster analysis to elucidate genetic lineages and metabolomic relations of grapevine cultivars. In the absence of genetic information, for example, where predecessor varieties are no longer extant, this may be a particularly useful approach.

Proteomic Profiling of Exosomes Leads to the Identification of Novel Biomarkers for Prostate Cancer

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

Duijvesz, Diederick; Burnum-Johnson, Kristin E.; Gritsenko, Marina A.

Introduction: Current markers for prostate cancer, such as PSA lack specificity. Therefore, novel biomarkers are needed. Unfortunately, biomarker discovery from body fluids is often hampered by the high abundance of many proteins unrelated to disease. An attractive alternative biomarker discovery approach is the isolation of small vesicles (exosomes, ~100 nm). They contain proteins that are specific to the tissue from which they are derived and therefore can be considered as treasure chests for disease-specific marker discovery. Profiling prostate cancer-derived exosomes could reveal new markers for this malignancy. Materials and Methods: Exosomes were isolated from 2 immortalized primary prostate epithelial cellsmore » (PNT2C2 and RWPE-1) and 2 PCa cell lines (PC346C and VCaP) by ultracentrifugation. Proteomic analyses utilized a nanoLC coupled with an LTQ-Orbitrap operated in tandem MS (MS/MS) mode, followed by the Accurate Mass and Time (AMT) tag approach. Exosomal proteins were validated by Western blotting. A Tissue Micro Array, containing 481 different PCa samples (radical prostatectomy), was used to correlate candidate markers with several clinical-pathological parameters such as PSA, Gleason score, biochemical recurrence, and (PCa-related) death. Results: Proteomic characterization resulted in the identification of 263 proteins by at least 2 peptides. Specifically analysis of exosomes from PNT2C2, RWPE-1, PC346C, and VCaP identified 248, 233, 169, and 216 proteins, respectively. Statistical analyses revealed 52 proteins differently expressed between PCa and control cells, 9 of which were more abundant in PCa. Validation by Western blotting confirmed a higher abundance of FASN, XPO1 and PDCD6IP (ALIX) in PCa exosomes. The Tissue Micro 4 Array showed strong correlation of higher Gleason scores and local recurrence with increased cytoplasmic XPO1 (P<0.001). Conclusions: Differentially abundant proteins of cell line-derived exosomes make a clear subdivision between benign and malignant origin. Validation showed a preferential abundance of PDCD6IP, FASN and XPO1. Cytoplasmic XPO1 is the most promising candidate biomarker.« less

Elemental profiling and geographical differentiation of Ethiopian coffee samples through inductively coupled plasma-optical emission spectroscopy (ICP-OES), ICP-mass spectrometry (ICP-MS) and direct mercury analyzer (DMA).

PubMed

Habte, Girum; Hwang, In Min; Kim, Jae Sung; Hong, Joon Ho; Hong, Young Sin; Choi, Ji Yeon; Nho, Eun Yeong; Jamila, Nargis; Khan, Naeem; Kim, Kyong Su

2016-12-01

This study was aimed to establish the elemental profiling and provenance of coffee samples collected from eleven major coffee producing regions of Ethiopia. A total of 129 samples were analyzed for forty-five elements using inductively coupled plasma (ICP)-optical emission spectroscopy (OES), ICP-mass spectrometry (MS) and direct mercury analyzer (DMA). Among the macro elements, K showed the highest levels whereas Fe was found to have the lowest concentration values. In all the samples, Ca, K, Mg, P and S contents were statistically significant (p<0.05). Micro elements showed the concentrations order of: Mn>Cu>Sr>Zn>Rb>Ni>B. Contents of the trace elements were lower than the permissible standard values. Inter-regions differentiation by cluster analysis (CA), linear discriminant analysis (LDA) and principal component analysis (PCA) showed that micro and trace elements are the best chemical descriptors of the analyzed coffee samples. Copyright © 2016 Elsevier Ltd. All rights reserved.

The impact of moderate wine consumption on the risk of developing prostate cancer.

PubMed

Vartolomei, Mihai Dorin; Kimura, Shoji; Ferro, Matteo; Foerster, Beat; Abufaraj, Mohammad; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2018-01-01

To investigate the impact of moderate wine consumption on the risk of prostate cancer (PCa). We focused on the differential effect of moderate consumption of red versus white wine. This study was a meta-analysis that includes data from case-control and cohort studies. A systematic search of Web of Science, Medline/PubMed, and Cochrane library was performed on December 1, 2017. Studies were deemed eligible if they assessed the risk of PCa due to red, white, or any wine using multivariable logistic regression analysis. We performed a formal meta-analysis for the risk of PCa according to moderate wine and wine type consumption (white or red). Heterogeneity between studies was assessed using Cochrane's Q test and I 2 statistics. Publication bias was assessed using Egger's regression test. A total of 930 abstracts and titles were initially identified. After removal of duplicates, reviews, and conference abstracts, 83 full-text original articles were screened. Seventeen studies (611,169 subjects) were included for final evaluation and fulfilled the inclusion criteria. In the case of moderate wine consumption: the pooled risk ratio (RR) for the risk of PCa was 0.98 (95% CI 0.92-1.05, p =0.57) in the multivariable analysis. Moderate white wine consumption increased the risk of PCa with a pooled RR of 1.26 (95% CI 1.10-1.43, p =0.001) in the multi-variable analysis. Meanwhile, moderate red wine consumption had a protective role reducing the risk by 12% (RR 0.88, 95% CI 0.78-0.999, p =0.047) in the multivariable analysis that comprised 222,447 subjects. In this meta-analysis, moderate wine consumption did not impact the risk of PCa. Interestingly, regarding the type of wine, moderate consumption of white wine increased the risk of PCa, whereas moderate consumption of red wine had a protective effect. Further analyses are needed to assess the differential molecular effect of white and red wine conferring their impact on PCa risk.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Temporal Processing of Dynamic Positron Emission Tomography via Principal Component Analysis in the Sinogram Domain

NASA Astrophysics Data System (ADS)

Chen, Zhe; Parker, B. J.; Feng, D. D.; Fulton, R.

2004-10-01

In this paper, we compare various temporal analysis schemes applied to dynamic PET for improved quantification, image quality and temporal compression purposes. We compare an optimal sampling schedule (OSS) design, principal component analysis (PCA) applied in the image domain, and principal component analysis applied in the sinogram domain; for region-of-interest quantification, sinogram-domain PCA is combined with the Huesman algorithm to quantify from the sinograms directly without requiring reconstruction of all PCA channels. Using a simulated phantom FDG brain study and three clinical studies, we evaluate the fidelity of the compressed data for estimation of local cerebral metabolic rate of glucose by a four-compartment model. Our results show that using a noise-normalized PCA in the sinogram domain gives similar compression ratio and quantitative accuracy to OSS, but with substantially better precision. These results indicate that sinogram-domain PCA for dynamic PET can be a useful preprocessing stage for PET compression and quantification applications.

Genome-wide copy number analysis reveals candidate gene loci that confer susceptibility to high-grade prostate cancer.

PubMed

Poniah, Prevathe; Mohd Zain, Shamsul; Abdul Razack, Azad Hassan; Kuppusamy, Shanggar; Karuppayah, Shankar; Sian Eng, Hooi; Mohamed, Zahurin

2017-09-01

Two key issues in prostate cancer (PCa) that demand attention currently are the need for a more precise and minimally invasive screening test owing to the inaccuracy of prostate-specific antigen and differential diagnosis to distinguish advanced vs. indolent cancers. This continues to pose a tremendous challenge in diagnosis and prognosis of PCa and could potentially lead to overdiagnosis and overtreatment complications. Copy number variations (CNVs) in the human genome have been linked to various carcinomas including PCa. Detection of these variants may improve clinical treatment as well as an understanding of the pathobiology underlying this complex disease. To this end, we undertook a pilot genome-wide CNV analysis approach in 36 subjects (18 patients with high-grade PCa and 18 controls that were matched by age and ethnicity) in search of more accurate biomarkers that could potentially explain susceptibility toward high-grade PCa. We conducted this study using the array comparative genomic hybridization technique. Array results were validated in 92 independent samples (46 high-grade PCa, 23 benign prostatic hyperplasia, and 23 healthy controls) using polymerase chain reaction-based copy number counting method. A total of 314 CNV regions were found to be unique to PCa subjects in this cohort (P<0.05). A log 2 ratio-based copy number analysis revealed 5 putative rare or novel CNV loci or both associated with susceptibility to PCa. The CNV gain regions were 1q21.3, 15q15, 7p12.1, and a novel CNV in PCa 12q23.1, harboring ARNT, THBS1, SLC5A8, and DDC genes that are crucial in the p53 and cancer pathways. A CNV loss and deletion event was observed at 8p11.21, which contains the SFRP1 gene from the Wnt signaling pathway. Cross-comparison analysis with genes associated to PCa revealed significant CNVs involved in biological processes that elicit cancer pathogenesis via cytokine production and endothelial cell proliferation. In conclusion, we postulated that the CNVs identified in this study could provide an insight into the development of advanced PCa. Copyright © 2017 Elsevier Inc. All rights reserved.

Lack of association between NAT2 polymorphism and prostate cancer risk: a meta-analysis and trial sequential analysis

PubMed Central

Tang, Jingyuan; Xu, Lingyan; Xu, Haoxiang; Li, Ran; Han, Peng; Yang, Haiwei

2017-01-01

Previous studies have investigated the association between NAT2 polymorphism and the risk of prostate cancer (PCa). However, the findings from these studies remained inconsistent. Hence, we performed a meta-analysis to provide a more reliable conclusion about such associations. In the present meta-analysis, 13 independent case-control studies were included with a total of 14,469 PCa patients and 10,689 controls. All relevant studies published were searched in the databates PubMed, EMBASE, and Web of Science, till March 1st, 2017. We used the pooled odds ratios (ORs) with 95% confidence intervals (CIs) to evaluate the strength of the association between NAT2*4 allele and susceptibility to PCa. Subgroup analysis was carried out by ethnicity, source of controls and genotyping method. What's more, we also performed trial sequential analysis (TSA) to reduce the risk of type I error and evaluate whether the evidence of the results was firm. Firstly, our results indicated that NAT2*4 allele was not associated with PCa susceptibility (OR = 1.00, 95% CI= 0.95–1.05; P = 0.100). However, after excluding two studies for its heterogeneity and publication bias, no significant relationship was also detected between NAT2*4 allele and the increased risk of PCa, in fixed-effect model (OR = 0.99, 95% CI= 0.94–1.04; P = 0.451). Meanwhile, no significant increased risk of PCa was found in the subgroup analyses by ethnicity, source of controls and genotyping method. Moreover, TSA demonstrated that such association was confirmed in the present study. Therefore, this meta-analysis suggested that no significant association between NAT2 polymorphism and the risk of PCa was found. PMID:28915684

A reversible Renilla luciferase protein complementation assay for rapid identification of protein-protein interactions reveals the existence of an interaction network involved in xyloglucan biosynthesis in the plant Golgi apparatus.

PubMed

Lund, Christian H; Bromley, Jennifer R; Stenbæk, Anne; Rasmussen, Randi E; Scheller, Henrik V; Sakuragi, Yumiko

2015-01-01

A growing body of evidence suggests that protein-protein interactions (PPIs) occur amongst glycosyltransferases (GTs) required for plant glycan biosynthesis (e.g. cell wall polysaccharides and N-glycans) in the Golgi apparatus, and may control the functions of these enzymes. However, identification of PPIs in the endomembrane system in a relatively fast and simple fashion is technically challenging, hampering the progress in understanding the functional coordination of the enzymes in Golgi glycan biosynthesis. To solve the challenges, we adapted and streamlined a reversible Renilla luciferase protein complementation assay (Rluc-PCA), originally reported for use in human cells, for transient expression in Nicotiana benthamiana. We tested Rluc-PCA and successfully identified luminescence complementation amongst Golgi-localizing GTs known to form a heterodimer (GAUT1 and GAUT7) and those which homooligomerize (ARAD1). In contrast, no interaction was shown between negative controls (e.g. GAUT7, ARAD1, IRX9). Rluc-PCA was used to investigate PPIs amongst Golgi-localizing GTs involved in biosynthesis of hemicelluloses. Although no PPI was identified among six GTs involved in xylan biosynthesis, Rluc-PCA confirmed three previously proposed interactions and identified seven novel PPIs amongst GTs involved in xyloglucan biosynthesis. Notably, three of the novel PPIs were confirmed by a yeast-based split-ubiquitin assay. Finally, Gateway-enabled expression vectors were generated, allowing rapid construction of fusion proteins to the Rluc reporters and epitope tags. Our results show that Rluc-PCA coupled with transient expression in N. benthamiana is a fast and versatile method suitable for analysis of PPIs between Golgi resident proteins in an easy and mid-throughput fashion in planta. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Clinical Significance of Retinoic Acid Receptor Beta Promoter Methylation in Prostate Cancer: A Meta-Analysis.

PubMed

Dou, MengMeng; Zhou, XueLiang; Fan, ZhiRui; Ding, XianFei; Li, LiFeng; Wang, ShuLing; Xue, Wenhua; Wang, Hui; Suo, Zhenhe; Deng, XiaoMing

2018-01-01

Retinoic acid receptor beta (RAR beta) is a retinoic acid receptor gene that has been shown to play key roles during multiple cancer processes, including cell proliferation, apoptosis, migration and invasion. Numerous studies have found that methylation of the RAR beta promoter contributed to the occurrence and development of malignant tumors. However, the connection between RAR beta promoter methylation and prostate cancer (PCa) remains unknown. This meta-analysis evaluated the clinical significance of RAR beta promoter methylation in PCa. We searched all published records relevant to RAR beta and PCa in a series of databases, including PubMed, Embase, Cochrane Library, ISI Web of Science and CNKI. The rates of RAR beta promoter methylation in the PCa and control groups (including benign prostatic hyperplasia and normal prostate tissues) were summarized. In addition, we evaluated the source region of available samples and the methods used to detect methylation. To compare the incidence and variation in RAR beta promoter methylation in PCa and non-PCa tissues, the odds ratio (OR) and 95% confidence interval (CI) were calculated accordingly. All the data were analyzed with the statistical software STATA 12.0. Based on the inclusion and exclusion criteria, 15 articles assessing 1,339 samples were further analyzed. These data showed that the RAR beta promoter methylation rates in PCa tissues were significantly higher than the rates in the non-PCa group (OR=21.65, 95% CI: 9.27-50.57). Subgroup analysis according to the source region of samples showed that heterogeneity in Asia was small (I2=0.0%, P=0.430). Additional subgroup analysis based on the method used to detect RAR beta promoter methylation showed that the heterogeneity detected by MSP (methylation-specific PCR) was relatively small (I2=11.3%, P=0.343). Although studies reported different rates for RAR beta promoter methylation in PCa tissues, the total analysis demonstrated that RAR beta promoter methylation may be correlated with PCa carcinogenesis and that the RAR beta gene is particularly susceptible. Additional studies with sufficient data are essential to further evaluate the clinical features and prognostic utility of RAR beta promoter methylation in PCa. © 2018 The Author(s). Published by S. Karger AG, Basel.

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

PubMed

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

2007-08-01

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

Meta-analysis of CDKN2A methylation to find its role in prostate cancer development and progression, and also to find the effect of CDKN2A expression on disease-free survival (PRISMA).

PubMed

Cao, Zipei; Wei, Lijuan; Zhu, Weizhi; Yao, Xuping

2018-03-01

Reduction of cyclin-dependent kinase inhibitor 2A (CDKN2A) (p16 and p14) expression through DNA methylation has been reported in prostate cancer (PCa). This meta-analysis was conducted to assess the difference of p16 and p14 methylation between PCa and different histological types of nonmalignant controls and the correlation of p16 or p14 methylation with clinicopathological features of PCa. According to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement criteria, articles were searched in PubMed, Embase, EBSCO, Wanfang, and CNKI databases. The strength of correlation was calculated by the pooled odds ratios (ORs) and their corresponding 95% confidence intervals (95% CIs). Trial sequential analysis (TSA) was used to estimate the required population information for significant results. A total of 20 studies published from 1997 to 2017 were identified in this meta-analysis, including 1140 PCa patients and 530 cases without cancer. Only p16 methylation in PCa was significantly higher than in benign prostatic lesions (OR = 4.72, P = .011), but had a similar level in PCa and adjacent tissues or high-grade prostatic intraepithelial neoplasias (HGPIN). TSA revealed that this analysis on p16 methylation is a false positive result in cancer versus benign prostatic lesions (the estimated required information size of 5116 participants). p16 methylation was not correlated with PCa in the urine and blood. Besides, p16 methylation was not linked to clinical stage, prostate-specific antigen (PSA) level, and Gleason score (GS) of patients with PCa. p14 methylation was not correlated with PCa in tissue and urine samples. No correlation was observed between p14 methylation and clinical stage or GS. CDKN2A mutation and copy number alteration were not associated with prognosis of PCa in overall survival and disease-free survival. CDKN2A expression was not correlated with the prognosis of PCa in overall survival (492 cases) (P > .1), while CDKN2A expression was significantly associated with a poor disease-free survival (P < .01). CDKN2A methylation may not be significantly associated with the development, progression of PCa. Although CDKN2A expression had an unfavorable prognosis in disease-free survival. More studies are needed to confirm our results.

Structure-properties relationships of novel poly(carbonate-co-amide) segmented copolymers with polyamide-6 as hard segments and polycarbonate as soft segments

NASA Astrophysics Data System (ADS)

Yang, Yunyun; Kong, Weibo; Yuan, Ye; Zhou, Changlin; Cai, Xufu

2018-04-01

Novel poly(carbonate-co-amide) (PCA) block copolymers are prepared with polycarbonate diol (PCD) as soft segments, polyamide-6 (PA6) as hard segments and 4,4'-diphenylmethane diisocyanate (MDI) as coupling agent through reactive processing. The reactive processing strategy is eco-friendly and resolve the incompatibility between polyamide segments and PCD segments in preparation processing. The chemical structure, crystalline properties, thermal properties, mechanical properties and water resistance were extensively studied by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Differential scanning calorimetry (DSC), Thermal gravity analysis (TGA), Dynamic mechanical analysis (DMA), tensile testing, water contact angle and water absorption, respectively. The as-prepared PCAs exhibit obvious microphase separation between the crystalline hard PA6 phase and amorphous PCD soft segments. Meanwhile, PCAs showed outstanding mechanical with the maximum tensile strength of 46.3 MPa and elongation at break of 909%. The contact angle and water absorption results indicate that PCAs demonstrate outstanding water resistance even though possess the hydrophilic surfaces. The TGA measurements prove that the thermal stability of PCA can satisfy the requirement of multiple-processing without decomposition.

Detection of Fungus Infection on Petals of Rapeseed (Brassica napus L.) Using NIR Hyperspectral Imaging

NASA Astrophysics Data System (ADS)

Zhao, Yan-Ru; Yu, Ke-Qiang; Li, Xiaoli; He, Yong

2016-12-01

Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. This research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874-1734 nm coupled with chemometrics. Reflectance was extracted from regions of interest (ROIs) in the hyperspectral image of each sample. Firstly, principal component analysis (PCA) was applied to conduct a cluster analysis with the first several principal components (PCs). Then, two methods including X-loadings of PCA and random frog (RF) algorithm were used and compared for optimizing wavebands selection. Least squares-support vector machine (LS-SVM) methodology was employed to establish discriminative models based on the optimal and full wavebands. Finally, area under the receiver operating characteristics curve (AUC) was utilized to evaluate classification performance of these LS-SVM models. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929. These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals.

Epigenetics-related genes in prostate cancer: expression profile in prostate cancer tissues, androgen-sensitive and -insensitive cell lines.

PubMed

Shaikhibrahim, Zaki; Lindstrot, Andreas; Ochsenfahrt, Jacqueline; Fuchs, Kerstin; Wernert, Nicolas

2013-01-01

Epigenetic changes have been suggested to drive prostate cancer (PCa) development and progression. Therefore, in this study, we aimed to identify novel epigenetics-related genes in PCa tissues, and to examine their expression in metastatic PCa cell lines. We analyzed the expression of epigenetics-related genes via a clustering analysis based on gene function in moderately and poorly differentiated PCa glands compared to normal glands of the peripheral zone (prostate proper) from PCa patients using Whole Human Genome Oligo Microarrays. Our analysis identified 12 epigenetics-related genes with a more than 2-fold increase or decrease in expression and a p-value <0.01. In modera-tely differentiated tumors compared to normal glands of the peripheral zone, we found the genes, TDRD1, IGF2, DICER1, ADARB1, HILS1, GLMN and TRIM27, to be upregulated, whereas TNRC6A and DGCR8 were found to be downregulated. In poorly differentiated tumors, we found TDRD1, ADARB and RBM3 to be upregulated, whereas DGCR8, PIWIL2 and BC069781 were downregulated. Our analysis of the expression level for each gene in the metastatic androgen-sensitive VCaP and LNCaP, and -insensitive PC3 and DU-145 PCa cell lines revealed differences in expression among the cell lines which may reflect the different biological properties of each cell line, and the potential role of each gene at different metastatic sites. The novel epigenetics-related genes that we identified in primary PCa tissues may provide further insight into the role that epigenetic changes play in PCa. Moreover, some of the genes that we identified may play important roles in primary PCa and metastasis, in primary PCa only, or in metastasis only. Follow-up studies are required to investigate the functional role and the role that the expression of these genes play in the outcome and progression of PCa using tissue microarrays.

Two worlds collide: Image analysis methods for quantifying structural variation in cluster molecular dynamics

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

Steenbergen, K. G., E-mail: kgsteen@gmail.com; Gaston, N.

2014-02-14

Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement formore » a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.« less

Two worlds collide: image analysis methods for quantifying structural variation in cluster molecular dynamics.

PubMed

Steenbergen, K G; Gaston, N

2014-02-14

Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement for a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.

Pepper seed variety identification based on visible/near-infrared spectral technology

NASA Astrophysics Data System (ADS)

Li, Cuiling; Wang, Xiu; Meng, Zhijun; Fan, Pengfei; Cai, Jichen

2016-11-01

Pepper is a kind of important fruit vegetable, with the expansion of pepper hybrid planting area, detection of pepper seed purity is especially important. This research used visible/near infrared (VIS/NIR) spectral technology to detect the variety of single pepper seed, and chose hybrid pepper seeds "Zhuo Jiao NO.3", "Zhuo Jiao NO.4" and "Zhuo Jiao NO.5" as research sample. VIS/NIR spectral data of 80 "Zhuo Jiao NO.3", 80 "Zhuo Jiao NO.4" and 80 "Zhuo Jiao NO.5" pepper seeds were collected, and the original spectral data was pretreated with standard normal variable (SNV) transform, first derivative (FD), and Savitzky-Golay (SG) convolution smoothing methods. Principal component analysis (PCA) method was adopted to reduce the dimension of the spectral data and extract principal components, according to the distribution of the first principal component (PC1) along with the second principal component(PC2) in the twodimensional plane, similarly, the distribution of PC1 coupled with the third principal component(PC3), and the distribution of PC2 combined with PC3, distribution areas of three varieties of pepper seeds were divided in each twodimensional plane, and the discriminant accuracy of PCA was tested through observing the distribution area of samples' principal components in validation set. This study combined PCA and linear discriminant analysis (LDA) to identify single pepper seed varieties, results showed that with the FD preprocessing method, the discriminant accuracy of pepper seed varieties was 98% for validation set, it concludes that using VIS/NIR spectral technology is feasible for identification of single pepper seed varieties.

Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis.

PubMed

Li, Zhan-Chao; Zhou, Xi-Bin; Dai, Zong; Zou, Xiao-Yong

2009-07-01

A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou's pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246-255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.

Bioinformatic prediction of G protein-coupled receptor encoding sequences from the transcriptome of the foreleg, including the Haller’s organ, of the cattle tick, Rhipicephalus australis

PubMed Central

Munoz, Sergio; Guerrero, Felix D.; Kellogg, Anastasia; Heekin, Andrew M.

2017-01-01

The cattle tick of Australia, Rhipicephalus australis, is a vector for microbial parasites that cause serious bovine diseases. The Haller’s organ, located in the tick’s forelegs, is crucial for host detection and mating. To facilitate the development of new technologies for better control of this agricultural pest, we aimed to sequence and annotate the transcriptome of the R. australis forelegs and associated tissues, including the Haller's organ. As G protein-coupled receptors (GPCRs) are an important family of eukaryotic proteins studied as pharmaceutical targets in humans, we prioritized the identification and classification of the GPCRs expressed in the foreleg tissues. The two forelegs from adult R. australis were excised, RNA extracted, and pyrosequenced with 454 technology. Reads were assembled into unigenes and annotated by sequence similarity. Python scripts were written to find open reading frames (ORFs) from each unigene. These ORFs were analyzed by different GPCR prediction approaches based on sequence alignments, support vector machines, hidden Markov models, and principal component analysis. GPCRs consistently predicted by multiple methods were further studied by phylogenetic analysis and 3D homology modeling. From 4,782 assembled unigenes, 40,907 possible ORFs were predicted. Using Blastp, Pfam, GPCRpred, TMHMM, and PCA-GPCR, a basic set of 46 GPCR candidates were compiled and a phylogenetic tree was constructed. With further screening of tertiary structures predicted by RaptorX, 6 likely GPCRs emerged and the strongest candidate was classified by PCA-GPCR to be a GABAB receptor. PMID:28231302

Bioinformatic prediction of G protein-coupled receptor encoding sequences from the transcriptome of the foreleg, including the Haller's organ, of the cattle tick, Rhipicephalus australis.

PubMed

Munoz, Sergio; Guerrero, Felix D; Kellogg, Anastasia; Heekin, Andrew M; Leung, Ming-Ying

2017-01-01

The cattle tick of Australia, Rhipicephalus australis, is a vector for microbial parasites that cause serious bovine diseases. The Haller's organ, located in the tick's forelegs, is crucial for host detection and mating. To facilitate the development of new technologies for better control of this agricultural pest, we aimed to sequence and annotate the transcriptome of the R. australis forelegs and associated tissues, including the Haller's organ. As G protein-coupled receptors (GPCRs) are an important family of eukaryotic proteins studied as pharmaceutical targets in humans, we prioritized the identification and classification of the GPCRs expressed in the foreleg tissues. The two forelegs from adult R. australis were excised, RNA extracted, and pyrosequenced with 454 technology. Reads were assembled into unigenes and annotated by sequence similarity. Python scripts were written to find open reading frames (ORFs) from each unigene. These ORFs were analyzed by different GPCR prediction approaches based on sequence alignments, support vector machines, hidden Markov models, and principal component analysis. GPCRs consistently predicted by multiple methods were further studied by phylogenetic analysis and 3D homology modeling. From 4,782 assembled unigenes, 40,907 possible ORFs were predicted. Using Blastp, Pfam, GPCRpred, TMHMM, and PCA-GPCR, a basic set of 46 GPCR candidates were compiled and a phylogenetic tree was constructed. With further screening of tertiary structures predicted by RaptorX, 6 likely GPCRs emerged and the strongest candidate was classified by PCA-GPCR to be a GABAB receptor.

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

PubMed

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 Periodicals, Inc.

PCA-LBG-based algorithms for VQ codebook generation

NASA Astrophysics Data System (ADS)

Tsai, Jinn-Tsong; Yang, Po-Yuan

2015-04-01

Vector quantisation (VQ) codebooks are generated by combining principal component analysis (PCA) algorithms with Linde-Buzo-Gray (LBG) algorithms. All training vectors are grouped according to the projected values of the principal components. The PCA-LBG-based algorithms include (1) PCA-LBG-Median, which selects the median vector of each group, (2) PCA-LBG-Centroid, which adopts the centroid vector of each group, and (3) PCA-LBG-Random, which randomly selects a vector of each group. The LBG algorithm finds a codebook based on the better vectors sent to an initial codebook by the PCA. The PCA performs an orthogonal transformation to convert a set of potentially correlated variables into a set of variables that are not linearly correlated. Because the orthogonal transformation efficiently distinguishes test image vectors, the proposed PCA-LBG-based algorithm is expected to outperform conventional algorithms in designing VQ codebooks. The experimental results confirm that the proposed PCA-LBG-based algorithms indeed obtain better results compared to existing methods reported in the literature.

Discrimination of Geographical Origin of Asian Garlic Using Isotopic and Chemical Datasets under Stepwise Principal Component Analysis.

PubMed

Liu, Tsang-Sen; Lin, Jhen-Nan; Peng, Tsung-Ren

2018-01-16

Isotopic compositions of δ 2 H, δ 18 O, δ 13 C, and δ 15 N and concentrations of 22 trace elements from garlic samples were analyzed and processed with stepwise principal component analysis (PCA) to discriminate garlic's country of origin among Asian regions including South Korea, Vietnam, Taiwan, and China. Results indicate that there is no single trace-element concentration or isotopic composition that can accomplish the study's purpose and the stepwise PCA approach proposed does allow for discrimination between countries on a regional basis. Sequentially, Step-1 PCA distinguishes garlic's country of origin among Taiwanese, South Korean, and Vietnamese samples; Step-2 PCA discriminates Chinese garlic from South Korean garlic; and Step-3 and Step-4 PCA, Chinese garlic from Vietnamese garlic. In model tests, countries of origin of all audit samples were correctly discriminated by stepwise PCA. Consequently, this study demonstrates that stepwise PCA as applied is a simple and effective approach to discriminating country of origin among Asian garlics. © 2018 American Academy of Forensic Sciences.

Plaque echodensity and textural features are associated with histologic carotid plaque instability.

PubMed

Doonan, Robert J; Gorgui, Jessica; Veinot, Jean P; Lai, Chi; Kyriacou, Efthyvoulos; Corriveau, Marc M; Steinmetz, Oren K; Daskalopoulou, Stella S

2016-09-01

Carotid plaque echodensity and texture features predict cerebrovascular symptomatology. Our purpose was to determine the association of echodensity and textural features obtained from a digital image analysis (DIA) program with histologic features of plaque instability as well as to identify the specific morphologic characteristics of unstable plaques. Patients scheduled to undergo carotid endarterectomy were recruited and underwent carotid ultrasound imaging. DIA was performed to extract echodensity and textural features using Plaque Texture Analysis software (LifeQ Medical Ltd, Nicosia, Cyprus). Carotid plaque surgical specimens were obtained and analyzed histologically. Principal component analysis (PCA) was performed to reduce imaging variables. Logistic regression models were used to determine if PCA variables and individual imaging variables predicted histologic features of plaque instability. Image analysis data from 160 patients were analyzed. Individual imaging features of plaque echolucency and homogeneity were associated with a more unstable plaque phenotype on histology. These results were independent of age, sex, and degree of carotid stenosis. PCA reduced 39 individual imaging variables to five PCA variables. PCA1 and PCA2 were significantly associated with overall plaque instability on histology (both P = .02), whereas PCA3 did not achieve statistical significance (P = .07). DIA features of carotid plaques are associated with histologic plaque instability as assessed by multiple histologic features. Importantly, unstable plaques on histology appear more echolucent and homogeneous on ultrasound imaging. These results are independent of stenosis, suggesting that image analysis may have a role in refining the selection of patients who undergo carotid endarterectomy. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Multivariate optimization of a synergistic blend of oleoresin sage (Salvia officinalis L.) and ascorbyl palmitate to stabilize sunflower oil.

PubMed

Upadhyay, Rohit; Mishra, Hari Niwas

2016-04-01

The simultaneous optimization of a synergistic blend of oleoresin sage (SAG) and ascorbyl palmitate (AP) in sunflower oil (SO) was performed using central composite and rotatable design coupled with principal component analysis (PCA) and response surface methodology (RSM). The physicochemical parameters viz., peroxide value, anisidine value, free fatty acids, induction period, total polar matter, antioxidant capacity and conjugated diene value were considered as response variables. PCA reduced the original set of correlated responses to few uncorrelated principal components (PC). The PC1 (eigen value, 5.78; data variance explained, 82.53 %) was selected for optimization using RSM. The quadratic model adequately described the data (R (2) = 0. 91, p < 0.05) and lack of fit was insignificant (p > 0.05). The contour plot of PC 1 score indicated the optimal synergistic combination of 1289.19 and 218.06 ppm for SAG and AP, respectively. This combination of SAG and AP resulted in shelf life of 320 days at 25 °C estimated using linear shelf life prediction model. In conclusion, the versatility of PCA-RSM approach has resulted in an easy interpretation in multiple response optimizations. This approach can be considered as a useful guide to develop new oil blends stabilized with food additives from natural sources.

Volatile Compounds Produced by Lactobacillus paracasei During Oat Fermentation.

PubMed

Lee, Sang Mi; Oh, Jieun; Hurh, Byung-Serk; Jeong, Gwi-Hwa; Shin, Young-Keum; Kim, Young-Suk

2016-12-01

This study investigated the profiles of volatile compounds produced by Lactobacillus paracasei during oat fermentation using gas chromatography-mass spectrometry coupled with headspace solid-phase microextraction method. A total of 60 compounds, including acids, alcohols, aldehydes, esters, furan derivatives, hydrocarbons, ketones, sulfur-containing compounds, terpenes, and other compounds, were identified in fermented oat. Lipid oxidation products such as 2-pentylfuran, 1-octen-3-ol, hexanal, and nonanal were found to be the main contributors to oat samples fermented by L. paracasei with the level of 2-pentylfuran being the highest. In addition, the contents of ketones, alcohols, acids, and furan derivatives in the oat samples consistently increased with the fermentation time. On the other hand, the contents of degradation products of amino acids, such as 3-methylbutanal, benzaldehyde, acetophenone, dimethyl sulfide, and dimethyl disulfide, decreased in oat samples during fermentation. Principal component analysis (PCA) was applied to discriminate the fermented oat samples according to different fermentation times. The fermented oats were clearly differentiated on PCA plots. The initial fermentation stage was mainly affected by aldehydes, whereas the later samples of fermented oats were strongly associated with acids, alcohols, furan derivatives, and ketones. The application of PCA to data of the volatile profiles revealed that the oat samples fermented by L. paracasei could be distinguished according to fermentation time. © 2016 Institute of Food Technologists®.

Amorphous solid dispersions of piroxicam and Soluplus(®): Qualitative and quantitative analysis of piroxicam recrystallization during storage.

PubMed

Lust, Andres; Strachan, Clare J; Veski, Peep; Aaltonen, Jaakko; Heinämäki, Jyrki; Yliruusi, Jouko; Kogermann, Karin

2015-01-01

The conversion of active pharmaceutical ingredient (API) from amorphous to crystalline form is the primary stability issue in formulating amorphous solid dispersions (SDs). The aim of the present study was to carry out qualitative and quantitative analysis of the physical solid-state stability of the SDs of poorly water-soluble piroxicam (PRX) and polyvinyl caprolactam-polyvinyl acetate-polyethylene-glycol graft copolymer (Soluplus(®)). The SDs were prepared by a solvent evaporation method and stored for six months at 0% RH/6 °C, 0% RH/25 °C, 40% RH/25 °C and 75% RH/25 °C. Fourier transform infrared spectroscopy equipped with attenuated total reflection accessory (ATR-FTIR) and Raman spectroscopy were used for characterizing the physical solid-state changes and drug-polymer interactions. The principal component analysis (PCA) and multivariate curve resolution alternating least squares (MCR-ALS) were used for the qualitative and quantitative analysis of Raman spectra collected during storage. When stored at 0% RH/6 °C and at 0% RH/25 °C, PRX in SDs remained in an amorphous form since no recrystallization was observed by ATR-FTIR and Raman spectroscopy. Raman spectroscopy coupled with PCA and MCR-ALS and ATR-FTIR spectroscopy enabled to detect the recrystallization of amorphous PRX in the samples stored at higher humidity. Copyright © 2015 Elsevier B.V. All rights reserved.

Prostate cancer gene 3 and multiparametric magnetic resonance can reduce unnecessary biopsies: decision curve analysis to evaluate predictive models.

PubMed

Busetto, Gian Maria; De Berardinis, Ettore; Sciarra, Alessandro; Panebianco, Valeria; Giovannone, Riccardo; Rosato, Stefano; D'Errigo, Paola; Di Silverio, Franco; Gentile, Vincenzo; Salciccia, Stefano

2013-12-01

To overcome the well-known prostate-specific antigen limits, several new biomarkers have been proposed. Since its introduction in clinical practice, the urinary prostate cancer gene 3 (PCA3) assay has shown promising results for prostate cancer (PC) detection. Furthermore, multiparametric magnetic resonance imaging (mMRI) has the ability to better describe several aspects of PC. A prospective study of 171 patients with negative prostate biopsy findings and a persistent high prostate-specific antigen level was conducted to assess the role of mMRI and PCA3 in identifying PC. All patients underwent the PCA3 test and mMRI before a second transrectal ultrasound-guided prostate biopsy. The accuracy and reliability of PCA3 (3 different cutoff points) and mMRI were evaluated. Four multivariate logistic regression models were analyzed, in terms of discrimination and the cost benefit, to assess the clinical role of PCA3 and mMRI in predicting the biopsy outcome. A decision curve analysis was also plotted. Repeated transrectal ultrasound-guided biopsy identified 68 new cases (41.7%) of PC. The sensitivity and specificity of the PCA3 test and mMRI was 68% and 49% and 74% and 90%, respectively. Evaluating the regression models, the best discrimination (area under the curve 0.808) was obtained using the full model (base clinical model plus mMRI and PCA3). The decision curve analysis, to evaluate the cost/benefit ratio, showed good performance in predicting PC with the model that included mMRI and PCA3. mMRI increased the accuracy and sensitivity of the PCA3 test, and the use of the full model significantly improved the cost/benefit ratio, avoiding unnecessary biopsies. Copyright © 2013 Elsevier Inc. All rights reserved.

Prostate Cancer Associated Lipid Signatures in Serum Studied by ESI-Tandem Mass Spectrometryas Potential New Biomarkers.

PubMed

Duscharla, Divya; Bhumireddy, Sudarshana Reddy; Lakshetti, Sridhar; Pospisil, Heike; Murthy, P V L N; Walther, Reinhard; Sripadi, Prabhakar; Ummanni, Ramesh

2016-01-01

Prostate cancer (PCa) is one amongst the most common cancersin western men. Incidence rate ofPCa is on the rise worldwide. The present study deals with theserum lipidome profiling of patients diagnosed with PCa to identify potential new biomarkers. We employed ESI-MS/MS and GC-MS for identification of significantly altered lipids in cancer patient's serum compared to controls. Lipidomic data revealed 24 lipids are significantly altered in cancer patinet's serum (n = 18) compared to normal (n = 18) with no history of PCa. By using hierarchical clustering and principal component analysis (PCA) we could clearly separate cancer patients from control group. Correlation and partition analysis along with Formal Concept Analysis (FCA) have identified that PC (39:6) and FA (22:3) could classify samples with higher certainty. Both the lipids, PC (39:6) and FA (22:3) could influence the cataloging of patients with 100% sensitivity (all 18 control samples are classified correctly) and 77.7% specificity (of 18 tumor samples 4 samples are misclassified) with p-value of 1.612×10-6 in Fischer's exact test. Further, we performed GC-MS to denote fatty acids altered in PCa patients and found that alpha-linolenic acid (ALA) levels are altered in PCa. We also performed an in vitro proliferation assay to determine the effect of ALA in survival of classical human PCa cell lines LNCaP and PC3. We hereby report that the altered lipids PC (39:6) and FA (22:3) offer a new set of biomarkers in addition to the existing diagnostic tests that could significantly improve sensitivity and specificity in PCa diagnosis.

Prostate Cancer Associated Lipid Signatures in Serum Studied by ESI-Tandem Mass Spectrometryas Potential New Biomarkers

PubMed Central

Duscharla, Divya; Bhumireddy, Sudarshana Reddy; Lakshetti, Sridhar; Pospisil, Heike; Murthy, P. V. L. N.; Walther, Reinhard; Sripadi, Prabhakar; Ummanni, Ramesh

2016-01-01

Prostate cancer (PCa) is one amongst the most common cancersin western men. Incidence rate ofPCa is on the rise worldwide. The present study deals with theserum lipidome profiling of patients diagnosed with PCa to identify potential new biomarkers. We employed ESI-MS/MS and GC-MS for identification of significantly altered lipids in cancer patient’s serum compared to controls. Lipidomic data revealed 24 lipids are significantly altered in cancer patinet’s serum (n = 18) compared to normal (n = 18) with no history of PCa. By using hierarchical clustering and principal component analysis (PCA) we could clearly separate cancer patients from control group. Correlation and partition analysis along with Formal Concept Analysis (FCA) have identified that PC (39:6) and FA (22:3) could classify samples with higher certainty. Both the lipids, PC (39:6) and FA (22:3) could influence the cataloging of patients with 100% sensitivity (all 18 control samples are classified correctly) and 77.7% specificity (of 18 tumor samples 4 samples are misclassified) with p-value of 1.612×10−6 in Fischer’s exact test. Further, we performed GC-MS to denote fatty acids altered in PCa patients and found that alpha-linolenic acid (ALA) levels are altered in PCa. We also performed an in vitro proliferation assay to determine the effect of ALA in survival of classical human PCa cell lines LNCaP and PC3. We hereby report that the altered lipids PC (39:6) and FA (22:3) offer a new set of biomarkers in addition to the existing diagnostic tests that could significantly improve sensitivity and specificity in PCa diagnosis. PMID:26958841

Identification, classification, and discrimination of agave syrups from natural sweeteners by infrared spectroscopy and HPAEC-PAD.

PubMed

Mellado-Mojica, Erika; López, Mercedes G

2015-01-15

Agave syrups are gaining popularity as new natural sweeteners. Identification, classification and discrimination by infrared spectroscopy coupled to chemometrics (NIR-MIR-SIMCA-PCA) and HPAEC-PAD of agave syrups from natural sweeteners were achieved. MIR-SIMCA-PCA allowed us to classify the natural sweeteners according to their natural source. Natural syrups exhibited differences in the MIR spectra region 1500-900 cm(-1). The agave syrups displayed strong absorption in the MIR spectra region 1061-1,063 cm(-1), in agreement with their high fructose content. Additionally, MIR-SIMCA-PCA allowed us to differentiate among syrups from different Agave species (Agavetequilana and Agavesalmiana). Thin-layer chromatography and HPAEC-PAD revealed glucose, fructose, and sucrose as the principal carbohydrates in all of the syrups. Oligosaccharide profiles showed that A. tequilana syrups are mainly composed of fructose (>60%) and fructooligosaccharides, while A. salmiana syrups contain more sucrose (28-32%). We conclude that MIR-SIMCA-PCA and HPAEC-PAD can be used to unequivocally identify and classified agave syrups. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

PubMed

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

PubMed Central

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J.

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. PMID:25849483

Identification and classification of upper limb motions using PCA.

PubMed

Veer, Karan; Vig, Renu

2018-03-28

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

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

PubMed

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

2014-09-15

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

The impact of moderate wine consumption on the risk of developing prostate cancer

PubMed Central

Ferro, Matteo; Foerster, Beat; Abufaraj, Mohammad; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2018-01-01

Objective To investigate the impact of moderate wine consumption on the risk of prostate cancer (PCa). We focused on the differential effect of moderate consumption of red versus white wine. Design This study was a meta-analysis that includes data from case–control and cohort studies. Materials and methods A systematic search of Web of Science, Medline/PubMed, and Cochrane library was performed on December 1, 2017. Studies were deemed eligible if they assessed the risk of PCa due to red, white, or any wine using multivariable logistic regression analysis. We performed a formal meta-analysis for the risk of PCa according to moderate wine and wine type consumption (white or red). Heterogeneity between studies was assessed using Cochrane’s Q test and I2 statistics. Publication bias was assessed using Egger’s regression test. Results A total of 930 abstracts and titles were initially identified. After removal of duplicates, reviews, and conference abstracts, 83 full-text original articles were screened. Seventeen studies (611,169 subjects) were included for final evaluation and fulfilled the inclusion criteria. In the case of moderate wine consumption: the pooled risk ratio (RR) for the risk of PCa was 0.98 (95% CI 0.92–1.05, p=0.57) in the multivariable analysis. Moderate white wine consumption increased the risk of PCa with a pooled RR of 1.26 (95% CI 1.10–1.43, p=0.001) in the multi-variable analysis. Meanwhile, moderate red wine consumption had a protective role reducing the risk by 12% (RR 0.88, 95% CI 0.78–0.999, p=0.047) in the multivariable analysis that comprised 222,447 subjects. Conclusions In this meta-analysis, moderate wine consumption did not impact the risk of PCa. Interestingly, regarding the type of wine, moderate consumption of white wine increased the risk of PCa, whereas moderate consumption of red wine had a protective effect. Further analyses are needed to assess the differential molecular effect of white and red wine conferring their impact on PCa risk. PMID:29713200

Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation.

PubMed

Onizuka, Miho; Hoang, Huu; Kawato, Mitsuo; Tokuda, Isao T; Schweighofer, Nicolas; Katori, Yuichi; Aihara, Kazuyuki; Lang, Eric J; Toyama, Keisuke

2013-11-01

The inferior olive (IO) possesses synaptic glomeruli, which contain dendritic spines from neighboring neurons and presynaptic terminals, many of which are inhibitory and GABAergic. Gap junctions between the spines electrically couple neighboring neurons whereas the GABAergic synaptic terminals are thought to act to decrease the effectiveness of this coupling. Thus, the glomeruli are thought to be important for determining the oscillatory and synchronized activity displayed by IO neurons. Indeed, the tendency to display such activity patterns is enhanced or reduced by the local administration of the GABA-A receptor blocker picrotoxin (PIX) or the gap junction blocker carbenoxolone (CBX), respectively. We studied the functional roles of the glomeruli by solving the inverse problem of estimating the inhibitory (gi) and gap-junctional conductance (gc) using an IO network model. This model was built upon a prior IO network model, in which the individual neurons consisted of soma and dendritic compartments, by adding a glomerular compartment comprising electrically coupled spines that received inhibitory synapses. The model was used in the forward mode to simulate spike data under PIX and CBX conditions for comparison with experimental data consisting of multi-electrode recordings of complex spikes from arrays of Purkinje cells (complex spikes are generated in a one-to-one manner by IO spikes and thus can substitute for directly measuring IO spike activity). The spatiotemporal firing dynamics of the experimental and simulation spike data were evaluated as feature vectors, including firing rates, local variation, auto-correlogram, cross-correlogram, and minimal distance, and were contracted onto two-dimensional principal component analysis (PCA) space. gc and gi were determined as the solution to the inverse problem such that the simulation and experimental spike data were closely matched in the PCA space. The goodness of the match was confirmed by an analysis of variance (ANOVA) of the PCA scores between the experimental and simulation spike data. In the PIX condition, gi was found to decrease to approximately half its control value. CBX caused an approximately 30% decrease in gc from control levels. These results support the hypothesis that the glomeruli are control points for determining the spatiotemporal characteristics of olivocerebellar activity and thus may shape its ability to convey signals to the cerebellum that may be used for motor learning or motor control purposes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Whole milk intake is associated with prostate cancer-specific mortality among U.S. male physicians.

PubMed

Song, Yan; Chavarro, Jorge E; Cao, Yin; Qiu, Weiliang; Mucci, Lorelei; Sesso, Howard D; Stampfer, Meir J; Giovannucci, Edward; Pollak, Michael; Liu, Simin; Ma, Jing

2013-02-01

Previous studies have associated higher milk intake with greater prostate cancer (PCa) incidence, but little data are available concerning milk types and the relation between milk intake and risk of fatal PCa. We investigated the association between intake of dairy products and the incidence and survival of PCa during a 28-y follow-up. We conducted a cohort study in the Physicians' Health Study (n = 21,660) and a survival analysis among the incident PCa cases (n = 2806). Information on dairy product consumption was collected at baseline. PCa cases and deaths (n = 305) were confirmed during follow-up. The intake of total dairy products was associated with increased PCa incidence [HR = 1.12 (95% CI: 0.93, 1.35); >2.5 servings/d vs. ≤0.5 servings/d]. Skim/low-fat milk intake was positively associated with risk of low-grade, early stage, and screen-detected cancers, whereas whole milk intake was associated only with fatal PCa [HR = 1.49 (95% CI: 0.97, 2.28); ≥237 mL/d (1 serving/d) vs. rarely consumed]. In the survival analysis, whole milk intake remained associated with risk of progression to fatal disease after diagnosis [HR = 2.17 (95% CI: 1.34, 3.51)]. In this prospective cohort, higher intake of skim/low-fat milk was associated with a greater risk of nonaggressive PCa. Most importantly, only whole milk was consistently associated with higher incidence of fatal PCa in the entire cohort and higher PCa-specific mortality among cases. These findings add further evidence to suggest the potential role of dairy products in the development and prognosis of PCa.

Activation of Beta-Catenin Signaling in Androgen Receptor–Negative Prostate Cancer Cells

PubMed Central

Wan, Xinhai; Liu, Jie; Lu, Jing-Fang; Tzelepi, Vassiliki; Yang, Jun; Starbuck, Michael W.; Diao, Lixia; Wang, Jing; Efstathiou, Eleni; Vazquez, Elba S.; Troncoso, Patricia; Maity, Sankar N.; Navone, Nora M.

2012-01-01

Purpose To study Wnt/beta-catenin in castrate-resistant prostate cancer (CRPC) and understand its function independently of the beta-catenin–androgen receptor (AR) interaction. Experimental Design We performed beta-catenin immunocytochemical analysis, evaluated TOP-flash reporter activity (a reporter of beta-catenin–mediated transcription), and sequenced the beta-catenin gene in MDA PCa 118a, MDA PCa 118b, MDA PCa 2b, and PC-3 prostate cancer (PCa) cells. We knocked down beta-catenin in AR-negative MDA PCa 118b cells and performed comparative gene-array analysis. We also immunohistochemically analyzed beta-catenin and AR in 27 bone metastases of human CRPCs. Results Beta-catenin nuclear accumulation and TOP-flash reporter activity were high in MDA PCa 118b but not in MDA PCa 2b or PC-3 cells. MDA PCa 118a and 118b cells carry a mutated beta-catenin at codon 32 (D32G). Ten genes were expressed differently (false discovery rate, 0.05) in MDA PCa 118b cells with downregulated beta-catenin. One such gene, hyaluronan synthase 2 (HAS2), synthesizes hyaluronan, a core component of the extracellular matrix. We confirmed HAS2 upregulation in PC-3 cells transfected with D32G-mutant beta-catenin. Finally, we found nuclear localization of beta-catenin in 10 of 27 human tissue specimens; this localization was inversely associated with AR expression (P = 0.056, Fisher’s exact test), suggesting that reduced AR expression enables Wnt/beta-catenin signaling. Conclusion We identified a previously unknown downstream target of beta-catenin, HAS2, in PCa, and found that high beta-catenin nuclear localization and low or no AR expression may define a subpopulation of men with bone-metastatic PCa. These findings may guide physicians in managing these patients. PMID:22298898

Association of microRNA-21 expression with clinicopathological characteristics and the risk of progression in advanced prostate cancer patients receiving androgen deprivation therapy.

PubMed

Guan, Yangbo; Wu, You; Liu, Yifei; Ni, Jian; Nong, Shaojun

2016-08-01

Despite androgen deprivation therapy (ADT) remains the mainstay therapy for advanced prostate cancer (PCa), the patients have widely variable durations of response to ADT. Unfortunately, there is limited knowledge of pre-treatment prognostic factors for response to ADT. Recently, microRNA-21 (miR-21) has been reported to play an important role in development of castration resistance of CaP. However, little is known about the expression of miR-21 in advanced PCa biopsy tissues, and data on its potential predictive value in advanced PCa are completely lacking. In this study, paraffin-embedded prostate carcinoma tissues obtained by needle biopsy from 85 advanced PCa patients were evaluated for the expression levels of miR-21 by quantitative real-time PCR (qRT-PCR). In situ hybridization (ISH) analysis was performed to further confirm the qRT-PCR results. Kaplan-Meier analysis and Cox proportional hazards regression models were performed to investigate the correlation between miR-21 expression and time to progression of advanced PCa patients. Compared with adjacent non-cancerous prostate tissues, the expression level of miR-21 was significantly increased in PCa tissues (PCa vs. non-cancerous prostate: 1.3273 ± 0.3207 vs. 0.9970 ± 0.2054, P < 0.001). By and large, in ISH analysis miR-21 was expressed at a higher level in tumor areas than in adjacent non-cancerous areas. Additionally, PCa patients with higher expression of miR-21 were significantly more likely to be of high Gleason score and high clinical stage (P < 0.05). There was no significant association between miR-21 expression and the initial prostate-specific antigen (PSA) level or age at diagnosis. Moreover, Kaplan-Meier survival analysis found that PCa patients with high miR-21 expression have shorter progression-free survival than those with low miR-21 expression. Furthermore, Multivariate Cox analysis revealed both miR-21 expression status (P = 0.040) and clinical stage (P = 0.042) were all independent predictive factor for progression-free survival for advanced PCa. These findings suggest for the first time that the up-regulation of miR-21 may serve as an independent predictor of progress-free survival in patients with advanced PCa. Prostate 76:986-993, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Combinations of elevated tissue miRNA-17-92 cluster expression and serum prostate-specific antigen as potential diagnostic biomarkers for prostate cancer.

PubMed

Feng, Sujuan; Qian, Xiaosong; Li, Han; Zhang, Xiaodong

2017-12-01

The aim of the present study was to investigate the effectiveness of the miR-17-92 cluster as a disease progression marker in prostate cancer (PCa). Reverse transcription-quantitative polymerase chain reaction analysis was used to detect the microRNA (miR)-17-92 cluster expression levels in tissues from patients with PCa or benign prostatic hyperplasia (BPH), in addition to in PCa and BPH cell lines. Spearman correlation was used for comparison and estimation of correlations between miRNA expression levels and clinicopathological characteristics such as the Gleason score and prostate-specific antigen (PSA). Receiver operating curve (ROC) analysis was performed for evaluation of specificity and sensitivity of miR-17-92 cluster expression levels for discriminating patients with PCa from patients with BPH. Kaplan-Meier analysis was plotted to investigate the predictive potential of miR-17-92 cluster for PCa biochemical recurrence. Expression of the majority of miRNAs in the miR-17-92 cluster was identified to be significantly increased in PCa tissues and cell lines. Bivariate correlation analysis indicated that the high expression of unregulated miRNAs was positively correlated with Gleason grade, but had no significant association with PSA. ROC curves demonstrated that high expression of miR-17-92 cluster predicted a higher diagnostic accuracy compared with PSA. Improved discriminating quotients were observed when combinations of unregulated miRNAs with PSA were used. Survival analysis confirmed a high combined miRNA score of miR-17-92 cluster was associated with shorter biochemical recurrence interval. miR-17-92 cluster could be a potential diagnostic and prognostic biomarker for PCa, and the combination of the miR-17-92 cluster and serum PSA may enhance the accuracy for diagnosis of PCa.

Evolutionary algorithm based optimization of hydraulic machines utilizing a state-of-the-art block coupled CFD solver and parametric geometry and mesh generation tools

NASA Astrophysics Data System (ADS)

S, Kyriacou; E, Kontoleontos; S, Weissenberger; L, Mangani; E, Casartelli; I, Skouteropoulou; M, Gattringer; A, Gehrer; M, Buchmayr

2014-03-01

An efficient hydraulic optimization procedure, suitable for industrial use, requires an advanced optimization tool (EASY software), a fast solver (block coupled CFD) and a flexible geometry generation tool. EASY optimization software is a PCA-driven metamodel-assisted Evolutionary Algorithm (MAEA (PCA)) that can be used in both single- (SOO) and multiobjective optimization (MOO) problems. In MAEAs, low cost surrogate evaluation models are used to screen out non-promising individuals during the evolution and exclude them from the expensive, problem specific evaluation, here the solution of Navier-Stokes equations. For additional reduction of the optimization CPU cost, the PCA technique is used to identify dependences among the design variables and to exploit them in order to efficiently drive the application of the evolution operators. To further enhance the hydraulic optimization procedure, a very robust and fast Navier-Stokes solver has been developed. This incompressible CFD solver employs a pressure-based block-coupled approach, solving the governing equations simultaneously. This method, apart from being robust and fast, also provides a big gain in terms of computational cost. In order to optimize the geometry of hydraulic machines, an automatic geometry and mesh generation tool is necessary. The geometry generation tool used in this work is entirely based on b-spline curves and surfaces. In what follows, the components of the tool chain are outlined in some detail and the optimization results of hydraulic machine components are shown in order to demonstrate the performance of the presented optimization procedure.

Simultaneous qualitative and quantitative evaluation of Ilex kudingcha C. J. tseng by using UPLC and UHPLC-qTOF-MS/MS.

PubMed

Zhou, Jie; Yi, Huan; Zhao, Zhong-Xiang; Shang, Xue-Ying; Zhu, Ming-Juan; Kuang, Guo-Jun; Zhu, Chen-Chen; Zhang, Lei

2018-06-05

In this study, a systematic method was established for the holistic quality control of Ilex kudingcha C. J. Tseng, a popular functional drink for adjuvant treatment of diabetes, hypertension, obesity and hyperlipidemia. Both qualitative and quantitative analyses were conducted. For qualitative analysis, an ultra high performance liquid chromatography (UHPLC) coupled with an electrospray ionization quadrupole time-of-flight mass spectrometry (ESI-qTOF-MS) method was established for rapid separation and structural identification of the constituents in Ilex kudingcha. Samples were separated on an ACQUITY UPLC HSS T3C 18 column (2.1 mm × 100 mm, 1.8 μm) by gradient elution using 0.1% (v/v) formic acid (solvent A) and acetonitrile (solvent B) as mobile phases at a flow rate of 0.25 mL min -1 . The chromatographic profiling of Ilex kudingcha by UHPLC-qTOF-MS/MS resulted in the characterization of 53 compounds, comprising 18 compounds that were unambiguously identified by comparison with reference standards. For quantitative analysis, 18 major compounds from 15 batches of Ilex kudingcha samples were simultaneously detected by UPLC-DAD at wavelengths of 210 nm, 260 nm, and 326 nm. The method was validated with respect to precision, linearity, repeatability, stability, accuracy, and so on. The contents of the 18 target compounds were applied for hierarchical clustering analysis (HCA) and principal component analysis (PCA) to differentiate between the samples. The results of HCA and PCA were consistent with each other. Sample No. 1 differed significantly based on HCA and PCA, and the differentiating components were confirmed to originate from different batches of samples. Phenolic acids and triterpenes were found to be the main ingredients in Ilex kudingcha. This strategy was effective and straightforward, and provided a potential approach for holistic quality control of Ilex kudingcha. Copyright © 2018. Published by Elsevier B.V.

Decreased expression of serine protease inhibitor family G1 (SERPING1) in prostate cancer can help distinguish high-risk prostate cancer and predicts malignant progression.

PubMed

Peng, Shengmeng; Du, Tao; Wu, Wanhua; Chen, Xianju; Lai, Yiming; Zhu, Dingjun; Wang, Qiong; Ma, Xiaoming; Lin, Chunhao; Li, Zean; Guo, Zhenghui; Huang, Hai

2018-06-11

The aim of this study was to investigate the associations of serine proteinase inhibitor family G1 (SERPING1) down-regulation with poor prognosis in patients with prostate cancer (PCa). Furthermore, we aim to find more novel and effective PCa molecular markers to provide an early screening of PCa, distinguish patients with aggressive PCa, predict the prognosis, or reduce the economic burden of PCa. SERPING1 protein expression in both human PCa and normal prostate tissues was detected by immunohistochemical staining, which intensity was analyzed in association with clinical pathological parameters such Gleason score, pathological grade, clinical stage, tumor stage, lymph node metastasis, and distant metastasis. Moreover, we used The Cancer Genome Atlas (TCGA) Database, Taylor Database, and Oncomine dataset to validate our immunohistochemical results and investigated the value of SERPING1 in PCa at mRNA level. Kaplan-Meier analysis and Cox regression analysis were performed to evaluate the relationship between SERPING1 and prognosis of patients with PCa. The outcome showed that SERPING1 was expressed mainly in cytoplasm of grand cells of prostate tissue and was significantly expressed less in PCa (P<0.001). Furthermore, in the tissue microarray of our samples, decreasing expression of SERPING1 was correlated with the higher Gleason score (P = 0.004), the higher pathological grade (P = 0.01) and the advanced tumor stage (P = 0.005) at protein level. In TCGA dataset and Taylor Dataset, low-expressed SERPING1 was correlated with the younger patient (P = 0.02 in TCGA, P = 0.044 in Taylor) and the higher Gleason score (P = 0.019 in TCGA, P<0.001 in Taylor) at mRNA level. Kaplan-Meier analysis revealed that the lower mRNA of SERPING1 predicted lower overall survivals (P = 0.027 in TCGA), lower disease-free survival (P = 0.029) and lower biochemical recurrence-free survival (P = 0.011 in Taylor). Data from Oncomine database shown that SERPING1 low expression implying higher malignancy of prostate lesions. Using multivariate analysis, we also found that SERPING1 expression was independent prognostic marker of poor disease-free survival and biochemical recurrence-free survival. SERPING1 may play an important role in PCa and can be serve as a novel marker in diagnosis and prognostic prediction in PCa. In addition, levels of SERPING1 can help identify low-risk prostate to provide reference for patients with PCa to accept active surveillance and reduce overtreatment. Copyright © 2018 Elsevier Inc. All rights reserved.

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

PubMed

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

2012-08-16

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

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

PubMed

Worley, Bradley; Powers, Robert

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

ToF-SIMS PCA analysis of Myrtus communis L.

NASA Astrophysics Data System (ADS)

Piras, F. M.; Dettori, M. F.; Magnani, A.

2009-06-01

Nowadays there is a growing interest of researchers for the application of sophisticated analytical techniques in conjunction with statistical data analysis methods to the characterization of natural products to assure their authenticity and quality, and for the possibility of direct analysis of food to obtain maximum information. In this work, time-of-flight secondary ion mass spectrometry (ToF-SIMS) in conjunction with principal components analysis (PCA) are applied to study the chemical composition and variability of Sardinian myrtle ( Myrtus communis L.) through the analysis of both berries alcoholic extracts and berries epicarp. ToF-SIMS spectra of berries epicarp show that the epicuticular waxes consist mainly of carboxylic acids with chain length ranging from C20 to C30, or identical species formed from fragmentation of long-chain esters. PCA of ToF-SIMS data from myrtle berries epicarp distinguishes two groups characterized by a different surface concentration of triacontanoic acid. Variability in antocyanins, flavonols, α-tocopherol, and myrtucommulone contents is showed by ToF-SIMS PCA analysis of myrtle berries alcoholic extracts.

Adaptive online monitoring for ICU patients by combining just-in-time learning and principal component analysis.

PubMed

Li, Xuejian; Wang, Youqing

2016-12-01

Offline general-type models are widely used for patients' monitoring in intensive care units (ICUs), which are developed by using past collected datasets consisting of thousands of patients. However, these models may fail to adapt to the changing states of ICU patients. Thus, to be more robust and effective, the monitoring models should be adaptable to individual patients. A novel combination of just-in-time learning (JITL) and principal component analysis (PCA), referred to learning-type PCA (L-PCA), was proposed for adaptive online monitoring of patients in ICUs. JITL was used to gather the most relevant data samples for adaptive modeling of complex physiological processes. PCA was used to build an online individual-type model and calculate monitoring statistics, and then to judge whether the patient's status is normal or not. The adaptability of L-PCA lies in the usage of individual data and the continuous updating of the training dataset. Twelve subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and five vital signs of each subject were chosen. The proposed method was compared with the traditional PCA and fast moving-window PCA (Fast MWPCA). The experimental results demonstrated that the fault detection rates respectively increased by 20 % and 47 % compared with PCA and Fast MWPCA. L-PCA is first introduced into ICU patients monitoring and achieves the best monitoring performance in terms of adaptability to changes in patient status and sensitivity for abnormality detection.

Experimental Researches on the Durability Indicators and the Physiological Comfort of Fabrics using the Principal Component Analysis (PCA) Method

NASA Astrophysics Data System (ADS)

Hristian, L.; Ostafe, M. M.; Manea, L. R.; Apostol, L. L.

2017-06-01

The work pursued the distribution of combed wool fabrics destined to manufacturing of external articles of clothing in terms of the values of durability and physiological comfort indices, using the mathematical model of Principal Component Analysis (PCA). Principal Components Analysis (PCA) applied in this study is a descriptive method of the multivariate analysis/multi-dimensional data, and aims to reduce, under control, the number of variables (columns) of the matrix data as much as possible to two or three. Therefore, based on the information about each group/assortment of fabrics, it is desired that, instead of nine inter-correlated variables, to have only two or three new variables called components. The PCA target is to extract the smallest number of components which recover the most of the total information contained in the initial data.

An Efficient Method Coupling Kernel Principal Component Analysis with Adjoint-Based Optimal Control and Its Goal-Oriented Extensions

NASA Astrophysics Data System (ADS)

Thimmisetty, C.; Talbot, C.; Tong, C. H.; Chen, X.

2016-12-01

The representativeness of available data poses a significant fundamental challenge to the quantification of uncertainty in geophysical systems. Furthermore, the successful application of machine learning methods to geophysical problems involving data assimilation is inherently constrained by the extent to which obtainable data represent the problem considered. We show how the adjoint method, coupled with optimization based on methods of machine learning, can facilitate the minimization of an objective function defined on a space of significantly reduced dimension. By considering uncertain parameters as constituting a stochastic process, the Karhunen-Loeve expansion and its nonlinear extensions furnish an optimal basis with respect to which optimization using L-BFGS can be carried out. In particular, we demonstrate that kernel PCA can be coupled with adjoint-based optimal control methods to successfully determine the distribution of material parameter values for problems in the context of channelized deformable media governed by the equations of linear elasticity. Since certain subsets of the original data are characterized by different features, the convergence rate of the method in part depends on, and may be limited by, the observations used to furnish the kernel principal component basis. By determining appropriate weights for realizations of the stochastic random field, then, one may accelerate the convergence of the method. To this end, we present a formulation of Weighted PCA combined with a gradient-based means using automatic differentiation to iteratively re-weight observations concurrent with the determination of an optimal reduced set control variables in the feature space. We demonstrate how improvements in the accuracy and computational efficiency of the weighted linear method can be achieved over existing unweighted kernel methods, and discuss nonlinear extensions of the algorithm.

EMPCA and Cluster Analysis of Quasar Spectra: Construction and Application to Simulated Spectra

NASA Astrophysics Data System (ADS)

Marrs, Adam; Leighly, Karen; Wagner, Cassidy; Macinnis, Francis

2017-01-01

Quasars have complex spectra with emission lines influenced by many factors. Therefore, to fully describe the spectrum requires specification of a large number of parameters, such as line equivalent width, blueshift, and ratios. Principal Component Analysis (PCA) aims to construct eigenvectors-or principal components-from the data with the goal of finding a few key parameters that can be used to predict the rest of the spectrum fairly well. Analysis of simulated quasar spectra was used to verify and justify our modified application of PCA.We used a variant of PCA called Weighted Expectation Maximization PCA (EMPCA; Bailey 2012) along with k-means cluster analysis to analyze simulated quasar spectra. Our approach combines both analytical methods to address two known problems with classical PCA. EMPCA uses weights to account for uncertainty and missing points in the spectra. K-means groups similar spectra together to address the nonlinearity of quasar spectra, specifically variance in blueshifts and widths of the emission lines.In producing and analyzing simulations, we first tested the effects of varying equivalent widths and blueshifts on the derived principal components, and explored the differences between standard PCA and EMPCA. We also tested the effects of varying signal-to-noise ratio. Next we used the results of fits to composite quasar spectra (see accompanying poster by Wagner et al.) to construct a set of realistic simulated spectra, and subjected those spectra to the EMPCA /k-means analysis. We concluded that our approach was validated when we found that the mean spectra from our k-means clusters derived from PCA projection coefficients reproduced the trends observed in the composite spectra.Furthermore, our method needed only two eigenvectors to identify both sets of correlations used to construct the simulations, as well as indicating the linear and nonlinear segments. Comparing this to regular PCA, which can require a dozen or more components, or to direct spectral analysis that may need measurement of 20 fit parameters, shows why the dual application of these two techniques is such a powerful tool.

Effect of emodin on Candida albicans growth investigated by microcalorimetry combined with chemometric analysis.

PubMed

Kong, W J; Wang, J B; Jin, C; Zhao, Y L; Dai, C M; Xiao, X H; Li, Z L

2009-07-01

Using the 3114/3115 thermal activity monitor (TAM) air isothermal microcalorimeter, ampoule mode, the heat output of Candida albicans growth at 37 degrees C was measured, and the effect of emodin on C. albicans growth was evaluated by microcalorimetry coupled with chemometric methods. The similarities between the heat flow power (HFP)-time curves of C. albicans growth affected by different concentrations of emodin were calculated by similarity analysis (SA). In the correspondence analysis (CA) diagram of eight quantitative parameters taken from the HFP-time curves, it could be deduced that emodin had definite dose-effect relationship as the distance between different concentrations of it increased along with the dosage and the effect. From the principal component analysis (PCA) on eight quantitative parameters, the action of emodin on C. albicans growth could be easily evaluated by analyzing the change of values of the main two parameters, growth rate constant k (2) and maximum power output P(2)(m). The coherent results of SA, CA, and PCA showed that emodin at different concentrations had different effects on C. albicans growth metabolism: A low concentration (0-10 microg ml(-1)) poorly inhibited the growth of C. albicans, and a high concentration (15-35 microg ml(-1)) could notably inhibit growth of this fungus. This work provided a useful idea of the combination of microcalorimetry and chemometric analysis for investigating the effect of drug and other compounds on microbes.

Characterization and forensic analysis of soil samples using laser-induced breakdown spectroscopy (LIBS).

PubMed

Jantzi, Sarah C; Almirall, José R

2011-07-01

A method for the quantitative elemental analysis of surface soil samples using laser-induced breakdown spectroscopy (LIBS) was developed and applied to the analysis of bulk soil samples for discrimination between specimens. The use of a 266 nm laser for LIBS analysis is reported for the first time in forensic soil analysis. Optimization of the LIBS method is discussed, and the results compared favorably to a laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) method previously developed. Precision for both methods was <10% for most elements. LIBS limits of detection were <33 ppm and bias <40% for most elements. In a proof of principle study, the LIBS method successfully discriminated samples from two different sites in Dade County, FL. Analysis of variance, Tukey's post hoc test and Student's t test resulted in 100% discrimination with no type I or type II errors. Principal components analysis (PCA) resulted in clear groupings of the two sites. A correct classification rate of 99.4% was obtained with linear discriminant analysis using leave-one-out validation. Similar results were obtained when the same samples were analyzed by LA-ICP-MS, showing that LIBS can provide similar information to LA-ICP-MS. In a forensic sampling/spatial heterogeneity study, the variation between sites, between sub-plots, between samples and within samples was examined on three similar Dade sites. The closer the sampling locations, the closer the grouping on a PCA plot and the higher the misclassification rate. These results underscore the importance of careful sampling for geographic site characterization.

Coupled Mercury–Cell Sorption, Reduction, and Oxidation on Methylmercury Production by Geobacter sulfurreducens PCA

DOE PAGES

Lin, Hui; Morrell-Falvey, Jennifer L.; Rao, Balaji; ...

2014-09-30

G. sulfurreducens PCA cells have been shown to reduce, sorb, and methylate Hg(II) species, but it is unclear whether this organism can oxidize and methylate dissolved elemental Hg(0) as shown for Desulfovibrio desulfuricans ND132. Using Hg(II) and Hg(0) separately as Hg sources in washed cell assays in phosphate buffered saline (pH 7.4), in this paper we report how cell-mediated Hg reduction and oxidation compete or synergize with sorption, thus affecting the production of toxic methylmercury by PCA cells. Methylation is found to be positively correlated to Hg sorption (r = 0.73) but negatively correlated to Hg reduction (r = -0.62).more » These reactions depend on the Hg and cell concentrations or the ratio of Hg to cellular thiols (-SH). Oxidation and methylation of Hg(0) are favored at relatively low Hg to cell–SH molar ratios (e.g., <1). Increasing Hg to cell ratios from 0.25 × 10 –19 to 25 × 10 –19 moles-Hg/cell (equivalent to Hg/cell–SH of 0.71 to 71) shifts the major reaction from oxidation to reduction. In the absence of five outer membrane c-type cytochromes, mutant ΔomcBESTZ also shows decreases in Hg reduction and increases in methylation. However, the presence of competing thiol-binding ions such as Zn 2+ leads to increased Hg reduction and decreased methylation. Finally, these results suggest that the coupled cell-Hg sorption and redox transformations are important in controlling the rates of Hg uptake and methylation by G. sulfurreducens PCA in anoxic environments.« less

Metabolomics approach of infant formula for the evaluation of contamination and degradation using hydrophilic interaction liquid chromatography coupled with mass spectrometry.

PubMed

Inoue, Koichi; Tanada, Chihiro; Sakamoto, Tasuku; Tsutsui, Haruhito; Akiba, Takashi; Min, Jun Zhe; Todoroki, Kenichiro; Yamano, Yutaka; Toyo'oka, Toshimasa

2015-08-15

In this study including the field of metabolomics approach for food, the evaluation of untargeted compounds using HILIC-ESI/TOF/MS and multivariate statistical analysis method is proposed for the assessment of classification, contamination and degradation of infant formula. HILIC mode is used to monitor more detected numbers in infant formulas in the ESI-positive scan mode than the reversed phase. The repeatability of the non-targeted contents from 4 kinds of infant formulas based on PCA was less than the relative standard deviation of 15% in all groups. The PCA pattern showed that significant differences in the classification of types and origins, the contamination of melamine and the degradations for one week were evaluated using HILIC-ESI/TOF/MS. In the S-plot from the degradation test, we could identify two markers by comparison to standards as nicotinic acid and nicotinamide. With this strategy, the differences from the untargeted compounds could be utilized for quality and safety assessment of infant formula. Copyright © 2015 Elsevier Ltd. All rights reserved.

Free energy landscape of a biomolecule in dihedral principal component space: sampling convergence and correspondence between structures and minima.

PubMed

Maisuradze, Gia G; Leitner, David M

2007-05-15

Dihedral principal component analysis (dPCA) has recently been developed and shown to display complex features of the free energy landscape of a biomolecule that may be absent in the free energy landscape plotted in principal component space due to mixing of internal and overall rotational motion that can occur in principal component analysis (PCA) [Mu et al., Proteins: Struct Funct Bioinfo 2005;58:45-52]. Another difficulty in the implementation of PCA is sampling convergence, which we address here for both dPCA and PCA using a tetrapeptide as an example. We find that for both methods the sampling convergence can be reached over a similar time. Minima in the free energy landscape in the space of the two largest dihedral principal components often correspond to unique structures, though we also find some distinct minima to correspond to the same structure. 2007 Wiley-Liss, Inc.

Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map

PubMed Central

An, Yan; Zou, Zhihong; Li, Ranran

2016-01-01

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009–2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data. PMID:26761018

Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map.

PubMed

An, Yan; Zou, Zhihong; Li, Ranran

2016-01-08

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009-2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data.

Analysis of Zinc-Exporters Expression in Prostate Cancer.

PubMed

Singh, Chandra K; Malas, Kareem M; Tydrick, Caitlin; Siddiqui, Imtiaz A; Iczkowski, Kenneth A; Ahmad, Nihal

2016-11-11

Maintaining optimal intracellular zinc (Zn) concentration is crucial for critical cellular functions. Depleted Zn has been associated with prostate cancer (PCa) progression. Solute carrier family 30 (SLC30A) proteins maintain cytoplasmic Zn balance by exporting Zn out to the extracellular space or by sequestering cytoplasmic Zn into intracellular compartments. In this study, we determined the involvement of Zn-exporters, SLC30A 1-10 in PCa, in the context of racial health disparity in human PCa samples obtained from European-American (EA) and African-American (AA) populations. We also analyzed the levels of Zn-exporters in a panel of PCa cells derived from EA and AA populations. We further explored the expression profile of Zn-exporters in PCa using Oncomine database. Zn-exporters were found to be differentially expressed at the mRNA level, with a significant upregulation of SLC30A1, SLC30A9 and SLC30A10, and downregulation of SLC30A5 and SLC30A6 in PCa, compared to benign prostate. Moreover, Ingenuity Pathway analysis revealed several interactions of Zn-exporters with certain tumor suppressor and promoter proteins known to be modulated in PCa. Our study provides an insight regarding Zn-exporters in PCa, which may open new avenues for future studies aimed at enhancing the levels of Zn by modulating Zn-transporters via pharmacological means.

Analysis of Zinc-Exporters Expression in Prostate Cancer

PubMed Central

Singh, Chandra K.; Malas, Kareem M.; Tydrick, Caitlin; Siddiqui, Imtiaz A.; Iczkowski, Kenneth A.; Ahmad, Nihal

2016-01-01

Maintaining optimal intracellular zinc (Zn) concentration is crucial for critical cellular functions. Depleted Zn has been associated with prostate cancer (PCa) progression. Solute carrier family 30 (SLC30A) proteins maintain cytoplasmic Zn balance by exporting Zn out to the extracellular space or by sequestering cytoplasmic Zn into intracellular compartments. In this study, we determined the involvement of Zn-exporters, SLC30A 1–10 in PCa, in the context of racial health disparity in human PCa samples obtained from European-American (EA) and African-American (AA) populations. We also analyzed the levels of Zn-exporters in a panel of PCa cells derived from EA and AA populations. We further explored the expression profile of Zn-exporters in PCa using Oncomine database. Zn-exporters were found to be differentially expressed at the mRNA level, with a significant upregulation of SLC30A1, SLC30A9 and SLC30A10, and downregulation of SLC30A5 and SLC30A6 in PCa, compared to benign prostate. Moreover, Ingenuity Pathway analysis revealed several interactions of Zn-exporters with certain tumor suppressor and promoter proteins known to be modulated in PCa. Our study provides an insight regarding Zn-exporters in PCa, which may open new avenues for future studies aimed at enhancing the levels of Zn by modulating Zn-transporters via pharmacological means. PMID:27833104

Detecting phase separation of freeze-dried binary amorphous systems using pair-wise distribution function and multivariate data analysis.

PubMed

Chieng, Norman; Trnka, Hjalte; Boetker, Johan; Pikal, Michael; Rantanen, Jukka; Grohganz, Holger

2013-09-15

The purpose of this study is to investigate the use of multivariate data analysis for powder X-ray diffraction-pair-wise distribution function (PXRD-PDF) data to detect phase separation in freeze-dried binary amorphous systems. Polymer-polymer and polymer-sugar binary systems at various ratios were freeze-dried. All samples were analyzed by PXRD, transformed to PDF and analyzed by principal component analysis (PCA). These results were validated by differential scanning calorimetry (DSC) through characterization of glass transition of the maximally freeze-concentrate solute (Tg'). Analysis of PXRD-PDF data using PCA provides a more clear 'miscible' or 'phase separated' interpretation through the distribution pattern of samples on a score plot presentation compared to residual plot method. In a phase separated system, samples were found to be evenly distributed around the theoretical PDF profile. For systems that were miscible, a clear deviation of samples away from the theoretical PDF profile was observed. Moreover, PCA analysis allows simultaneous analysis of replicate samples. Comparatively, the phase behavior analysis from PXRD-PDF-PCA method was in agreement with the DSC results. Overall, the combined PXRD-PDF-PCA approach improves the clarity of the PXRD-PDF results and can be used as an alternative explorative data analytical tool in detecting phase separation in freeze-dried binary amorphous systems. Copyright © 2013 Elsevier B.V. All rights reserved.

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

Code of Federal Regulations, 2010 CFR

2010-04-01

... PROGRAM (MARK-TO-MARKET) Restructuring Plan § 401.451 PAE Physical Condition Analysis (PCA). (a) Review and certification of owner evaluation. (1) The PAE must independently evaluate the physical condition... 24 Housing and Urban Development 2 2010-04-01 2010-04-01 false PAE Physical Condition Analysis...

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

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 2 2013-04-01 2013-04-01 false PAE Physical Condition Analysis... PROGRAM (MARK-TO-MARKET) Restructuring Plan § 401.451 PAE Physical Condition Analysis (PCA). (a) Review and certification of owner evaluation. (1) The PAE must independently evaluate the physical condition...

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

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 2 2011-04-01 2011-04-01 false PAE Physical Condition Analysis... PROGRAM (MARK-TO-MARKET) Restructuring Plan § 401.451 PAE Physical Condition Analysis (PCA). (a) Review and certification of owner evaluation. (1) The PAE must independently evaluate the physical condition...

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

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 2 2012-04-01 2012-04-01 false PAE Physical Condition Analysis... PROGRAM (MARK-TO-MARKET) Restructuring Plan § 401.451 PAE Physical Condition Analysis (PCA). (a) Review and certification of owner evaluation. (1) The PAE must independently evaluate the physical condition...

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

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 2 2014-04-01 2014-04-01 false PAE Physical Condition Analysis... PROGRAM (MARK-TO-MARKET) Restructuring Plan § 401.451 PAE Physical Condition Analysis (PCA). (a) Review and certification of owner evaluation. (1) The PAE must independently evaluate the physical condition...

Physical activity in relation to risk of prostate cancer: a systematic review and meta-analysis.

PubMed

Benke, I N; Leitzmann, M F; Behrens, G; Schmid, D

2018-05-01

Prostate cancer (PCa) is one of the most common cancers among men, yet little is known about its modifiable risk and protective factors. This study aims to quantitatively summarize observational studies relating physical activity (PA) to PCa incidence and mortality. Published articles pertaining to PA and PCa incidence and mortality were retrieved in July 2017 using the Medline and EMBASE databases. The literature review yielded 48 cohort studies and 24 case-control studies with a total of 151 748 PCa cases. The mean age of the study participants at baseline was 61 years. In random-effects models, comparing the highest versus the lowest level of overall PA showed a summary relative risk (RR) estimate for total PCa incidence close to the null [RR = 0.99, 95% confidence interval (CI) = 0.94-1.04]. The corresponding RRs for advanced and non-advanced PCa were 0.92 (95% CI = 0.80-1.06) and 0.95 (95% CI = 0.85-1.07), respectively. We noted a statistically significant inverse association between long-term occupational activity and total PCa (RR = 0.83, 95% CI = 0.71-0.98, n studies = 13), although that finding became statistically non-significant when individual studies were removed from the analysis. When evaluated by cancer subtype, an inverse association with long-term occupational activity was noted for non-advanced/non-aggressive PCa (RR = 0.51, 95% CI = 0.37-0.71, n studies = 2) and regular recreational activity was inversely related to advanced/aggressive PCa (RR = 0.75, 95% CI = 0.60-0.95, n studies = 2), although these observations are based on a low number of studies. Moreover, PA after diagnosis was related to reduced risk of PCa mortality among survivors of PCa (summary RR based on four studies = 0.69, 95% CI = 0.55-0.85). Whether PA protects against PCa remains elusive. Further investigation taking into account the complex clinical and pathologic nature of PCa is needed to clarify the PA and PCa incidence relation. Moreover, future studies are needed to confirm whether PA after diagnosis reduces risk of PCa mortality.

Characterizing the molecular features of ERG-positive tumors in primary and castration resistant prostate cancer

PubMed Central

Roudier, Martine P; Winters, Brian R; Coleman, Ilsa; Lam, Hung-Ming; Zhang, Xiaotun; Coleman, Roger; Chéry, Lisly; True, Lawrence D.; Higano, Celestia S.; Montgomery, Bruce; Lange, Paul H.; Snyder, Linda A.; Srivistava, Shiv; Corey, Eva; Vessella, Robert L.; Nelson, Peter S.; Üren, Aykut; Morrissey, Colm

2017-01-01

Background The TMPRSS2-ERG gene fusion is detected in approximately half of primary prostate cancers (PCa) yet the prognostic significance remains unclear. We hypothesized that ERG promotes the expression of common genes in primary PCa and metastatic castration-resistant PCa (CRPC), with the objective of identifying ERG-associated pathways, which may promote the transition from primary PCa to CRPC. Methods We constructed tissue microarrays (TMA) from 127 radical prostatectomy specimens, 20 LuCaP patient-derived xenografts (PDX), and 152 CRPC metastases obtained immediately at time of death. Nuclear ERG was assessed by immunohistochemistry (IHC). To characterize the molecular features of ERG-expressing PCa, a subset of IHC confirmed ERG+ or ERG-specimens including 11 radical prostatectomies, 20 LuCaP PDXs, and 45 CRPC metastases underwent gene expression analysis. Genes were ranked based on expression in primary PCa and CRPC. Common genes of interest were targeted for IHC analysis and expression compared with biochemical recurrence (BCR) status. Results IHC revealed that 43% of primary PCa, 35% of the LuCaP PDXs, and 18% of the CRPC metastases were ERG+ (12 of 48 patients [25%] had at least 1 ERG+ metastasis). Based on gene expression data and previous literature, two proteins involved in calcium signaling (NCALD, CACNA1D), a protein involved in inflammation (HLA-DMB), CD3 positive immune cells, and a novel ERG-associated protein, DCLK1 were evaluated in primary PCa and CRPC metastases. In ERG+ primary PCa, a weak association was seen with NCALD and CACNA1D protein expression. HLA-DMB expression and the presence of CD3 positive immune cells were decreased in CRPC metastases compared to primary PCa. DCLK1 was upregulated at the protein level in unpaired ERG+ primary PCa and CRPC metastases (p=0.0013 and p<0.0001, respectively). In primary PCa, ERG status or expression of targeted proteins was not associated with BCR-free survival. However for primary PCa, ERG+DCLK1+ patients exhibited shorter time to BCR (p=0.06) compared with ERG+DCLK1- patients. Conclusions This study examined ERG expression in primary PCa and CRPC. We have identified altered levels of inflammatory mediators associated with ERG expression. We determined expression of DCLK1 correlates with ERG expression and may play a role in primary PCa progression to metastatic CPRC. PMID:26990456

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

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

PubMed

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

2013-11-15

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

Application of principal component analysis for improvement of X-ray fluorescence images obtained by polycapillary-based micro-XRF technique

NASA Astrophysics Data System (ADS)

Aida, S.; Matsuno, T.; Hasegawa, T.; Tsuji, K.

2017-07-01

Micro X-ray fluorescence (micro-XRF) analysis is repeated as a means of producing elemental maps. In some cases, however, the XRF images of trace elements that are obtained are not clear due to high background intensity. To solve this problem, we applied principal component analysis (PCA) to XRF spectra. We focused on improving the quality of XRF images by applying PCA. XRF images of the dried residue of standard solution on the glass substrate were taken. The XRF intensities for the dried residue were analyzed before and after PCA. Standard deviations of XRF intensities in the PCA-filtered images were improved, leading to clear contrast of the images. This improvement of the XRF images was effective in cases where the XRF intensity was weak.

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

PubMed

Ernst, Matthias; Sittel, Florian; Stock, Gerhard

2015-12-28

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

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

NASA Astrophysics Data System (ADS)

Ernst, Matthias; Sittel, Florian; Stock, Gerhard

2015-12-01

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

Urinary microRNAs for prostate cancer diagnosis, prognosis, and treatment response: are we there yet?

PubMed

Balacescu, Ovidiu; Petrut, Bogdan; Tudoran, Oana; Feflea, Dragos; Balacescu, Loredana; Anghel, Andrei; Sirbu, Ioan O; Seclaman, Edward; Marian, Catalin

2017-11-01

Prostate cancer (PCa) remains one of the leading causes of cancer-related deaths in men. Despite the tremendous progress in research over the years, a suitable minimally invasive PCa biomarker is yet to be discovered. The recent advances regarding the roles of microRNAs as biomarkers has allowed for their study in PCa as well, especially as blood-based markers. However, there are several studies that used urine as biological sample to evaluate microRNAs as biomarkers for PCa diagnosis, prognosis, and treatment response, which were reviewed herein. A high degree of inconsistency among reports has been observed, which could be due to several analytical aspects, starting with different urinary fractions used for analysis and continuing with the employment of various analytical platforms and methods of statistical analysis. However, a few microRNAs were found to be dysregulated in the urine of PCa patients, which alone or together with serum prostate-specific antigen seem to improve diagnostic power even in the gray zone of PCa. These results warrant further confirmation by larger prospective studies, preferably using a standardized protocol for analysis. WIREs RNA 2017, 8:e1438. doi: 10.1002/wrna.1438 For further resources related to this article, please visit the WIREs website. © 2017 Wiley Periodicals, Inc.

Microarray Analysis Gene Expression Profiles in Laryngeal Muscle After Recurrent Laryngeal Nerve Injury.

PubMed

Bijangi-Vishehsaraei, Khadijeh; Blum, Kevin; Zhang, Hongji; Safa, Ahmad R; Halum, Stacey L

2016-03-01

The pathophysiology of recurrent laryngeal nerve (RLN) transection injury is rare in that it is characteristically followed by a high degree of spontaneous reinnervation, with reinnervation of the laryngeal adductor complex (AC) preceding that of the abducting posterior cricoarytenoid (PCA) muscle. Here, we aim to elucidate the differentially expressed myogenic factors following RLN injury that may be at least partially responsible for the spontaneous reinnervation. F344 male rats underwent RLN injury (n = 12) or sham surgery (n = 12). One week after RLN injury, larynges were harvested following euthanasia. The mRNA was extracted from PCA and AC muscles bilaterally, and microarray analysis was performed using a full rat genome array. Microarray analysis of denervated AC and PCA muscles demonstrated dramatic differences in gene expression profiles, with 205 individual probes that were differentially expressed between the denervated AC and PCA muscles and only 14 genes with similar expression patterns. The differential expression patterns of the AC and PCA suggest different mechanisms of reinnervation. The PCA showed the gene patterns of Wallerian degeneration, while the AC expressed the gene patterns of reinnervation by adjacent axonal sprouting. This finding may reveal important therapeutic targets applicable to RLN and other peripheral nerve injuries. © The Author(s) 2015.

Prostate Cancer Patients-Negative Biopsy Controls Discrimination by Untargeted Metabolomics Analysis of Urine by LC-QTOF: Upstream Information on Other Omics

NASA Astrophysics Data System (ADS)

Fernández-Peralbo, M. A.; Gómez-Gómez, E.; Calderón-Santiago, M.; Carrasco-Valiente, J.; Ruiz-García, J.; Requena-Tapia, M. J.; Luque de Castro, M. D.; Priego-Capote, F.

2016-12-01

The existing clinical biomarkers for prostate cancer (PCa) diagnosis are far from ideal (e.g., the prostate specific antigen (PSA) serum level suffers from lack of specificity, providing frequent false positives leading to over-diagnosis). A key step in the search for minimum invasive tests to complement or replace PSA should be supported on the changes experienced by the biochemical pathways in PCa patients as compared to negative biopsy control individuals. In this research a comprehensive global analysis by LC-QTOF was applied to urine from 62 patients with a clinically significant PCa and 42 healthy individuals, both groups confirmed by biopsy. An unpaired t-test (p-value < 0.05) provided 28 significant metabolites tentatively identified in urine, used to develop a partial least squares discriminant analysis (PLS-DA) model characterized by 88.4 and 92.9% of sensitivity and specificity, respectively. Among the 28 significant metabolites 27 were present at lower concentrations in PCa patients than in control individuals, while only one reported higher concentrations in PCa patients. The connection among the biochemical pathways in which they are involved (DNA methylation, epigenetic marks on histones and RNA cap methylation) could explain the concentration changes with PCa and supports, once again, the role of metabolomics in upstream processes.

Metabonomics study of the therapeutic mechanism of fenugreek galactomannan on diabetic hyperglycemia in rats, by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry.

PubMed

Jiang, Wenyue; Gao, Lu; Li, Pengdong; Kan, Hong; Qu, Jiale; Men, Lihui; Liu, Zhiqiang; Liu, Zhongying

2017-02-15

Fenugreek is a traditional plant for the treatment of diabetes. Galactomannan, an active major component in fenugreek seeds, has shown hypoglycemic activity. The present study was performed to investigate the therapeutic mechanism underlying fenugreek galactomannan (F-GAL) in treating diabetes, using a metabonomics approach based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). The F-GAL used for study was highly purified, and its yield, purity, and galactose/mannose ratio were characterized by capillary zone electrophoresis (CZE) and a modified phenol-sulfuric acid method. After treatment of streptozotocin (STZ)-induced diabetic rats with F-GAL for 28days, urine and serum samples were analyzed by UPLC-QTOF/MS. Multivariate statistical approaches such as principal component analysis (PCA) and orthogonal projection to latent structures squares-discriminant analysis (OPLS-DA) were applied to distinguish the non-diabetic/untreated, diabetic/untreated, and diabetic/F-GAL-treated groups. Then, potential biomarkers were identified that may help elucidate the underlying therapeutic mechanism of F-GAL in diabetes. The results demonstrated that there was a clear separation among the three groups in the PCA model. Fourteen potential biomarkers were identified by OPLS-DA, and they were determined to be produced in response to the therapeutic effects of F-GAL. These biomarkers were involved in histidine metabolism, tryptophan metabolism, energy metabolism, phenylalanine metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and arachidonic acid metabolism. In conclusion, our study demonstrates that a metabonomics approach is a powerful, novel tool that can be used to evaluate the underlying therapeutic mechanisms of herb extracts. Copyright © 2016 Elsevier B.V. All rights reserved.

Can Prostate Imaging Reporting and Data System Version 2 reduce unnecessary prostate biopsies in men with PSA levels of 4-10 ng/ml?

PubMed

Xu, Ning; Wu, Yu-Peng; Chen, Dong-Ning; Ke, Zhi-Bin; Cai, Hai; Wei, Yong; Zheng, Qing-Shui; Huang, Jin-Bei; Li, Xiao-Dong; Xue, Xue-Yi

2018-05-01

To explore the value of Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2) for predicting prostate biopsy results in patients with prostate specific antigen (PSA) levels of 4-10 ng/ml. We retrospectively reviewed multi-parameter magnetic resonance images from 528 patients with PSA levels of 4-10 ng/ml who underwent transrectal ultrasound-guided prostate biopsies between May 2015 and May 2017. Among them, 137 were diagnosed with prostate cancer (PCa), and we further subdivided them according to pathological results into the significant PCa (S-PCa) and insignificant significant PCa (Ins-PCa) groups (121 cases were defined by surgical pathological specimen and 16 by biopsy). Age, PSA, percent free PSA, PSA density (PSAD), prostate volume (PV), and PI-RADS score were collected. Logistic regression analysis was performed to determine predictors of pathological results. Receiver operating characteristic curves were constructed to analyze the diagnostic value of PI-RADS v2 in PCa. Multivariate analysis indicated that age, PV, percent free PSA, and PI-RADS score were independent predictors of biopsy findings, while only PI-RADS score was an independent predictor of S-PCa (P < 0.05). The areas under the receiver operating characteristic curve for diagnosing PCa with respect to age, PV, percent free PSA, and PI-RADS score were 0.570, 0.430, 0.589 and 0.836, respectively. The area under the curve for diagnosing S-PCa with respect to PI-RADS score was 0.732. A PI-RADS score of 3 was the best cutoff for predicting PCa, and 4 was the best cutoff for predicting S-PCa. Thus, 92.8% of patients with PI-RADS scores of 1-2 would have avoided biopsy, but at the cost of missing 2.2% of the potential PCa cases. Similarly, 83.82% of patients with a PI-RADS score ≤ 3 would have avoided biopsy, but at the cost of missing 3.3% of the potential S-PCa cases. PI-RADS v2 could be used to reduce unnecessary prostate biopsies in patients with PSA levels of 4-10 ng/ml.

The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer.

PubMed

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

2015-04-01

To evaluate histogram analysis of intravoxel incoherent motion (IVIM) for discriminating the Gleason grade of prostate cancer (PCa). A total of 48 patients pathologically confirmed as having clinically significant PCa (size > 0.5 cm) underwent preoperative DW-MRI (b of 0-900 s/mm(2)). Data was post-processed by monoexponential and IVIM model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analysis was performed by outlining entire-tumour regions of interest (ROIs) from histological-radiological correlation. The ability of imaging indices to differentiate low-grade (LG, Gleason score (GS) ≤6) from intermediate/high-grade (HG, GS > 6) PCa was analysed by ROC regression. Eleven patients had LG tumours (18 foci) and 37 patients had HG tumours (42 foci) on pathology examination. HG tumours had significantly lower ADCs and D in terms of mean, median, 10th and 75th percentiles, combined with higher histogram kurtosis and skewness for ADCs, D and f, than LG PCa (p < 0.05). Histogram D showed relatively higher correlations (ñ = 0.641-0.668 vs. ADCs: 0.544-0.574) with ordinal GS of PCa; and its mean, median and 10th percentile performed better than ADCs did in distinguishing LG from HG PCa. It is feasible to stratify the pathological grade of PCa by IVIM with histogram metrics. D performed better in distinguishing LG from HG tumour than conventional ADCs. • GS had relatively higher correlation with tumour D than ADCs. • Difference of histogram D among two-grade tumours was statistically significant. • D yielded better individual features in demonstrating tumour grade than ADC. • D* and f failed to determine tumour grade of PCa.

Detecting most influencing courses on students grades using block PCA

NASA Astrophysics Data System (ADS)

Othman, Osama H.; Gebril, Rami Salah

2014-12-01

One of the modern solutions adopted in dealing with the problem of large number of variables in statistical analyses is the Block Principal Component Analysis (Block PCA). This modified technique can be used to reduce the vertical dimension (variables) of the data matrix Xn×p by selecting a smaller number of variables, (say m) containing most of the statistical information. These selected variables can then be employed in further investigations and analyses. Block PCA is an adapted multistage technique of the original PCA. It involves the application of Cluster Analysis (CA) and variable selection throughout sub principal components scores (PC's). The application of Block PCA in this paper is a modified version of the original work of Liu et al (2002). The main objective was to apply PCA on each group of variables, (established using cluster analysis), instead of involving the whole large pack of variables which was proved to be unreliable. In this work, the Block PCA is used to reduce the size of a huge data matrix ((n = 41) × (p = 251)) consisting of Grade Point Average (GPA) of the students in 251 courses (variables) in the faculty of science in Benghazi University. In other words, we are constructing a smaller analytical data matrix of the GPA's of the students with less variables containing most variation (statistical information) in the original database. By applying the Block PCA, (12) courses were found to `absorb' most of the variation or influence from the original data matrix, and hence worth to be keep for future statistical exploring and analytical studies. In addition, the course Independent Study (Math.) was found to be the most influencing course on students GPA among the 12 selected courses.

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

NASA Astrophysics Data System (ADS)

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

2011-07-01

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

Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis.

PubMed

Zhang, Han; Zuo, Xi-Nian; Ma, Shuang-Ye; Zang, Yu-Feng; Milham, Michael P; Zhu, Chao-Zhe

2010-07-15

Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction. Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and generates variable decompositions. In this study, a rigorous theoretical analysis was conducted to prove the existence of such variability. Simulated and real fMRI experiments were used to demonstrate the subject-order-induced variability of TC-GICA results using multiple PCA data reductions. To solve this problem, we propose a new subject order-independent group ICA (SOI-GICA). Both simulated and real fMRI data experiments demonstrated the high robustness and accuracy of the SOI-GICA results compared to those of traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI studies, especially those with large data sets. Copyright 2010 Elsevier Inc. All rights reserved.

Comparison of the Trace Elements and Active Components of Lonicera japonica flos and Lonicera flos Using ICP-MS and HPLC-PDA.

PubMed

Zhao, Yueran; Dou, Deqiang; Guo, Yueqiu; Qi, Yue; Li, Jun; Jia, Dong

2018-06-01

Thirteen trace elements and active constituents of 40 batches of Lonicera japonica flos and Lonicera flos were comparatively studied using inductively coupled plasma mass-spectrometry (ICP-MS) and high-performance liquid chromatography-photodiode array (HPLC-PDA). The trace elements were 24 Mg, 52 Cr, 55 Mn, 57 Fe, 60 Ni, 63 Cu, 66 Zn, 75 As, 82 Se, 98 Mo, 114 Cd, 202 Hg, and 208 Pb, and the active compounds were chlorogenic acid, 3,5-O-dicaffeoylquinc acid, 4,5-O-dicaffeoylquinc acid, luteolin-7-O-glucoside, and 4-O-caffeoylquinic acid. The data of 18 variables were statistically processed using principal component analysis (PCA) and discriminate analysis (DA) to classify L. japonica flos and L. flos. The validated method was developed to divide the 40 samples into two groups based on the PCA in terms of 18 variables. Furthermore, the species of Lonicera was better discriminated by using DA with 12 variables. These results suggest that the method and statistical analysis of the contents of trace elements and chemical components can classify the L. japonica flos and L. flos using 12 variables, such as 3,5-O-dicaffeoylquincacid, luteolin-7-O-glucoside, Cd, Mn, Hg, Pb, Ni, 4-O-caffeoyl-quinic acid, 4,5-O-dicaffeoylquinc acid, Fe, Mg, and Cr.

PBOV1 as a potential biomarker for more advanced prostate cancer based on protein and digital histomorphometric analysis.

PubMed

Carleton, Neil M; Zhu, Guangjing; Gorbounov, Mikhail; Miller, M Craig; Pienta, Kenneth J; Resar, Linda M S; Veltri, Robert W

2018-05-01

There are few tissue-based biomarkers that can accurately predict prostate cancer (PCa) progression and aggressiveness. We sought to evaluate the clinical utility of prostate and breast overexpressed 1 (PBOV1) as a potential PCa biomarker. Patient tumor samples were designated by Grade Groups using the 2014 Gleason grading system. Primary radical prostatectomy tumors were obtained from 48 patients and evaluated for PBOV1 levels using Western blot analysis in matched cancer and benign cancer-adjacent regions. Immunohistochemical evaluation of PBOV1 was subsequently performed in 80 cancer and 80 benign cancer-adjacent patient samples across two tissue microarrays (TMAs) to verify protein levels in epithelial tissue and to assess correlation between PBOV1 proteins and nuclear architectural changes in PCa cells. Digital histomorphometric analysis was used to track 22 parameters that characterized nuclear changes in PBOV1-stained cells. Using a training and test set for validation, multivariate logistic regression (MLR) models were used to identify significant nuclear parameters that distinguish Grade Group 3 and above PCa from Grade Group 1 and 2 PCa regions. PBOV1 protein levels were increased in tumors from Grade Group 3 and above (GS 4 + 3 and ≥ 8) regions versus Grade Groups 1 and 2 (GS 3 + 3 and 3 + 4) regions (P = 0.005) as assessed by densitometry of immunoblots. Additionally, by immunoblotting, PBOV1 protein levels differed significantly between Grade Group 2 (GS 3 + 4) and Grade Group 3 (GS 4 + 3) PCa samples (P = 0.028). In the immunohistochemical analysis, measures of PBOV1 staining intensity strongly correlated with nuclear alterations in cancer cells. An MLR model retaining eight parameters describing PBOV1 staining intensity and nuclear architecture discriminated Grade Group 3 and above PCa from Grade Group 1 and 2 PCa and benign cancer-adjacent regions with a ROC-AUC of 0.90 and 0.80, respectively, in training and test sets. Our study demonstrates that the PBOV1 protein could be used to discriminate Grade Group 3 and above PCa. Additionally, the PBOV1 protein could be involved in modulating changes to the nuclear architecture of PCa cells. Confirmatory studies are warranted in an independent population for further validation. © 2018 Wiley Periodicals, Inc.

Piloted Simulation Tests of Propulsion Control as Backup to Loss of Primary Flight Controls for a B747-400 Jet Transport

NASA Technical Reports Server (NTRS)

Bull, John; Mah, Robert; Hardy, Gordon; Sullivan, Barry; Jones, Jerry; Williams, Diane; Soukup, Paul; Winters, Jose

1997-01-01

Partial failures of aircraft primary flight control systems and structural damages to aircraft during flight have led to catastrophic accidents with subsequent loss of lives (e.g. DC-10, B-747, C-5, B-52, and others). Following the DC-10 accident at Sioux City, Iowa in 1989, the National Transportation Safety Board recommended 'Encourage research and development of backup flight control systems for newly certified wide-body airplanes that utilize an alternate source of motive power separate from that source used for the conventional control system.' This report describes the concept of a propulsion controlled aircraft (PCA), discusses pilot controls, displays, and procedures; and presents the results of a PCA piloted simulation test and evaluation of the B747-400 airplane conducted at NASA Ames Research Center in December, 1996. The purpose of the test was to develop and evaluate propulsion control throughout the full flight envelope of the B747-400 including worst case scenarios of engine failures and out of trim moments. Pilot ratings of PCA performance ranged from adequate to satisfactory. PCA performed well in unusual attitude recoveries at 35,000 ft altitude, performed well in fully coupled ILS approaches, performed well in single engine failures, and performed well at aft cg. PCA performance was primarily limited by out-of-trim moments.

Lgr4 promotes prostate tumorigenesis through the Jmjd2a/AR signaling pathway.

PubMed

Zhang, Jianwei; Li, Qi; Zhang, Shaojin; Xu, Quanquan; Wang, Tianen

2016-11-15

Lgr4 (leucine-rich repeat domain containing G protein-coupled receptor 4) is implicated in the transcriptional regulation of multiple histone demethylases in the progression of diverse cancers, but there are few reports concerning the molecular mechanism by which Lgr4 regulates histone demethylase activation in prostate cancer (PCa) progression. As Jmjd2a is a histone demethylase, in the current study, we investigated the relationship between interaction Lgr4 with Jmjd 2a and Jmjd2a/androgen receptor (AR) signaling pathway in PCa progression. Firstly, Lgr4 was overexpressed by transfecting pcDNA3.1(+)/Lgr4 plasmids into PCa (LNCaP and PC-3) cell lines. Next, we found that Lgr4 overexpression promoted Jmjd2a mRNA expression, reduced cell apoptosis and arrested cell cycle in the S phase, these effects were reversed by Jmjd2a silencing. Moreover, Lgr4 overexpression markedly elevated AR levels and its interaction with Jmjd2a, which was tested by co-immunoprecipitation and luciferase reporter assays. Furthermore, interaction AR with PSA promoter (containing an AR response element) was obviously improved by Lgr4 overexpression, and PSA silencing reduced Lgr4-induced cell apoptosis and cell cycle arrest in PCa cells. Taken together, Lgr4 may be a novel tumor marker providing new mechanistic insights into PCa progression. Lgr4 activates Jmjd2a/AR signaling pathway to promote interaction AR with PSA promoter, causing reduction of PCa apoptosis and cell cycle arrest. Copyright © 2016 Elsevier Inc. All rights reserved.

Physicochemical and mechanical properties of paracetamol cocrystal with 5-nitroisophthalic acid.

PubMed

Hiendrawan, Stevanus; Veriansyah, Bambang; Widjojokusumo, Edward; Soewandhi, Sundani Nurono; Wikarsa, Saleh; Tjandrawinata, Raymond R

2016-01-30

We report novel pharmaceutical cocrystal of a popular antipyretic drug paracetamol (PCA) with coformer 5-nitroisophhthalic acid (5NIP) to improve its tabletability. The cocrystal (PCA-5NIP at molar ratio of 1:1) was synthesized by solvent evaporation technique using methanol as solvent. The physicochemical properties of cocrystal were characterized by powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), thermogravimetry analysis (TGA), fourier transform infrared spectroscopy (FTIR), hot stage polarized microscopy (HSPM) and scanning electron microscopy (SEM). Stability of the cocrystal was assessed by storing them at 40°C/75% RH for one month. Compared to PCA, the cocrystal displayed superior tableting performance. PCA-5NIP cocrystal showed a similar dissolution profile as compared to PCA and exhibited good stability. This study showed the utility of PCA-5NIP cocrystal for improving mechanical properties of PCA. Copyright © 2015 Elsevier B.V. All rights reserved.

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

PubMed

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.

RECENT APPLICATIONS OF SOURCE APPORTIONMENT METHODS AND RELATED NEEDS

EPA Science Inventory

Traditional receptor modeling studies have utilized factor analysis (like principal component analysis, PCA) and/or Chemical Mass Balance (CMB) to assess source influences. The limitations with these approaches is that PCA is qualitative and CMB requires the input of source pr...

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

PubMed

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

2012-01-01

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

Using the prostate imaging reporting and data system version 2 (PI-RIDS v2) to detect prostate cancer can prevent unnecessary biopsies and invasive treatment.

PubMed

Liu, Chang; Liu, Shi-Liang; Wang, Zhi-Xian; Yu, Kai; Feng, Chun-Xiang; Ke, Zan; Wang, Liang; Zeng, Xiao-Yong

2018-04-13

Prostate cancer (PCa) is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) to classify men with PCa, clinically significant PCa (CSPCa), or no PCa, especially among those with serum total prostate-specific antigen (tPSA) levels in the "gray zone" (4-10 ng ml -1 ). A total of 308 patients (355 lesions) were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density (PSAD) were independent predictors of PCa and CSPCa. A PI-RADS v2 score ≥4 provided high negative predictive values (91.39% for PCa and 95.69% for CSPCa). A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group (PI-RADS score = 5 and PSAD ≥0.15 ng ml -1 cm -3 , with tPSA in the gray zone, or PI-RADS score ≥4 with high tPSA level) with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.

Prognostic Value of Neutrophil-to-Lymphocyte Ratio in Localized and Advanced Prostate Cancer: A Systematic Review and Meta-Analysis.

PubMed

Tang, Lu; Li, Xintao; Wang, Baojun; Luo, Guoxiong; Gu, Liangyou; Chen, Luyao; Liu, Kan; Gao, Yu; Zhang, Xu

2016-01-01

Increasing evidence suggests that inflammation plays an essential role in cancer development and progression. The inflammation marker neutrophil-lymphocyte ratio (NLR) is correlated with prognosis across a wide variety of tumor types, but its prognostic value in prostate cancer (PCa) remains controversial. In the present meta-analysis, the prognostic value of NLR in PCa patients is investigated. We performed a meta-analysis to determine the predictive value of NLR for overall survival (OS), recurrence-free survival (RFS), and clinical features in patients with PCa. We systematically searched PubMed, ISI Web of Science, and Embase for relevant studies published up to October 2015. A total of 9418 patients from 18 studies were included in the meta-analysis. Elevated pretreatment NLR predicted poor OS (HR 1.628, 95% CI 1.410-1.879) and RFS (HR 1.357, 95% CI 1.126-1.636) in all patients with PCa. However, NLR was insignificantly associated with OS in the subgroup of patients with localized PCa (HR 1.439, 95% CI 0.753-2.75). Increased NLR was also significantly correlated with lymph node involvement (OR 1.616, 95% CI 1.167-2.239) but not with pathological stage (OR 0.827, 95% CI 0.637-1.074) or Gleason score (OR 0.761, 95% CI 0.555-1.044). The present meta-analysis indicated that NLR could predict the prognosis for patients with locally advanced or castration-resistant PCa. Patients with higher NLR are more likely to have poorer prognosis than those with lower NLR.

The Burden of Urinary Incontinence and Urinary Bother Among Elderly Prostate Cancer Survivors

PubMed Central

Kopp, Ryan P.; Marshall, Lynn M.; Wang, Patty Y.; Bauer, Douglas C.; Barrett-Connor, Elizabeth; Parsons, J. Kellogg

2014-01-01

Background Data describing urinary health in elderly, community-dwelling prostate cancer (PCa) survivors are limited. Objective To elucidate the prevalence of lower urinary tract symptoms, urinary bother, and incontinence in elderly PCa survivors compared with peers without PCa. Design, setting, and participants A cross-sectional analysis of 5990 participants in the Osteoporotic Fractures in Men Research Group, a cohort study of community-dwelling men ≥65 yr. Outcome measurements and statistical analysis We characterized urinary health using self-reported urinary incontinence and the American Urological Association Symptom Index (AUA-SI). We compared urinary health measures according to type of PCa treatment in men with PCa and men without PCa using multivariate log-binomial regression to generate prevalence ratios (PRs). Results and limitations At baseline, 706 men (12%) reported a history of PCa, with a median time since diagnosis of 6.3 yr. Of these men, 426 (60%) reported urinary incontinence. In adjusted analyses, observation (PR: 1.92; 95% confidence interval [CI], 1.15–3.21; p = 0.01), surgery (PR: 4.68; 95% CI, 4.11–5.32; p < 0.0001), radiation therapy (PR: 1.64; 95% CI, 1.20– 2.23; p = 0.002), and androgen-deprivation therapy (ADT) (PR: 2.01; 95% CI, 1.35–2.99; p = 0.0006) were each associated with daily incontinence. Daily incontinence risk increased with time since diagnosis independently of age. Observation (PR: 1.33; 95% CI, 1.00–1.78; p = 0.05), surgery (PR: 1.25; 95% CI, 1.10–1.42; p = 0.0008), and ADT (PR: 1.50; 95% CI, 1.26–1.79; p < 0.0001) were associated with increased AUA-SI bother scores. Cancer stage and use of adjuvant or salvage therapies were not available for analysis. Conclusions Compared with their peers without PCa, elderly PCa survivors had a two-fold to five-fold greater prevalence of urinary incontinence, which rose with increasing survivorship duration. Observation, surgery, and ADT were each associated with increased urinary bother. These data suggest a substantially greater burden of urinary health problems among elderly PCa survivors than previously recognized. PMID:23587870

A Review of Analytical Methods for p-Coumaric Acid in Plant-Based Products, Beverages, and Biological Matrices.

PubMed

Ferreira, Paula Scanavez; Victorelli, Francesca Damiani; Fonseca-Santos, Bruno; Chorilli, Marlus

2018-05-14

p-Coumaric acid (p-CA), also known as 4-hydroxycinnamic acid, is a phenolic acid, which has been widely studied due to its beneficial effects against several diseases and its wide distribution in the plant kingdom. This phenolic compound can be found in the free form or conjugated with other molecules; therefore, its bioavailability and the pathways via which it is metabolized change according to its chemical structure. p-CA has potential pharmacological effects because it has high free radical scavenging, anti-inflammatory, antineoplastic, and antimicrobial activities, among other biological properties. It is therefore essential to choose the most appropriate and effective analytical method for qualitative and quantitative determination of p-CA in different matrices, such as plasma, urine, plant extracts, and drug delivery systems. The most-reported analytical method for this purpose is high-performance liquid chromatography, which is mostly coupled with some type of detectors, such as UV/Vis detector. However, other analytical techniques are also used to evaluate this compound. This review presents a summary of p-CA in terms of its chemical and pharmacokinetic properties, pharmacological effects, drug delivery systems, and the analytical methods described in the literature that are suitable for its quantification.

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

PubMed

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

2014-12-01

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

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

ERIC Educational Resources Information Center

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…

Germline Mutations in ATM and BRCA1/2 Distinguish Risk for Lethal and Indolent Prostate Cancer and are Associated with Early Age at Death

PubMed Central

Na, Rong; Zheng, S. Lilly; Han, Misop; Yu, Hongjie; Jiang, Deke; Shah, Sameep; Ewing, Charles M.; Zhang, Liti; Novakovic, Kristian; Petkewicz, Jacqueline; Gulukota, Kamalakar; Helseth, Donald L.; Quinn, Margo; Humphries, Elizabeth; Wiley, Kathleen E.; Isaacs, Sarah D.; Wu, Yishuo; Liu, Xu; Zhang, Ning; Wang, Chi-Hsiung; Khandekar, Janardan; Hulick, Peter J.; Shevrin, Daniel H.; Cooney, Kathleen A.; Shen, Zhoujun; Partin, Alan W.; Carter, H. Ballentine; Carducci, Michael A.; Eisenberger, Mario A.; Denmeade, Sam R.; McGuire, Michael; Walsh, Patrick C.; Helfand, Brian T.; Brendler, Charles B.; Ding, Qiang; Xu, Jianfeng; Isaacs, William B.

2017-01-01

Background Germline mutations in BRCA1/2 and ATM have been associated with prostate cancer (PCa) risk. Objective To directly assess whether germline mutations in these three genes distinguish lethal from indolent PCa and whether they confer any effect on age at death. Design, setting, and participants A retrospective case-case study of 313 patients who died of PCa and 486 patients with low-risk localized PCa of European, African, and Chinese descent. Germline DNA of each of the 799 patients was sequenced for these three genes. Outcome measurements and statistical analysis Mutation carrier rates and their effect on lethal PCa were analyzed using the Fisher’s exact test and Cox regression analysis, respectively. Results and limitations The combined BRCA1/2 and ATM mutation carrier rate was significantly higher in lethal PCa patients (6.07%) than localized PCa patients (1.44%), p = 0.0007. The rate also differed significantly among lethal PCa patients as a function of age at death (10.00%, 9.08%, 8.33%, 4.94%, and 2.97% in patients who died ≤60 yr, 61–65 yr, 66–70 yr, 71–75 yr, and over 75 yr, respectively, p = 0.046) and time to death after diagnosis (12.26%, 4.76%, and 0.98% in patients who died ≤5 yr, 6–10 yr, and > 10 yr after a PCa diagnosis, respectively, p = 0.0006). Survival analysis in the entire cohort revealed mutation carriers remained an independent predictor of lethal PCa after adjusting for race and age, prostate-specific antigen, and Gleason score at the time of diagnosis (hazard ratio = 2.13, 95% confidence interval: 1.24–3.66, p = 0.004). A limitation of this study is that other DNA repair genes were not analyzed. Conclusions Mutation status of BRCA1/2 and ATM distinguishes risk for lethal and indolent PCa and is associated with earlier age at death and shorter survival time. Patient summary Prostate cancer patients with inherited mutations in BRCA1/2 and ATM are more likely to die of prostate cancer and do so at an earlier age. PMID:27989354

Prolactin- and testosterone-induced carboxypeptidase-D correlates with increased nitrotyrosines and Ki67 in prostate cancer.

PubMed

Thomas, Lynn N; Merrimen, Jennifer; Bell, David G; Rendon, Ricardo; Too, Catherine K L

2015-11-01

Carboxypeptidase-D (CPD) cleaves C-terminal arginine for conversion to nitric oxide (NO) by nitric oxide synthase (NOS). Prolactin (PRL) and androgens stimulate CPD gene transcription and expression, which increases intracellular production of NO to promote viability of prostate cancer (PCa) cells in vitro. The current study evaluated whether hormonal upregulation of CPD and NO promote PCa cell viabilty in vivo, by correlating changes in expression of CPD and nitrotyrosine residues (products of NO action) with proliferation marker Ki67 and associated proteins during PCa development and progression. Fresh prostate tissues, obtained from 40 men with benign prostatic hyperplasia (BPH) or PCa, were flash-frozen at the time of surgery and used for RT-qPCR analysis of CPD, androgen receptor (AR), PRL receptor (PRLR), eNOS, and Ki67 levels. Archival paraffin-embedded tissues from 113 men with BPH or PCa were used for immunohistochemical (IHC) analysis of CPD, nitrotyrosines, phospho-Stat5 (for activated PRLR), AR, eNOS/iNOS, and Ki67. RT-qPCR and IHC analyses showed strong AR and PRLR expression in benign and malignant prostates. CPD mRNA levels increased ∼threefold in PCa compared to BPH, which corresponded to a twofold increase in Ki67 mRNA levels. IHC analysis showed a progressive increase in CPD from 11.4 ± 2.1% in benign to 21.8 ± 3.2% in low-grade (P = 0.007), 40.7 ± 4.0% in high-grade (P < 0.0001) and 50.0 ± 9.5% in castration-recurrent PCa (P < 0.0001). Immunostaining for nitrotyrosines and Ki67 mirrored these increases during PCa progression. CPD, nitrotyrosines, and Ki67 tended to co-localize, as did phospho-Stat5. CPD, nitrotyrosine, and Ki67 levels were higher in PCa than in benign and tended to co-localize, along with phospho-Stat5. The strong correlation in expression of these proteins in benign and malignant prostate tissues, combined with abundant AR and PRLR, supports in vitro evidence that the CPD-Arg-NO pathway is involved in the regulation of PCa cell proliferation. It further highlights a role for PRL in the development and progression of PCa. © 2015 Wiley Periodicals, Inc.

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

NASA Astrophysics Data System (ADS)

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.

Exercise and prostate cancer: From basic science to clinical applications.

PubMed

Campos, Christian; Sotomayor, Paula; Jerez, Daniel; González, Javier; Schmidt, Camila B; Schmidt, Katharina; Banzer, Winfried; Godoy, Alejandro S

2018-06-01

Prostate cancer (PCa) is a disease of increasing medical significance worldwide. In developed countries, PCa is the most common non-skin cancer in men, and one of the leading causes of cancer-related deaths. Exercise is one of the environmental factors that have been shown to influence cancer risk. Moreover, systemic reviews and meta-analysis have suggested that total physical activity is related to a decrease in the risk of developing PCa. In addition, epidemiological studies have shown that exercise, after diagnosis, has benefits regarding PCa development, and positive outcome in patients under treatment. The standard treatment for locally advanced or metastatic PCa is Androgen deprivation therapy (ADT). ADT produces diverse side effects, including loss of libido, changes in body composition (increase abdominal fat), and reduced muscle mass, and muscle tone. Analysis of numerous research publications showed that aerobic and/or resistance training improve patient's physical condition, such us, cardiorespiratory fitness, muscle strength, physical function, body composition, and fatigue. Therefore, exercise might counteract several ADT treatment-induced side effects. In addition of the aforementioned benefits, epidemiological, and in vitro studies have shown that exercise might decrease PCa development. Thus, physical activity might attenuate the risk of PCa and supervised exercise intervention might improve deleterious effects of cancer treatment, such as ADT side effects. This review article provides evidence indicating that exercise could complement, and potentiate, the current standard treatments for advanced PCa, probably by creating an unfavorable microenvironment that can negatively affect tumor development, and progression. © 2018 Wiley Periodicals, Inc.

Proton Nuclear Magnetic Resonance-Spectroscopic Discrimination of Wines Reflects Genetic Homology of Several Different Grape (V. vinifera L.) Cultivars

PubMed Central

Zhu, Yong; Wen, Wen; Zhang, Fengmin; Hardie, Jim W.

2015-01-01

Background and Aims Proton nuclear magnetic resonance spectroscopy coupled multivariate analysis (1H NMR-PCA/PLS-DA) is an important tool for the discrimination of wine products. Although 1H NMR has been shown to discriminate wines of different cultivars, a grape genetic component of the discrimination has been inferred only from discrimination of cultivars of undefined genetic homology and in the presence of many confounding environmental factors. We aimed to confirm the influence of grape genotypes in the absence of those factors. Methods and Results We applied 1H NMR-PCA/PLS-DA and hierarchical cluster analysis (HCA) to wines from five, variously genetically-related grapevine (V. vinifera) cultivars; all grown similarly on the same site and vinified similarly. We also compared the semi-quantitative profiles of the discriminant metabolites of each cultivar with previously reported chemical analyses. The cultivars were clearly distinguishable and there was a general correlation between their grouping and their genetic homology as revealed by recent genomic studies. Between cultivars, the relative amounts of several of the cultivar-related discriminant metabolites conformed closely with reported chemical analyses. Conclusions Differences in grape-derived metabolites associated with genetic differences alone are a major source of 1H NMR-based discrimination of wines and 1H NMR has the capacity to discriminate between very closely related cultivars. Significance of the Study The study confirms that genetic variation among grape cultivars alone can account for the discrimination of wine by 1H NMR-PCA/PLS and indicates that 1H NMR spectra of wine of single grape cultivars may in future be used in tandem with hierarchical cluster analysis to elucidate genetic lineages and metabolomic relations of grapevine cultivars. In the absence of genetic information, for example, where predecessor varieties are no longer extant, this may be a particularly useful approach. PMID:26658757

Rapid analysis of pharmaceutical drugs using LIBS coupled with multivariate analysis.

PubMed

Tiwari, P K; Awasthi, S; Kumar, R; Anand, R K; Rai, P K; Rai, A K

2018-02-01

Type 2 diabetes drug tablets containing voglibose having dose strengths of 0.2 and 0.3 mg of various brands have been examined, using laser-induced breakdown spectroscopy (LIBS) technique. The statistical methods such as the principal component analysis (PCA) and the partial least square regression analysis (PLSR) have been employed on LIBS spectral data for classifying and developing the calibration models of drug samples. We have developed the ratio-based calibration model applying PLSR in which relative spectral intensity ratios H/C, H/N and O/N are used. Further, the developed model has been employed to predict the relative concentration of element in unknown drug samples. The experiment has been performed in air and argon atmosphere, respectively, and the obtained results have been compared. The present model provides rapid spectroscopic method for drug analysis with high statistical significance for online control and measurement process in a wide variety of pharmaceutical industrial applications.

Prognostic value of transformer 2β expression in prostate cancer.

PubMed

Diao, Yan; Wu, Dong; Dai, Zhijun; Kang, Huafeng; Wang, Ziming; Wang, Xijing

2015-01-01

Deregulation of transformer 2β (Tra2β) has been implicated in several cancers. However, the role of Tra2β expression in prostate cancer (PCa) is unclear. Therefore, this study was to investigate the expression of Tra2β in PCa and evaluated its association with clinicopathological variables and prognosis. Thirty paired fresh PCa samples were analyzed for Tra2β expression by Western blot analysis. Immunohistochemistry (IHC) assay was performed in 160 PCa samples after radical prostatectomy and adjacent non-cancerous tissues. Tra2β protein expression was divided into high expression group and low expression group by IHC. We also investigated the association of Tra2β expression with clinical and pathologic parameters. Kaplan-Meier plots and Cox proportional hazards regression model were used to analyze the association between Tra2β protein expression and prognosis of PCa patients. Our results showed that Tra2β was significantly upregulated in PCa tissues by western blot and IHC. Our data indicated that high expression of Tra2β was significantly associated with lymph node metastasis (P=0.002), clinical stage (P=0.015), preoperative prostate-specific antigen (P=0.003), Gleason score (P=0.001), and biochemical recurrence (P=0.021). High Tra2β expression was a significant predictor of poor biochemical recurrence free survival and overall survival both in univariate and multivariate analysis. We show that Tra2β was significantly upregulated in PCa patients after radical prostatectomy, and multivariate analysis confirmed Tra2β as an independent prognostic factor.

Circular RNA Myosin Light Chain Kinase (MYLK) Promotes Prostate Cancer Progression through Modulating Mir-29a Expression.

PubMed

Dai, Yuanqing; Li, Dongjie; Chen, Xiong; Tan, Xinji; Gu, Jie; Chen, Mingquan; Zhang, Xiaobo

2018-05-25

BACKGROUND In developed countries, prostate cancer (PCa) is a frequently diagnosed cancer with the second highest fatality rate. Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs (ncRNAs) stably expressed in cells and involved in a series of carcinomas. However, few research studies have reported on the role of circRNAs in PCa. MATERIAL AND METHODS We used qRT-PCR to detect the expression of circMYLK (circRNA ID: hsa_circ_0141940) and miR-29a in PCa tissues and cell lines. MTT, colony formation, and TUNEL assays were performed to analysis the cell viability of PCa cells. Transwell and wound scratch assays were performed to investigate the cell invasion and migration of PCa cells. RESULTS In the present study, we confirmed that circMYLK expression level was significantly higher in PCa samples and PCa cells than in normal tissues and normal prostatic cells. The upregulated circRNA-MYLK promoted PCa cells proliferation, invasion, and migration; however, si-circRNA-MYLK significantly accelerated the PCa cell apoptosis. We also observed that the aforementioned function of circRNA-MYLK on PCa cells was affected through targeting miR-29a. CONCLUSIONS We confirmed circRNA-MYLK was an oncogene in PCa and revealed a novel mechanism underlying circRNA-MYLK in PC progression.

The theoretical and experimental study on dicalcium phosphate dehydrate loading with protocatechuic aldehyde.

PubMed

Guo, Yuehua; Qu, Shuxin; Lu, Xiong; Xie, Haodong; Zhang, Hongping; Weng, Jie

2010-07-01

The aim of this study is to investigate the interaction between dicalcium phosphate dihydrate (CaHPO(4) x 2H(2)O, DCPD) and Protocatechuic aldehyde (C(7)H(6)O(3), Pca), which is the water-soluble constituents of Chinese Medicine, Salvia Miltiorrhiza Bunge (SMB), by calculating the absorption energy through molecular dynamics simulation. Furthermore, the effects of functional groups of Pca and temperature on Pca adsorbed by DCPD are calculated respectively. DCPD/Pca and DCPD were analyzed by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TG). The simulation results showed that Pca mostly absorbed on the (0 2 0) surface of DCPD. The aldehyde group of Pca played a moren important role on the adsorption of Pca on DCPD than hydroxyl did, while temperature had no distinct effects on the adsorption. XRD results indicated that Pca induced the preferential growth of (0 2 0) crystal surface in DCPC/Pca whereas it had no influence on the crystal structure, the crystallinity and grain size of DCPD. FTIR and TG results showed that the characteristic peak of Pca was at 1295 cm(-1) and the content of Pca in DCPD was 16%, respectively. The present results show that molecular dynamics simulation is a very effective and complementary method to study the interaction between materials and medicine.

Characterizing the molecular features of ERG-positive tumors in primary and castration resistant prostate cancer.

PubMed

Roudier, Martine P; Winters, Brian R; Coleman, Ilsa; Lam, Hung-Ming; Zhang, Xiaotun; Coleman, Roger; Chéry, Lisly; True, Lawrence D; Higano, Celestia S; Montgomery, Bruce; Lange, Paul H; Snyder, Linda A; Srivastava, Shiv; Corey, Eva; Vessella, Robert L; Nelson, Peter S; Üren, Aykut; Morrissey, Colm

2016-06-01

The TMPRSS2-ERG gene fusion is detected in approximately half of primary prostate cancers (PCa) yet the prognostic significance remains unclear. We hypothesized that ERG promotes the expression of common genes in primary PCa and metastatic castration-resistant PCa (CRPC), with the objective of identifying ERG-associated pathways, which may promote the transition from primary PCa to CRPC. We constructed tissue microarrays (TMA) from 127 radical prostatectomy specimens, 20 LuCaP patient-derived xenografts (PDX), and 152 CRPC metastases obtained immediately at time of death. Nuclear ERG was assessed by immunohistochemistry (IHC). To characterize the molecular features of ERG-expressing PCa, a subset of IHC confirmed ERG+ or ERG- specimens including 11 radical prostatectomies, 20 LuCaP PDXs, and 45 CRPC metastases underwent gene expression analysis. Genes were ranked based on expression in primary PCa and CRPC. Common genes of interest were targeted for IHC analysis and expression compared with biochemical recurrence (BCR) status. IHC revealed that 43% of primary PCa, 35% of the LuCaP PDXs, and 18% of the CRPC metastases were ERG+ (12 of 48 patients [25%] had at least one ERG+ metastasis). Based on gene expression data and previous literature, two proteins involved in calcium signaling (NCALD, CACNA1D), a protein involved in inflammation (HLA-DMB), CD3 positive immune cells, and a novel ERG-associated protein, DCLK1 were evaluated in primary PCa and CRPC metastases. In ERG+ primary PCa, a weak association was seen with NCALD and CACNA1D protein expression. HLA-DMB association with ERG was decreased and CD3 cell number association with ERG was changed from positive to negative in CRPC metastases compared to primary PCa. DCLK1 was upregulated at the protein level in unpaired ERG+ primary PCa and CRPC metastases (P = 0.0013 and P < 0.0001, respectively). In primary PCa, ERG status or expression of targeted proteins was not associated with BCR-free survival. However, for primary PCa, ERG+DCLK1+ patients exhibited shorter time to BCR (P = 0.06) compared with ERG+DCLK1- patients. This study examined ERG expression in primary PCa and CRPC. We have identified altered levels of inflammatory mediators associated with ERG expression. We determined expression of DCLK1 correlates with ERG expression and may play a role in primary PCa progression to metastatic CPRC. Prostate 76:810-822, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

PubMed

Ye, Jieping; Li, Qi

2005-06-01

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

Identification of species of the Euterpe genus by rare earth elements using inductively coupled plasma mass spectrometry and linear discriminant analysis.

PubMed

Santos, Vívian Silva; Nardini, Viviani; Cunha, Luís Carlos; Barbosa, Fernando; De Almeida Teixeira, Gustavo Henrique

2014-06-15

The açaí (Euterpe oleracea Mart.) and juçara (Euterpe edulis Mart.) produce similar fruits which are rich in energy, minerals, vitamins and natural compounds with antioxidant and anti-inflammatory properties. Although the drink obtained from these species is similar, it is important to develop tools to establish the identity of the fruit species and growing regions. To assess claims of origin and for other purposes, we use multivariate analysis to investigate the differentiation of açaí and juçara fruits based on rare earth element (REE) content determined by Inductively Coupled Plasma Mass Spectrometry. REE content, in particular Sm, Th, La, Pr, Gd, and especially Ce and Nd varied between species. PCA analysis was not efficient in differentiating açaí from juçara fruit samples. In contrast, LDA analysis permitted a correct differentiation between species with a predictive ability of 83.3%. The methodology that we have applied confirms that REE can be used to differentiate between açaí and juçara fruit samples and to identify their origin. Copyright © 2013 Elsevier Ltd. All rights reserved.

A comprehensive evaluation of CHEK2 germline mutations in men with prostate cancer.

PubMed

Wu, Yishuo; Yu, Hongjie; Zheng, S Lilly; Na, Rong; Mamawala, Mufaddal; Landis, Tricia; Wiley, Kathleen; Petkewicz, Jacqueline; Shah, Sameep; Shi, Zhuqing; Novakovic, Kristian; McGuire, Michael; Brendler, Charles B; Ding, Qiang; Helfand, Brian T; Carter, H Ballentine; Cooney, Kathleen A; Isaacs, William B; Xu, Jianfeng

2018-06-01

Germline mutations in CHEK2 have been associated with prostate cancer (PCa) risk. Our objective is to examine whether germline pathogenic CHEK2 mutations can differentiate risk of lethal from indolent PCa. A case-case study of 703 lethal PCa patients and 1455 patients with low-risk localized PCa of European, African, and Chinese origin was performed. Germline DNA samples from these patients were sequenced for CHEK2. Mutation carrier rates and their association with lethal PCa were analyzed using the Fisher exact test and Kaplan-Meier survival analysis. In the entire study population, 40 (1.85%) patients were identified as carrying one of 15 different germline CHEK2 pathogenic or likely pathogenic mutations. CHEK2 mutations were detected in 16 (2.28%) of 703 lethal PCa patients compared with 24 (1.65%) of 1455 low-risk PCa patients (P = 0.31). No association was found between CHEK2 mutation status and early-diagnosis or PCa-specific survival time. However, the most common mutation in CHEK2, c.1100delC (p.T367 fs), had a significantly higher carrier rate (1.28%) in lethal PCa patients than low-risk PCa patients of European American origin (0.16%), P = 0.0038. The estimated Odds Ratio of this mutation for lethal PCa was 7.86. The carrier rate in lethal PCa was also significantly higher than that (0.46%) in 32 461 non-Finnish European subjects from the Exome Aggregation Consortium (ExAC) (P = 0.01). While overall CHEK2 mutations were not significantly more common in men with lethal compared to low-risk PCa, the specific CHEK2 mutation, c.1100delC, appears to contribute to an increased risk of lethal PCa in European American men. © 2018 Wiley Periodicals, Inc.

Sparse principal component analysis in medical shape modeling

NASA Astrophysics Data System (ADS)

Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus

2006-03-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis

PubMed Central

Zheng, Lu-Lu; Niu, Shen; Hao, Pei; Feng, KaiYan; Cai, Yu-Dong; Li, Yixue

2011-01-01

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations. PMID:22174779

A new statistical PCA-ICA algorithm for location of R-peaks in ECG.

PubMed

Chawla, M P S; Verma, H K; Kumar, Vinod

2008-09-16

The success of ICA to separate the independent components from the mixture depends on the properties of the electrocardiogram (ECG) recordings. This paper discusses some of the conditions of independent component analysis (ICA) that could affect the reliability of the separation and evaluation of issues related to the properties of the signals and number of sources. Principal component analysis (PCA) scatter plots are plotted to indicate the diagnostic features in the presence and absence of base-line wander in interpreting the ECG signals. In this analysis, a newly developed statistical algorithm by authors, based on the use of combined PCA-ICA for two correlated channels of 12-channel ECG data is proposed. ICA technique has been successfully implemented in identifying and removal of noise and artifacts from ECG signals. Cleaned ECG signals are obtained using statistical measures like kurtosis and variance of variance after ICA processing. This analysis also paper deals with the detection of QRS complexes in electrocardiograms using combined PCA-ICA algorithm. The efficacy of the combined PCA-ICA algorithm lies in the fact that the location of the R-peaks is bounded from above and below by the location of the cross-over points, hence none of the peaks are ignored or missed.

Variants on 8q24 and prostate cancer risk in Chinese population: a meta-analysis.

PubMed

Ren, Xiao-Qiang; Zhang, Jian-Guo; Xin, Shi-Yong; Cheng, Tao; Li, Liang; Ren, Wei-Hua

2015-01-01

Previous studies have identified 8q24 as an important region to prostate cancer (PCa) susceptibility. The aim of this study was to investigate the role of six genetic variants on 8q24 (rs1447295, A; rs6983267, G; rs6983561, C; rs7837688, T; rs10090154, T and rs16901979, A) on PCa risk in Chinese population. Online electronic databases were searched to retrieve related articles concerning the association between 8q24 variants and PCa risk in men of Chinese population published between 2000 and 2014. Odds ratio (ORs) with its 95% correspondence interval (CI) were employed to assess the strength of association. Total eleven case-control studies were screened out, including 2624 PCa patients and 2438 healthy controls. Our results showed that three risk alleles of rs1447295 A (OR=1.35, 95% CI=1.19-1.53, P<0.00001), rs6983561 C (C vs. A: OR=1.41, 95% CI=1.21-1.63, P<0.00001) and rs10090154 T (T vs. C: OR=1.48, 95% CI=1.22-1.80, P<0.00001) on8q24 were significantly associated with PCa risk in Chinese population. Furthermore, genotypes of rs1447295, AA+AC; rs6983561, CC+AC and CC; rs10090154, TT+TC; and rs16901979, AA were associated with PCa as well (P<0.01). No association was found between rs6983267, rs7837688 and PCa risk. In conclusions, variants including rs1447295, rs6983561, rs10090154 and rs16901979 on 8q24 might be associated with PCa risk in Chinese population, indicating these four variations may contribute risk to this disease. This meta-analysis was the first study to assess the role of 8q24 variants on PCa risk in Chinese population.

An Estimate of the Incidence of Prostate Cancer in Africa: A Systematic Review and Meta-Analysis

PubMed Central

Aderemi, Adewale Victor; Iseolorunkanmi, Alexander; Oyedokun, Ayo; Ayo, Charles K.

2016-01-01

Background Prostate cancer (PCa) is rated the second most common cancer and sixth leading cause of cancer deaths among men globally. Reports show that African men suffer disproportionately from PCa compared to men from other parts of the world. It is still quite difficult to accurately describe the burden of PCa in Africa due to poor cancer registration systems. We systematically reviewed the literature on prostate cancer in Africa and provided a continent-wide incidence rate of PCa based on available data in the region. Methods A systematic literature search of Medline, EMBASE and Global Health from January 1980 to June 2015 was conducted, with additional search of Google Scholar, International Association of Cancer Registries (IACR), International Agency for Research on Cancer (IARC), and WHO African region websites, for studies that estimated incidence rate of PCa in any African location. Having assessed quality and consistency across selected studies, we extracted incidence rates of PCa and conducted a random effects meta-analysis. Results Our search returned 9766 records, with 40 studies spreading across 16 African countries meeting our selection criteria. We estimated a pooled PCa incidence rate of 22.0 (95% CI: 19.93–23.97) per 100,000 population, and also reported a median incidence rate of 19.5 per 100,000 population. We observed an increasing trend in PCa incidence with advancing age, and over the main years covered. Conclusion Effective cancer registration and extensive research are vital to appropriately quantifying PCa burden in Africa. We hope our findings may further assist at identifying relevant gaps, and contribute to improving knowledge, research, and interventions targeted at prostate cancer in Africa. PMID:27073921

Decision tree and PCA-based fault diagnosis of rotating machinery

NASA Astrophysics Data System (ADS)

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.

On a PCA-based lung motion model

NASA Astrophysics Data System (ADS)

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

2011-09-01

Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772-81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921-9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1 mm (0.7 ± 0.1 mm). When a single artificial internal marker was used to derive the lung motion, the average 3D error was found to be within 2 mm (1.8 ± 0.3 mm) through comprehensive statistical analysis. The optimal number of PCA coefficients needs to be determined on a patient-by-patient basis and two PCA coefficients seem to be sufficient for accurate modeling of the lung motion for most patients. In conclusion, we have presented thorough theoretical analysis and clinical validation of the PCA lung motion model. The feasibility of deriving the entire lung motion using a single marker has also been demonstrated on clinical data using a simulation approach.

Fuzzy clustering evaluation of the discrimination power of UV-Vis and (±) ESI-MS detection system in individual or coupled RPLC for characterization of Ginkgo Biloba standardized extracts.

PubMed

Medvedovici, Andrei; Albu, Florin; Naşcu-Briciu, Rodica Domnica; Sârbu, Costel

2014-02-01

Discrimination power evaluation of UV-Vis and (±) electrospray ionization/mass spectrometric techniques, (ESI-MS) individually considered or coupled as detectors to reversed phase liquid chromatography (RPLC) in the characterization of Ginkgo Biloba standardized extracts, is used in herbal medicines and/or dietary supplements with the help of Fuzzy hierarchical clustering (FHC). Seventeen batches of Ginkgo Biloba commercially available standardized extracts from seven manufacturers were measured during experiments. All extracts were within the criteria of the official monograph dedicated to dried refined and quantified Ginkgo extracts, in the European Pharmacopoeia. UV-Vis and (±) ESI-MS spectra of the bulk standardized extracts in methanol were acquired. Additionally, an RPLC separation based on a simple gradient elution profile was applied to the standardized extracts. Detection was made through monitoring UV absorption at 220 nm wavelength or the total ion current (TIC) produced through (±) ESI-MS analysis. FHC was applied to raw, centered and scaled data sets, for evaluating the discrimination power of the method with respect to the origins of the extracts and to the batch to batch variability. The discrimination power increases with the increase of the intrinsic selectivity of the spectral technique being used: UV-Vis

The influence of stigma on the quality of life for prostate cancer survivors.

PubMed

Wood, Andrew W; Barden, Sejal; Terk, Mitchell; Cesaretti, Jamie

2017-01-01

The purpose of the present study was to investigate the influence of stigma on prostate cancer (PCa) survivors' quality of life. Stigma for lung cancer survivors has been the focus of considerable research (Else-Quest & Jackson, 2014); however, gaps remain in understanding the experience of PCa stigma. A cross-sectional correlational study was designed to assess the incidence of PCa stigma and its influence on the quality of life of survivors. Eighty-five PCa survivors were administered survey packets consisting of a stigma measure, a PCa-specific quality of life measure, and a demographic survey during treatment of their disease. A linear regression analysis was conducted with the data received from PCa survivors. Results indicated that PCa stigma has a significant, negative influence on the quality of life for survivors (R 2 = 0.33, F(4, 80) = 11.53, p < 0.001). There were no statistically significant differences in PCa stigma based on demographic variables (e.g., race and age). Implications for physical and mental health practitioners and researchers are discussed.

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.

PubMed

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. .

A stable systemic risk ranking in China's banking sector: Based on principal component analysis

NASA Astrophysics Data System (ADS)

Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing

2018-02-01

In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.

Comparison of water extraction methods in Tibet based on GF-1 data

NASA Astrophysics Data System (ADS)

Jia, Lingjun; Shang, Kun; Liu, Jing; Sun, Zhongqing

2018-03-01

In this study, we compared four different water extraction methods with GF-1 data according to different water types in Tibet, including Support Vector Machine (SVM), Principal Component Analysis (PCA), Decision Tree Classifier based on False Normalized Difference Water Index (FNDWI-DTC), and PCA-SVM. The results show that all of the four methods can extract large area water body, but only SVM and PCA-SVM can obtain satisfying extraction results for small size water body. The methods were evaluated by both overall accuracy (OAA) and Kappa coefficient (KC). The OAA of PCA-SVM, SVM, FNDWI-DTC, PCA are 96.68%, 94.23%, 93.99%, 93.01%, and the KCs are 0.9308, 0.8995, 0.8962, 0.8842, respectively, in consistent with visual inspection. In summary, SVM is better for narrow rivers extraction and PCA-SVM is suitable for water extraction of various types. As for dark blue lakes, the methods using PCA can extract more quickly and accurately.

The Children, Intimate Relationships, and Conflictual Life Events (CIRCLE) interview for simultaneous measurement of intimate partner and parent to child aggression.

PubMed

Marshall, Amy D; Feinberg, Mark E; Jones, Damon E; Chote, Daniel R

2017-08-01

Despite substantial rates of parent to child aggression (PCA) and intimate partner aggression (IPA) co-occurrence within families, the co-occurrence of PCA and IPA within incidents of aggression has not previously been examined. To do so, we developed the Children, Intimate Relationships, and Conflictual Life Events (CIRCLE) interview to simultaneously measure incidents of psychological and physical PCA and IPA. The CIRCLE interview was administered quarterly for approximately 1 year to 109 women and 94 men from 111 couples with a first born child approximately 32 months of age at study initiation. Demonstrating the CIRCLE interview's ability to yield new knowledge about the nature of family aggression, we describe the frequency of aggressive incidents, the average number of aggressive behaviors within incidents, the daily occurrence of multiple aggressive incidents, and rates of within-incident PCA and IPA co-occurrence. With the exception of men's physical IPA, aggression scores derived from the CIRCLE interview exhibited a relatively high degree of interpartner reporting concordance, as well as structural validity and convergent validity with common aggression measures. Aggression reports via repeated testing were not influenced by social desirability or attempts to avoid aggression. Participants who perceived enhanced memory for aggression as a function of study participation reported increasing PCA and IPA frequencies over time. In the prediction of child conduct and emotional problems, the CIRCLE interview demonstrated predictive validity and incremental validity over traditional aggression measures. For the first time, within-incident co-occurrence of PCA and IPA was documented and shown to uniquely impact child outcomes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Development status of the small community solar power system

NASA Technical Reports Server (NTRS)

Pons, R. L.

1982-01-01

The development status and test results for the Small Community Solar Thermal Power Experiment are presented. Activities on the phase 2 power module development effort are presented with emphasis on the receiver, the plant control subsystem, and the energy transport subsystem. The components include a single prototype power module consisting of a parabolic dish concentrator, a power conversion assembly (PCA), and a multiple-module plant control subsystem. The PCA consists of a cavity receiver coupled to an organic Rankine cycle engine-alternator unit defined as the power conversion subsystem; the PCA is mounted at the focus of a parabolic dish concentrator. At a solar insolation of 100 W/sq m and ambient temperature of 28 C (82 F), the power module produces approximately 20 kW of 3-phase, 3 kHz ac power, depending on the concentrator employed. A ground-mounted rectifier to the central collection site where it is supplied directly to the common dc bus which collects the power from all modules in the plant.

The androgen receptor controls expression of the cancer-associated sTn antigen and cell adhesion through induction of ST6GalNAc1 in prostate cancer

PubMed Central

Munkley, Jennifer; Oltean, Sebastian; Vodák, Daniel; Wilson, Brian T.; Livermore, Karen E.; Zhou, Yan; Star, Eleanor; Floros, Vasileios I.; Johannessen, Bjarne; Knight, Bridget; McCullagh, Paul; McGrath, John; Crundwell, Malcolm; Skotheim, Rolf I.; Robson, Craig N.; Leung, Hing Y.; Harries, Lorna W.; Rajan, Prabhakar; Mills, Ian G.; Elliott, David J.

2015-01-01

Patterns of glycosylation are important in cancer, but the molecular mechanisms that drive changes are often poorly understood. The androgen receptor drives prostate cancer (PCa) development and progression to lethal metastatic castration-resistant disease. Here we used RNA-Seq coupled with bioinformatic analyses of androgen-receptor (AR) binding sites and clinical PCa expression array data to identify ST6GalNAc1 as a direct and rapidly activated target gene of the AR in PCa cells. ST6GalNAc1 encodes a sialytransferase that catalyses formation of the cancer-associated sialyl-Tn antigen (sTn), which we find is also induced by androgen exposure. Androgens induce expression of a novel splice variant of the ST6GalNAc1 protein in PCa cells. This splice variant encodes a shorter protein isoform that is still fully functional as a sialyltransferase and able to induce expression of the sTn-antigen. Surprisingly, given its high expression in tumours, stable expression of ST6GalNAc1 in PCa cells reduced formation of stable tumours in mice, reduced cell adhesion and induced a switch towards a more mesenchymal-like cell phenotype in vitro. ST6GalNAc1 has a dynamic expression pattern in clinical datasets, being significantly up-regulated in primary prostate carcinoma but relatively down-regulated in established metastatic tissue. ST6GalNAc1 is frequently upregulated concurrently with another important glycosylation enzyme GCNT1 previously associated with prostate cancer progression and implicated in Sialyl Lewis X antigen synthesis. Together our data establishes an androgen-dependent mechanism for sTn antigen expression in PCa, and are consistent with a general role for the androgen receptor in driving important coordinate changes to the glycoproteome during PCa progression. PMID:26452038

Stacking and determination of phenazine-1-carboxylic acid with low pKa in soil via moving reaction boundary formed by alkaline and double acidic buffers in capillary electrophoresis.

PubMed

Sun, Chong; Yang, Xiao-Di; Fan, Liu-Yin; Zhang, Wei; Xu, Yu-Quan; Cao, Cheng-Xi

2011-04-01

As shown herein, a normal moving reaction boundary (MRB) formed by an alkaline buffer and a single acidic buffer had poor stacking to the new important plant growth promoter of phenazine-1-carboxylic acid (PCA) in soil due to the leak induced by its low pK(a). To stack the PCA with low pK(a) efficiently, a novel stacking system of MRB was developed, which was formed by an alkaline buffer and double acidic buffers (viz., acidic sample and blank buffers). With the novel system, the PCA leaking into the blank buffer from the sample buffer could be well stacked by the prolonged MRB formed between the alkaline buffer and blank buffer. The relevant mechanism of stacking was discussed briefly. The stacking system, coupled with sample pretreatment, could achieve a 214-fold increase of PCA sensitivity under the optimal conditions (15 mM (pH 11.5) Gly-NaOH as the alkaline buffer, 15 mM (pH 3.0) Gly-HCl-acetonitrile (20%, v/v) as the acidic sample buffer, 15 mM (pH 3.0) Gly-HCl as the blank buffer, 3 min 13 mbar injection of double acidic buffers, benzoic acid as the internal standard, 75 μm i.d. × 53 cm (44 cm effective length) capillary, 25 kV and 248 nm). The limit of detection of PCA in soil was decreased to 17 ng/g, the intra-day and inter-day precision values (expressed as relative standard deviations) were 3.17-4.24% and 4.17-4.87%, respectively, and the recoveries of PCA at three concentration levels changed from 52.20% to 102.61%. The developed method could be used for the detection of PCA in soil at trace level.

Analysis of cytokine release assay data using machine learning approaches.

PubMed

Xiong, Feiyu; Janko, Marco; Walker, Mindi; Makropoulos, Dorie; Weinstock, Daniel; Kam, Moshe; Hrebien, Leonid

2014-10-01

The possible onset of Cytokine Release Syndrome (CRS) is an important consideration in the development of monoclonal antibody (mAb) therapeutics. In this study, several machine learning approaches are used to analyze CRS data. The analyzed data come from a human blood in vitro assay which was used to assess the potential of mAb-based therapeutics to produce cytokine release similar to that induced by Anti-CD28 superagonistic (Anti-CD28 SA) mAbs. The data contain 7 mAbs and two negative controls, a total of 423 samples coming from 44 donors. Three (3) machine learning approaches were applied in combination to observations obtained from that assay, namely (i) Hierarchical Cluster Analysis (HCA); (ii) Principal Component Analysis (PCA) followed by K-means clustering; and (iii) Decision Tree Classification (DTC). All three approaches were able to identify the treatment that caused the most severe cytokine response. HCA was able to provide information about the expected number of clusters in the data. PCA coupled with K-means clustering allowed classification of treatments sample by sample, and visualizing clusters of treatments. DTC models showed the relative importance of various cytokines such as IFN-γ, TNF-α and IL-10 to CRS. The use of these approaches in tandem provides better selection of parameters for one method based on outcomes from another, and an overall improved analysis of the data through complementary approaches. Moreover, the DTC analysis showed in addition that IL-17 may be correlated with CRS reactions, although this correlation has not yet been corroborated in the literature. Copyright © 2014 Elsevier B.V. All rights reserved.

Cell cycle-coupled expansion of AR activity promotes cancer progression.

PubMed

McNair, C; Urbanucci, A; Comstock, C E S; Augello, M A; Goodwin, J F; Launchbury, R; Zhao, S G; Schiewer, M J; Ertel, A; Karnes, J; Davicioni, E; Wang, L; Wang, Q; Mills, I G; Feng, F Y; Li, W; Carroll, J S; Knudsen, K E

2017-03-23

The androgen receptor (AR) is required for prostate cancer (PCa) survival and progression, and ablation of AR activity is the first line of therapeutic intervention for disseminated disease. While initially effective, recurrent tumors ultimately arise for which there is no durable cure. Despite the dependence of PCa on AR activity throughout the course of disease, delineation of the AR-dependent transcriptional network that governs disease progression remains elusive, and the function of AR in mitotically active cells is not well understood. Analyzing AR activity as a function of cell cycle revealed an unexpected and highly expanded repertoire of AR-regulated gene networks in actively cycling cells. New AR functions segregated into two major clusters: those that are specific to cycling cells and retained throughout the mitotic cell cycle ('Cell Cycle Common'), versus those that were specifically enriched in a subset of cell cycle phases ('Phase Restricted'). Further analyses identified previously unrecognized AR functions in major pathways associated with clinical PCa progression. Illustrating the impact of these unmasked AR-driven pathways, dihydroceramide desaturase 1 was identified as an AR-regulated gene in mitotically active cells that promoted pro-metastatic phenotypes, and in advanced PCa proved to be highly associated with development of metastases, recurrence after therapeutic intervention and reduced overall survival. Taken together, these findings delineate AR function in mitotically active tumor cells, thus providing critical insight into the molecular basis by which AR promotes development of lethal PCa and nominate new avenues for therapeutic intervention.

Combined serum and EPS-urine proteomic analysis using iTRAQ technology for discovery of potential prostate cancer biomarkers.

PubMed

Zhang, Mo; Chen, Lizhu; Yuan, Zhengwei; Yang, Zeyu; Li, Yue; Shan, Liping; Yin, Bo; Fei, Xiang; Miao, Jianing; Song, Yongsheng

2016-11-01

Prostate cancer (PCa) is one of the most common malignant tumors and a major cause of cancer-related death for men worldwide. The aim of our study was to identify potential non-invasive serum and expressed prostatic secretion (EPS)-urine biomarkers for accurate diagnosis of PCa. Here, we performed a combined isobaric tags for relative and absolute quantification (iTRAQ) proteomic analysis to compare protein profiles using pooled serum and EPS-urine samples from 4 groups of patients: benign prostate hyperplasia (BPH), high grade prostatic intraepithelial neoplasia (HGPIN), localized PCa and metastatic PCa. The differentially expressed proteins were rigorously selected and further validated in a large and independent cohort using classical ELISA and Western blot assays. Finally, we established a multiplex biomarker panel consisting of 3 proteins (serum PF4V1, PSA, and urinary CRISP3) with an excellent diagnostic capacity to differentiate PCa from BPH [area under the receiver operating characteristic curve (AUC) of 0.941], which showed an evidently greater discriminatory ability than PSA alone (AUC, 0.757) (P<0.001). Importantly, even when PSA level was in the gray zone (4-10 ng/mL), a combination of PF4V1 and CRISP3 could achieve a relatively high diagnostic efficacy (AUC, 0.895). Furthermore, their combination also had the potential to distinguish PCa from HGPIN (AUC, 0.934). Our results demonstrated that the combined application of serum and EPS-urine biomarkers can improve the diagnosis of PCa and provide a new prospect for non-invasive PCa detection.

Characterization of Argentine honeys on the basis of their mineral content and some typical quality parameters

PubMed Central

2014-01-01

Background The levels of 19 elements (As, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Se, Tl, U, V, Zn) from sixteen different Argentine production sites of unifloral [eucalyptus (Eucaliptus rostrata), chilca (Baccharis salicifolia), Algarrobo (Prosopis sp.), mistol (Ziziphus mistol) and citric] and multifloral honeys were measured with the aim to test the quality of the selected samples. Typical quality parameters of honeys were also determined (pH, sugar content, moisture). Mineral elements were determined by using inductively coupled plasma mass spectrometer (ICP-MS DRC). We also evaluated the suitability of honey as a possible biomonitor of environmental pollution. Thus, the sites were classified through cluster analysis (CA) and then pattern recognition methods such as Principal Component Analysis (PCA) and discriminant analysis (DA) were applied. Results Mean values for quality parameters were: pH, 4.12 and 3.81; sugar 82.1 and 82.0 °brix; moisture, 16.90 and 17.00% for unifloral and multifloral honeys respectively. The water content showed good maturity. Likewise, the other parameters confirmed the good quality of the honeys analysed. Potassium was quantitatively the most abundant metal, accounting for 92,5% of the total metal contents with an average concentration of 832.0 and 816.2 μg g-1 for unifloral and multifloral honeys respectively. Sodium was the second most abundant major metal in honeys with a mean value of 32.16 and 33.19 μg g-1 for unifloral and multifloral honeys respectively. Mg, Ca, Fe, Mn, Zn and Cu were present at low-intermediate concentrations. For the other 11 trace elements determined in this study (As, Be, Cd, Co, Cr, Ni, Pb, Se, Tl, U and V), the mean concentrations were very low or below of the LODs. The sites were classified through CA by using elements’ and physicochemical parameters data, then DA on the PCA factors was applied. Dendrograms identified three main groups. PCA explained 52.03% of the total variability with the first two factors. Conclusions In general, there are no evidences of pollution for the analysed honeys. The analytical results obtained for the Argentine honeys indicate the products’ high quality. In fact, most of the toxic elements were below LODs. The chemometric analysis combining CA, DA and PCA showed their aptness as useful tools for honey’s classification. Eventually, this study confirms that the use of honey as biomonitor of environmental contamination is not reliable for sites with low levels of contamination. PMID:25057287

Flight test of a propulsion controlled aircraft system on the NASA F-15 airplane

NASA Technical Reports Server (NTRS)

Burcham, Frank W., Jr.; Maine, Trindel A.

1995-01-01

Flight tests of the propulsion controlled aircraft (PCA) system on the NASA F-15 airplane evolved as a result of a long series of simulation and flight tests. Initially, the simulation results were very optimistic. Early flight tests showed that manual throttles-only control was much more difficult than the simulation, and a flight investigation was flown to acquire data to resolve this discrepancy. The PCA system designed and developed by MDA evolved as these discrepancies were found and resolved, requiring redesign of the PCA software and modification of the flight test plan. Small throttle step inputs were flown to provide data for analysis, simulation update, and control logic modification. The PCA flight tests quickly revealed less than desired performance, but the extensive flexibility built into the flight PCA software allowed rapid evaluation of alternate gains, filters, and control logic, and within 2 weeks, the PCA system was functioning well. The initial objective of achieving adequate control for up-and-away flying and approaches was satisfied, and the option to continue to actual landings was achieved. After the PCA landings were accomplished, other PCA features were added, and additional maneuvers beyond those originally planned were flown. The PCA system was used to recover from extreme upset conditions, descend, and make approaches to landing. A heading mode was added, and a single engine plus rudder PCA mode was also added and flown. The PCA flight envelope was expanded far beyond that originally designed for. Guest pilots from the USAF, USN, NASA, and the contractor also flew the PCA system and were favorably impressed.

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

PubMed Central

Thomas, John E.; Sem, Daniel S.

2009-01-01

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

Direct analysis in real time mass spectrometry and multivariate data analysis: a novel approach to rapid identification of analytical markers for quality control of traditional Chinese medicine preparation.

PubMed

Zeng, Shanshan; Wang, Lu; Chen, Teng; Wang, Yuefei; Mo, Huanbiao; Qu, Haibin

2012-07-06

The paper presents a novel strategy to identify analytical markers of traditional Chinese medicine preparation (TCMP) rapidly via direct analysis in real time mass spectrometry (DART-MS). A commonly used TCMP, Danshen injection, was employed as a model. The optimal analysis conditions were achieved by measuring the contribution of various experimental parameters to the mass spectra. Salvianolic acids and saccharides were simultaneously determined within a single 1-min DART-MS run. Furthermore, spectra of Danshen injections supplied by five manufacturers were processed with principal component analysis (PCA). Obvious clustering was observed in the PCA score plot, and candidate markers were recognized from the contribution plots of PCA. The suitability of potential markers was then confirmed by contrasting with the results of traditional analysis methods. Using this strategy, fructose, glucose, sucrose, protocatechuic aldehyde and salvianolic acid A were rapidly identified as the markers of Danshen injections. The combination of DART-MS with PCA provides a reliable approach to the identification of analytical markers for quality control of TCMP. Copyright © 2012 Elsevier B.V. All rights reserved.

Multivariate statistical analysis of the polyphenolic constituents in kiwifruit juices to trace fruit varieties and geographical origins.

PubMed

Guo, Jing; Yuan, Yahong; Dou, Pei; Yue, Tianli

2017-10-01

Fifty-one kiwifruit juice samples of seven kiwifruit varieties from five regions in China were analyzed to determine their polyphenols contents and to trace fruit varieties and geographical origins by multivariate statistical analysis. Twenty-one polyphenols belonging to four compound classes were determined by ultra-high-performance liquid chromatography coupled with ultra-high-resolution TOF mass spectrometry. (-)-Epicatechin, (+)-catechin, procyanidin B1 and caffeic acid derivatives were the predominant phenolic compounds in the juices. Principal component analysis (PCA) allowed a clear separation of the juices according to kiwifruit varieties. Stepwise linear discriminant analysis (SLDA) yielded satisfactory categorization of samples, provided 100% success rate according to kiwifruit varieties and 92.2% success rate according to geographical origins. The result showed that polyphenolic profiles of kiwifruit juices contain enough information to trace fruit varieties and geographical origins. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

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.

[Analyzing and modeling methods of near infrared spectroscopy for in-situ prediction of oil yield from oil shale].

PubMed

Liu, Jie; Zhang, Fu-Dong; Teng, Fei; Li, Jun; Wang, Zhi-Hong

2014-10-01

In order to in-situ detect the oil yield of oil shale, based on portable near infrared spectroscopy analytical technology, with 66 rock core samples from No. 2 well drilling of Fuyu oil shale base in Jilin, the modeling and analyzing methods for in-situ detection were researched. By the developed portable spectrometer, 3 data formats (reflectance, absorbance and K-M function) spectra were acquired. With 4 different modeling data optimization methods: principal component-mahalanobis distance (PCA-MD) for eliminating abnormal samples, uninformative variables elimination (UVE) for wavelength selection and their combina- tions: PCA-MD + UVE and UVE + PCA-MD, 2 modeling methods: partial least square (PLS) and back propagation artificial neural network (BPANN), and the same data pre-processing, the modeling and analyzing experiment were performed to determine the optimum analysis model and method. The results show that the data format, modeling data optimization method and modeling method all affect the analysis precision of model. Results show that whether or not using the optimization method, reflectance or K-M function is the proper spectrum format of the modeling database for two modeling methods. Using two different modeling methods and four different data optimization methods, the model precisions of the same modeling database are different. For PLS modeling method, the PCA-MD and UVE + PCA-MD data optimization methods can improve the modeling precision of database using K-M function spectrum data format. For BPANN modeling method, UVE, UVE + PCA-MD and PCA- MD + UVE data optimization methods can improve the modeling precision of database using any of the 3 spectrum data formats. In addition to using the reflectance spectra and PCA-MD data optimization method, modeling precision by BPANN method is better than that by PLS method. And modeling with reflectance spectra, UVE optimization method and BPANN modeling method, the model gets the highest analysis precision, its correlation coefficient (Rp) is 0.92, and its standard error of prediction (SEP) is 0.69%.

Low-Molecular-Weight Protein Tyrosine Phosphatase Predicts Prostate Cancer Outcome by Increasing the Metastatic Potential.

PubMed

Ruela-de-Sousa, Roberta R; Hoekstra, Elmer; Hoogland, A Marije; Queiroz, Karla C Souza; Peppelenbosch, Maikel P; Stubbs, Andrew P; Pelizzaro-Rocha, Karin; van Leenders, Geert J L H; Jenster, Guido; Aoyama, Hiroshi; Ferreira, Carmen V; Fuhler, Gwenny M

2016-04-01

Low-risk patients suffering from prostate cancer (PCa) are currently placed under active surveillance rather than undergoing radical prostatectomy. However, clear parameters for selecting the right patient for each strategy are not available, and new biomarkers and treatment modalities are needed. Low-molecular-weight protein tyrosine phosphatase (LMWPTP) could present such a target. To correlate expression levels of LMWPTP in primary PCa to clinical outcome, and determine the role of LMWPTP in prostate tumor cell biology. Acid phosphatase 1, soluble (ACP1) expression was analyzed on microarray data sets, which were subsequently used in Ingenuity Pathway Analysis. Immunohistochemistry was performed on a tissue microarray containing material of 481 PCa patients whose clinicopathologic data were recorded. PCa cell line models were used to investigate the role of LMWPTP in cell proliferation, migration, adhesion, and anoikis resistance. The association between LMWPTP expression and clinical and pathologic outcomes was calculated using chi-square correlations and multivariable Cox regression analysis. Functional consequences of LMWPTP overexpression or downregulation were determined using migration and adhesion assays, confocal microscopy, Western blotting, and proliferation assays. LMWPTP expression was significantly increased in human PCa and correlated with earlier recurrence of disease (hazard ratio [HR]:1.99; p<0.001) and reduced patient survival (HR: 1.53; p=0.04). Unbiased Ingenuity analysis comparing cancer and normal prostate suggests migratory propensities in PCa. Indeed, overexpression of LMWPTP increases PCa cell migration, anoikis resistance, and reduces activation of focal adhesion kinase/paxillin, corresponding to decreased adherence. Overexpression of LMWPTP in PCa confers a malignant phenotype with worse clinical outcome. Prospective follow-up should determine the clinical potential of LMWPTP overexpression. These findings implicate low-molecular-weight protein tyrosine phosphatase as a novel oncogene in prostate cancer and could offer the possibility of using this protein as biomarker or target for treatment of this disease. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Near-Infrared Neodymium Tag for Quantifying Targeted Biomarker and Counting Its Host Circulating Tumor Cells.

PubMed

Liu, Chunlan; Lu, Shu; Yang, Limin; Chen, Peijie; Bai, Peiming; Wang, Qiuquan

2017-09-05

Quantitative information on a targeted analyte in a complex biological system is the most basic premise for understanding its involved mechanisms, and thus precise diagnosis of a disease if it is a so-called biomarker. Here, we designed and synthesized a neodymium (Nd)-cored tag [1,4,7,10-tetraazacyclododecane-1,4,7-trisacetic acid (DOTA)-Nd complex together with a light-harvesting antenna aminofluorescein (AMF, λ ex/em = 494/520 nm), AMF-DOTA-Nd] with duplex signals, second near-infrared (NIR) window luminescence (λ em = 1065 nm, 2.5 μs), and stable isotopic mass ( 142 Nd). AMF-DOTA-Nd covalently linked with a urea-based peptidomimetic targeting group, 2-[3-(1,3-dicarboxypropyl)-ureido]pentanedioic acid (DUPA)-8-Aoc-Phe-Phe-Cys (DUPAaFFC) (DUPAaFFC-AMF-DOTA-Nd), allowing us to detect and quantify prostate-specific membrane antigen (PSMA) and its splice variants (total PSMA, tPSMA), which was set as an example of targeted biomarkers in this study, using NIR and inductively coupled plasma mass spectrometry (ICPMS) with the limit of detection (LOD) (3σ) of 0.3 ng/mL. When it was applied to the analysis of 80 blood samples from prostate cancer (PCa) and benign prostatic hyperplasia (BPH) patients as well as healthy volunteers, we found that 320 and 600 ng/mL tPSMA could be recommended as the threshold values to differentiate BPH from PCa and for the diagnosis of PCa. Moreover, PSMA-positive circulating tumor cells (CTCs) were counted using ICPMS being from 134 to 773 CTCs in the PCa blood samples of the Gleason score from 6 to 9 when the cell membrane-spanning mPSMA was tagged. Such a methodology developed could be expected to be applicable to other clinic-meaningful biomolecules and their host CTCs in liquid biopsy, when other specific targeting groups are modified to the NIR Nd tag.

[Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology].

PubMed

Li, Xun-lan; Yi, Shi-lai; He, Shao-lan; Lü, Qiang; Xie, Rang-jin; Zheng, Yong-qiang; Deng, Lie

2015-09-01

Existing methods for the identification of pummelo cultivars are usually time-consuming and costly, and are therefore inconvenient to be used in cases that a rapid identification is needed. This research was aimed at identifying different pummelo cultivars by hyperspectral imaging technology which can achieve a rapid and highly sensitive measurement. A total of 240 leaf samples, 60 for each of the four cultivars were investigated. Samples were divided into two groups such as calibration set (48 samples of each cultivar) and validation set (12 samples of each cultivar) by a Kennard-Stone-based algorithm. Hyperspectral images of both adaxial and abaxial surfaces of each leaf were obtained, and were segmented into a region of interest (ROI) using a simple threshold. Spectra of leaf samples were extracted from ROI. To remove the absolute noises of the spectra, only the date of spectral range 400~1000 nm was used for analysis. Multiplicative scatter correction (MSC) and standard normal variable (SNV) were utilized for data preprocessing. Principal component analysis (PCA) was used to extract the best principal components, and successive projections algorithm (SPA) was used to extract the effective wavelengths. Least squares support vector machine (LS-SVM) was used to obtain the discrimination model of the four different pummelo cultivars. To find out the optimal values of σ2 and γ which were important parameters in LS-SVM modeling, Grid-search technique and Cross-Validation were applied. The first 10 and 11 principal components were extracted by PCA for the hyperspectral data of adaxial surface and abaxial surface, respectively. There were 31 and 21 effective wavelengths selected by SPA based on the hyperspectral data of adaxial surface and abaxial surface, respectively. The best principal components and the effective wavelengths were used as inputs of LS-SVM models, and then the PCA-LS-SVM model and the SPA-LS-SVM model were built. The results showed that 99.46% and 98.44% of identification accuracy was achieved in the calibration set for the PCA-LS-SVM model and the SPA-LS-SVM model, respectively, and a 95.83% of identification accuracy was achieved in the validation set for both the PCA-LS-SVM and the SPA- LS-SVM models, which were built based on the hyperspectral data of adaxial surface. Comparatively, the results of the PCA-LS-SVM and the SPA-LS-SVM models built based on the hyperspectral data of abaxial surface both achieved identification accuracies of 100% for both calibration set and validation set. The overall results demonstrated that use of hyperspectral data of adaxial and abaxial leaf surfaces coupled with the use of PCA-LS-SVM and the SPA-LS-SVM could achieve an accurate identification of pummelo cultivars. It was feasible to use hyperspectral imaging technology to identify different pummelo cultivars, and hyperspectral imaging technology provided an alternate way of rapid identification of pummelo cultivars. Moreover, the results in this paper demonstrated that the data from the abaxial surface of leaf was more sensitive in identifying pummelo cultivars. This study provided a new method for to the fast discrimination of pummelo cultivars.

Contrast-Enhanced Ultrasound (CEUS) and Quantitative Perfusion Analysis in Patients with Suspicion for Prostate Cancer.

PubMed

Maxeiner, Andreas; Fischer, Thomas; Schwabe, Julia; Baur, Alexander Daniel Jacques; Stephan, Carsten; Peters, Robert; Slowinski, Torsten; von Laffert, Maximilian; Marticorena Garcia, Stephan Rodrigo; Hamm, Bernd; Jung, Ernst-Michael

2018-06-06

 The aim of this study was to investigate contrast-enhanced ultrasound (CEUS) parameters acquired by software during magnetic resonance imaging (MRI) US fusion-guided biopsy for prostate cancer (PCa) detection and discrimination.  From 2012 to 2015, 158 out of 165 men with suspicion for PCa and with at least 1 negative biopsy of the prostate were included and underwent a multi-parametric 3 Tesla MRI and an MRI/US fusion-guided biopsy, consecutively. CEUS was conducted during biopsy with intravenous bolus application of 2.4 mL of SonoVue ® (Bracco, Milan, Italy). In the latter CEUS clips were investigated using quantitative perfusion analysis software (VueBox, Bracco). The area of strongest enhancement within the MRI pre-located region was investigated and all available parameters from the quantification tool box were collected and analyzed for PCa and its further differentiation was based on the histopathological results.  The overall detection rate was 74 (47 %) PCa cases in 158 included patients. From these 74 PCa cases, 49 (66 %) were graded Gleason ≥ 3 + 4 = 7 (ISUP ≥ 2) PCa. The best results for cancer detection over all quantitative perfusion parameters were rise time (p = 0.026) and time to peak (p = 0.037). Within the subgroup analysis (> vs ≤ 3 + 4 = 7a (ISUP 2)), peak enhancement (p = 0.012), wash-in rate (p = 0.011), wash-out rate (p = 0.007) and wash-in perfusion index (p = 0.014) also showed statistical significance.  The quantification of CEUS parameters was able to discriminate PCa aggressiveness during MRI/US fusion-guided prostate biopsy. © Georg Thieme Verlag KG Stuttgart · New York.

Recruitment Methods and Show Rates to a Prostate Cancer Early Detection Program for High-Risk Men: A Comprehensive Analysis

PubMed Central

Giri, Veda N.; Coups, Elliot J.; Ruth, Karen; Goplerud, Julia; Raysor, Susan; Kim, Taylor Y.; Bagden, Loretta; Mastalski, Kathleen; Zakrzewski, Debra; Leimkuhler, Suzanne; Watkins-Bruner, Deborah

2009-01-01

Purpose Men with a family history (FH) of prostate cancer (PCA) and African American (AA) men are at higher risk for PCA. Recruitment and retention of these high-risk men into early detection programs has been challenging. We report a comprehensive analysis on recruitment methods, show rates, and participant factors from the Prostate Cancer Risk Assessment Program (PRAP), which is a prospective, longitudinal PCA screening study. Materials and Methods Men 35–69 years are eligible if they have a FH of PCA, are AA, or have a BRCA1/2 mutation. Recruitment methods were analyzed with respect to participant demographics and show to the first PRAP appointment using standard statistical methods Results Out of 707 men recruited, 64.9% showed to the initial PRAP appointment. More individuals were recruited via radio than from referral or other methods (χ2 = 298.13, p < .0001). Men recruited via radio were more likely to be AA (p<0.001), less educated (p=0.003), not married or partnered (p=0.007), and have no FH of PCA (p<0.001). Men recruited via referrals had higher incomes (p=0.007). Men recruited via referral were more likely to attend their initial PRAP visit than those recruited by radio or other methods (χ2 = 27.08, p < .0001). Conclusions This comprehensive analysis finds that radio leads to higher recruitment of AA men with lower socioeconomic status. However, these are the high-risk men that have lower show rates for PCA screening. Targeted motivational measures need to be studied to improve show rates for PCA risk assessment for these high-risk men. PMID:19758657

Once upon Multivariate Analyses: When They Tell Several Stories about Biological Evolution.

PubMed

Renaud, Sabrina; Dufour, Anne-Béatrice; Hardouin, Emilie A; Ledevin, Ronan; Auffray, Jean-Christophe

2015-01-01

Geometric morphometrics aims to characterize of the geometry of complex traits. It is therefore by essence multivariate. The most popular methods to investigate patterns of differentiation in this context are (1) the Principal Component Analysis (PCA), which is an eigenvalue decomposition of the total variance-covariance matrix among all specimens; (2) the Canonical Variate Analysis (CVA, a.k.a. linear discriminant analysis (LDA) for more than two groups), which aims at separating the groups by maximizing the between-group to within-group variance ratio; (3) the between-group PCA (bgPCA) which investigates patterns of between-group variation, without standardizing by the within-group variance. Standardizing within-group variance, as performed in the CVA, distorts the relationships among groups, an effect that is particularly strong if the variance is similarly oriented in a comparable way in all groups. Such shared direction of main morphological variance may occur and have a biological meaning, for instance corresponding to the most frequent standing genetic variation in a population. Here we undertake a case study of the evolution of house mouse molar shape across various islands, based on the real dataset and simulations. We investigated how patterns of main variance influence the depiction of among-group differentiation according to the interpretation of the PCA, bgPCA and CVA. Without arguing about a method performing 'better' than another, it rather emerges that working on the total or between-group variance (PCA and bgPCA) will tend to put the focus on the role of direction of main variance as line of least resistance to evolution. Standardizing by the within-group variance (CVA), by dampening the expression of this line of least resistance, has the potential to reveal other relevant patterns of differentiation that may otherwise be blurred.

DCE-MRI of the prostate using shutter-speed vs. Tofts model for tumor characterization and assessment of aggressiveness.

PubMed

Hectors, Stefanie J; Besa, Cecilia; Wagner, Mathilde; Jajamovich, Guido H; Haines, George K; Lewis, Sara; Tewari, Ashutosh; Rastinehad, Ardeshir; Huang, Wei; Taouli, Bachir

2017-09-01

To quantify Tofts model (TM) and shutter-speed model (SSM) perfusion parameters in prostate cancer (PCa) and noncancerous peripheral zone (PZ) and to compare the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to Prostate Imaging and Reporting and Data System (PI-RADS) classification for the assessment of PCa aggressiveness. Fifty PCa patients (mean age 60 years old) who underwent MRI at 3.0T followed by prostatectomy were included in this Institutional Review Board-approved retrospective study. DCE-MRI parameters (K trans , v e , k ep [TM&SSM] and intracellular water molecule lifetime τ i [SSM]) were determined in PCa and PZ. Differences in DCE-MRI parameters between PCa and PZ, and between models were assessed using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis for differentiation between PCa and PZ was performed for individual and combined DCE-MRI parameters. Diagnostic performance of DCE-MRI parameters for identification of aggressive PCa (Gleason ≥8, grade group [GG] ≥3 or pathology stage pT3) was assessed using ROC analysis and compared with PI-RADSv2 scores. DCE-MRI parameters were significantly different between TM and SSM and between PZ and PCa (P < 0.037). Diagnostic performances of TM and SSM for differentiation of PCa from PZ were similar (highest AUC TM: K trans +k ep 0.76, SSM: τ i +k ep 0.80). PI-RADS outperformed TM and SSM DCE-MRI for identification of Gleason ≥8 lesions (AUC PI-RADS: 0.91, highest AUC DCE-MRI: K trans +τ i SSM 0.61, P = 0.002). The diagnostic performance of PI-RADS and DCE-MRI for identification of GG ≥3 and pT3 PCa was not significantly different (P > 0.213). SSM DCE-MRI did not increase the diagnostic performance of DCE-MRI for PCa characterization. PI-RADS outperformed both TM and SSM DCE-MRI for identification of aggressive cancer. 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:837-849. © 2017 International Society for Magnetic Resonance in Medicine.

Tumor volume in insignificant prostate cancer: increasing threshold gains increasing risk.

PubMed

Schiffmann, Jonas; Connan, Judith; Salomon, Georg; Boehm, Katharina; Beyer, Burkhard; Schlomm, Thorsten; Tennstedt, Pierre; Sauter, Guido; Karakiewicz, Pierre I; Graefen, Markus; Huland, Hartwig

2015-01-01

An increased tumor volume threshold (<2.5 ml) is suggested to define insignificant prostate cancer (iPCa). We hypothesize that an increasing tumor volume within iPCa patients increases the risk of biochemical recurrence (BCR) after radical prostatectomy (RP). We relied on RP patients treated between 1992 and 2008. Multivariable Cox regression analyses predicting BCR within patients harboring favorable pathological characteristics (≤pT2, pN0/Nx, Gleason 3 + 3). Kaplan-Meier analysis was performed for BCR-free survival within iPCa patients (≤pT2, pN0/Nx, Gleason 3 + 3, tumor volume: <0.5 vs. 0.5-2.49 ml). From 1,829 patients, 141 (7.7%) and 310 (16.9%) harbored iPCa (tumor volume: <0.5 vs. 0.5-2.49 ml), respectively. Of those, 21 (14.9%) versus 31 (10.0%) had PSA >10 ng/ml. Tumor volume achieved independent predictor status for BCR. Specifically, iPCa patients with increasing tumor volume (0.5-2.49 ml) were at higher risk of BCR after RP than those with tumor volume <0.5 ml (HR: 8.8, 95% CI: 1.2-65.9, P = 0.04). Kaplan-Meier analysis recorded superior BCR-free survival in iPCa patients with lower tumor volume (<0.5 ml) (log-rank P = 0.009). The 10-year cancer-specific death rate was 0 versus 0.5%. Contemporary iPCa definition incorporates intermediate and high-risk patients (PSA: 10-20 and >20 ng/ml). Despite most favorable pathological characteristics, iPCa patients are not devoid of BCR after RP. Moreover, iPCa patients were at higher risk of BCR, when increasing tumor volume up to 2.49 ml was at play. Taken together the contemporary concept of iPCa is suboptimal. Especially, an increased tumor volume threshold for defining iPCa cannot be recommended according to our data. Clinicians might take these considerations into account during decision-making process. © 2014 Wiley Periodicals, Inc.

Picture agnosia as a characteristic of posterior cortical atrophy.

PubMed

Sugimoto, Azusa; Midorikawa, Akira; Koyama, Shinichi; Futamura, Akinori; Hieda, Sotaro; Kawamura, Mitsuru

2012-01-01

Posterior cortical atrophy (PCA) is a degenerative disease characterized by progressive visual agnosia with posterior cerebral atrophy. We examine the role of the picture naming test and make a number of suggestions with regard to diagnosing PCA as atypical dementia. We investigated 3 cases of early-stage PCA with 7 control cases of Alzheimer disease (AD). The patients and controls underwent a naming test with real objects and colored photographs of familiar objects. We then compared rates of correct answers. Patients with early-stage PCA showed significant inability to recognize photographs compared to real objects (F = 196.284, p = 0.0000) as measured by analysis of variants. This difficulty was also significant to AD controls (F = 58.717, p = 0.0000). Picture agnosia is a characteristic symptom of early-stage PCA, and the picture naming test is useful for the diagnosis of PCA as atypical dementia at an early stage. Copyright © 2012 S. Karger AG, Basel.

Principal component analysis as a tool for library design: a case study investigating natural products, brand-name drugs, natural product-like libraries, and drug-like libraries.

PubMed

Wenderski, Todd A; Stratton, Christopher F; Bauer, Renato A; Kopp, Felix; Tan, Derek S

2015-01-01

Principal component analysis (PCA) is a useful tool in the design and planning of chemical libraries. PCA can be used to reveal differences in structural and physicochemical parameters between various classes of compounds by displaying them in a convenient graphical format. Herein, we demonstrate the use of PCA to gain insight into structural features that differentiate natural products, synthetic drugs, natural product-like libraries, and drug-like libraries, and show how the results can be used to guide library design.

Principal Component Analysis as a Tool for Library Design: A Case Study Investigating Natural Products, Brand-Name Drugs, Natural Product-Like Libraries, and Drug-Like Libraries

PubMed Central

Wenderski, Todd A.; Stratton, Christopher F.; Bauer, Renato A.; Kopp, Felix; Tan, Derek S.

2015-01-01

Principal component analysis (PCA) is a useful tool in the design and planning of chemical libraries. PCA can be used to reveal differences in structural and physicochemical parameters between various classes of compounds by displaying them in a convenient graphical format. Herein, we demonstrate the use of PCA to gain insight into structural features that differentiate natural products, synthetic drugs, natural product-like libraries, and drug-like libraries, and show how the results can be used to guide library design. PMID:25618349

Principal Component Analysis of Thermographic Data

NASA Technical Reports Server (NTRS)

Winfree, William P.; Cramer, K. Elliott; Zalameda, Joseph N.; Howell, Patricia A.; Burke, Eric R.

2015-01-01

Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. While a reliable technique for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the "good" material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued where a fixed set of eigenvectors, generated from an analytic model of the thermal response of the material under examination, is used to process the thermal data from composite materials. This method has been applied for characterization of flaws.

Evaluation of skin melanoma in spectral range 450-950 nm using principal component analysis

NASA Astrophysics Data System (ADS)

Jakovels, D.; Lihacova, I.; Kuzmina, I.; Spigulis, J.

2013-06-01

Diagnostic potential of principal component analysis (PCA) of multi-spectral imaging data in the wavelength range 450- 950 nm for distant skin melanoma recognition is discussed. Processing of the measured clinical data by means of PCA resulted in clear separation between malignant melanomas and pigmented nevi.

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

USDA-ARS?s Scientific Manuscript database

A useful biological interpretation of diploid heterozygotes is in terms of the dose of the common allele (0, 1 or 2 copies). We have developed a PERL script that converts FASTA files into coded spreadsheets suitable for Principal Component Analysis (PCA). In combination with R and R Commander, two- ...

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

EPA Science Inventory

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

Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins

PubMed Central

David, Charles C.; Jacobs, Donald J.

2015-01-01

It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. While most popular molecular dynamics packages inevitably provide PCA tools to analyze protein trajectories, researchers often make inferences of their results without having insight into how to make interpretations, and they are often unaware of limitations and generalizations of such analysis. Here we review best practices for applying standard PCA, describe useful variants, discuss why one may wish to make comparison studies, and describe a set of metrics that make comparisons possible. In practice, one will be forced to make inferences about the essential dynamics of a protein without having the desired amount of samples. Therefore, considerable time is spent on describing how to judge the significance of results, highlighting pitfalls. The topic of PCA is reviewed from the perspective of many practical considerations, and useful recipes are provided. PMID:24061923

Principal component analysis: a method for determining the essential dynamics of proteins.

PubMed

David, Charles C; Jacobs, Donald J

2014-01-01

It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. While most popular molecular dynamics packages inevitably provide PCA tools to analyze protein trajectories, researchers often make inferences of their results without having insight into how to make interpretations, and they are often unaware of limitations and generalizations of such analysis. Here we review best practices for applying standard PCA, describe useful variants, discuss why one may wish to make comparison studies, and describe a set of metrics that make comparisons possible. In practice, one will be forced to make inferences about the essential dynamics of a protein without having the desired amount of samples. Therefore, considerable time is spent on describing how to judge the significance of results, highlighting pitfalls. The topic of PCA is reviewed from the perspective of many practical considerations, and useful recipes are provided.

Fatty acidomics: Evaluation of the effects of thermal treatments on commercial mussels through an extended characterization of their free fatty acids by liquid chromatography - Fourier transform mass spectrometry.

PubMed

Losito, Ilario; Facchini, Laura; Valentini, Alessandra; Cataldi, Tommaso R I; Palmisano, Francesco

2018-07-30

An unprecedented characterization of free fatty acids (FFA) in the lipid extracts of fresh or thermally treated mussels of sp. Mytilus galloprovincialis, including up to 128 saturated, mono- or poly-unsaturated and 63 oxidized (i.e., modified by hydroxylic, carbonylic and/or epoxylic groups) compounds, was achieved using reverse phase chromatography coupled to electrospray ionization-Fourier transform single and tandem mass spectrometry (RPC-ESI-FTMS,MS/MS). Subsequent Principal Components Analysis (PCA) evidenced several effects of thermal treatments on the mussel FFA profiles. In particular, death-inducing low temperature treatments (freezing at -16 °C or refrigeration at 4 °C for several days) induced a peculiar increase in the incidence of FFA, whereas the effect was absent in mussels undergoing death upon prolonged storage at room temperature (25 °C, 6 h) or fast cooking (100 °C, 5 min). Alive mussels, either fresh or resulting from short term (up to 48 h) refrigeration were actually indistinguishable by PCA, although subtle seasonal effects were observed. Copyright © 2018. Published by Elsevier Ltd.

On a PCA-based lung motion model

PubMed Central

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

2014-01-01

Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772–81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921–9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1 mm (0.7 ± 0.1 mm). When a single artificial internal marker was used to derive the lung motion, the average 3D error was found to be within 2 mm (1.8 ± 0.3 mm) through comprehensive statistical analysis. The optimal number of PCA coefficients needs to be determined on a patient-by-patient basis and two PCA coefficients seem to be sufficient for accurate modeling of the lung motion for most patients. In conclusion, we have presented thorough theoretical analysis and clinical validation of the PCA lung motion model. The feasibility of deriving the entire lung motion using a single marker has also been demonstrated on clinical data using a simulation approach. PMID:21865624

Population Analysis of Disabled Children by Departments in France

NASA Astrophysics Data System (ADS)

Meidatuzzahra, Diah; Kuswanto, Heri; Pech, Nicolas; Etchegaray, Amélie

2017-06-01

In this study, a statistical analysis is performed by model the variations of the disabled about 0-19 years old population among French departments. The aim is to classify the departments according to their profile determinants (socioeconomic and behavioural profiles). The analysis is focused on two types of methods: principal component analysis (PCA) and multiple correspondences factorial analysis (MCA) to review which one is the best methods for interpretation of the correlation between the determinants of disability (independent variable). The PCA is the best method for interpretation of the correlation between the determinants of disability (independent variable). The PCA reduces 14 determinants of disability to 4 axes, keeps 80% of total information, and classifies them into 7 classes. The MCA reduces the determinants to 3 axes, retains only 30% of information, and classifies them into 4 classes.

Rapid discrimination of sea buckthorn berries from different H. rhamnoides subspecies by multi-step IR spectroscopy coupled with multivariate data analysis

NASA Astrophysics Data System (ADS)

Liu, Yue; Zhang, Ying; Zhang, Jing; Fan, Gang; Tu, Ya; Sun, Suqin; Shen, Xudong; Li, Qingzhu; Zhang, Yi

2018-03-01

As an important ethnic medicine, sea buckthorn was widely used to prevent and treat various diseases due to its nutritional and medicinal properties. According to the Chinese Pharmacopoeia, sea buckthorn was originated from H. rhamnoides, which includes five subspecies distributed in China. Confusion and misidentification usually occurred due to their similar morphology, especially in dried and powdered forms. Additionally, these five subspecies have vital differences in quality and physiological efficacy. This paper focused on the quick classification and identification method of sea buckthorn berry powders from five H. rhamnoides subspecies using multi-step IR spectroscopy coupled with multivariate data analysis. The holistic chemical compositions revealed by the FT-IR spectra demonstrated that flavonoids, fatty acids and sugars were the main chemical components. Further, the differences in FT-IR spectra regarding their peaks, positions and intensities were used to identify H. rhamnoides subspecies samples. The discrimination was achieved using principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). The results showed that the combination of multi-step IR spectroscopy and chemometric analysis offered a simple, fast and reliable method for the classification and identification of the sea buckthorn berry powders from different H. rhamnoides subspecies.

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-01-01

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

A Seven-Gene Locus for Synthesis of Phenazine-1-Carboxylic Acid by Pseudomonas fluorescens 2-79

PubMed Central

Mavrodi, Dmitri V.; Ksenzenko, Vladimir N.; Bonsall, Robert F.; Cook, R. James; Boronin, Alexander M.; Thomashow, Linda S.

1998-01-01

Pseudomonas fluorescens 2-79 produces the broad-spectrum antibiotic phenazine-1-carboxylic acid (PCA), which is active against a variety of fungal root pathogens. In this study, seven genes designated phzABCDEFG that are sufficient for synthesis of PCA were localized within a 6.8-kb BglII-XbaI fragment from the phenazine biosynthesis locus of strain 2-79. Polypeptides corresponding to all phz genes were identified by analysis of recombinant plasmids in a T7 promoter/polymerase expression system. Products of the phzC, phzD, and phzE genes have similarities to enzymes of shikimic acid and chorismic acid metabolism and, together with PhzF, are absolutely necessary for PCA production. PhzG is similar to pyridoxamine-5′-phosphate oxidases and probably is a source of cofactor for the PCA-synthesizing enzyme(s). Products of the phzA and phzB genes are highly homologous to each other and may be involved in stabilization of a putative PCA-synthesizing multienzyme complex. Two new genes, phzX and phzY, that are homologous to phzA and phzB, respectively, were cloned and sequenced from P. aureofaciens 30-84, which produces PCA, 2-hydroxyphenazine-1-carboxylic acid, and 2-hydroxyphenazine. Based on functional analysis of the phz genes from strains 2-79 and 30-84, we postulate that different species of fluorescent pseudomonads have similar genetic systems that confer the ability to synthesize PCA. PMID:9573209

IMPROVED SEARCH OF PRINCIPAL COMPONENT ANALYSIS DATABASES FOR SPECTRO-POLARIMETRIC INVERSION

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

Casini, R.; Lites, B. W.; Ramos, A. Asensio

2013-08-20

We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 2{sup 4n} bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of ''compatible'' models for the inversion of amore » given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 2{sup 4n} as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing.« less

Facilitating Neuronal Connectivity Analysis of Evoked Responses by Exposing Local Activity with Principal Component Analysis Preprocessing: Simulation of Evoked MEG

PubMed Central

Gao, Lin; Zhang, Tongsheng; Wang, Jue; Stephen, Julia

2014-01-01

When connectivity analysis is carried out for event related EEG and MEG, the presence of strong spatial correlations from spontaneous activity in background may mask the local neuronal evoked activity and lead to spurious connections. In this paper, we hypothesized PCA decomposition could be used to diminish the background activity and further improve the performance of connectivity analysis in event related experiments. The idea was tested using simulation, where we found that for the 306-channel Elekta Neuromag system, the first 4 PCs represent the dominant background activity, and the source connectivity pattern after preprocessing is consistent with the true connectivity pattern designed in the simulation. Improving signal to noise of the evoked responses by discarding the first few PCs demonstrates increased coherences at major physiological frequency bands when removing the first few PCs. Furthermore, the evoked information was maintained after PCA preprocessing. In conclusion, it is demonstrated that the first few PCs represent background activity, and PCA decomposition can be employed to remove it to expose the evoked activity for the channels under investigation. Therefore, PCA can be applied as a preprocessing approach to improve neuronal connectivity analysis for event related data. PMID:22918837

Facilitating neuronal connectivity analysis of evoked responses by exposing local activity with principal component analysis preprocessing: simulation of evoked MEG.

PubMed

Gao, Lin; Zhang, Tongsheng; Wang, Jue; Stephen, Julia

2013-04-01

When connectivity analysis is carried out for event related EEG and MEG, the presence of strong spatial correlations from spontaneous activity in background may mask the local neuronal evoked activity and lead to spurious connections. In this paper, we hypothesized PCA decomposition could be used to diminish the background activity and further improve the performance of connectivity analysis in event related experiments. The idea was tested using simulation, where we found that for the 306-channel Elekta Neuromag system, the first 4 PCs represent the dominant background activity, and the source connectivity pattern after preprocessing is consistent with the true connectivity pattern designed in the simulation. Improving signal to noise of the evoked responses by discarding the first few PCs demonstrates increased coherences at major physiological frequency bands when removing the first few PCs. Furthermore, the evoked information was maintained after PCA preprocessing. In conclusion, it is demonstrated that the first few PCs represent background activity, and PCA decomposition can be employed to remove it to expose the evoked activity for the channels under investigation. Therefore, PCA can be applied as a preprocessing approach to improve neuronal connectivity analysis for event related data.

An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis

PubMed Central

Bravo, Ignacio; Mazo, Manuel; Lázaro, José L.; Gardel, Alfredo; Jiménez, Pedro; Pizarro, Daniel

2010-01-01

This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices. PMID:22163406

An intelligent architecture based on Field Programmable Gate Arrays designed to detect moving objects by using Principal Component Analysis.

PubMed

Bravo, Ignacio; Mazo, Manuel; Lázaro, José L; Gardel, Alfredo; Jiménez, Pedro; Pizarro, Daniel

2010-01-01

This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices.

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

PubMed

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

2017-11-01

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

Extraction methods of Amaranthus sp. grain oil isolation.

PubMed

Krulj, Jelena; Brlek, Tea; Pezo, Lato; Brkljača, Jovana; Popović, Sanja; Zeković, Zoran; Bodroža Solarov, Marija

2016-08-01

Amaranthus sp. is a fast-growing crop with well-known beneficial nutritional values (rich in protein, fat, dietary fiber, ash, and minerals, especially calcium and sodium, and containing a higher amount of lysine than conventional cereals). Amaranthus sp. is an underexploited plant source of squalene, a compound of high importance in the food, cosmetic and pharmaceutical industries. This paper has examined the effects of the different extraction methods (Soxhlet, supercritical fluid and accelerated solvent extraction) on the oil and squalene yield of three genotypes of Amaranthus sp. grain. The highest yield of the extracted oil (78.1 g kg(-1) ) and squalene (4.7 g kg(-1) ) in grain was obtained by accelerated solvent extraction (ASE) in genotype 16. Post hoc Tukey's HSD test at 95% confidence limit showed significant differences between observed samples. Principal component analysis (PCA) and cluster analysis (CA) were used for assessing the effect of different genotypes and extraction methods on oil and squalene yield, and also the fatty acid composition profile. Using coupled PCA and CA of observed samples, possible directions for improving the quality of product can be realized. The results of this study indicate that it is very important to choose both the right genotype and the right method of extraction for optimal oil and squalene yield. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Forensic surface metrology: tool mark evidence.

PubMed

Gambino, Carol; McLaughlin, Patrick; Kuo, Loretta; Kammerman, Frani; Shenkin, Peter; Diaczuk, Peter; Petraco, Nicholas; Hamby, James; Petraco, Nicholas D K

2011-01-01

Over the last several decades, forensic examiners of impression evidence have come under scrutiny in the courtroom due to analysis methods that rely heavily on subjective morphological comparisons. Currently, there is no universally accepted system that generates numerical data to independently corroborate visual comparisons. Our research attempts to develop such a system for tool mark evidence, proposing a methodology that objectively evaluates the association of striated tool marks with the tools that generated them. In our study, 58 primer shear marks on 9 mm cartridge cases, fired from four Glock model 19 pistols, were collected using high-resolution white light confocal microscopy. The resulting three-dimensional surface topographies were filtered to extract all "waviness surfaces"-the essential "line" information that firearm and tool mark examiners view under a microscope. Extracted waviness profiles were processed with principal component analysis (PCA) for dimension reduction. Support vector machines (SVM) were used to make the profile-gun associations, and conformal prediction theory (CPT) for establishing confidence levels. At the 95% confidence level, CPT coupled with PCA-SVM yielded an empirical error rate of 3.5%. Complementary, bootstrap-based computations for estimated error rates were 0%, indicating that the error rate for the algorithmic procedure is likely to remain low on larger data sets. Finally, suggestions are made for practical courtroom application of CPT for assigning levels of confidence to SVM identifications of tool marks recorded with confocal microscopy. Copyright © 2011 Wiley Periodicals, Inc.

Infrared monitoring of dinitrotoluenes in sunflower and maize roots.

PubMed

Dokken, K M; Davis, L C

2011-01-01

Infrared microspectroscopy (IMS) is emerging as an important analytical tool for the structural analysis of biological tissue. This report describes the use of IMS coupled to a synchrotron source combined with principal components analysis (PCA) to monitor the fate and effect of dinitrotoluenes in the roots of maize and sunflower plants. Infrared imaging revealed that maize roots metabolized 2,4-dinitrotoluene (DNT) and 2,6-DNT. The DNTs and their derivative aromatic amines were predominantly associated with epidermis and xylem. Both isomers of DNT altered the structure and production of pectin and pectic polysaccharides in maize and sunflower plant roots. Infrared peaks diagnostic for aromatic amines were seen at the 5 mg L concentrations for both DNTs in maize and sunflower treated tissue. However, only infrared peaks for nitro groups, not aromatic amines, were present in the maize treated at 10 mg L For sunflower, the 10 mg L level was toxic and also produced very dark root systems making spectra difficult to obtain. Maize and sunflower seem unable to metabolize effectively at concentrations higher than about 5 mg L DNT in hydroponic solution. Based on the results of this study, IMS combined with PCA can be an effective means of determining the fate and metabolism of organic contaminants in plant tissue when isotopically labeled compounds are not available. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.

A new strategy for statistical analysis-based fingerprint establishment: Application to quality assessment of Semen sojae praeparatum.

PubMed

Guo, Hui; Zhang, Zhen; Yao, Yuan; Liu, Jialin; Chang, Ruirui; Liu, Zhao; Hao, Hongyuan; Huang, Taohong; Wen, Jun; Zhou, Tingting

2018-08-30

Semen sojae praeparatum with homology of medicine and food is a famous traditional Chinese medicine. A simple and effective quality fingerprint analysis, coupled with chemometrics methods, was developed for quality assessment of Semen sojae praeparatum. First, similarity analysis (SA) and hierarchical clusting analysis (HCA) were applied to select the qualitative markers, which obviously influence the quality of Semen sojae praeparatum. 21 chemicals were selected and characterized by high resolution ion trap/time-of-flight mass spectrometry (LC-IT-TOF-MS). Subsequently, principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted to select the quantitative markers of Semen sojae praeparatum samples from different origins. Moreover, 11 compounds with statistical significance were determined quantitatively, which provided an accurate and informative data for quality evaluation. This study proposes a new strategy for "statistic analysis-based fingerprint establishment", which would be a valuable reference for further study. Copyright © 2018 Elsevier Ltd. All rights reserved.

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

PubMed Central

Yu, Ke-Qiang; Zhao, Yan-Ru; Liu, Fei; He, Yong

2016-01-01

The aim of this work was to analyze the variety of soil by laser-induced breakdown spectroscopy (LIBS) coupled with chemometrics methods. 6 certified reference materials (CRMs) of soil samples were selected and their LIBS spectra were captured. Characteristic emission lines of main elements were identified based on the LIBS curves and corresponding contents. From the identified emission lines, LIBS spectra in 7 lines with high signal-to-noise ratio (SNR) were chosen for further analysis. Principal component analysis (PCA) was carried out using the LIBS spectra at 7 selected lines and an obvious cluster of 6 soils was observed. Soft independent modeling of class analogy (SIMCA) and least-squares support vector machine (LS-SVM) were introduced to establish discriminant models for classifying the 6 types of soils, and they offered the correct discrimination rates of 90% and 100%, respectively. Receiver operating characteristic (ROC) curve was used to evaluate the performance of models and the results demonstrated that the LS-SVM model was promising. Lastly, 8 types of soils from different places were gathered to conduct the same experiments for verifying the selected 7 emission lines and LS-SVM model. The research revealed that LIBS technology coupled with chemometrics could conduct the variety discrimination of soil. PMID:27279284

Evaluation of sulfur isotopic enrichment of urine metabolites for the differentiation of healthy and prostate cancer mice after the administration of 34S labelled yeast.

PubMed

Galilea San Blas, Oscar; Moreno Sanz, Fernando; Herrero Espílez, Pilar; Sainz Menéndez, Rosa María; Mayo Barallo, Juan Carlos; Marchante-Gayón, Juan Manuel; García Alonso, José Ignacio

2017-01-01

Sulfur isotopic enrichment of urine metabolites in healthy and prostate cancer mice using 34 S enriched yeast and High Performance Liquid Chromatography coupled to Multicollector Inductively Coupled Plasma Mass Spectrometry (HPLC-MC-ICP-MS) has been evaluated. A 30 weeks experiment (since the eleventh to the fortieth week of life) was carried out collecting the urine of three healthy mice and three transgenic mice with prostate cancer during 24h after a single oral administration of a 34 S enriched yeast slurry. The isotopic enrichment of different sulphur metabolites was monitored by coupling a C18 reverse phase HPLC column with a multicollector ICP-MS using a membrane desolvating system. Quantification of sulfur in the chromatographic peaks was carried out by post-column isotope dilution using a 33 S enriched spike. Differences between the 34 S enrichment in the urine metabolites of healthy and prostate cancer mice were found from the beginning of the disease. Both populations could be differentiated using a principal component analysis (PCA). Finally, 7 unknown mice were correctly classified in each population using a linear discriminant analysis. Copyright © 2016 Elsevier GmbH. All rights reserved.

A Systematic Review and Meta-analysis of the Diagnostic Accuracy of Prostate Health Index and 4-Kallikrein Panel Score in Predicting Overall and High-grade Prostate Cancer.

PubMed

Russo, Giorgio Ivan; Regis, Federica; Castelli, Tommaso; Favilla, Vincenzo; Privitera, Salvatore; Giardina, Raimondo; Cimino, Sebastiano; Morgia, Giuseppe

2017-08-01

Markers for prostate cancer (PCa) have progressed over recent years. In particular, the prostate health index (PHI) and the 4-kallikrein (4K) panel have been demonstrated to improve the diagnosis of PCa. We aimed to review the diagnostic accuracy of PHI and the 4K panel for PCa detection. We performed a systematic literature search of PubMed, EMBASE, Cochrane, and Academic One File databases until July 2016. We included diagnostic accuracy studies that used PHI or 4K panel for the diagnosis of PCa or high-grade PCa. The methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Twenty-eight studies including 16,762 patients have been included for the analysis. The pooled data showed a sensitivity of 0.89 and 0.74 for PHI and 4K panel, respectively, for PCa detection and a pooled specificity of 0.34 and 0.60 for PHI and 4K panel, respectively. The derived area under the curve (AUC) from the hierarchical summary receiver operating characteristic (HSROC) showed an accuracy of 0.76 and 0.72 for PHI and 4K panel respectively. For high-grade PCa detection, the pooled sensitivity was 0.93 and 0.87 for PHI and 4K panel, respectively, whereas the pooled specificity was 0.34 and 0.61 for PHI and 4K panel, respectively. The derived AUC from the HSROC showed an accuracy of 0.82 and 0.81 for PHI and 4K panel, respectively. Both PHI and the 4K panel provided good diagnostic accuracy in detecting overall and high-grade PCa. Copyright © 2016 Elsevier Inc. All rights reserved.

Urinary MicroRNAs of Prostate Cancer: Virus-Encoded hsv1-miRH18 and hsv2-miR-H9-5p Could Be Valuable Diagnostic Markers.

PubMed

Yun, Seok Joong; Jeong, Pildu; Kang, Ho Won; Kim, Ye-Hwan; Kim, Eun-Ah; Yan, Chunri; Choi, Young-Ki; Kim, Dongho; Kim, Jung Min; Kim, Seon-Kyu; Kim, Seon-Young; Kim, Sang Tae; Kim, Won Tae; Lee, Ok-Jun; Koh, Gou-Young; Moon, Sung-Kwon; Kim, Isaac Yi; Kim, Jayoung; Choi, Yung-Hyun; Kim, Wun-Jae

2015-06-01

MicroRNAs (miRNAs) in biological fluids are potential biomarkers for the diagnosis and assessment of urological diseases such as benign prostatic hyperplasia (BPH) and prostate cancer (PCa). The aim of the study was to identify and validate urinary cell-free miRNAs that can segregate patients with PCa from those with BPH. In total, 1,052 urine, 150 serum, and 150 prostate tissue samples from patients with PCa or BPH were used in the study. A urine-based miRNA microarray analysis suggested the presence of differentially expressed urinary miRNAs in patients with PCa, and these were further validated in three independent PCa cohorts, using a quantitative reverse transcriptionpolymerase chain reaction analysis. The expression levels of hsa-miR-615-3p, hsv1-miR-H18, hsv2-miR-H9-5p, and hsa-miR-4316 were significantly higher in urine samples of patients with PCa than in those of BPH controls. In particular, herpes simplex virus (hsv)-derived hsv1-miR-H18 and hsv2-miR-H9-5p showed better diagnostic performance than did the serum prostate-specific antigen (PSA) test for patients in the PSA gray zone. Furthermore, a combination of urinary hsv2-miR-H9-5p with serum PSA showed high sensitivity and specificity, providing a potential clinical benefit by reducing unnecessary biopsies. Our findings showed that hsv-encoded hsv1-miR-H18 and hsv2-miR-H9-5p are significantly associated with PCa and can facilitate early diagnosis of PCa for patients within the serum PSA gray zone.

Urinary MicroRNAs of Prostate Cancer: Virus-Encoded hsv1-miRH18 and hsv2-miR-H9-5p Could Be Valuable Diagnostic Markers

PubMed Central

Yun, Seok Joong; Jeong, Pildu; Kang, Ho Won; Kim, Ye-Hwan; Kim, Eun-Ah; Yan, Chunri; Choi, Young-Ki; Kim, Dongho; Kim, Jung Min; Kim, Seon-Kyu; Kim, Seon-Young; Kim, Sang Tae; Kim, Won Tae; Lee, Ok-Jun; Koh, Gou-Young; Moon, Sung-Kwon; Kim, Isaac Yi; Kim, Jayoung; Choi, Yung-Hyun; Kim, Wun-Jae

2015-01-01

Purpose: MicroRNAs (miRNAs) in biological fluids are potential biomarkers for the diagnosis and assessment of urological diseases such as benign prostatic hyperplasia (BPH) and prostate cancer (PCa). The aim of the study was to identify and validate urinary cell-free miRNAs that can segregate patients with PCa from those with BPH. Methods: In total, 1,052 urine, 150 serum, and 150 prostate tissue samples from patients with PCa or BPH were used in the study. A urine-based miRNA microarray analysis suggested the presence of differentially expressed urinary miRNAs in patients with PCa, and these were further validated in three independent PCa cohorts, using a quantitative reverse transcriptionpolymerase chain reaction analysis. Results: The expression levels of hsa-miR-615-3p, hsv1-miR-H18, hsv2-miR-H9-5p, and hsa-miR-4316 were significantly higher in urine samples of patients with PCa than in those of BPH controls. In particular, herpes simplex virus (hsv)-derived hsv1-miR-H18 and hsv2-miR-H9-5p showed better diagnostic performance than did the serum prostate-specific antigen (PSA) test for patients in the PSA gray zone. Furthermore, a combination of urinary hsv2-miR-H9-5p with serum PSA showed high sensitivity and specificity, providing a potential clinical benefit by reducing unnecessary biopsies. Conclusions: Our findings showed that hsv-encoded hsv1-miR-H18 and hsv2-miR-H9-5p are significantly associated with PCa and can facilitate early diagnosis of PCa for patients within the serum PSA gray zone. PMID:26126436

Novel potential serological prostate cancer biomarkers using CT100+ cancer antigen microarray platform in a multi-cultural South African cohort

PubMed Central

Adeola, Henry A.; Smith, Muneerah; Kaestner, Lisa; Blackburn, Jonathan M.; Zerbini, Luiz F.

2016-01-01

There is a growing need for high throughput diagnostic tools for early diagnosis and treatment monitoring of prostate cancer (PCa) in Africa. The role of cancer-testis antigens (CTAs) in PCa in men of African descent is poorly researched. Hence, we aimed to elucidate the role of 123 Tumour Associated Antigens (TAAs) using antigen microarray platform in blood samples (N = 67) from a South African PCa, Benign prostatic hyperplasia (BPH) and disease control (DC) cohort. Linear (fold-over-cutoff) and differential expression quantitation of autoantibody signal intensities were performed. Molecular signatures of candidate PCa antigen biomarkers were identified and analyzed for ethnic group variation. Potential cancer diagnostic and immunotherapeutic inferences were drawn. We identified a total of 41 potential diagnostic/therapeutic antigen biomarkers for PCa. By linear quantitation, four antigens, GAGE1, ROPN1, SPANXA1 and PRKCZ were found to have higher autoantibody titres in PCa serum as compared with BPH where MAGEB1 and PRKCZ were highly expressed. Also, p53 S15A and p53 S46A were found highly expressed in the disease control group. Statistical analysis by differential expression revealed twenty-four antigens as upregulated in PCa samples, while 11 were downregulated in comparison to BPH and DC (FDR = 0.01). FGFR2, COL6A1and CALM1 were verifiable biomarkers of PCa analysis using urinary shotgun proteomics. Functional pathway annotation of identified biomarkers revealed similar enrichment both at genomic and proteomic level and ethnic variations were observed. Cancer antigen arrays are emerging useful in potential diagnostic and immunotherapeutic antigen biomarker discovery. PMID:26885621

Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

PubMed

Badran, M; Morsy, R; Soliman, H; Elnimr, T

2016-01-01

The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus. Copyright © 2015 Elsevier GmbH. All rights reserved.

Metabolomics Coupled with Multivariate Data and Pathway Analysis on Potential Biomarkers in Gastric Ulcer and Intervention Effects of Corydalis yanhusuo Alkaloid

PubMed Central

Shuai, Wang; Yongrui, Bao; Shanshan, Guan; Bo, Liu; Lu, Chen; Lei, Wang; Xiaorong, Ran

2014-01-01

Metabolomics, the systematic analysis of potential metabolites in a biological specimen, has been increasingly applied to discovering biomarkers, identifying perturbed pathways, measuring therapeutic targets, and discovering new drugs. By analyzing and verifying the significant difference in metabolic profiles and changes of metabolite biomarkers, metabolomics enables us to better understand substance metabolic pathways which can clarify the mechanism of Traditional Chinese Medicines (TCM). Corydalis yanhusuo alkaloid (CA) is a major component of Qizhiweitong (QZWT) prescription which has been used for treating gastric ulcer for centuries and its mechanism remains unclear completely. Metabolite profiling was performed by high-performance liquid chromatography combined with time-of-flight mass spectrometry (HPLC/ESI-TOF-MS) and in conjunction with multivariate data analysis and pathway analysis. The statistic software Mass Profiller Prossional (MPP) and statistic method including ANOVA and principal component analysis (PCA) were used for discovering novel potential biomarkers to clarify mechanism of CA in treating acid injected rats with gastric ulcer. The changes in metabolic profiling were restored to their base-line values after CA treatment according to the PCA score plots. Ten different potential biomarkers and seven key metabolic pathways contributing to the treatment of gastric ulcer were discovered and identified. Among the pathways, sphingophospholipid metabolism and fatty acid metabolism related network were acutely perturbed. Quantitative real time polymerase chain reaction (RT-PCR) analysis were performed to evaluate the expression of genes related to the two pathways for verifying the above results. The results show that changed biomarkers and pathways may provide evidence to insight into drug action mechanisms and enable us to increase research productivity toward metabolomics drug discovery. PMID:24454691

[Study on HPLC fingerprint of Oldenlandia diffusa].

PubMed

Chen, Yan; Yao, Zhi-Hong; Dai, Yi; Cheng, Hong; Wen, Li-Rong; Zhou, Guang-Xiong; Yao, Xin-Sheng

2012-06-01

To establish the HPLC fingerprint chromatogram of Oldenlandia diffusa coupled with chemometrics means for the quality control of multi-batches of medicinal material. The separation was developed on C18 column(4.6 mm x 250 mm, 5 microm) by gradient elution with acetonitrile-water(both containing 0.1 per thousand (V/V) ocetic acid) as mobile phase at a flow rate of 0.8 mL/min, the detection wavelength at 238 nm and column temperature at 30 degrees C. The HPLC fingerprint chromatogram of Oldenlandia diffusa was set up and the main characteristic peaks were identified by comparing with chemical reference substance. The quality of 22 batches of medicinal material was evaluated by similarity assay as well as principal component analysis (PCA) and cluster analysis. The established HPLC fingerprint chromatogram of Oldenlandia diffusa was specific, precise, reproducible and stable. 11 peaks were chemically identified. The similarity of 17 batches of Oldenlandia diffusa was obviously higher than 5 batches of adulterants. PCA showed that 17 batches of Oldenlandia diffusa were in a domain and 5 batches of adulterants were far apart from the domain. The cluster analysis of the 22 batches of medicinal material showed that 17 batches of Oldenlandia diffusa were in a cluster while 5 batches of adulterants were excluded. Further cluster analysis was carried out for the quality consistency of 17 batches of Oldenlandia diffusa and accordingly they were devided into 4 clusters. With the combination of chemometrics means, the HPLC fingerprint chromatogram provides a method for evaluation of authenticity and quality control of Oldenlandia diffusa, which is favorable to improve overall quality control of Oldenlandia diffusa.

Facilitating text reading in posterior cortical atrophy.

PubMed

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

2015-07-28

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

Facilitating text reading in posterior cortical atrophy

PubMed Central

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

2015-01-01

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

Principal components analysis in clinical studies.

PubMed

Zhang, Zhongheng; Castelló, Adela

2017-09-01

In multivariate analysis, independent variables are usually correlated to each other which can introduce multicollinearity in the regression models. One approach to solve this problem is to apply principal components analysis (PCA) over these variables. This method uses orthogonal transformation to represent sets of potentially correlated variables with principal components (PC) that are linearly uncorrelated. PCs are ordered so that the first PC has the largest possible variance and only some components are selected to represent the correlated variables. As a result, the dimension of the variable space is reduced. This tutorial illustrates how to perform PCA in R environment, the example is a simulated dataset in which two PCs are responsible for the majority of the variance in the data. Furthermore, the visualization of PCA is highlighted.

Discrimination of almonds (Prunus dulcis) geographical origin by minerals and fatty acids profiling.

PubMed

Amorello, Diana; Orecchio, Santino; Pace, Andrea; Barreca, Salvatore

2016-09-01

Twenty-one almond samples from three different geographical origins (Sicily, Spain and California) were investigated by determining minerals and fatty acids compositions. Data were used to discriminate by chemometry almond origin by linear discriminant analysis. With respect to previous PCA profiling studies, this work provides a simpler analytical protocol for the identification of almonds geographical origin. Classification by using mineral contents data only was correct in 77% of the samples, while, by using fatty acid profiles, the percentages of samples correctly classified reached 82%. The coupling of mineral contents and fatty acid profiles lead to an increased efficiency of the classification with 87% of samples correctly classified.

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

Multi-Centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection

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

Chen, Pin-Yu; Choudhury, Sutanay; Hero, Alfred

Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the centered graph adjacency matrix (graph Laplacian), we propose spectral decomposition approaches to graph PCA and graph dictionary learning that integrate multiple features, including graph walk statistics, centrality measures and graph distances to reference nodes. In this paper we propose a new PCA method for single graph analysis, called multi-centrality graph PCA (MC-GPCA), and a new dictionary learning method for ensembles ofmore » graphs, called multi-centrality graph dictionary learning (MC-GDL), both based on spectral decomposition of multi-centrality matrices. As an application to cyber intrusion detection, MC-GPCA can be an effective indicator of anomalous connectivity pattern and MC-GDL can provide discriminative basis for attack classification.« less

Revealing the ultrafast outflow in IRAS 13224-3809 through spectral variability

NASA Astrophysics Data System (ADS)

Parker, M. L.; Alston, W. N.; Buisson, D. J. K.; Fabian, A. C.; Jiang, J.; Kara, E.; Lohfink, A.; Pinto, C.; Reynolds, C. S.

2017-08-01

We present an analysis of the long-term X-ray variability of the extreme narrow-line Seyfert 1 galaxy IRAS 13224-3809 using principal component analysis (PCA) and fractional excess variability (Fvar) spectra to identify model-independent spectral components. We identify a series of variability peaks in both the first PCA component and Fvar spectrum which correspond to the strongest predicted absorption lines from the ultrafast outflow (UFO) discovered by Parker et al. (2017). We also find higher order PCA components, which correspond to variability of the soft excess and reflection features. The subtle differences between RMS and PCA results argue that the observed flux-dependence of the absorption is due to increased ionization of the gas, rather than changes in column density or covering fraction. This result demonstrates that we can detect outflows from variability alone and that variability studies of UFOs are an extremely promising avenue for future research.

Racism, Racial Resilience, and African American Youth Development: Person-Centered Analysis as a Tool to Promote Equity and Justice.

PubMed

Neblett, Enrique W; Sosoo, Effua E; Willis, Henry A; Bernard, Donte L; Bae, Jiwoon; Billingsley, Janelle T

Racism constitutes a significant risk to the healthy development of African American youth. Fortunately, however, not all youth who experience racism evidence negative developmental outcomes. In this chapter, we examine person-centered analysis (PCA)-a quantitative technique that investigates how variables combine across individuals-as a useful tool for elucidating racial and ethnic protective processes that mitigate the negative impact of racism. We review recent studies employing PCA in examinations of racial identity, racial socialization, and other race-related experiences, as well as how these constructs correlate with and impact African American youth development. We also consider challenges and limitations of PCA and conclude with a discussion of future research and how PCA might be used to promote equity and justice for African American and other racial and ethnic minority youth who experience racism. © 2016 Elsevier Inc. All rights reserved.

Multiscale 3D Shape Analysis using Spherical Wavelets

PubMed Central

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

2013-01-01

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data. PMID:16685992

Multiscale 3D shape analysis using spherical wavelets.

PubMed

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen R

2005-01-01

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.

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

PubMed Central

2012-01-01

Background 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. Methods 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. Results 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. Conclusions 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 possibilities of using PCA3 knockdown as an additional therapeutic strategy for PCa control. PMID:23130941

Optimizing the clinical utility of PCA3 to diagnose prostate cancer in initial prostate biopsy.

PubMed

Rubio-Briones, Jose; Borque, Angel; Esteban, Luis M; Casanova, Juan; Fernandez-Serra, Antonio; Rubio, Luis; Casanova-Salas, Irene; Sanz, Gerardo; Domínguez-Escrig, Jose; Collado, Argimiro; Gómez-Ferrer, Alvaro; Iborra, Inmaculada; Ramírez-Backhaus, Miguel; Martínez, Francisco; Calatrava, Ana; Lopez-Guerrero, Jose A

2015-09-11

PCA3 has been included in a nomogram outperforming previous clinical models for the prediction of any prostate cancer (PCa) and high grade PCa (HGPCa) at the initial prostate biopsy (IBx). Our objective is to validate such IBx-specific PCA3-based nomogram. We also aim to optimize the use of this nomogram in clinical practice through the definition of risk groups. Independent external validation. Clinical and biopsy data from a contemporary cohort of 401 men with the same inclusion criteria to those used to build up the reference's nomogram in IBx. The predictive value of the nomogram was assessed by means of calibration curves and discrimination ability through the area under the curve (AUC). Clinical utility of the nomogram was analyzed by choosing thresholds points that minimize the overlapping between probability density functions (PDF) in PCa and no PCa and HGPCa and no HGPCa groups, and net benefit was assessed by decision curves. We detect 28% of PCa and 11 % of HGPCa in IBx, contrasting to the 46 and 20% at the reference series. Due to this, there is an overestimation of the nomogram probabilities shown in the calibration curve for PCa. The AUC values are 0.736 for PCa (C.I.95%:0.68-0.79) and 0.786 for HGPCa (C.I.95%:0.71-0.87) showing an adequate discrimination ability. PDF show differences in the distributions of nomogram probabilities in PCa and not PCa patient groups. A minimization of the overlapping between these curves confirms the threshold probability of harboring PCa >30 % proposed by Hansen is useful to indicate a IBx, but a cut-off > 40% could be better in series of opportunistic screening like ours. Similar results appear in HGPCa analysis. The decision curve also shows a net benefit of 6.31% for the threshold probability of 40%. PCA3 is an useful tool to select patients for IBx. Patients with a calculated probability of having PCa over 40% should be counseled to undergo an IBx if opportunistic screening is required.

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

PubMed

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

2017-09-19

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

The burden of urinary incontinence and urinary bother among elderly prostate cancer survivors.

PubMed

Kopp, Ryan P; Marshall, Lynn M; Wang, Patty Y; Bauer, Douglas C; Barrett-Connor, Elizabeth; Parsons, J Kellogg

2013-10-01

Data describing urinary health in elderly, community-dwelling prostate cancer (PCa) survivors are limited. To elucidate the prevalence of lower urinary tract symptoms, urinary bother, and incontinence in elderly PCa survivors compared with peers without PCa. A cross-sectional analysis of 5990 participants in the Osteoporotic Fractures in Men Research Group, a cohort study of community-dwelling men ≥ 65 yr. We characterized urinary health using self-reported urinary incontinence and the American Urological Association Symptom Index (AUA-SI). We compared urinary health measures according to type of PCa treatment in men with PCa and men without PCa using multivariate log-binomial regression to generate prevalence ratios (PRs). At baseline, 706 men (12%) reported a history of PCa, with a mean time since diagnosis of 6.3 yr. Of these men, 426 (60%) reported urinary incontinence. In adjusted analyses, observation (PR: 2.11; 95% confidence interval [CI], 1.22-3.65; p=0.007), surgery (PR: 4.41; 95% CI, 3.79-5.13; p<0.0001), radiation therapy (PR: 1.49; 95% CI, 1.06-2.08; p=0.02), and androgen-deprivation therapy (ADT) (PR: 2.02; 95% CI, 1.31-3.13; p=0.002) were each associated with daily incontinence. Daily incontinence risk increased with time since diagnosis independently of age. Observation (PR: 1.33; 95% CI, 1.00-1.78; p=0.05), surgery (PR: 1.25; 95% CI, 1.10-1.42; p=0.0008), and ADT (PR: 1.50; 95% CI, 1.26-1.79; p<0.0001) were associated with increased AUA-SI bother scores. Cancer stage and use of adjuvant or salvage therapies were not available for analysis. Compared with their peers without PCa, elderly PCa survivors had a two-fold to five-fold greater prevalence of urinary incontinence, which rose with increasing survivorship duration. Observation, surgery, and ADT were each associated with increased urinary bother. These data suggest a substantially greater burden of urinary health problems among elderly PCa survivors than previously recognized. Copyright © 2013 European Association of Urology. Published by Elsevier B.V. All rights reserved.

MiR-145 detection in urinary extracellular vesicles increase diagnostic efficiency of prostate cancer based on hydrostatic filtration dialysis method.

PubMed

Xu, Yong; Qin, Sihua; An, Taixue; Tang, Yueting; Huang, Yiyao; Zheng, Lei

2017-07-01

Extracellular vesicles (EVs) can be detected in body fluids and may serve as disease biomarkers. Increasing evidence suggests that circulating miRNAs in serum and urine may be potential non-invasive biomarkers for prostate cancer (PCa). In the present study, we aimed to investigate whether hydrostatic filtration dialysis (HFD) is suitable for urinary EVs (UEVs) isolation and whether such reported PCa-related miRNAs can be detected in UEVs as PCa biomarkers. To analyze EVs miRNAs, we searched for an easy and economic method to enrich EVs from urine samples. We compared the efficiency of HFD method and conventional ultracentrifugation (UC) in isolating UEVs. Subsequently, UEVs were isolated from patients with PCa, patients with benign prostate hyperplasia (BPH) and healthy individuals. Differential expression of four PCa-related miRNAs (miR-572, miR-1290, miR-141, and miR-145) were measured in UEVs and paired serum EVs using SYBR Green-based quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The overall performance of HFD was similar to UC. In miRNA yield, both HFD and UC can meet the needs of further analysis. The level of miR-145 in UEVs was significantly increased in patients with PCa compared with the patients with BPH (P = 0.018). In addition, significant increase was observed in miR-145 levels when patients with Gleason score ≥8 tumors compared with Gleason score ≤7 (P = 0.020). Receiver-operating characteristic curve (ROC) revealed that miR-145 in UEVs combined with serum PSA could differentiate PCa from BPH better than PSA alone (AUC 0.863 and AUC 0.805, respectively). In serum EVs, four miRNAs were significantly higher in patients with PCa than with BPH. HFD is appropriate for UEVs isolation and miRNA analysis when compared with conventional UC. miR-145 in UEVs is upregulated from PCa patients compared BPH patients and healthy controls. We suggest the potential use of UEVs miR-145 as a biomarker of PCa. © 2017 Wiley Periodicals, Inc.

Untargeted Identification of Wood Type-Specific Markers in Particulate Matter from Wood Combustion.

PubMed

Weggler, Benedikt A; Ly-Verdu, Saray; Jennerwein, Maximilian; Sippula, Olli; Reda, Ahmed A; Orasche, Jürgen; Gröger, Thomas; Jokiniemi, Jorma; Zimmermann, Ralf

2016-09-20

Residential wood combustion emissions are one of the major global sources of particulate and gaseous organic pollutants. However, the detailed chemical compositions of these emissions are poorly characterized due to their highly complex molecular compositions, nonideal combustion conditions, and sample preparation steps. In this study, the particulate organic emissions from a masonry heater using three types of wood logs, namely, beech, birch, and spruce, were chemically characterized using thermal desorption in situ derivatization coupled to a GCxGC-ToF/MS system. Untargeted data analyses were performed using the comprehensive measurements. Univariate and multivariate chemometric tools, such as analysis of variance (ANOVA), principal component analysis (PCA), and ANOVA simultaneous component analysis (ASCA), were used to reduce the data to highly significant and wood type-specific features. This study reveals substances not previously considered in the literature as meaningful markers for differentiation among wood types.

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

PubMed

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

2016-10-01

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

The screening research of anti-inflammatory bioactive markers from different flowering phases of Flos Lonicerae Japonicae.

PubMed

Jiang, Min; Han, Yan-qi; Zhou, Meng-ge; Zhao, Hong-zhi; Xiao, Xue; Hou, Yuan-yuan; Gao, Jie; Bai, Gang; Luo, Guo-an

2014-01-01

Flos Lonicerae Japonicae (FLJ) is an important cash crop in eastern Asia, and it is an anti-inflammatory Traditional Chinese Medicine. There are large variations in the quality of the marketed FLJ products. To find marker ingredients useful for quality control, a tandem technology integrating ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF), principal component analysis (PCA), heat map analysis and hierarchical cluster analysis coupled with a NF-κB luciferase reporter gene assay were used to identify the different ingredients from the green bud, white bud, flowering stage and leaf stages, as well as to screen the anti-inflammatory activity of FLJ compositions. As flowering progressed, the anti-inflammatory effects of FLJ gradually decreased; however, chlorogenic acid, swertiamarin and sweroside should be used to evaluate the quality of FLJ products.

The Screening Research of Anti-Inflammatory Bioactive Markers from Different Flowering Phases of Flos Lonicerae Japonicae

PubMed Central

Jiang, Min; Han, Yan-qi; Zhou, Meng-ge; Zhao, Hong-zhi; Xiao, Xue; Hou, Yuan-yuan; Gao, Jie; Bai, Gang; Luo, Guo-an

2014-01-01

Flos Lonicerae Japonicae (FLJ) is an important cash crop in eastern Asia, and it is an anti-inflammatory Traditional Chinese Medicine. There are large variations in the quality of the marketed FLJ products. To find marker ingredients useful for quality control, a tandem technology integrating ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF), principal component analysis (PCA), heat map analysis and hierarchical cluster analysis coupled with a NF-κB luciferase reporter gene assay were used to identify the different ingredients from the green bud, white bud, flowering stage and leaf stages, as well as to screen the anti-inflammatory activity of FLJ compositions. As flowering progressed, the anti-inflammatory effects of FLJ gradually decreased; however, chlorogenic acid, swertiamarin and sweroside should be used to evaluate the quality of FLJ products. PMID:24809338

Nanocrystalline Nb-Al-Ge mixtures fabricated using wet mechanical milling

NASA Astrophysics Data System (ADS)

Pusceddu, E.; Charlton, S.; Hampshire, D. P.

2008-02-01

An investigation into Nb-Al-Ge mixtures is presented with special attention to the superconducting compounds Nb3(Al1-xGex) with x = 0, 0.3 and 1, which are reported to provide the highest upper critical field values for Nb-based compounds. Wet mechanical milling using copper milling media and distilled water as a process control agent (PCA) was used with the intention of improving the yield, properties and the performance of these materials. Very high yields of nanocrystalline material were achieved but significant copper contamination occurred - confirmed using inductively-coupled-plasma atomic-emission-spectroscopy. Simultaneous thermogravimetric measurements and differential scanning calorimetry were performed on powders milled for up to 20 h with different PCA content, to quantify the work done on the powders. A typical grain size of a few nm was obtained for the Nb-Al-Ge mixtures after several hours milling. Powder ground for 20 h with 5% PCA was processed using a hot isostatic press (HIP) operating at 2000 atm and temperatures up to 750 °C. The room temperature resistivity decreased as the temperature of the HIPing increased. Unfortunately, despite the nanocrystalline microstructure of the powders and the high HIP temperatures, if superconducting material was formed it was below the detection level of resistivity, Ac. susceptibility and SQUID measurements. We conclude that during milling there was widespread contamination of the powders by the PCA so that milling with distilled water as a PCA is not to be recommended for fabricating nanocrystalline Nb3(Al1-xGex) A15 superconducting compounds.

Overexpression of a BAHD Acyltransferase, OsAt10, Alters Rice Cell Wall Hydroxycinnamic Acid Content and Saccharification1[C][W][OA

PubMed Central

Bartley, Laura E.; Peck, Matthew L.; Kim, Sung-Ryul; Ebert, Berit; Manisseri, Chithra; Chiniquy, Dawn M.; Sykes, Robert; Gao, Lingfang; Rautengarten, Carsten; Vega-Sánchez, Miguel E.; Benke, Peter I.; Canlas, Patrick E.; Cao, Peijian; Brewer, Susan; Lin, Fan; Smith, Whitney L.; Zhang, Xiaohan; Keasling, Jay D.; Jentoff, Rolf E.; Foster, Steven B.; Zhou, Jizhong; Ziebell, Angela; An, Gynheung; Scheller, Henrik V.; Ronald, Pamela C.

2013-01-01

Grass cell wall properties influence food, feed, and biofuel feedstock usage efficiency. The glucuronoarabinoxylan of grass cell walls is esterified with the phenylpropanoid-derived hydroxycinnamic acids ferulic acid (FA) and para-coumaric acid (p-CA). Feruloyl esters undergo oxidative coupling with neighboring phenylpropanoids on glucuronoarabinoxylan and lignin. Examination of rice (Oryza sativa) mutants in a grass-expanded and -diverged clade of BAHD acyl-coenzyme A-utilizing transferases identified four mutants with altered cell wall FA or p-CA contents. Here, we report on the effects of overexpressing one of these genes, OsAt10 (LOC_Os06g39390), in rice. An activation-tagged line, OsAT10-D1, shows a 60% reduction in matrix polysaccharide-bound FA and an approximately 300% increase in p-CA in young leaf tissue but no discernible phenotypic alterations in vegetative development, lignin content, or lignin composition. Two additional independent OsAt10 overexpression lines show similar changes in FA and p-CA content. Cell wall fractionation and liquid chromatography-mass spectrometry experiments isolate the cell wall alterations in the mutant to ester conjugates of a five-carbon sugar with p-CA and FA. These results suggest that OsAT10 is a p-coumaroyl coenzyme A transferase involved in glucuronoarabinoxylan modification. Biomass from OsAT10-D1 exhibits a 20% to 40% increase in saccharification yield depending on the assay. Thus, OsAt10 is an attractive target for improving grass cell wall quality for fuel and animal feed. PMID:23391577

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

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2017-01-01

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

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

PubMed

Liu, Chengjun

2004-05-01

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

Multivariate Analysis of Fruit Antioxidant Activities of Blackberry Treated with 1-Methylcyclopropene or Vacuum Precooling

PubMed Central

Li, Jian; Ma, Guowei; Ma, Lin; Bao, Xiaolin; Li, Liping; Zhao, Qian

2018-01-01

Effects of 1-methylcyclopropene (1-MCP) and vacuum precooling on quality and antioxidant properties of blackberries (Rubus spp.) were evaluated using one-way analysis of variance, principal component analysis (PCA), partial least squares (PLS), and path analysis. Results showed that the activities of antioxidant enzymes were enhanced by both 1-MCP treatment and vacuum precooling. PCA could discriminate 1-MCP treated fruit and the vacuum precooled fruit and showed that the radical-scavenging activities in vacuum precooled fruit were higher than those in 1-MCP treated fruit. The scores of PCA showed that H2O2 content was the most important variables of blackberry fruit. PLSR results showed that peroxidase (POD) activity negatively correlated with H2O2 content. The results of path coefficient analysis indicated that glutathione (GSH) also had an indirect effect on H2O2 content. PMID:29487622

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

PubMed

Ghosh, Debasree; Chattopadhyay, Parimal

2012-06-01

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

Fixed Eigenvector Analysis of Thermographic NDE Data

NASA Technical Reports Server (NTRS)

Cramer, K. Elliott; Winfree, William P.

2011-01-01

Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. This paper will discuss an alternative method of analysis that has been developed where a predetermined set of eigenvectors is used to process the thermal data from both reinforced carbon-carbon (RCC) and graphiteepoxy honeycomb materials. These eigenvectors can be generated either from an analytic model of the thermal response of the material system under examination, or from a large set of experimental data. This paper provides the details of the analytic model, an overview of the PCA process, as well as a quantitative signal-to-noise comparison of the results of performing both conventional PCA and fixed eigenvector analysis on thermographic data from two specimens, one Reinforced Carbon-Carbon with flat bottom holes and the second a sandwich construction with graphite-epoxy face sheets and aluminum honeycomb core.

L-dopa decarboxylase (DDC) gene expression is related to outcome in patients with prostate cancer.

PubMed

Koutalellis, Georgios; Stravodimos, Konstantinos; Avgeris, Margaritis; Mavridis, Konstantinos; Scorilas, Andreas; Lazaris, Andreas; Constantinides, Constantinos

2012-09-01

What's known on the subject? and What does the study add? L-dopa decarboxylase (DDC) has been documented as a novel co-activator of androgen receptor transcriptional activity. Recently, it was shown that DDC gene expression is significantly higher in patients with PCa than in those with BPH. In the present study, there was a significant association between the DDC gene expression levels and the pathological stage and Gleason score of patients with prostate cancer (PCa). Moreover, DDC expression was shown to be an unfavourable prognostic marker of biochemical recurrence and disease-free survival in patients with PCa treated by radical prostatectomy. To determine whether L-dopa decarboxylase gene (DDC) expression levels in patients with prostate cancer (PCa) correlate to biochemical recurrence and disease prognosis after radical prostatectomy (RP). The present study consisted of 56 samples with confirmed malignancy from patients with PCa who had undergone RP at a single tertiary academic centre. Total RNA was isolated from tissue specimens and a SYBR Green fluorescence-based quantitative real-time polymerase chain reaction methodology was developed for the determination of DDC mRNA expression levels of the tested tissues. Follow-up time ranged between 1.0 and 62.0 months (mean ± SE, 28.6 ± 2.1 month; median, 31.5 months). Time to biochemical recurrence was defined as the interval between the surgery and the measurement of two consecutive values of prostate-specific antigen (PSA) ≥0.2 ng/mL. DDC expression levels were found to be positively correlated with the tumour-node-metastasis stage (P = 0.021) and Gleason score (P = 0.036) of the patients with PCa. Patients with PCa with raised DDC expression levels run a significantly higher risk of biochemical recurrence after RP, as indicated by Cox proportional regression analysis (P = 0.021). Multivariate Cox proportional regression models revealed the preoperative PSA-, age- and digital rectal examination-independent prognostic value of DDC expression for the prediction of disease-free survival (DFS) among patients with PCa (P = 0.036). Kaplan-Meier survival analysis confirms the significantly shorter DFS after RP of PCa with higher DDC expression levels (P = 0.015). This is the first study indicating the potential of DDC expression as a novel prognostic biomarker in patients with PCa who have undergone RP. For further evaluation and clinical application of the findings of the present study, a direct analysis of mRNA and/or its protein expression level in preoperative biopsy, blood serum and urine should be conducted. © 2012 BJU INTERNATIONAL.

Perturbational formulation of principal component analysis in molecular dynamics simulation.

PubMed

Koyama, Yohei M; Kobayashi, Tetsuya J; Tomoda, Shuji; Ueda, Hiroki R

2008-10-01

Conformational fluctuations of a molecule are important to its function since such intrinsic fluctuations enable the molecule to respond to the external environmental perturbations. For extracting large conformational fluctuations, which predict the primary conformational change by the perturbation, principal component analysis (PCA) has been used in molecular dynamics simulations. However, several versions of PCA, such as Cartesian coordinate PCA and dihedral angle PCA (dPCA), are limited to use with molecules with a single dominant state or proteins where the dihedral angle represents an important internal coordinate. Other PCAs with general applicability, such as the PCA using pairwise atomic distances, do not represent the physical meaning clearly. Therefore, a formulation that provides general applicability and clearly represents the physical meaning is yet to be developed. For developing such a formulation, we consider the conformational distribution change by the perturbation with arbitrary linearly independent perturbation functions. Within the second order approximation of the Kullback-Leibler divergence by the perturbation, the PCA can be naturally interpreted as a method for (1) decomposing a given perturbation into perturbations that independently contribute to the conformational distribution change or (2) successively finding the perturbation that induces the largest conformational distribution change. In this perturbational formulation of PCA, (i) the eigenvalue measures the Kullback-Leibler divergence from the unperturbed to perturbed distributions, (ii) the eigenvector identifies the combination of the perturbation functions, and (iii) the principal component determines the probability change induced by the perturbation. Based on this formulation, we propose a PCA using potential energy terms, and we designate it as potential energy PCA (PEPCA). The PEPCA provides both general applicability and clear physical meaning. For demonstrating its power, we apply the PEPCA to an alanine dipeptide molecule in vacuum as a minimal model of a nonsingle dominant conformational biomolecule. The first and second principal components clearly characterize two stable states and the transition state between them. Positive and negative components with larger absolute values of the first and second eigenvectors identify the electrostatic interactions, which stabilize or destabilize each stable state and the transition state. Our result therefore indicates that PCA can be applied, by carefully selecting the perturbation functions, not only to identify the molecular conformational fluctuation but also to predict the conformational distribution change by the perturbation beyond the limitation of the previous methods.

Perturbational formulation of principal component analysis in molecular dynamics simulation

NASA Astrophysics Data System (ADS)

Koyama, Yohei M.; Kobayashi, Tetsuya J.; Tomoda, Shuji; Ueda, Hiroki R.

2008-10-01

Conformational fluctuations of a molecule are important to its function since such intrinsic fluctuations enable the molecule to respond to the external environmental perturbations. For extracting large conformational fluctuations, which predict the primary conformational change by the perturbation, principal component analysis (PCA) has been used in molecular dynamics simulations. However, several versions of PCA, such as Cartesian coordinate PCA and dihedral angle PCA (dPCA), are limited to use with molecules with a single dominant state or proteins where the dihedral angle represents an important internal coordinate. Other PCAs with general applicability, such as the PCA using pairwise atomic distances, do not represent the physical meaning clearly. Therefore, a formulation that provides general applicability and clearly represents the physical meaning is yet to be developed. For developing such a formulation, we consider the conformational distribution change by the perturbation with arbitrary linearly independent perturbation functions. Within the second order approximation of the Kullback-Leibler divergence by the perturbation, the PCA can be naturally interpreted as a method for (1) decomposing a given perturbation into perturbations that independently contribute to the conformational distribution change or (2) successively finding the perturbation that induces the largest conformational distribution change. In this perturbational formulation of PCA, (i) the eigenvalue measures the Kullback-Leibler divergence from the unperturbed to perturbed distributions, (ii) the eigenvector identifies the combination of the perturbation functions, and (iii) the principal component determines the probability change induced by the perturbation. Based on this formulation, we propose a PCA using potential energy terms, and we designate it as potential energy PCA (PEPCA). The PEPCA provides both general applicability and clear physical meaning. For demonstrating its power, we apply the PEPCA to an alanine dipeptide molecule in vacuum as a minimal model of a nonsingle dominant conformational biomolecule. The first and second principal components clearly characterize two stable states and the transition state between them. Positive and negative components with larger absolute values of the first and second eigenvectors identify the electrostatic interactions, which stabilize or destabilize each stable state and the transition state. Our result therefore indicates that PCA can be applied, by carefully selecting the perturbation functions, not only to identify the molecular conformational fluctuation but also to predict the conformational distribution change by the perturbation beyond the limitation of the previous methods.

Application of GC/MS-based metabonomic profiling in studying the lipid-regulating effects of Ginkgo biloba extract on diet-induced hyperlipidemia in rats

PubMed Central

Zhang, Qi; Wang, Guang-ji; A, Ji-ye; Wu, Di; Zhu, Ling-ling; Ma, Bo; Du, Yu

2009-01-01

Aim: To evaluate the lipid-regulating effects of extract from Ginkgo biloba leaves (EGB) using pharmacological methods and metabonomic profiling in a rat model of diet-induced hyperlipidemia. Methods: EGB was orally administered at a dose level of 40 mg/kg in both the EGB-prevention and -treatment groups. All rat samples obtained were examined for known and potential biomarkers and enzyme activity using commercial assay kits and GC/MS-based metabonomic profiling coupled with principal component analysis (PCA). Results: The data obtained from the assay kits indicated that EGB reduced total cholesterol and low density lipoprotein cholesterol levels and increased high density lipoprotein cholesterol levels in rat plasma obtained from both the EGB-prevention and –treatment groups compared with those of the diet-induced hyperlipidemia group. EGB also increased the activities of lipoprotein lipase and hepatic lipase and excretion of fecal bile acid in rats from the EGB-prevention and–treatment groups. Using GC/MS-based metabonomic analysis, more than 40 endogenous metabolites were identified in rat plasma. PCA of rat plasma samples obtained using GC/MS produced a distinctive separation of the four treatment groups and sampling points within each group. Metabolic changes during hyperlipidemia formation and improvement resulting from EGB treatment were definitively monitored with PCA score plots. Furthermore, elevated levels of sorbitol, tyrosine, glutamine and glucose, and decreased levels of citric acid, galactose, palmitic acid, arachidonic acid, acetic acid, cholesterol, butyrate, creatinine, linoleate, ornithine and proline, were observed in the plasma of rats treated with EGB. Conclusion: EGB exerts multi-directional lipid-lowering effects on the rat metabonome, including limitation of the absorption of cholesterol, inactivation of HMGCoA and favorable regulation of profiles of essential polyunsaturated fatty acid (EFA). Further experiments are warranted to explore the mechanisms of action underlying the lipid-regulating effects of EGB against hyperlipidemia. PMID:19960012

Application of GC/MS-based metabonomic profiling in studying the lipid-regulating effects of Ginkgo biloba extract on diet-induced hyperlipidemia in rats.

PubMed

Zhang, Qi; Wang, Guang-ji; A, Ji-ye; Wu, Di; Zhu, Ling-ling; Ma, Bo; Du, Yu

2009-12-01

To evaluate the lipid-regulating effects of extract from Ginkgo biloba leaves (EGB) using pharmacological methods and metabonomic profiling in a rat model of diet-induced hyperlipidemia. EGB was orally administered at a dose level of 40 mg/kg in both the EGB-prevention and -treatment groups. All rat samples obtained were examined for known and potential biomarkers and enzyme activity using commercial assay kits and GC/MS-based metabonomic profiling coupled with principal component analysis (PCA). The data obtained from the assay kits indicated that EGB reduced total cholesterol and low density lipoprotein cholesterol levels and increased high density lipoprotein cholesterol levels in rat plasma obtained from both the EGB-prevention and -treatment groups compared with those of the diet-induced hyperlipidemia group. EGB also increased the activities of lipoprotein lipase and hepatic lipase and excretion of fecal bile acid in rats from the EGB-prevention and-treatment groups. Using GC/MS-based metabonomic analysis, more than 40 endogenous metabolites were identified in rat plasma. PCA of rat plasma samples obtained using GC/MS produced a distinctive separation of the four treatment groups and sampling points within each group. Metabolic changes during hyperlipidemia formation and improvement resulting from EGB treatment were definitively monitored with PCA score plots. Furthermore, elevated levels of sorbitol, tyrosine, glutamine and glucose, and decreased levels of citric acid, galactose, palmitic acid, arachidonic acid, acetic acid, cholesterol, butyrate, creatinine, linoleate, ornithine and proline, were observed in the plasma of rats treated with EGB. EGB exerts multi-directional lipid-lowering effects on the rat metabonome, including limitation of the absorption of cholesterol, inactivation of HMGCoA and favorable regulation of profiles of essential polyunsaturated fatty acid (EFA). Further experiments are warranted to explore the mechanisms of action underlying the lipid-regulating effects of EGB against hyperlipidemia.

Cell wall composition and digestibility alterations in Brachypodium distachyon achieved through reduced expression of the UDP-arabinopyranose mutase

PubMed Central

Rancour, David M.; Hatfield, Ronald D.; Marita, Jane M.; Rohr, Nicholas A.; Schmitz, Robert J.

2015-01-01

Nucleotide-activated sugars are essential substrates for plant cell-wall carbohydrate-polymer biosynthesis. The most prevalent grass cell wall (CW) sugars are glucose (Glc), xylose (Xyl), and arabinose (Ara). These sugars are biosynthetically related via the UDP–sugar interconversion pathway. We sought to target and generate UDP–sugar interconversion pathway transgenic Brachypodium distachyon lines resulting in CW carbohydrate composition changes with improved digestibility and normal plant stature. Both RNAi-mediated gene-suppression and constitutive gene-expression approaches were performed. CWs from 336 T0 transgenic plants with normal appearance were screened for complete carbohydrate composition. RNAi mutants of BdRGP1, a UDP-arabinopyranose mutase, resulted in large alterations in CW carbohydrate composition with significant decreases in CW Ara content but with minimal change in plant stature. Five independent RNAi-RGP1 T1 plant lines were used for in-depth analysis of plant CWs. Real-time PCR analysis indicated that gene expression levels for BdRGP1, BdRGP2, and BdRGP3 were reduced in RNAi-RGP1 plants to 15–20% of controls. CW Ara content was reduced by 23–51% of control levels. No alterations in CW Xyl and Glc content were observed. Corresponding decreases in CW ferulic acid (FA) and ferulic acid-dimers (FA-dimers) were observed. Additionally, CW p-coumarates (pCA) were decreased. We demonstrate the CW pCA decrease corresponds to Ara-coupled pCA. Xylanase-mediated digestibility of RNAi-RGP1 Brachypodium CWs resulted in a near twofold increase of released total carbohydrate. However, cellulolytic hydrolysis of CW material was inhibited in leaves of RNAi-RGP1 mutants. Our results indicate that targeted manipulation of UDP–sugar biosynthesis can result in biomass with substantially altered compositions and highlights the complex effect CW composition has on digestibility. PMID:26136761

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

PubMed Central

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

2014-01-01

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. PMID:24981110

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

PubMed

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.

Biotransformation of ferulic acid to protocatechuic acid by Corynebacterium glutamicum ATCC 21420 engineered to express vanillate O-demethylase.

PubMed

Okai, Naoko; Masuda, Takaya; Takeshima, Yasunobu; Tanaka, Kosei; Yoshida, Ken-Ichi; Miyamoto, Masanori; Ogino, Chiaki; Kondo, Akihiko

2017-12-01

Ferulic acid (4-hydroxy-3-methoxycinnamic acid, FA) is a lignin-derived phenolic compound abundant in plant biomass. The utilization of FA and its conversion to valuable compounds is desired. Protocatechuic acid (3,4-dihydroxybenzoic acid, PCA) is a precursor of polymers and plastics and a constituent of food. A microbial conversion system to produce PCA from FA was developed in this study using a PCA-producing strain of Corynebacterium glutamicum F (ATCC 21420). C. glutamicum strain F grown at 30 °C for 48 h utilized 2 mM each of FA and vanillic acid (4-hydroxy-3-methoxybenzoic acid, VA) to produce PCA, which was secreted into the medium. FA may be catabolized by C. glutamicum through proposed (I) non-β-oxidative, CoA-dependent or (II) β-oxidative, CoA-dependent phenylpropanoid pathways. The conversion of VA to PCA is the last step in each pathway. Therefore, the vanillate O-demethylase gene (vanAB) from Corynebacterium efficiens NBRC 100395 was expressed in C. glutamicum F (designated strain FVan) cultured at 30 °C in AF medium containing FA. Strain C. glutamicum FVan converted 4.57 ± 0.07 mM of FA into 2.87 ± 0.01 mM PCA after 48 h with yields of 62.8% (mol/mol), and 6.91 mM (1064 mg/L) of PCA was produced from 16.0 mM of FA after 12 h of fed-batch biotransformation. Genomic analysis of C. glutamicum ATCC 21420 revealed that the PCA-utilization genes (pca cluster) were conserved in strain ATCC 21420 and that mutations were present in the PCA importer gene pcaK.

A genetic variant in SLC28A3, rs56350726, is associated with progression to castration-resistant prostate cancer in a Korean population with metastatic prostate cancer.

PubMed

Jo, Jung Ku; Oh, Jong Jin; Kim, Yong Tae; Moon, Hong Sang; Choi, Hong Yong; Park, Seunghyun; Ho, Jin-Nyoung; Yoon, Sungroh; Park, Hae Young; Byun, Seok-Soo

2017-11-14

Genetic variation which related with progression to castration-resistant prostate cancer (CRPC) during androgen-deprivation therapy (ADT) has not been elucidated in patients with metastatic prostate cancer (mPCa). Therefore, we assessed the association between genetic variats in mPCa and progession to CRPC. Analysis of exome genotypes revealed that 42 SNPs were significantly associated with mPCa. The top five polymorphisms were statistically significantly associated with metastatic disease. In addition, one of these SNPs, rs56350726, was significantly associated with time to CRPC in Kaplan-Meier analysis (Log-rank test, p = 0.011). In multivariable Cox regression, rs56350726 was strongly associated with progression to CRPC (HR = 4.172 95% CI = 1.223-14.239, p = 0.023). We assessed genetic variation among 1000 patients with PCa with or without metastasis, using 242,221 single nucleotide polymorphisms (SNPs) on the custom HumanExome BeadChip v1.0 (Illuminam Inc.). We analyzed the time to CRPC in 110 of the 1000 patients who were treated with ADT. Genetic data were analyzed using unconditional logistic regression and odds ratios calculated as estimates of relative risk of metastasis. We identified SNPs associated with metastasis and analyzed the relationship between these SNPs and time to CRPC in mPCa. Based on a genetic variation, the five top SNPs were observed to associate with mPCa. And one (SLC28A3, rs56350726) of five SNP was found the association with the progression to CRPC in patients with mPCa.

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

PubMed

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.

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

PubMed

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

2018-02-01

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

Reading Achievement in Relation to Phonological Coding and Awareness in Deaf Readers: A Meta-Analysis

ERIC Educational Resources Information Center

Mayberry, Rachel I.; del Giudice, Alex A.; Lieberman, Amy M.

2011-01-01

The relation between reading ability and phonological coding and awareness (PCA) skills in individuals who are severely and profoundly deaf was investigated with a meta-analysis. From an initial set of 230 relevant publications, 57 studies were analyzed that experimentally tested PCA skills in 2,078 deaf participants. Half of the studies found…

Identification of regional activation by factorization of high-density surface EMG signals: A comparison of Principal Component Analysis and Non-negative Matrix factorization.

PubMed

Gallina, Alessio; Garland, S Jayne; Wakeling, James M

2018-05-22

In this study, we investigated whether principal component analysis (PCA) and non-negative matrix factorization (NMF) perform similarly for the identification of regional activation within the human vastus medialis. EMG signals from 64 locations over the VM were collected from twelve participants while performing a low-force isometric knee extension. The envelope of the EMG signal of each channel was calculated by low-pass filtering (8 Hz) the monopolar EMG signal after rectification. The data matrix was factorized using PCA and NMF, and up to 5 factors were considered for each algorithm. Association between explained variance, spatial weights and temporal scores between the two algorithms were compared using Pearson correlation. For both PCA and NMF, a single factor explained approximately 70% of the variance of the signal, while two and three factors explained just over 85% or 90%. The variance explained by PCA and NMF was highly comparable (R > 0.99). Spatial weights and temporal scores extracted with non-negative reconstruction of PCA and NMF were highly associated (all p < 0.001, mean R > 0.97). Regional VM activation can be identified using high-density surface EMG and factorization algorithms. Regional activation explains up to 30% of the variance of the signal, as identified through both PCA and NMF. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production

PubMed Central

Mueller, Daniela; Ferrão, Marco Flôres; Marder, Luciano; da Costa, Adilson Ben; de Cássia de Souza Schneider, Rosana

2013-01-01

The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources—canola, cotton, corn, palm, sunflower and soybeans—were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples. PMID:23539030

A multivariate approach using attenuated total reflectance mid-infrared spectroscopy to measure the surface mannoproteins and β-glucans of yeast cell walls during wine fermentations.

PubMed

Moore, John P; Zhang, Song-Lei; Nieuwoudt, Hélène; Divol, Benoit; Trygg, Johan; Bauer, Florian F

2015-11-18

Yeast cells possess a cell wall comprising primarily glycoproteins, mannans, and glucan polymers. Several yeast phenotypes relevant for fermentation, wine processing, and wine quality are correlated with cell wall properties. To investigate the effect of wine fermentation on cell wall composition, a study was performed using mid-infrared (MIR) spectroscopy coupled with multivariate methods (i.e., PCA and OPLS-DA). A total of 40 yeast strains were evaluated, including Saccharomyces strains (laboratory and industrial) and non-Saccharomyces species. Cells were fermented in both synthetic MS300 and Chardonnay grape must to stationery phase, processed, and scanned in the MIR spectrum. PCA of the fingerprint spectral region showed distinct separation of Saccharomyces strains from non-Saccharomyces species; furthermore, industrial wine yeast strains separated from laboratory strains. PCA loading plots and the use of OPLS-DA to the data sets suggested that industrial strains were enriched with cell wall proteins (e.g., mannoproteins), whereas laboratory strains were composed mainly of mannan and glucan polymers.

Validation of the BUGJEFF311.BOLIB, BUGENDF70.BOLIB and BUGLE-B7 broad-group libraries on the PCA-Replica (H2O/Fe) neutron shielding benchmark experiment

NASA Astrophysics Data System (ADS)

Pescarini, Massimo; Orsi, Roberto; Frisoni, Manuela

2016-03-01

The PCA-Replica 12/13 (H2O/Fe) neutron shielding benchmark experiment was analysed using the TORT-3.2 3D SN code. PCA-Replica reproduces a PWR ex-core radial geometry with alternate layers of water and steel including a pressure vessel simulator. Three broad-group coupled neutron/photon working cross section libraries in FIDO-ANISN format with the same energy group structure (47 n + 20 γ) and based on different nuclear data were alternatively used: the ENEA BUGJEFF311.BOLIB (JEFF-3.1.1) and UGENDF70.BOLIB (ENDF/B-VII.0) libraries and the ORNL BUGLE-B7 (ENDF/B-VII.0) library. Dosimeter cross sections derived from the IAEA IRDF-2002 dosimetry file were employed. The calculated reaction rates for the Rh-103(n,n')Rh-103m, In-115(n,n')In-115m and S-32(n,p)P-32 threshold activation dosimeters and the calculated neutron spectra are compared with the corresponding experimental results.

Statistical techniques applied to aerial radiometric surveys (STAARS): principal components analysis user's manual. [NURE program

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

Koch, C.D.; Pirkle, F.L.; Schmidt, J.S.

1981-01-01

A Principal Components Analysis (PCA) has been written to aid in the interpretation of multivariate aerial radiometric data collected by the US Department of Energy (DOE) under the National Uranium Resource Evaluation (NURE) program. The variations exhibited by these data have been reduced and classified into a number of linear combinations by using the PCA program. The PCA program then generates histograms and outlier maps of the individual variates. Black and white plots can be made on a Calcomp plotter by the application of follow-up programs. All programs referred to in this guide were written for a DEC-10. From thismore » analysis a geologist may begin to interpret the data structure. Insight into geological processes underlying the data may be obtained.« less

Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth.

PubMed

Thon, Anika; Teichgräber, Ulf; Tennstedt-Schenk, Cornelia; Hadjidemetriou, Stathis; Winzler, Sven; Malich, Ansgar; Papageorgiou, Ismini

2017-01-01

Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary™) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade. To assess the performance of Watson Elementary™ in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies. The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary™ utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth. The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ2 test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation). The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis.

Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth

PubMed Central

Thon, Anika; Teichgräber, Ulf; Tennstedt-Schenk, Cornelia; Hadjidemetriou, Stathis; Winzler, Sven; Malich, Ansgar

2017-01-01

Background Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary™) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade. Aim/Objective To assess the performance of Watson Elementary™ in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies. Methods The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary™ utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth. Results The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ2 test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation). Conclusion The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis. PMID:29023572

Authentication of animal origin of heparin and low molecular weight heparin including ovine, porcine and bovine species using 1D NMR spectroscopy and chemometric tools.

PubMed

Monakhova, Yulia B; Diehl, Bernd W K; Fareed, Jawed

2018-02-05

High resolution (600MHz) nuclear magnetic resonance (NMR) spectroscopy is used to distinguish heparin and low-molecular weight heparins (LMWHs) produced from porcine, bovine and ovine mucosal tissues as well as their blends. For multivariate analysis several statistical methods such as principal component analysis (PCA), factor discriminant analysis (FDA), partial least squares - discriminant analysis (PLS-DA), linear discriminant analysis (LDA) were utilized for the modeling of NMR data of more than 100 authentic samples. Heparin and LMWH samples from the independent test set (n=15) were 100% correctly classified according to its animal origin. Moreover, by using 1 H NMR coupled with chemometrics and several batches of bovine heparins from two producers were differentiated. Thus, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of animal origin and process based manufacturing difference in heparin products. Copyright © 2017 Elsevier B.V. All rights reserved.

Differentiation of fresh and frozen-thawed fish samples using Raman spectroscopy coupled with chemometric analysis.

PubMed

Velioğlu, Hasan Murat; Temiz, Havva Tümay; Boyaci, Ismail Hakki

2015-04-01

The potential of Raman spectroscopy was investigated in terms of its capability to discriminate the species of the fish samples and determine their freshness according to the number of freezing/thawing cycles they exposed. Species discrimination analysis was carried out on sixty-four fish samples from six different species, namely horse mackerel (Trachurus trachurus), European anchovy (Engraulis encrasicolus), red mullet (Mullus surmuletus), Bluefish (Pomatamus saltatrix), Atlantic salmon (Salmo salar) and flying gurnard (Trigla lucerna). Afterwards, fish samples were exposed to different numbers of freezing/thawing cycles and separated into three batches, namely (i) fresh, (ii) once frozen-thawed (OF) and (iii) twice frozen-thawed (TF) samples, in order to perform the freshness analysis. Raman data collected were used as inputs for chemometric analysis, which enabled us to develop two main PCA models to successfully terminate the studies for both species discrimination and freshness determination analysis. Copyright © 2014 Elsevier Ltd. All rights reserved.

Qualitative and quantitative analysis of milk for the detection of adulteration by Laser Induced Breakdown Spectroscopy (LIBS).

PubMed

Moncayo, S; Manzoor, S; Rosales, J D; Anzano, J; Caceres, J O

2017-10-01

The present work focuses on the development of a fast and cost effective method based on Laser Induced Breakdown Spectroscopy (LIBS) to the quality control, traceability and detection of adulteration in milk. Two adulteration cases have been studied; a qualitative analysis for the discrimination between different milk blends and quantification of melamine in adulterated toddler milk powder. Principal Component Analysis (PCA) and neural networks (NN) have been used to analyze LIBS spectra obtaining a correct classification rate of 98% with a 100% of robustness. For the quantification of melamine, two methodologies have been developed; univariate analysis using CN emission band and multivariate calibration NN model obtaining correlation coefficient (R 2 ) values of 0.982 and 0.999 respectively. The results of the use of LIBS technique coupled with chemometric analysis are discussed in terms of its potential use in the food industry to perform the quality control of this dairy product. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Bednarz, Natalia; Eltze, Elke; Semjonow, Axel

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

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia.

PubMed

Pérez-Rambla, Clara; Puchades-Carrasco, Leonor; García-Flores, María; Rubio-Briones, José; López-Guerrero, José Antonio; Pineda-Lucena, Antonio

2017-01-01

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. Serum prostate specific antigen (PSA) level has been extensively used as a biomarker to detect PCa. However, PSA is not cancer-specific and various non-malignant conditions, including benign prostatic hyperplasia (BPH), can cause a rise in PSA blood levels, thus leading to many false positive results. In this study, we evaluated the potential of urinary metabolomic profiling for discriminating PCa from BPH. Urine samples from 64 PCa patients and 51 individuals diagnosed with BPH were analysed using 1 H nuclear magnetic resonance ( 1 H-NMR). Comparative analysis of urinary metabolomic profiles was carried out using multivariate and univariate statistical approaches. The urine metabolomic profile of PCa patients is characterised by increased concentrations of branched-chain amino acids (BCAA), glutamate and pseudouridine, and decreased concentrations of glycine, dimethylglycine, fumarate and 4-imidazole-acetate compared with individuals diagnosed with BPH. PCa patients have a specific urinary metabolomic profile. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be useful to discriminate PCa from BPH in a clinical context.

Regulators of gene expression as biomarkers for prostate cancer

PubMed Central

Willard, Stacey S; Koochekpour, Shahriar

2012-01-01

Recent technological advancements in gene expression analysis have led to the discovery of a promising new group of prostate cancer (PCa) biomarkers that have the potential to influence diagnosis and the prediction of disease severity. The accumulation of deleterious changes in gene expression is a fundamental mechanism of prostate carcinogenesis. Aberrant gene expression can arise from changes in epigenetic regulation or mutation in the genome affecting either key regulatory elements or gene sequences themselves. At the epigenetic level, a myriad of abnormal histone modifications and changes in DNA methylation are found in PCa patients. In addition, many mutations in the genome have been associated with higher PCa risk. Finally, over- or underexpression of key genes involved in cell cycle regulation, apoptosis, cell adhesion and regulation of transcription has been observed. An interesting group of biomarkers are emerging from these studies which may prove more predictive than the standard prostate specific antigen (PSA) serum test. In this review, we discuss recent results in the field of gene expression analysis in PCa including the most promising biomarkers in the areas of epigenetics, genomics and the transcriptome, some of which are currently under investigation as clinical tests for early detection and better prognostic prediction of PCa. PMID:23226612

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

PubMed

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

2018-03-20

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

Shift work, night work, and the risk of prostate cancer: A meta-analysis based on 9 cohort studies.

PubMed

Du, Hong-Bing; Bin, Kai-Yun; Liu, Wen-Hong; Yang, Feng-Sheng

2017-11-01

Epidemiology studies suggested that shift work or night work may be linked to prostate cancer (PCa); the relationship, however, remains controversy. PubMed, ScienceDirect, and Embase (Ovid) databases were searched before (started from the building of the databases) February 4, 2017 for eligible cohort studies. We pooled the evidence included by a random- or fixed-effect model, according to the heterogeneity. A predefined subgroup analysis was conducted to see the potential discrepancy between groups. Sensitivity analysis was used to test whether our results were stale. Nine cohort studies were eligible for meta-analysis with 2,570,790 male subjects. Our meta-analysis showed that, under the fixed-effect model, the pooled relevant risk (RR) of PCa was 1.05 (95% confidence interval [CI]: 1.00, 1.11; P = .06; I = 24.00%) for men who had ever engaged in night shift work; and under the random-effect model, the pooled RR was 1.08 (0.99, 1.17; P = .08; I = 24.00%). Subgroup analysis showed the RR of PCa among males in western countries was 1.05 (95% CI: 0.99, 1.11; P = .09; I = 0.00%), while among Asian countries it was 2.45 (95% CI: 1.19, 5.04; P = .02; I = 0.00%); and the RR was 1.04 (95% CI: 0.95, 1.14; P = .40; I = 29.20%) for the high-quality group compared with 1.21 (95% CI: 1.03, 1.41; P = .02; I = 0.00%) for the moderate/low-quality group. Sensitivity analysis showed robust results. Based on the current evidence of cohort studies, we found no obvious association between night shift work and PCa. However, our subgroup analysis suggests that night shift work may increase the risk of PCa in Asian men. Some evidence of a small study effect was observed in this meta-analysis.

A case-control study of lower urinary-tract infections, associated antibiotics and the risk of developing prostate cancer using PCBaSe 3.0.

PubMed

Russell, Beth; Garmo, Hans; Beckmann, Kerri; Stattin, Pär; Adolfsson, Jan; Van Hemelrijck, Mieke

2018-01-01

To investigate the association between lower urinary-tract infections, their associated antibiotics and the subsequent risk of developing PCa. Using data from the Swedish PCBaSe 3.0, we performed a matched case-control study (8762 cases and 43806 controls). Conditional logistic regression analysis was used to assess the association between lower urinary-tract infections, related antibiotics and PCa, whilst adjusting for civil status, education, Charlson Comorbidity Index and time between lower urinary-tract infection and PCa diagnosis. It was found that lower urinary-tract infections did not affect PCa risk, however, having a lower urinary-tract infection or a first antibiotic prescription 6-12 months before PCa were both associated with an increased risk of PCa (OR: 1.50, 95% CI: 1.23-1.82 and 1.96, 1.71-2.25, respectively), as compared to men without lower urinary-tract infections. Compared to men with no prescriptions for antibiotics, men who were prescribed ≥10 antibiotics, were 15% less likely to develop PCa (OR: 0.85, 95% CI: 0.78-0.91). PCa was not found to be associated with diagnosis of a urinary-tract infection or frequency, but was positively associated with short time since diagnoses of lower urinary-tract infection or receiving prescriptions for antibiotics. These observations can likely be explained by detection bias, which highlights the importance of data on the diagnostic work-up when studying potential risk factors for PCa.

Putative Prostate Cancer Risk SNP in an Androgen Receptor‐Binding Site of the Melanophilin Gene Illustrates Enrichment of Risk SNPs in Androgen Receptor Target Sites

PubMed Central

Bu, Huajie; Narisu, Narisu; Schlick, Bettina; Rainer, Johannes; Manke, Thomas; Schäfer, Georg; Pasqualini, Lorenza; Chines, Peter; Schweiger, Michal R.; Fuchsberger, Christian

2015-01-01

ABSTRACT Genome‐wide association studies have identified genomic loci, whose single‐nucleotide polymorphisms (SNPs) predispose to prostate cancer (PCa). However, the mechanisms of most of these variants are largely unknown. We integrated chromatin‐immunoprecipitation‐coupled sequencing and microarray expression profiling in TMPRSS2‐ERG gene rearrangement positive DUCaP cells with the GWAS PCa risk SNPs catalog to identify disease susceptibility SNPs localized within functional androgen receptor‐binding sites (ARBSs). Among the 48 GWAS index risk SNPs and 3,917 linked SNPs, 80 were found located in ARBSs. Of these, rs11891426:T>G in an intron of the melanophilin gene (MLPH) was within a novel putative auxiliary AR‐binding motif, which is enriched in the neighborhood of canonical androgen‐responsive elements. T→G exchange attenuated the transcriptional activity of the ARBS in an AR reporter gene assay. The expression of MLPH in primary prostate tumors was significantly lower in those with the G compared with the T allele and correlated significantly with AR protein. Higher melanophilin level in prostate tissue of patients with a favorable PCa risk profile points out a tumor‐suppressive effect. These results unravel a hidden link between AR and a functional putative PCa risk SNP, whose allele alteration affects androgen regulation of its host gene MLPH. PMID:26411452

Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures.

PubMed

Bouhlel, Jihéne; Jouan-Rimbaud Bouveresse, Delphine; Abouelkaram, Said; Baéza, Elisabeth; Jondreville, Catherine; Travel, Angélique; Ratel, Jérémy; Engel, Erwan; Rutledge, Douglas N

2018-02-01

The aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the "orthogonalized", "orthogonalized and Pareto-scaled", and "orthogonalized and autoscaled" data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not highlight the most influencing variables for each separation, whereas the ICA Loadings highlighted the same variables as did CCA. This study shows the potential of CCA for the extraction of pertinent information from a data matrix, using a procedure based on an original optimisation criterion, to produce results that are complementary, and in some cases may be superior, to those of PCA and ICA. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

PubMed

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.

Prostate extracellular vesicles in patient plasma as a liquid biopsy platform for prostate cancer using nanoscale flow cytometry.

PubMed

Biggs, Colleen N; Siddiqui, Khurram M; Al-Zahrani, Ali A; Pardhan, Siddika; Brett, Sabine I; Guo, Qiu Q; Yang, Jun; Wolf, Philipp; Power, Nicholas E; Durfee, Paul N; MacMillan, Connor D; Townson, Jason L; Brinker, Jeffrey C; Fleshner, Neil E; Izawa, Jonathan I; Chambers, Ann F; Chin, Joseph L; Leong, Hon S

2016-02-23

Extracellular vesicles released by prostate cancer present in seminal fluid, urine, and blood may represent a non-invasive means to identify and prioritize patients with intermediate risk and high risk of prostate cancer. We hypothesize that enumeration of circulating prostate microparticles (PMPs), a type of extracellular vesicle (EV), can identify patients with Gleason Score≥4+4 prostate cancer (PCa) in a manner independent of PSA. Plasmas from healthy volunteers, benign prostatic hyperplasia patients, and PCa patients with various Gleason score patterns were analyzed for PMPs. We used nanoscale flow cytometry to enumerate PMPs which were defined as submicron events (100-1000nm) immunoreactive to anti-PSMA mAb when compared to isotype control labeled samples. Levels of PMPs (counts/µL of plasma) were also compared to CellSearch CTC Subclasses in various PCa metastatic disease subtypes (treatment naïve, castration resistant prostate cancer) and in serially collected plasma sets from patients undergoing radical prostatectomy. PMP levels in plasma as enumerated by nanoscale flow cytometry are effective in distinguishing PCa patients with Gleason Score≥8 disease, a high-risk prognostic factor, from patients with Gleason Score≤7 PCa, which carries an intermediate risk of PCa recurrence. PMP levels were independent of PSA and significantly decreased after surgical resection of the prostate, demonstrating its prognostic potential for clinical follow-up. CTC subclasses did not decrease after prostatectomy and were not effective in distinguishing localized PCa patients from metastatic PCa patients. PMP enumeration was able to identify patients with Gleason Score ≥8 PCa but not patients with Gleason Score 4+3 PCa, but offers greater confidence than CTC counts in identifying patients with metastatic prostate cancer. CTC Subclass analysis was also not effective for post-prostatectomy follow up and for distinguishing metastatic PCa and localized PCa patients. Nanoscale flow cytometry of PMPs presents an emerging biomarker platform for various stages of prostate cancer.

Prostate extracellular vesicles in patient plasma as a liquid biopsy platform for prostate cancer using nanoscale flow cytometry

PubMed Central

Al-Zahrani, Ali A.; Pardhan, Siddika; Brett, Sabine I.; Guo, Qiu Q.; Yang, Jun; Wolf, Philipp; Power, Nicholas E.; Durfee, Paul N.; MacMillan, Connor D.; Townson, Jason L.; Brinker, Jeffrey C.; Fleshner, Neil E.; Izawa, Jonathan I.; Chambers, Ann F.; Chin, Joseph L.; Leong, Hon S.

2016-01-01

Background Extracellular vesicles released by prostate cancer present in seminal fluid, urine, and blood may represent a non-invasive means to identify and prioritize patients with intermediate risk and high risk of prostate cancer. We hypothesize that enumeration of circulating prostate microparticles (PMPs), a type of extracellular vesicle (EV), can identify patients with Gleason Score≥4+4 prostate cancer (PCa) in a manner independent of PSA. Patients and Methods Plasmas from healthy volunteers, benign prostatic hyperplasia patients, and PCa patients with various Gleason score patterns were analyzed for PMPs. We used nanoscale flow cytometry to enumerate PMPs which were defined as submicron events (100-1000nm) immunoreactive to anti-PSMA mAb when compared to isotype control labeled samples. Levels of PMPs (counts/μL of plasma) were also compared to CellSearch CTC Subclasses in various PCa metastatic disease subtypes (treatment naïve, castration resistant prostate cancer) and in serially collected plasma sets from patients undergoing radical prostatectomy. Results PMP levels in plasma as enumerated by nanoscale flow cytometry are effective in distinguishing PCa patients with Gleason Score≥8 disease, a high-risk prognostic factor, from patients with Gleason Score≤7 PCa, which carries an intermediate risk of PCa recurrence. PMP levels were independent of PSA and significantly decreased after surgical resection of the prostate, demonstrating its prognostic potential for clinical follow-up. CTC subclasses did not decrease after prostatectomy and were not effective in distinguishing localized PCa patients from metastatic PCa patients. Conclusions PMP enumeration was able to identify patients with Gleason Score ≥8 PCa but not patients with Gleason Score 4+3 PCa, but offers greater confidence than CTC counts in identifying patients with metastatic prostate cancer. CTC Subclass analysis was also not effective for post-prostatectomy follow up and for distinguishing metastatic PCa and localized PCa patients. Nanoscale flow cytometry of PMPs presents an emerging biomarker platform for various stages of prostate cancer. PMID:26814433

The Influence Function of Principal Component Analysis by Self-Organizing Rule.

PubMed

Higuchi; Eguchi

1998-07-28

This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

From measurements to metrics: PCA-based indicators of cyber anomaly

NASA Astrophysics Data System (ADS)

Ahmed, Farid; Johnson, Tommy; Tsui, Sonia

2012-06-01

We present a framework of the application of Principal Component Analysis (PCA) to automatically obtain meaningful metrics from intrusion detection measurements. In particular, we report the progress made in applying PCA to analyze the behavioral measurements of malware and provide some preliminary results in selecting dominant attributes from an arbitrary number of malware attributes. The results will be useful in formulating an optimal detection threshold in the principal component space, which can both validate and augment existing malware classifiers.

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

PubMed

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

2016-07-01

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

Human Prostatic Acid Phosphatase: Structure, Function and Regulation

PubMed Central

Muniyan, Sakthivel; Chaturvedi, Nagendra K.; Dwyer, Jennifer G.; LaGrange, Chad A.; Chaney, William G.; Lin, Ming-Fong

2013-01-01

Human prostatic acid phosphatase (PAcP) is a 100 kDa glycoprotein composed of two subunits. Recent advances demonstrate that cellular PAcP (cPAcP) functions as a protein tyrosine phosphatase by dephosphorylating ErbB-2/Neu/HER-2 at the phosphotyrosine residues in prostate cancer (PCa) cells, which results in reduced tumorigenicity. Further, the interaction of cPAcP and ErbB-2 regulates androgen sensitivity of PCa cells. Knockdown of cPAcP expression allows androgen-sensitive PCa cells to develop the castration-resistant phenotype, where cells proliferate under an androgen-reduced condition. Thus, cPAcP has a significant influence on PCa cell growth. Interestingly, promoter analysis suggests that PAcP expression can be regulated by NF-κB, via a novel binding sequence in an androgen-independent manner. Further understanding of PAcP function and regulation of expression will have a significant impact on understanding PCa progression and therapy. PMID:23698773

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

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

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

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

Complexity of free energy landscapes of peptides revealed by nonlinear principal component analysis.

PubMed

Nguyen, Phuong H

2006-12-01

Employing the recently developed hierarchical nonlinear principal component analysis (NLPCA) method of Saegusa et al. (Neurocomputing 2004;61:57-70 and IEICE Trans Inf Syst 2005;E88-D:2242-2248), the complexities of the free energy landscapes of several peptides, including triglycine, hexaalanine, and the C-terminal beta-hairpin of protein G, were studied. First, the performance of this NLPCA method was compared with the standard linear principal component analysis (PCA). In particular, we compared two methods according to (1) the ability of the dimensionality reduction and (2) the efficient representation of peptide conformations in low-dimensional spaces spanned by the first few principal components. The study revealed that NLPCA reduces the dimensionality of the considered systems much better, than did PCA. For example, in order to get the similar error, which is due to representation of the original data of beta-hairpin in low dimensional space, one needs 4 and 21 principal components of NLPCA and PCA, respectively. Second, by representing the free energy landscapes of the considered systems as a function of the first two principal components obtained from PCA, we obtained the relatively well-structured free energy landscapes. In contrast, the free energy landscapes of NLPCA are much more complicated, exhibiting many states which are hidden in the PCA maps, especially in the unfolded regions. Furthermore, the study also showed that many states in the PCA maps are mixed up by several peptide conformations, while those of the NLPCA maps are more pure. This finding suggests that the NLPCA should be used to capture the essential features of the systems. (c) 2006 Wiley-Liss, Inc.

[Identification of varieties of textile fibers by using Vis/NIR infrared spectroscopy technique].

PubMed

Wu, Gui-Fang; He, Yong

2010-02-01

The aim of the present paper was to provide new insight into Vis/NIR spectroscopic analysis of textile fibers. In order to achieve rapid identification of the varieties of fibers, the authors selected 5 kinds of fibers of cotton, flax, wool, silk and tencel to do a study with Vis/NIR spectroscopy. Firstly, the spectra of each kind of fiber were scanned by spectrometer, and principal component analysis (PCA) method was used to analyze the characteristics of the pattern of Vis/NIR spectra. Principal component scores scatter plot (PC1 x PC2 x PC3) of fiber indicated the classification effect of five varieties of fibers. The former 6 principal components (PCs) were selected according to the quantity and size of PCs. The PCA classification model was optimized by using the least-squares support vector machines (LS-SVM) method. The authors used the 6 PCs extracted by PCA as the inputs of LS-SVM, and PCA-LS-SVM model was built to achieve varieties validation as well as mathematical model building and optimization analysis. Two hundred samples (40 samples for each variety of fibers) of five varieties of fibers were used for calibration of PCA-LS-SVM model, and the other 50 samples (10 samples for each variety of fibers) were used for validation. The result of validation showed that Vis/NIR spectroscopy technique based on PCA-LS-SVM had a powerful classification capability. It provides a new method for identifying varieties of fibers rapidly and real time, so it has important significance for protecting the rights of consumers, ensuring the quality of textiles, and implementing rationalization production and transaction of textile materials and its production.

TARGETED PRINCIPLE COMPONENT ANALYSIS: A NEW MOTION ARTIFACT CORRECTION APPROACH FOR NEAR-INFRARED SPECTROSCOPY

PubMed Central

YÜCEL, MERYEM A.; SELB, JULIETTE; COOPER, ROBERT J.; BOAS, DAVID A.

2014-01-01

As near-infrared spectroscopy (NIRS) broadens its application area to different age and disease groups, motion artifacts in the NIRS signal due to subject movement is becoming an important challenge. Motion artifacts generally produce signal fluctuations that are larger than physiological NIRS signals, thus it is crucial to correct for them before obtaining an estimate of stimulus evoked hemodynamic responses. There are various methods for correction such as principle component analysis (PCA), wavelet-based filtering and spline interpolation. Here, we introduce a new approach to motion artifact correction, targeted principle component analysis (tPCA), which incorporates a PCA filter only on the segments of data identified as motion artifacts. It is expected that this will overcome the issues of filtering desired signals that plagues standard PCA filtering of entire data sets. We compared the new approach with the most effective motion artifact correction algorithms on a set of data acquired simultaneously with a collodion-fixed probe (low motion artifact content) and a standard Velcro probe (high motion artifact content). Our results show that tPCA gives statistically better results in recovering hemodynamic response function (HRF) as compared to wavelet-based filtering and spline interpolation for the Velcro probe. It results in a significant reduction in mean-squared error (MSE) and significant enhancement in Pearson’s correlation coefficient to the true HRF. The collodion-fixed fiber probe with no motion correction performed better than the Velcro probe corrected for motion artifacts in terms of MSE and Pearson’s correlation coefficient. Thus, if the experimental study permits, the use of a collodion-fixed fiber probe may be desirable. If the use of a collodion-fixed probe is not feasible, then we suggest the use of tPCA in the processing of motion artifact contaminated data. PMID:25360181

Comparison of multi-subject ICA methods for analysis of fMRI data

PubMed Central

Erhardt, Erik Barry; Rachakonda, Srinivas; Bedrick, Edward; Allen, Elena; Adali, Tülay; Calhoun, Vince D.

2010-01-01

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi-subject ICA approaches in combination with data reduction methods for simulated and fMRI task data. For multi-subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject-specific, spatial concatenation, and group data mean subject-level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject-specific and group data mean subject-level PCA are preferred because of well-estimated TCs and SMs. Second, aggregate independent components are estimated using either noise free ICA or probabilistic ICA (PICA). Third, subject-specific SMs and TCs are estimated using back-reconstruction. We compare several direct group ICA (GICA) back-reconstruction approaches (GICA1-GICA3) and an indirect back-reconstruction approach, spatio-temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed-component artifacts in estimated SMs. Our evidence-based recommendation is to use GICA3, introduced here, with subject-specific PCA and noise-free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation. PMID:21162045

Decision analysis for a data collection system of patient-controlled analgesia with a multi-attribute utility model.

PubMed

Lee, I-Jung; Huang, Shih-Yu; Tsou, Mei-Yung; Chan, Kwok-Hon; Chang, Kuang-Yi

2010-10-01

Data collection systems are very important for the practice of patient-controlled analgesia (PCA). This study aimed to evaluate 3 PCA data collection systems and selected the most favorable system with the aid of multiattribute utility (MAU) theory. We developed a questionnaire with 10 items to evaluate the PCA data collection system and 1 item for overall satisfaction based on MAU theory. Three systems were compared in the questionnaire, including a paper record, optic card reader and personal digital assistant (PDA). A pilot study demonstrated a good internal and test-retest reliability of the questionnaire. A weighted utility score combining the relative importance of individual items assigned by each participant and their responses to each question was calculated for each system. Sensitivity analyses with distinct weighting protocols were conducted to evaluate the stability of the final results. Thirty potential users of a PCA data collection system were recruited in the study. The item "easy to use" had the highest median rank and received the heaviest mean weight among all items. MAU analysis showed that the PDA system had a higher utility score than that in the other 2 systems. Sensitivity analyses revealed that both inverse and reciprocal weighting processes favored the PDA system. High correlations between overall satisfaction and MAU scores from miscellaneous weighting protocols suggested a good predictive validity of our MAU-based questionnaire. The PDA system was selected as the most favorable PCA data collection system by the MAU analysis. The item "easy to use" was the most important attribute of the PCA data collection system. MAU theory can evaluate alternatives by taking into account individual preferences of stakeholders and aid in better decision-making. Copyright © 2010 Elsevier. Published by Elsevier B.V. All rights reserved.

Bedside ROP screening and telemedicine interpretation integrated to a neonatal transport system: Economic aspects and return on investment analysis.

PubMed

Kovács, Gábor; Somogyvári, Zsolt; Maka, Erika; Nagyjánosi, László

Peter Cerny Ambulance Service - Premature Eye Rescue Program (PCA-PERP) uses digital retinal imaging (DRI) with remote interpretation in bedside ROP screening, which has advantages over binocular indirect ophthalmoscopy (BIO) in screening of premature newborns. We aimed to demonstrate that PCA-PERP provides good value for the money and to model the cost ramifications of a similar newly launched system. As DRI was demonstrated to have high diagnostic performance, only the costs of bedside DRI-based screening were compared to those of traditional transport and BIO-based screening (cost-minimization analysis). The total costs of investment and maintenance were analyzed with micro-costing method. A ten-year analysis time-horizon and service provider's perspective were applied. From the launch of PCA-PERP up to the end of 2014, 3722 bedside examinations were performed in the PCA covered central region of Hungary. From 2009 to 2014, PCA-PERP saved 92,248km and 3633 staff working hours, with an annual nominal cost-savings ranging from 17,435 to 35,140 Euro. The net present value was 127,847 Euro at the end of 2014, with a payback period of 4.1years and an internal rate of return of 20.8%. Our model presented the NPVs of different scenarios with different initial investments, annual number of transports and average transport distances. PCA-PERP as bedside screening with remote interpretation, when compared to a transport-based screening with BIO, produced better cost-savings from the perspective of the service provider and provided a return on initial investment within five years after the project initiation. Copyright © 2017 Elsevier B.V. All rights reserved.

Principal component analysis of the CT density histogram to generate parametric response maps of COPD

NASA Astrophysics Data System (ADS)

Zha, N.; Capaldi, D. P. I.; Pike, D.; McCormack, D. G.; Cunningham, I. A.; Parraga, G.

2015-03-01

Pulmonary x-ray computed tomography (CT) may be used to characterize emphysema and airways disease in patients with chronic obstructive pulmonary disease (COPD). One analysis approach - parametric response mapping (PMR) utilizes registered inspiratory and expiratory CT image volumes and CT-density-histogram thresholds, but there is no consensus regarding the threshold values used, or their clinical meaning. Principal-component-analysis (PCA) of the CT density histogram can be exploited to quantify emphysema using data-driven CT-density-histogram thresholds. Thus, the objective of this proof-of-concept demonstration was to develop a PRM approach using PCA-derived thresholds in COPD patients and ex-smokers without airflow limitation. Methods: Fifteen COPD ex-smokers and 5 normal ex-smokers were evaluated. Thoracic CT images were also acquired at full inspiration and full expiration and these images were non-rigidly co-registered. PCA was performed for the CT density histograms, from which the components with the highest eigenvalues greater than one were summed. Since the values of the principal component curve correlate directly with the variability in the sample, the maximum and minimum points on the curve were used as threshold values for the PCA-adjusted PRM technique. Results: A significant correlation was determined between conventional and PCA-adjusted PRM with 3He MRI apparent diffusion coefficient (p<0.001), with CT RA950 (p<0.0001), as well as with 3He MRI ventilation defect percent, a measurement of both small airways disease (p=0.049 and p=0.06, respectively) and emphysema (p=0.02). Conclusions: PRM generated using PCA thresholds of the CT density histogram showed significant correlations with CT and 3He MRI measurements of emphysema, but not airways disease.

Investigation of gastric cancers in nude mice using X-ray in-line phase contrast imaging.

PubMed

Tao, Qiang; Luo, Shuqian

2014-07-24

This paper is to report the new imaging of gastric cancers without the use of imaging agents. Both gastric normal regions and gastric cancer regions can be distinguished by using the principal component analysis (PCA) based on the gray level co-occurrence matrix (GLCM). Human gastric cancer BGC823 cells were implanted into the stomachs of nude mice. Then, 3, 5, 7, 9 or 11 days after cancer cells implantation, the nude mice were sacrificed and their stomachs were removed. X-ray in-line phase contrast imaging (XILPCI), an X-ray phase contrast imaging method, has greater soft tissue contrast than traditional absorption radiography and generates higher-resolution images. The gastric specimens were imaged by an XILPCIs' charge coupled device (CCD) of 9 μm image resolution. The PCA of the projective images' region of interests (ROIs) based on GLCM were extracted to discriminate gastric normal regions and gastric cancer regions. Different stages of gastric cancers were classified by using support vector machines (SVMs). The X-ray in-line phase contrast images of nude mice gastric specimens clearly show the gastric architectures and the details of the early gastric cancers. The phase contrast computed tomography (CT) images of nude mice gastric cancer specimens are better than the traditional absorption CT images without the use of imaging agents. The results of the PCA of the texture parameters based on GLCM of normal regions is (F1+F2) >8.5, but those of cancer regions is (F1+F2) <8.5. The classification accuracy is 83.3% that classifying gastric specimens into different stages using SVMs. This is a very preliminary feasibility study. With further researches, XILPCI could become a noninvasive method for future the early detection of gastric cancers or medical researches.

Brachyury as a potential modulator of androgen receptor activity and a key player in therapy resistance in prostate cancer

PubMed Central

Pinto, Filipe; Pértega-Gomes, Nelma; Vizcaíno, José R.; Andrade, Raquel P.; Cárcano, Flavio M.; Reis, Rui Manuel

2016-01-01

Prostate cancer (PCa) is the most commonly diagnosed neoplasm and the second leading cause of cancer-related deaths in men. Acquisition of resistance to conventional therapy is a major problem for PCa patient management. Several mechanisms have been described to promote therapy resistance in PCa, such as androgen receptor (AR) activation, epithelial-to-mesenchymal transition (EMT), acquisition of stem cell properties and neuroendocrine transdifferentiation (NEtD). Recently, we identified Brachyury as a new biomarker of PCa aggressiveness and poor prognosis. In the present study we aimed to assess the role of Brachyury in PCa therapy resistance. We showed that Brachyury overexpression in prostate cancer cells lines increased resistance to docetaxel and cabazitaxel drugs, whereas Brachyury abrogation induced decrease in therapy resistance. Through ChiP-qPCR assays we further demonstrated that Brachyury is a direct regulator of AR expression as well as of the biomarker AMACR and the mesenchymal markers Snail and Fibronectin. Furthermore, in vitro Brachyury was also able to increase EMT and stem properties. By in silico analysis, clinically human Brachyury-positive PCa samples were associated with biomarkers of PCa aggressiveness and therapy resistance, including PTEN loss, and expression of NEtD markers, ERG and Bcl-2. Taken together, our results indicate that Brachyury contributes to tumor chemotherapy resistance, constituting an attractive target for advanced PCa patients. PMID:27049720

Dietary protocatechuic acid ameliorates dextran sulphate sodium-induced ulcerative colitis and hepatotoxicity in rats.

PubMed

Farombi, Ebenezer O; Adedara, Isaac A; Awoyemi, Omolola V; Njoku, Chinonye R; Micah, Gabriel O; Esogwa, Cynthia U; Owumi, Solomon E; Olopade, James O

2016-02-01

The present study investigated the antioxidant and anti-inflammatory effects of dietary protocatechuic acid (PCA), a simple hydrophilic phenolic compound commonly found in many edible vegetables, on dextran sulphate sodium (DSS)-induced ulcerative colitis and its associated hepatotoxicity in rats. PCA was administered orally at 10 mg kg(-1) to dextran sulphate sodium exposed rats for five days. The result revealed that administration of PCA significantly (p < 0.05) prevented the incidence of diarrhea and bleeding, the decrease in the body weight gain, shortening of colon length and the increase in colon mass index in DSS-treated rats. Furthermore, PCA prevented the increase in the plasma levels of pro-inflammatory cytokines, markers of liver toxicity and markedly suppressed the DSS-mediated elevation in colonic nitric oxide concentration and myeloperoxidase activity in the treated rats. Administration of PCA significantly protected against colonic and hepatic oxidative damage by increasing the antioxidant status and concomitantly decreased hydrogen peroxide and lipid peroxidation levels in the DSS-treated rats. Moreover, histological examinations confirmed PCA chemoprotection against colon and liver damage. Immunohistochemical analysis showed that PCA significantly inhibited cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) protein expression in the colon of DSS-treated rats. In conclusion, the effective chemoprotective role of PCA in colitis and the associated hepatotoxicity is related to its intrinsic anti-inflammatory and anti-oxidative properties.

Discrimination of Schisandrae Chinensis Fructus and Schisandrae Sphenantherae Fructus based on fingerprint profiles of hydrophilic components by high-performance liquid chromatography with ultraviolet detection.

PubMed

Oshima, Ryusei; Kotani, Akira; Kuroda, Minpei; Yamamoto, Kazuhiro; Mimaki, Yoshihiro; Hakamata, Hideki

2018-03-01

High-performance liquid chromatography with ultraviolet detection (HPLC-UV) using 20 mM phosphate mobile phase and an octadecylsilyl column (Triart C18, 150 × 3.0 mm i.d., 3 μm) has been developed for the analysis of hydrophilic compounds in the water extract of Schisandrae Fructus samples. The present HPLC-UV method permits the accurate and precise determination of malic, citric, and protocatechuic acids in the Japanese Pharmacopoeia (JP) Schisandrae Fructus, Schisandrae Chinensis Fructus and Schisandrae Sphenantherae Fructus. The JP Schisandrae Fructus studied contains 27.98 mg/g malic, 107.08 mg/g citric, and 0.42 mg/g protocatechuic acids, with a relative standard deviation (RSD) of repeatability of <0.9% (n = 6). The content of malic acids in Schisandrae Chinensis Fructus is approximately ten times that in Schisandrae Sphenantherae Fructus. To examine whether the HPLC-UV method is applicable to the fingerprint-based discrimination of Schisandrae Fructus samples obtained from Chinese markets, principal component analysis (PCA) was performed using the determined contents of organic acids and the ratio of six characteristic unknown peaks derived from hydrophilic components to internal standard peak areas. On the score plots, Schisandrae Chinensis Fructus and Schisandrae Sphenantherae Fructus samples are clearly discriminated. Therefore, the HPLC-UV method for the analysis of hydrophilic components coupled with PCA has been shown to be practical and useful in the quality control of Schisandrae Fructus.

Comparing two metabolic profiling approaches (liquid chromatography and gas chromatography coupled to mass spectrometry) for extra-virgin olive oil phenolic compounds analysis: A botanical classification perspective.

PubMed

Bajoub, Aadil; Pacchiarotta, Tiziana; Hurtado-Fernández, Elena; Olmo-García, Lucía; García-Villalba, Rocío; Fernández-Gutiérrez, Alberto; Mayboroda, Oleg A; Carrasco-Pancorbo, Alegría

2016-01-08

Over the last decades, the phenolic compounds from virgin olive oil (VOO) have become the subject of intensive research because of their biological activities and their influence on some of the most relevant attributes of this interesting matrix. Developing metabolic profiling approaches to determine them in monovarietal virgin olive oils could help to gain a deeper insight into olive oil phenolic compounds composition as well as to promote their use for botanical origin tracing purposes. To this end, two approaches were comparatively investigated (LC-ESI-TOF MS and GC-APCI-TOF MS) to evaluate their capacity to properly classify 25 olive oil samples belonging to five different varieties (Arbequina, Cornicabra, Hojiblanca, Frantoio and Picual), using the entire chromatographic phenolic profiles combined to chemometrics (principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA)). The application of PCA to LC-MS and GC-MS data showed the natural clustering of the samples, seeing that 2 varieties were dominating the models (Arbequina and Frantoio), suppressing any possible discrimination among the other cultivars. Afterwards, PLS-DA was used to build four different efficient predictive models for varietal classification of the samples under study. The varietal markers pointed out by each platform were compared. In general, with the exception of one GC-MS model, all exhibited proper quality parameters. The models constructed by using the LC-MS data demonstrated superior classification ability. Copyright © 2015 Elsevier B.V. All rights reserved.

Characterization and discrimination of human breast cancer and normal breast tissues using resonance Raman spectroscopy

NASA Astrophysics Data System (ADS)

Wu, Binlin; Smith, Jason; Zhang, Lin; Gao, Xin; Alfano, Robert R.

2018-02-01

Worldwide breast cancer incidence has increased by more than twenty percent in the past decade. It is also known that in that time, mortality due to the affliction has increased by fourteen percent. Using optical-based diagnostic techniques, such as Raman spectroscopy, has been explored in order to increase diagnostic accuracy in a more objective way along with significantly decreasing diagnostic wait-times. In this study, Raman spectroscopy with 532-nm excitation was used in order to incite resonance effects to enhance Stokes Raman scattering from unique biomolecular vibrational modes. Seventy-two Raman spectra (41 cancerous, 31 normal) were collected from nine breast tissue samples by performing a ten-spectra average using a 500-ms acquisition time at each acquisition location. The raw spectral data was subsequently prepared for analysis with background correction and normalization. The spectral data in the Raman Shift range of 750- 2000 cm-1 was used for analysis since the detector has highest sensitivity around in this range. The matrix decomposition technique nonnegative matrix factorization (NMF) was then performed on this processed data. The resulting leave-oneout cross-validation using two selective feature components resulted in sensitivity, specificity and accuracy of 92.6%, 100% and 96.0% respectively. The performance of NMF was also compared to that using principal component analysis (PCA), and NMF was shown be to be superior to PCA in this study. This study shows that coupling the resonance Raman spectroscopy technique with subsequent NMF decomposition method shows potential for high characterization accuracy in breast cancer detection.

PTEN genomic deletion predicts prostate cancer recurrence and is associated with low AR expression and transcriptional activity

PubMed Central

2012-01-01

Background Prostate cancer (PCa), a leading cause of cancer death in North American men, displays a broad range of clinical outcome from relatively indolent to lethal metastatic disease. Several genomic alterations have been identified in PCa which may serve as predictors of progression. PTEN, (10q23.3), is a negative regulator of the phosphatidylinositol 3-kinase (PIK3)/AKT survival pathway and a tumor suppressor frequently deleted in PCa. The androgen receptor (AR) signalling pathway is known to play an important role in PCa and its blockade constitutes a commonly used treatment modality. In this study, we assessed the deletion status of PTEN along with AR expression levels in 43 primary PCa specimens with clinical follow-up. Methods Fluorescence In Situ Hybridization (FISH) was done on formalin fixed paraffin embedded (FFPE) PCa samples to examine the deletion status of PTEN. AR expression levels were determined using immunohistochemistry (IHC). Results Using FISH, we found 18 cases of PTEN deletion. Kaplan-Meier analysis showed an association with disease recurrence (P=0.03). Concurrently, IHC staining for AR found significantly lower levels of AR expression within those tumors deleted for PTEN (P<0.05). To validate these observations we interrogated a copy number alteration and gene expression profiling dataset of 64 PCa samples, 17 of which were PTEN deleted. We confirmed the predictive value of PTEN deletion in disease recurrence (P=0.03). PTEN deletion was also linked to diminished expression of PTEN (P<0.01) and AR (P=0.02). Furthermore, gene set enrichment analysis revealed a diminished expression of genes downstream of AR signalling in PTEN deleted tumors. Conclusions Altogether, our data suggest that PTEN deleted tumors expressing low levels of AR may represent a worse prognostic subset of PCa establishing a challenge for therapeutic management. PMID:23171135

Identification of genetic risk associated with prostate cancer using ancestry informative markers

PubMed Central

Ricks-Santi, LJ; Apprey, V; Mason, T; Wilson, B; Abbas, M; Hernandez, W; Hooker, S; Doura, M; Bonney, G; Dunston, G; Kittles, R; Ahaghotu, C

2014-01-01

BACKGROUND Prostate cancer (PCa) is a common malignancy and a leading cause of cancer death among men in the United States with African-American (AA) men having the highest incidence and mortality rates. Given recent results from admixture mapping and genome-wide association studies for PCa in AA men, it is clear that many risk alleles are enriched in men with West African genetic ancestry. METHODS A total of 77 ancestry informative markers (AIMs) within surrounding candidate gene regions were genotyped and haplotyped using Pyrosequencing in 358 unrelated men enrolled in a PCa genetic association study at the Howard University Hospital between 2000 and 2004. Sequence analysis of promoter region single-nucleotide polymorphisms (SNPs) to evaluate disruption of transcription factor-binding sites was conducted using in silico methods. RESULTS Eight AIMs were significantly associated with PCa risk after adjusting for age and West African ancestry. SNP rs1993973 (intervening sequences) had the strongest association with PCa using the log-additive genetic model (P = 0.002). SNPs rs1561131 (genotypic, P = 0.007), rs1963562 (dominant, P = 0.01) and rs615382 (recessive, P = 0.009) remained highly significant after adjusting for both age and ancestry. We also tested the independent effect of each significantly associated SNP and rs1561131 (P = 0.04) and rs1963562 (P = 0.04) remained significantly associated with PCa development. After multiple comparisons testing using the false discovery rate, rs1993973 remained significant. Analysis of the rs156113–, rs1963562–rs615382l and rs1993973–rs585224 haplotypes revealed that the least frequently found haplotypes in this population were significantly associated with a decreased risk of PCa (P = 0.032 and 0.0017, respectively). CONCLUSIONS The approach for SNP selection utilized herein showed that AIMs may not only leverage increased linkage disequilibrium in populations to identify risk and protective alleles, but may also be informative in dissecting the biology of PCa and other health disparities. PMID:22801071

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

PubMed

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.

Identifying natural and anthropogenic sources of metals in urban and rural soils using GIS-based data, PCA, and spatial interpolation

PubMed Central

Davis, Harley T.; Aelion, C. Marjorie; McDermott, Suzanne; Lawson, Andrew B.

2009-01-01

Determining sources of neurotoxic metals in rural and urban soils is important for mitigating human exposure. Surface soil from four areas with significant clusters of mental retardation and developmental delay (MR/DD) in children, and one control site were analyzed for nine metals and characterized by soil type, climate, ecological region, land use and industrial facilities using readily-available GIS-based data. Kriging, principal component analysis (PCA) and cluster analysis (CA) were used to identify commonalities of metal distribution. Three MR/DD areas (one rural and two urban) had similar soil types and significantly higher soil metal concentrations. PCA and CA results suggested that Ba, Be and Mn were consistently from natural sources; Pb and Hg from anthropogenic sources; and As, Cr, Cu, and Ni from both sources. Arsenic had low commonality estimates, was highly associated with a third PCA factor, and had a complex distribution, complicating mitigation strategies to minimize concentrations and exposures. PMID:19361902

SESNPCA: Principal Component Analysis Applied to Stripped-Envelope Core-Collapse Supernovae

NASA Astrophysics Data System (ADS)

Williamson, Marc; Bianco, Federica; Modjaz, Maryam

2018-01-01

In the new era of time-domain astronomy, it will become increasingly important to have rigorous, data driven models for classifying transients, including supernovae (SNe). We present the first application of principal component analysis (PCA) to stripped-envelope core-collapse supernovae (SESNe). Previous studies of SNe types Ib, IIb, Ic, and broad-line Ic (Ic-BL) focus only on specific spectral features, while our PCA algorithm uses all of the information contained in each spectrum. We use one of the largest compiled datasets of SESNe, containing over 150 SNe, each with spectra taken at multiple phases. Our work focuses on 49 SNe with spectra taken 15 ± 5 days after maximum V-band light where better distinctions can be made between SNe type Ib and Ic spectra. We find that spectra of SNe type IIb and Ic-BL are separable from the other types in PCA space, indicating that PCA is a promising option for developing a purely data driven model for SESNe classification.

The role of CD147 expression in prostate cancer: a systematic review and meta-analysis.

PubMed

Ye, Yun; Li, Su-Liang; Wang, Yao; Yao, Yang; Wang, Juan; Ma, Yue-Yun; Hao, Xiao-Ke

2016-01-01

There are a number of studies which show that expression of CD147 is increased significantly in prostate cancer (PCa). However, conflicting conclusions have also been reported by other researchers lately. In order to arrive at a clear conclusion, a meta-analysis of eligible studies was conducted. We searched PubMed, MEDLINE, Cochrane Library, and the China National Knowledge Infrastructure databases to identify all the published case-control studies on the relationship between the expression of CD147 and PCa until February 2016. In the end, a total of 930 patients in eight studies were included in the meta-analysis. CD147 expression in the PCa patients increased significantly (odds ratio [OR], 4.65; 95% confidence interval [CI], 3.52-6.14; Z=10.79; P<0.05), but there was obvious heterogeneity between studies (I (2)=92.9%, P<0.05). Subgroup analysis showed that positive expression of CD147 was associated with PCa among the Asian population (OR, 21.01; 95% CI, 12.88-34.28; Z=12.19; P<0.05). Furthermore, it was significantly related to TNM stage (OR, 0.24; 95% CI, 0.17-0.35; Z=7.74; P<0.05), Gleason score (OR, 0.41; 95% CI, 0.31-0.56; Z=5.62; P<0.05), differentiation grade (OR, 0.27; 95% CI, 0.13-0.56; Z=3.47; P<0.05), and pretreatment serum prostate-specific antigen level (OR, 0.07; 95% CI, 0.03-0.16; Z=6.47; P<0.05). Positive expression of CD147 was related to PCa, significant heterogeneity was not found between Asian studies, and the result became more significant. The positive expression of CD147 was significantly related to the clinicopathological characteristics of PCa. This suggests that CD147 plays an essential role in poor prognosis and recurrence prediction.

The role of CD147 expression in prostate cancer: a systematic review and meta-analysis

PubMed Central

Ye, Yun; Li, Su-Liang; Wang, Yao; Yao, Yang; Wang, Juan; Ma, Yue-Yun; Hao, Xiao-Ke

2016-01-01

Background There are a number of studies which show that expression of CD147 is increased significantly in prostate cancer (PCa). However, conflicting conclusions have also been reported by other researchers lately. In order to arrive at a clear conclusion, a meta-analysis of eligible studies was conducted. Materials and methods We searched PubMed, MEDLINE, Cochrane Library, and the China National Knowledge Infrastructure databases to identify all the published case–control studies on the relationship between the expression of CD147 and PCa until February 2016. In the end, a total of 930 patients in eight studies were included in the meta-analysis. Results CD147 expression in the PCa patients increased significantly (odds ratio [OR], 4.65; 95% confidence interval [CI], 3.52–6.14; Z=10.79; P<0.05), but there was obvious heterogeneity between studies (I2=92.9%, P<0.05). Subgroup analysis showed that positive expression of CD147 was associated with PCa among the Asian population (OR, 21.01; 95% CI, 12.88–34.28; Z=12.19; P<0.05). Furthermore, it was significantly related to TNM stage (OR, 0.24; 95% CI, 0.17–0.35; Z=7.74; P<0.05), Gleason score (OR, 0.41; 95% CI, 0.31–0.56; Z=5.62; P<0.05), differentiation grade (OR, 0.27; 95% CI, 0.13–0.56; Z=3.47; P<0.05), and pretreatment serum prostate-specific antigen level (OR, 0.07; 95% CI, 0.03–0.16; Z=6.47; P<0.05). Conclusion Positive expression of CD147 was related to PCa, significant heterogeneity was not found between Asian studies, and the result became more significant. The positive expression of CD147 was significantly related to the clinicopathological characteristics of PCa. This suggests that CD147 plays an essential role in poor prognosis and recurrence prediction. PMID:27536064

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

PubMed

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

2010-03-26

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

Rapid Isolation of Phenol Degrading Bacteria by Fourier Transform Infrared (FTIR) Spectroscopy.

PubMed

Li, Fei; Song, Wen-jun; Wei, Ji-ping; Wang, Su-ying; Liu, Chong-ji

2015-05-01

Phenol is an important chemical engineering material and ubiquitous in industry wastewater, its existence has become a thorny issue in many developed and developing country. More and more stringent standards for effluent all over the world with human realizing the toxicity of phenol have been announced. Many advanced biological methods are applied to industrial wastewater treatment with low cost, high efficiency and no secondary pollution, but the screening of function microorganisms is certain cumbersome process. In our study a rapid procedure devised for screening bacteria on solid medium can degrade phenol coupled with attenuated total reflection fourier transform infrared (ATR-FTIR) which is a detection method has the characteristics of efficient, fast, high fingerprint were used. Principal component analysis (PCA) is a method in common use to extract fingerprint peaks effectively, it couples with partial least squares (PLS) statistical method could establish a credible model. The model we created using PCA-PLS can reach 99. 5% of coefficient determination and validation data get 99. 4%, which shows the promising fitness and forecasting of the model. The high fitting model is used for predicting the concentration of phenol at solid medium where the bacteria were grown. The highly consistent result of two screening methods, solid cultural with ATR-FTIR detected and traditional liquid cultural detected by GC methods, suggests the former can rapid isolate the bacteria which can degrade substrates as well as traditional cumbersome liquid cultural method. Many hazardous substrates widely existed in industry wastewater, most of them has specialize fingerprint peaks detected by ATR-FTIR, thereby this detected method could be used as a rapid detection for isolation of functional microorganisms those can degrade many other toxic substrates.

The Pattern of Brain Amyloid Load in Posterior Cortical Atrophy Using 18F-AV45: Is Amyloid the Principal Actor in the Disease?

PubMed Central

Beaufils, Emilie; Ribeiro, Maria Joao; Vierron, Emilie; Vercouillie, Johnny; Dufour-Rainfray, Diane; Cottier, Jean-Philippe; Camus, Vincent; Mondon, Karl; Guilloteau, Denis; Hommet, Caroline

2014-01-01

Background Posterior cortical atrophy (PCA) is characterized by progressive higher-order visuoperceptual dysfunction and praxis declines. This syndrome is related to a number of underlying diseases, including, in most cases, Alzheimer's disease (AD). The aim of this study was to compare the amyloid load with 18F-AV45 positron emission tomography (PET) between PCA and AD subjects. Methods We performed 18F-AV45 PET, cerebrospinal fluid (CSF) biomarker analysis and a neuropsychological assessment in 11 PCA patients and 12 AD patients. Results The global and regional 18F-AV45 uptake was similar in the PCA and AD groups. No significant correlation was observed between global 18F-AV45 uptake and CSF biomarkers or between regional 18F-AV45 uptake and cognitive and affective symptoms. Conclusion This 18F-AV45 PET amyloid imaging study showed no specific regional pattern of cortical 18F-AV45 binding in PCA patients. These results confirm that a distinct clinical phenotype in amnestic AD and PCA is not related to amyloid distribution. PMID:25538727

Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection

NASA Astrophysics Data System (ADS)

Li, Shao-Xin; Zeng, Qiu-Yao; Li, Lin-Fang; Zhang, Yan-Jiao; Wan, Ming-Ming; Liu, Zhi-Ming; Xiong, Hong-Lian; Guo, Zhou-Yi; Liu, Song-Hao

2013-02-01

The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30) and the other group from healthy volunteers (n=31). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.

Fast Steerable Principal Component Analysis

PubMed Central

Zhao, Zhizhen; Shkolnisky, Yoel; Singer, Amit

2016-01-01

Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2-D images as large as a few hundred pixels in each direction. Here, we introduce an algorithm that efficiently and accurately performs principal component analysis (PCA) for a large set of 2-D images, and, for each image, the set of its uniform rotations in the plane and their reflections. For a dataset consisting of n images of size L × L pixels, the computational complexity of our algorithm is O(nL3 + L4), while existing algorithms take O(nL4). The new algorithm computes the expansion coefficients of the images in a Fourier–Bessel basis efficiently using the nonuniform fast Fourier transform. We compare the accuracy and efficiency of the new algorithm with traditional PCA and existing algorithms for steerable PCA. PMID:27570801

Raman signatures of ferroic domain walls captured by principal component analysis.

PubMed

Nataf, G F; Barrett, N; Kreisel, J; Guennou, M

2018-01-24

Ferroic domain walls are currently investigated by several state-of-the art techniques in order to get a better understanding of their distinct, functional properties. Here, principal component analysis (PCA) of Raman maps is used to study ferroelectric domain walls (DWs) in LiNbO 3 and ferroelastic DWs in NdGaO 3 . It is shown that PCA allows us to quickly and reliably identify small Raman peak variations at ferroelectric DWs and that the value of a peak shift can be deduced-accurately and without a priori-from a first order Taylor expansion of the spectra. The ability of PCA to separate the contribution of ferroelastic domains and DWs to Raman spectra is emphasized. More generally, our results provide a novel route for the statistical analysis of any property mapped across a DW.

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

PubMed

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

2012-09-26

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

Analysis of lard in meatball broth using Fourier transform infrared spectroscopy and chemometrics.

PubMed

Kurniawati, Endah; Rohman, Abdul; Triyana, Kuwat

2014-01-01

Meatball is one of the favorite foods in Indonesia. For the economic reason (due to the price difference), the substitution of beef meat with pork can occur. In this study, FTIR spectroscopy in combination with chemometrics of partial least square (PLS) and principal component analysis (PCA) was used for analysis of pork fat (lard) in meatball broth. Lard in meatball broth was quantitatively determined at wavenumber region of 1018-1284 cm(-1). The coefficient of determination (R(2)) and root mean square error of calibration (RMSEC) values obtained were 0.9975 and 1.34% (v/v), respectively. Furthermore, the classification of lard and beef fat in meatball broth as well as in commercial samples was performed at wavenumber region of 1200-1000 cm(-1). The results showed that FTIR spectroscopy coupled with chemometrics can be used for quantitative analysis and classification of lard in meatball broth for Halal verification studies. The developed method is simple in operation, rapid and not involving extensive sample preparation. © 2013.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Rotation of EOFs by the Independent Component Analysis: Towards A Solution of the Mixing Problem in the Decomposition of Geophysical Time Series

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)

2001-01-01

The Independent Component Analysis is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components, a stronger constraint that uses higher-order statistics, instead of the classical decorrelation, a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e. exploratory approach). We demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis, the Independent Component Analysis performs a rotation of the classical PCA (or EOF) solution. This rotation uses no localization criterion like other Rotation Techniques (RT), only the global generalization of decorrelation by statistical independence is used. This rotation of the PCA solution seems to be able to solve the tendency of PCA to mix several physical phenomena, even when the signal is just their linear sum.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

PubMed

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Applying Fourier Transform Mid Infrared Spectroscopy to Detect the Adulteration of Salmo salar with Oncorhynchus mykiss

PubMed Central

Moreira, Maria João

2018-01-01

The aim of this study was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy coupled with chemometric methods to detect fish adulteration. Muscles of Atlantic salmon (Salmo salar) (SS) and Salmon trout (Onconrhynchus mykiss) (OM) muscles were mixed in different percentages and transformed into mini-burgers. These were stored at 3 °C, then examined at 0, 72, 160, and 240 h for deteriorative microorganisms. Mini-burgers was submitted to Soxhlet extraction, following which lipid extracts were analyzed by FTIR. The principal component analysis (PCA) described the studied adulteration using four principal components with an explained variance of 95.60%. PCA showed that the absorbance in the spectral region from 721, 1097, 1370, 1464, 1655, 2805, to 2935, 3009 cm−1 may be attributed to biochemical fingerprints related to differences between SS and OM. The partial least squares regression (PLS-R) predicted the presence/absence of adulteration in fish samples of an external set with high accuracy. The proposed methods have the advantage of allowing quick measurements, despite the storage time of the adulterated fish. FTIR combined with chemometrics showed that a methodology to identify the adulteration of SS with OM can be established, even when stored for different periods of time. PMID:29621135

Volatile compounds in cryptic species of the Aneura pinguis complex and Aneura maxima (Marchantiophyta, Metzgeriidae).

PubMed

Wawrzyniak, Rafał; Wasiak, Wiesław; Bączkiewicz, Alina; Buczkowska, Katarzyna

2014-09-01

Aneura pinguis is one of the liverwort species complexes that consist of several cryptic species. Ten samples collected from different regions in Poland are in the focus of our research. Eight of the A. pinguis complex belonging to four cryptic species (A, B, C, E) and two samples of closely related species Aneura maxima were tested for the composition of volatile compounds. The HS-SPME technique coupled to GC/FID and GC/MS analysis has been applied. The fiber coated with DVB/CAR/PDMS has been used. The results of the present study, revealed the qualitative and quantitative differences in the composition of the volatile compounds between the studied species. Mainly they are from the group of sesquiterpenoids, oxygenated sesquiterpenoids and aliphatic hydrocarbons. The statistical methods (CA and PCA) showed that detected volatile compounds allow to distinguish cryptic species of A. pinguis. All examined cryptic species of the A. pinguis complex differ from A. maxima. Species A and E of A. pinguis, in CA and PCA, form separate clusters remote from two remaining cryptic species of A. pinguis (B and C) and A. maxima. Relationship between the cryptic species appeared from the chemical studies are in accordance with that revealed on the basis of DNA sequences. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Principal Component Analysis in the Spectral Analysis of the Dynamic Laser Speckle Patterns

NASA Astrophysics Data System (ADS)

Ribeiro, K. M.; Braga, R. A., Jr.; Horgan, G. W.; Ferreira, D. D.; Safadi, T.

2014-02-01

Dynamic laser speckle is a phenomenon that interprets an optical patterns formed by illuminating a surface under changes with coherent light. Therefore, the dynamic change of the speckle patterns caused by biological material is known as biospeckle. Usually, these patterns of optical interference evolving in time are analyzed by graphical or numerical methods, and the analysis in frequency domain has also been an option, however involving large computational requirements which demands new approaches to filter the images in time. Principal component analysis (PCA) works with the statistical decorrelation of data and it can be used as a data filtering. In this context, the present work evaluated the PCA technique to filter in time the data from the biospeckle images aiming the reduction of time computer consuming and improving the robustness of the filtering. It was used 64 images of biospeckle in time observed in a maize seed. The images were arranged in a data matrix and statistically uncorrelated by PCA technique, and the reconstructed signals were analyzed using the routine graphical and numerical methods to analyze the biospeckle. Results showed the potential of the PCA tool in filtering the dynamic laser speckle data, with the definition of markers of principal components related to the biological phenomena and with the advantage of fast computational processing.

Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements.

PubMed

Caprihan, A; Pearlson, G D; Calhoun, V D

2008-08-15

Principal component analysis (PCA) is often used to reduce the dimension of data before applying more sophisticated data analysis methods such as non-linear classification algorithms or independent component analysis. This practice is based on selecting components corresponding to the largest eigenvalues. If the ultimate goal is separation of data in two groups, then these set of components need not have the most discriminatory power. We measured the distance between two such populations using Mahalanobis distance and chose the eigenvectors to maximize it, a modified PCA method, which we call the discriminant PCA (DPCA). DPCA was applied to diffusion tensor-based fractional anisotropy images to distinguish age-matched schizophrenia subjects from healthy controls. The performance of the proposed method was evaluated by the one-leave-out method. We show that for this fractional anisotropy data set, the classification error with 60 components was close to the minimum error and that the Mahalanobis distance was twice as large with DPCA, than with PCA. Finally, by masking the discriminant function with the white matter tracts of the Johns Hopkins University atlas, we identified left superior longitudinal fasciculus as the tract which gave the least classification error. In addition, with six optimally chosen tracts the classification error was zero.

A case-control study of lower urinary-tract infections, associated antibiotics and the risk of developing prostate cancer using PCBaSe 3.0

PubMed Central

Garmo, Hans; Beckmann, Kerri; Stattin, Pär; Adolfsson, Jan; Van Hemelrijck, Mieke

2018-01-01

Objectives To investigate the association between lower urinary-tract infections, their associated antibiotics and the subsequent risk of developing PCa. Subjects/Patients (or materials) and methods Using data from the Swedish PCBaSe 3.0, we performed a matched case-control study (8762 cases and 43806 controls). Conditional logistic regression analysis was used to assess the association between lower urinary-tract infections, related antibiotics and PCa, whilst adjusting for civil status, education, Charlson Comorbidity Index and time between lower urinary-tract infection and PCa diagnosis. Results It was found that lower urinary-tract infections did not affect PCa risk, however, having a lower urinary-tract infection or a first antibiotic prescription 6–12 months before PCa were both associated with an increased risk of PCa (OR: 1.50, 95% CI: 1.23–1.82 and 1.96, 1.71–2.25, respectively), as compared to men without lower urinary-tract infections. Compared to men with no prescriptions for antibiotics, men who were prescribed ≥10 antibiotics, were 15% less likely to develop PCa (OR: 0.85, 95% CI: 0.78–0.91). Conclusion PCa was not found to be associated with diagnosis of a urinary-tract infection or frequency, but was positively associated with short time since diagnoses of lower urinary-tract infection or receiving prescriptions for antibiotics. These observations can likely be explained by detection bias, which highlights the importance of data on the diagnostic work-up when studying potential risk factors for PCa. PMID:29649268

Ghrelin O-acyltransferase (GOAT) enzyme is overexpressed in prostate cancer, and its levels are associated with patient's metabolic status: Potential value as a non-invasive biomarker.

PubMed

Hormaechea-Agulla, Daniel; Gómez-Gómez, Enrique; Ibáñez-Costa, Alejandro; Carrasco-Valiente, Julia; Rivero-Cortés, Esther; L-López, Fernando; Pedraza-Arevalo, Sergio; Valero-Rosa, José; Sánchez-Sánchez, Rafael; Ortega-Salas, Rosa; Moreno, María M; Gahete, Manuel D; López-Miranda, José; Requena, María J; Castaño, Justo P; Luque, Raúl M

2016-12-01

Ghrelin-O-acyltransferase (GOAT) is the key enzyme regulating ghrelin activity, and has been proposed as a potential therapeutic target for obesity/diabetes and as a biomarker in some endocrine-related cancers. However, GOAT presence and putative role in prostate-cancer (PCa) is largely unknown. Here, we demonstrate, for the first time, that GOAT is overexpressed (mRNA/protein-level) in prostatic tissues (n = 52) and plasma/urine-samples (n = 85) of PCa-patients, compared with matched controls [healthy prostate tissues (n = 12) and plasma/urine-samples from BMI-matched controls (n = 28), respectively]. Interestingly, GOAT levels in PCa-patients correlated with aggressiveness and metabolic conditions (i.e. diabetes). Actually, GOAT expression was regulated by metabolic inputs (i.e. In1-ghrelin, insulin/IGF-I) in cultured normal prostate cells and PCa-cell lines. Importantly, ROC-curve analysis unveiled a valuable diagnostic potential for GOAT to discriminate PCa at the tissue/plasma/urine-level with high sensitivity/specificity, particularly in non-diabetic individuals. Moreover, we discovered that GOAT is secreted by PCa-cells, and that its levels are higher in urine samples from a stimulated post-massage vs. pre-massage prostate-test. In conclusion, plasmatic GOAT levels exhibit high specificity/sensitivity to predict PCa-presence compared with other PCa-biomarkers, especially in non-diabetic individuals, suggesting that GOAT holds potential as a novel non-invasive PCa-biomarker. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The language profile of Posterior Cortical Atrophy

PubMed Central

Crutch, Sebastian J.; Lehmann, Manja; Warren, Jason D.; Rohrer, Jonathan D.

2015-01-01

Background Posterior Cortical Atrophy (PCA) is typically considered to be a visual syndrome, primarily characterised by progressive impairment of visuoperceptual and visuospatial skills. However patients commonly describe early difficulties with word retrieval. This paper details the first systematic analysis of linguistic function in PCA. Characterising and quantifying the aphasia associated with PCA is important for clarifying diagnostic and selection criteria for clinical and research studies. Methods Fifteen patients with PCA, 7 patients with logopenic/phonological aphasia (LPA) and 18 age-matched healthy participants completed a detailed battery of linguistic tests evaluating auditory input processing, repetition and working memory, lexical and grammatical comprehension, single word retrieval and fluency, and spontaneous speech. Results Relative to healthy controls, PCA patients exhibited language impairments across all the domains examined, but with anomia, reduced phonemic fluency and slowed speech rate the most prominent deficits. PCA performance most closely resembled that of LPA patients on tests of auditory input processing, repetition and digit span, but was relatively stronger on tasks of comprehension and spontaneous speech. Conclusions The study demonstrates that in addition to the well-reported degradation of vision, literacy and numeracy, PCA is characterised by a progressive oral language dysfunction with prominent word retrieval difficulties. Overlap in the linguistic profiles of PCA and LPA, which are both most commonly caused by Alzheimer’s disease, further emphasises the notion of a phenotypic continuum between typical and atypical manifestations of the disease. Clarifying the boundaries between AD phenotypes has important implications for diagnosis, clinical trial recruitment and investigations into biological factors driving phenotypic heterogeneity in AD. Rehabilitation strategies to ameliorate the phonological deficit in PCA are required. PMID:23138762

Epigenetics in prostate cancer: biologic and clinical relevance.

PubMed

Jerónimo, Carmen; Bastian, Patrick J; Bjartell, Anders; Carbone, Giuseppina M; Catto, James W F; Clark, Susan J; Henrique, Rui; Nelson, William G; Shariat, Shahrokh F

2011-10-01

Prostate cancer (PCa) is one of the most common human malignancies and arises through genetic and epigenetic alterations. Epigenetic modifications include DNA methylation, histone modifications, and microRNAs (miRNA) and produce heritable changes in gene expression without altering the DNA coding sequence. To review progress in the understanding of PCa epigenetics and to focus upon translational applications of this knowledge. PubMed was searched for publications regarding PCa and DNA methylation, histone modifications, and miRNAs. Reports were selected based on the detail of analysis, mechanistic support of data, novelty, and potential clinical applications. Aberrant DNA methylation (hypo- and hypermethylation) is the best-characterized alteration in PCa and leads to genomic instability and inappropriate gene expression. Global and locus-specific changes in chromatin remodeling are implicated in PCa, with evidence suggesting a causative dysfunction of histone-modifying enzymes. MicroRNA deregulation also contributes to prostate carcinogenesis, including interference with androgen receptor signaling and apoptosis. There are important connections between common genetic alterations (eg, E twenty-six fusion genes) and the altered epigenetic landscape. Owing to the ubiquitous nature of epigenetic alterations, they provide potential biomarkers for PCa detection, diagnosis, assessment of prognosis, and post-treatment surveillance. Altered epigenetic gene regulation is involved in the genesis and progression of PCa. Epigenetic alterations may provide valuable tools for the management of PCa patients and be targeted by pharmacologic compounds that reverse their nature. The potential for epigenetic changes in PCa requires further exploration and validation to enable translation to the clinic. Copyright © 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Triglyceride dependent differentiation of obesity in adipose tissues by FTIR spectroscopy coupled with chemometrics.

PubMed

Kucuk Baloglu, Fatma; Baloglu, Onur; Heise, Sebastian; Brockmann, Gudrun; Severcan, Feride

2017-10-01

The excess deposition of triglycerides in adipose tissue is the main reason of obesity and causes excess release of fatty acids to the circulatory system resulting in obesity and insulin resistance. Body mass index and waist circumference are not precise measure of obesity and obesity related metabolic diseases. Therefore, in the current study, it was aimed to propose triglyceride bands located at 1770-1720 cm -1 spectral region as a more sensitive obesity related biomarker using the diagnostic potential of Fourier Transform Infrared (FTIR) spectroscopy in subcutaneous (SCAT) and visceral (VAT) adipose tissues. The adipose tissue samples were obtained from 10 weeks old male control (DBA/2J) (n = 6) and four different obese BFMI mice lines (n = 6 per group). FTIR spectroscopy coupled with hierarchical cluster analysis (HCA) and principal component analysis (PCA) was applied to the spectra of triglyceride bands as a diagnostic tool in the discrimination of the samples. Successful discrimination of the obese, obesity related insulin resistant and control groups were achieved with high sensitivity and specificity. The results revealed the power of FTIR spectroscopy coupled with chemometric approaches in internal diagnosis of abdominal obesity based on the spectral differences in the triglyceride region that can be used as a spectral marker. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quorum sensing systems differentially regulate the production of phenazine-1-carboxylic acid in the rhizobacterium Pseudomonas aeruginosa PA1201

PubMed Central

Sun, Shuang; Zhou, Lian; Jin, Kaiming; Jiang, Haixia; He, Ya-Wen

2016-01-01

Pseudomonas aeruginosa strain PA1201 is a newly identified rhizobacterium that produces high levels of the secondary metabolite phenazine-1-carboxylic acid (PCA), the newly registered biopesticide Shenqinmycin. PCA production in liquid batch cultures utilizing a specialized PCA-promoting medium (PPM) typically occurs after the period of most rapid growth, and production is regulated in a quorum sensing (QS)-dependent manner. PA1201 contains two PCA biosynthetic gene clusters phz1 and phz2; both clusters contribute to PCA production, with phz2 making a greater contribution. PA1201 also contains a complete set of genes for four QS systems (LasI/LasR, RhlI/RhlR, PQS/MvfR, and IQS). By using several methods including gene deletion, the construction of promoter-lacZ fusion reporter strains, and RNA-Seq analysis, this study investigated the effects of the four QS systems on bacterial growth, QS signal production, the expression of phz1 and phz2, and PCA production. The possible mechanisms for the strain- and condition-dependent expression of phz1 and phz2 were discussed, and a schematic model was proposed. These findings provide a basis for further genetic engineering of the QS systems to improve PCA production. PMID:27456813

National economic and development indicators and international variation in prostate cancer incidence and mortality: an ecological analysis.

PubMed

Neupane, Subas; Bray, Freddie; Auvinen, Anssi

2017-06-01

Macroeconomic indicators are likely associated with prostate cancer (PCa) incidence and mortality globally, but have rarely been assessed. Data on PCa incidence in 2003-2007 for 49 countries with either nationwide cancer registry or at least two regional registries were obtained from Cancer Incidence in Five Continents Vol X and national PCa mortality for 2012 from GLOBOCAN 2012. We compared PCa incidence and mortality rates with various population-level indicators of health, economy and development in 2000. Poisson and linear regression methods were used to quantify the associations. PCa incidence varied more than 15-fold, being highest in high-income countries. PCa mortality exhibited less variation, with higher rates in many low- and middle-income countries. Healthcare expenditure (rate ratio, RR 1.46, 95 % CI 1.45-1.47) and population growth (RR 1.15, 95 % CI 1.14-1.16), as well as computer and mobile phone density, were associated with a higher PCa incidence, while gross domestic product, GDP (RR 0.94, 95 % CI 0.93-0.95) and overall mortality (RR 0.72, 95 % CI 0.71-0.73) were associated with a low incidence. GDP (RR 0.55, 95 % CI 0.46-0.66) was also associated with a low PCa mortality, while life expectancy (RR 3.93, 95 % CI 3.22-4.79) and healthcare expenditure (RR 1.20, 95 % CI 1.09-1.32) were associated with an elevated mortality. Our results show that healthcare expenditure and, thus, the availability of medical resources are an important contributor to the patterns of international variation in PCa incidence. This suggests that there is an iatrogenic component in the current global epidemic of PCa. On the other hand, higher healthcare expenditure is associated with lower PCa death rates.

Human factors issues and approaches in the spatial layout of a space station control room, including the use of virtual reality as a design analysis tool

NASA Technical Reports Server (NTRS)

Hale, Joseph P., II

1994-01-01

Human Factors Engineering support was provided for the 30% design review of the late Space Station Freedom Payload Control Area (PCA). The PCA was to be the payload operations control room, analogous to the Spacelab Payload Operations Control Center (POCC). This effort began with a systematic collection and refinement of the relevant requirements driving the spatial layout of the consoles and PCA. This information was used as input for specialized human factors analytical tools and techniques in the design and design analysis activities. Design concepts and configuration options were developed and reviewed using sketches, 2-D Computer-Aided Design (CAD) drawings, and immersive Virtual Reality (VR) mockups.

Fast principal component analysis for stacking seismic data

NASA Astrophysics Data System (ADS)

Wu, Juan; Bai, Min

2018-04-01

Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional average-based seismic stacking methods cannot obtain optimal performance when the ambient noise is extremely strong. We propose a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. Considering the computational bottleneck of the classic PCA algorithm in processing massive seismic data, we propose an efficient PCA algorithm to make the proposed method readily applicable for industrial applications. Two numerically designed examples and one real seismic data are used to demonstrate the performance of the presented method.

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2004-10-01

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

Spatial Mapping of Pyocyanin in Pseudomonas aeruginosa Bacterial Communities by Surface Enhanced Raman Scattering

PubMed Central

Polisetti, Sneha; Baig, Nameera F.; Morales-Soto, Nydia; Shrout, Joshua D.; Bohn, Paul W.

2017-01-01

Surface Enhanced Raman Spectroscopy (SERS) imaging was used in conjunction with Principal Component Analysis (PCA) for the in situ spatiotemporal mapping of the virulence factor pyocyanin, in communities of the pathogenic bacterium Pseudomonas aeruginosa. The combination of SERS imaging and PCA analysis provides a robust method for characterization of heterogeneous biological systems while circumventing issues associated with interference from sample autofluorescence and low reproducibility of SERS signals. The production of pyocyanin is found to depend both on the growth carbon source and on the specific strain of P. aeruginosa studied. A cystic fibrosis lung isolate strain of P. aeruginosa synthesizes and secretes pyocyanin when grown with glucose and glutamate, while the laboratory strain exhibits detectable production of pyocyanin only when grown with glutamate as the source of carbon. Pyocyanin production in the laboratory strain grown with glucose was below the limit of detection of SERS. In addition, the combination of SERS imaging and PCA can elucidate subtle differences in the molecular composition of biofilms. PCA loading plots from the clinical isolate exhibit features corresponding to vibrational bands of carbohydrates, which represent the mucoid biofilm matrix specific to that isolate, features that are not seen in the PCA loading plots of the laboratory strain. PMID:27354400

Exploring high-affinity binding properties of octamer peptides by principal component analysis of tetramer peptides.

PubMed

Kume, Akiko; Kawai, Shun; Kato, Ryuji; Iwata, Shinmei; Shimizu, Kazunori; Honda, Hiroyuki

2017-02-01

To investigate the binding properties of a peptide sequence, we conducted principal component analysis (PCA) of the physicochemical features of a tetramer peptide library comprised of 512 peptides, and the variables were reduced to two principal components. We selected IL-2 and IgG as model proteins and the binding affinity to these proteins was assayed using the 512 peptides mentioned above. PCA of binding affinity data showed that 16 and 18 variables were suitable for localizing IL-2 and IgG high-affinity binding peptides, respectively, into a restricted region of the PCA plot. We then investigated whether the binding affinity of octamer peptide libraries could be predicted using the identified region in the tetramer PCA. The results show that octamer high-affinity binding peptides were also concentrated in the tetramer high-affinity binding region of both IL-2 and IgG. The average fluorescence intensity of high-affinity binding peptides was 3.3- and 2.1-fold higher than that of low-affinity binding peptides for IL-2 and IgG, respectively. We conclude that PCA may be used to identify octamer peptides with high- or low-affinity binding properties from data from a tetramer peptide library. Copyright © 2016 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Scalable Robust Principal Component Analysis Using Grassmann Averages.

PubMed

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

2016-11-01

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

A measure for objects clustering in principal component analysis biplot: A case study in inter-city buses maintenance cost data

NASA Astrophysics Data System (ADS)

Ginanjar, Irlandia; Pasaribu, Udjianna S.; Indratno, Sapto W.

2017-03-01

This article presents the application of the principal component analysis (PCA) biplot for the needs of data mining. This article aims to simplify and objectify the methods for objects clustering in PCA biplot. The novelty of this paper is to get a measure that can be used to objectify the objects clustering in PCA biplot. Orthonormal eigenvectors, which are the coefficients of a principal component model representing an association between principal components and initial variables. The existence of the association is a valid ground to objects clustering based on principal axes value, thus if m principal axes used in the PCA, then the objects can be classified into 2m clusters. The inter-city buses are clustered based on maintenance costs data by using two principal axes PCA biplot. The buses are clustered into four groups. The first group is the buses with high maintenance costs, especially for lube, and brake canvass. The second group is the buses with high maintenance costs, especially for tire, and filter. The third group is the buses with low maintenance costs, especially for lube, and brake canvass. The fourth group is buses with low maintenance costs, especially for tire, and filter.

A novel urinary long non-coding RNA transcript improves diagnostic accuracy in patients undergoing prostate biopsy.

PubMed

Zhang, Wei; Ren, Shan-Cheng; Shi, Xiao-Lei; Liu, Ya-Wei; Zhu, Ya-Sheng; Jing, Tai-Le; Wang, Fu-Bo; Chen, Rui; Xu, Chuan-Liang; Wang, Hui-Qing; Wang, Hai-Feng; Wang, Yan; Liu, Bing; Li, Yao-Ming; Fang, Zi-Yu; Guo, Fei; Lu, Xin; Shen, Dan; Gao, Xu; Hou, Jian-Guo; Sun, Ying-Hao

2015-05-01

Long non-coding RNA (LncRNA) PCA3 has been a well-established urine biomarker for the detection of prostate cancer (PCa). Our previous study showed a novel LncRNA FR0348383 is up-regulated in over 70% of PCa compared with matched benign tissues. The aim of this study was to evaluate the diagnostic value of urinary FR0348383 for men undergoing prostate biopsy due to elevated PSA (PSA > 4.0 ng/ml) and/or abnormal digital rectal examination (DRE). Post-DRE first-catch urine specimens prior to prostate biopsies were prospectively collected. After the whole transcriptome amplification, quantitative real time polymerase chain reaction was applied to quantify urine FR0348383 and PSA levels. The FR0348383 score was calculated as the ratio of PSA and FR0348383 mRNA (PSA mRNA/FR0348383 mRNA × 1000). The diagnostic value of FR0348383 score was evaluated by logistic regression and decision curve analysis. 213 cases with urine samples containing sufficient mRNA were included, 94 cases had serum PSA level 4.0-10.0 ng/ml. PCa was identified in 72 cases. An increasing FR0348383 score was correlated with an increasing probability of a positive biopsy (P < 0.001). Multivariable logistic analysis indicated FR0348383 score (P < 0.001), PSA (P = 0.004), age (P = 0.007), prostate volume (P < 0.001) were independent predictors of PCa. ROC analysis demonstrated FR0348383 score outperformed PSA, %free PSA, and PSA Density in the prediction of PCa in the subgroup of patients with grey area PSA (AUC: 0.815 vs. 0.562 vs. 0.599 vs. 0.645). When using a probability threshold of 30% in the grey zone cohort, The FR0348383 score would save 52.0% of avoidable biopsies without missing any high grade cancers. FR0348383 transcript in post-DRE urine may be a novel biomarker for detection of PCa with great diagnostic value, especially in the grey zone cohort. The application of FR0348383 score in clinical practice might avoid unnecessary prostate biopsies and increase the specificity of PCa diagnosis. © 2015 Wiley Periodicals, Inc.

What Is the Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in Excluding Prostate Cancer at Biopsy? A Systematic Review and Meta-analysis from the European Association of Urology Prostate Cancer Guidelines Panel.

PubMed

Moldovan, Paul C; Van den Broeck, Thomas; Sylvester, Richard; Marconi, Lorenzo; Bellmunt, Joaquim; van den Bergh, Roderick C N; Bolla, Michel; Briers, Erik; Cumberbatch, Marcus G; Fossati, Nicola; Gross, Tobias; Henry, Ann M; Joniau, Steven; van der Kwast, Theo H; Matveev, Vsevolod B; van der Poel, Henk G; De Santis, Maria; Schoots, Ivo G; Wiegel, Thomas; Yuan, Cathy Yuhong; Cornford, Philip; Mottet, Nicolas; Lam, Thomas B; Rouvière, Olivier

2017-08-01

It remains unclear whether patients with a suspicion of prostate cancer (PCa) and negative multiparametric magnetic resonance imaging (mpMRI) can safely obviate prostate biopsy. To systematically review the literature assessing the negative predictive value (NPV) of mpMRI in patients with a suspicion of PCa. The Embase, Medline, and Cochrane databases were searched up to February 2016. Studies reporting prebiopsy mpMRI results using transrectal or transperineal biopsy as a reference standard were included. We further selected for meta-analysis studies with at least 10-core biopsies as the reference standard, mpMRI comprising at least T2-weighted and diffusion-weighted imaging, positive mpMRI defined as a Prostate Imaging Reporting Data System/Likert score of ≥3/5 or ≥4/5, and results reported at patient level for the detection of overall PCa or clinically significant PCa (csPCa) defined as Gleason ≥7 cancer. A total of 48 studies (9613 patients) were eligible for inclusion. At patient level, the median prevalence was 50.4% (interquartile range [IQR], 36.4-57.7%) for overall cancer and 32.9% (IQR, 28.1-37.2%) for csPCa. The median mpMRI NPV was 82.4% (IQR, 69.0-92.4%) for overall cancer and 88.1% (IQR, 85.7-92.3) for csPCa. NPV significantly decreased when cancer prevalence increased, for overall cancer (r=-0.64, p<0.0001) and csPCa (r=-0.75, p=0.032). Eight studies fulfilled the inclusion criteria for meta-analysis. Seven reported results for overall PCa. When the overall PCa prevalence increased from 30% to 60%, the combined NPV estimates decreased from 88% (95% confidence interval [95% CI], 77-99%) to 67% (95% CI, 56-79%) for a cut-off score of 3/5. Only one study selected for meta-analysis reported results for Gleason ≥7 cancers, with a positive biopsy rate of 29.3%. The corresponding NPV for a cut-off score of ≥3/5 was 87.9%. The NPV of mpMRI varied greatly depending on study design, cancer prevalence, and definitions of positive mpMRI and csPCa. As cancer prevalence was highly variable among series, risk stratification of patients should be the initial step before considering prebiopsy mpMRI and defining those in whom biopsy may be omitted when the mpMRI is negative. This systematic review examined if multiparametric magnetic resonance imaging (MRI) scan can be used to reliably predict the absence of prostate cancer in patients suspected of having prostate cancer, thereby avoiding a prostate biopsy. The results suggest that whilst it is a promising tool, it is not accurate enough to replace prostate biopsy in such patients, mainly because its accuracy is variable and influenced by the prostate cancer risk. However, its performance can be enhanced if there were more accurate ways of determining the risk of having prostate cancer. When such tools are available, it should be possible to use an MRI scan to avoid biopsy in patients at a low risk of prostate cancer. Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Chen, Weisheng; Wang, Yue; Chen, Rong; Zeng, Haishan

2013-01-01

The capability of using silver nanoparticle based near-infrared surface enhanced Raman scattering (SERS) spectroscopy combined with principal component analysis (PCA) and linear discriminate analysis (LDA) to differentiate esophageal cancer tissue from normal tissue was presented. Significant differences in Raman intensities of prominent SERS bands were observed between normal and cancer tissues. PCA-LDA multivariate analysis of the measured tissue SERS spectra achieved diagnostic sensitivity of 90.9% and specificity of 97.8%. This exploratory study demonstrated great potential for developing label-free tissue SERS analysis into a clinical tool for esophageal cancer detection.

Epigenetic regulation of EFEMP1 in prostate cancer: biological relevance and clinical potential

PubMed Central

Almeida, Mafalda; Costa, Vera L; Costa, Natália R; Ramalho-Carvalho, João; Baptista, Tiago; Ribeiro, Franclim R; Paulo, Paula; Teixeira, Manuel R; Oliveira, Jorge; Lothe, Ragnhild A; Lind, Guro E; Henrique, Rui; Jerónimo, Carmen

2014-01-01

Epigenetic alterations are common in prostate cancer (PCa) and seem to contribute decisively to its initiation and progression. Moreover, aberrant promoter methylation is a promising biomarker for non-invasive screening. Herein, we sought to characterize EFEMP1 as biomarker for PCa, unveiling its biological relevance in prostate carcinogenesis. Microarray analyses of treated PCa cell lines and primary tissues enabled the selection of differentially methylated genes, among which EFEMP1 was further validated by MSP and bisulfite sequencing. Assessment of biomarker performance was accomplished by qMSP. Expression analysis of EFEMP1 and characterization of histone marks were performed in tissue samples and cancer cell lines to determine the impact of epigenetic mechanisms on EFEMP1 transcriptional regulation. Phenotypic assays, using transfected cell lines, permitted the evaluation of EFEMP1’s role in PCa development. EFEMP1 methylation assay discriminated PCa from normal prostate tissue (NPT; P < 0.001, Kruskall–Wallis test) and renal and bladder cancers (96% sensitivity and 98% specificity). EFEMP1 transcription levels inversely correlated with promoter methylation and histone deacetylation, suggesting that both epigenetic mechanisms are involved in gene regulation. Phenotypic assays showed that EFEMP1 de novo expression reduces malignant phenotype of PCa cells. EFEMP1 promoter methylation is prevalent in PCa and accurately discriminates PCa from non-cancerous prostate tissues and other urological neoplasms. This epigenetic alteration occurs early in prostate carcinogenesis and, in association with histone deacetylation, progressively leads to gene down-regulation, fostering cell proliferation, invasion and evasion of apoptosis. PMID:25211630

Determination of tumor cell procoagulant activity by Sonoclot analysis in whole blood.

PubMed

Amirkhosravi, A; Biggerstaff, J P; Warnes, G; Francis, D A; Francis, J L

1996-12-01

Coagulation activation in cancer may be due to expression of procoagulant activity (PCA) by tumor cells. Some PCA activate coagulation, while others influence platelet aggregation. Thus, clotting time assessments of PCA may not reflect the potential for hypercoagulability. We therefore studied the effect of various malignant and non-malignant cells on whole blood coagulation using the Sonoclot Analyzer. Five human (HT29 colon, J82 bladder, MCF-7 breast, BT-474 breast and A375 malignant melanoma) and three rodent (MC28, WEHI-164 and Neuro2a) cell lines were used. Rat thymocytes and peritoneal macrophages and human endotoxin-stimulated mononuclear cells and umbilical vein endothelial cells (HUVEC) were used as non-malignant controls. All tumor cells markedly shortened the recalcification time of citrated blood and the most potent (HT29 and J82) also increased clot rate (P < 0.01). The breast cancer cells MCF-7 and BT-474 expressed only weak PCA and did not affect clotting rate. None of the non-malignant cells significantly affected clotting time or rate in whole blood. J82 and HT29 cells grown in serum-rich media shortened the recalcification time of both normal and FVII-deficient plasmas. However, cells grown in serum-free conditions had significantly less PCA in FVII-deficient plasma. We conclude that the Sonoclot Analyzer is useful for determining cellular PCA; significant PCA is a feature of malignant cells and cells grown in medium containing serum supplements cannot be reliably evaluated for PCA.

Heterogeneity of DNA methylation in multifocal prostate cancer.

PubMed

Serenaite, Inga; Daniunaite, Kristina; Jankevicius, Feliksas; Laurinavicius, Arvydas; Petroska, Donatas; Lazutka, Juozas R; Jarmalaite, Sonata

2015-01-01

Most prostate cancer (PCa) cases are multifocal, and separate foci display histological and molecular heterogeneity. DNA hypermethylation is a frequent alteration in PCa, but interfocal heterogeneity of these changes has not been extensively investigated. Ten pairs of foci from multifocal PCa and 15 benign prostatic hyperplasia (BPH) samples were obtained from prostatectomy specimens, resulting altogether in 35 samples. Methylation-specific PCR (MSP) was used to evaluate methylation status of nine tumor suppressor genes (TSGs), and a set of selected TSGs was quantitatively analyzed for methylation intensity by pyrosequencing. Promoter sequences of the RASSF1 and ESR1 genes were methylated in all paired PCa foci, and frequent (≥75 %) DNA methylation was detected in RARB, GSTP1, and ABCB1 genes. MSP revealed different methylation status of at least one gene in separate foci in 8 out of 10 multifocal tumors. The mean methylation level of ESR1, GSTP1, RASSF1, and RARB differed between the paired foci of all PCa cases. The intensity of DNA methylation in these TSGs was significantly higher in PCa cases than in BPH (p < 0.001). Hierarchical cluster analysis revealed a divergent methylation profile of paired PCa foci, while the foci from separate cases with biochemical recurrence showed similar methylation profile and the highest mean levels of DNA methylation. Our findings suggest that PCa tissue is heterogeneous, as between paired foci differences in DNA methylation status were found. Common epigenetic profile of recurrent tumors can be inferred from our data.

Fluorescence excitation-emission matrix (EEM) spectroscopy and cavity ring-down (CRD) absorption spectroscopy of oil-contaminated jet fuel using fiber-optic probes.

PubMed

Omrani, Hengameh; Barnes, Jack A; Dudelzak, Alexander E; Loock, Hans-Peter; Waechter, Helen

2012-06-21

Excitation emission matrix (EEM) and cavity ring-down (CRD) spectral signatures have been used to detect and quantitatively assess contamination of jet fuels with aero-turbine lubricating oil. The EEM spectrometer has been fiber-coupled to permit in situ measurements of jet turbine oil contamination of jet fuel. Parallel Factor (PARAFAC) analysis as well as Principal Component Analysis and Regression (PCA/PCR) were used to quantify oil contamination in a range from the limit of detection (10 ppm) to 1000 ppm. Fiber-loop cavity ring-down spectroscopy using a pulsed 355 nm laser was used to quantify the oil contamination in the range of 400 ppm to 100,000 ppm. Both methods in combination therefore permit the detection of oil contamination with a linear dynamic range of about 10,000.

Use of Geochemistry Data Collected by the Mars Exploration Rover Spirit in Gusev Crater to Teach Geomorphic Zonation through Principal Components Analysis

ERIC Educational Resources Information Center

Rodrigue, Christine M.

2011-01-01

This paper presents a laboratory exercise used to teach principal components analysis (PCA) as a means of surface zonation. The lab was built around abundance data for 16 oxides and elements collected by the Mars Exploration Rover Spirit in Gusev Crater between Sol 14 and Sol 470. Students used PCA to reduce 15 of these into 3 components, which,…

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

PubMed

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

2014-10-01

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

Haystack, a web-based tool for metabolomics research

PubMed Central

2014-01-01

Background Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species. Results To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins. Conclusion Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization options and supports non-biased differential profiling studies through a unique and flexible binning function that provides an alternative to conventional peak deconvolution analysis methods. PMID:25350247

Haystack, a web-based tool for metabolomics research.

PubMed

Grace, Stephen C; Embry, Stephen; Luo, Heng

2014-01-01

Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species. To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins. Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization options and supports non-biased differential profiling studies through a unique and flexible binning function that provides an alternative to conventional peak deconvolution analysis methods.

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

PubMed

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

2017-12-01

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

Chemometric Methods to Quantify 1D and 2D NMR Spectral Differences Among Similar Protein Therapeutics.

PubMed

Chen, Kang; Park, Junyong; Li, Feng; Patil, Sharadrao M; Keire, David A

2018-04-01

NMR spectroscopy is an emerging analytical tool for measuring complex drug product qualities, e.g., protein higher order structure (HOS) or heparin chemical composition. Most drug NMR spectra have been visually analyzed; however, NMR spectra are inherently quantitative and multivariate and thus suitable for chemometric analysis. Therefore, quantitative measurements derived from chemometric comparisons between spectra could be a key step in establishing acceptance criteria for a new generic drug or a new batch after manufacture change. To measure the capability of chemometric methods to differentiate comparator NMR spectra, we calculated inter-spectra difference metrics on 1D/2D spectra of two insulin drugs, Humulin R® and Novolin R®, from different manufacturers. Both insulin drugs have an identical drug substance but differ in formulation. Chemometric methods (i.e., principal component analysis (PCA), 3-way Tucker3 or graph invariant (GI)) were performed to calculate Mahalanobis distance (D M ) between the two brands (inter-brand) and distance ratio (D R ) among the different lots (intra-brand). The PCA on 1D inter-brand spectral comparison yielded a D M value of 213. In comparing 2D spectra, the Tucker3 analysis yielded the highest differentiability value (D M  = 305) in the comparisons made followed by PCA (D M  = 255) then the GI method (D M  = 40). In conclusion, drug quality comparisons among different lots might benefit from PCA on 1D spectra for rapidly comparing many samples, while higher resolution but more time-consuming 2D-NMR-data-based comparisons using Tucker3 analysis or PCA provide a greater level of assurance for drug structural similarity evaluation between drug brands.

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

NASA Astrophysics Data System (ADS)

Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

2017-06-01

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

Compositional differences among Chinese soy sauce types studied by (13)C NMR spectroscopy coupled with multivariate statistical analysis.

PubMed

Kamal, Ghulam Mustafa; Wang, Xiaohua; Bin Yuan; Wang, Jie; Sun, Peng; Zhang, Xu; Liu, Maili

2016-09-01

Soy sauce a well known seasoning all over the world, especially in Asia, is available in global market in a wide range of types based on its purpose and the processing methods. Its composition varies with respect to the fermentation processes and addition of additives, preservatives and flavor enhancers. A comprehensive (1)H NMR based study regarding the metabonomic variations of soy sauce to differentiate among different types of soy sauce available on the global market has been limited due to the complexity of the mixture. In present study, (13)C NMR spectroscopy coupled with multivariate statistical data analysis like principle component analysis (PCA), and orthogonal partial least square-discriminant analysis (OPLS-DA) was applied to investigate metabonomic variations among different types of soy sauce, namely super light, super dark, red cooking and mushroom soy sauce. The main additives in soy sauce like glutamate, sucrose and glucose were easily distinguished and quantified using (13)C NMR spectroscopy which were otherwise difficult to be assigned and quantified due to serious signal overlaps in (1)H NMR spectra. The significantly higher concentration of sucrose in dark, red cooking and mushroom flavored soy sauce can directly be linked to the addition of caramel in soy sauce. Similarly, significantly higher level of glutamate in super light as compared to super dark and mushroom flavored soy sauce may come from the addition of monosodium glutamate. The study highlights the potentiality of (13)C NMR based metabonomics coupled with multivariate statistical data analysis in differentiating between the types of soy sauce on the basis of level of additives, raw materials and fermentation procedures. Copyright © 2016 Elsevier B.V. All rights reserved.

Does Para-chloroaniline Really Form after Mixing Sodium Hypochlorite and Chlorhexidine?

PubMed

Orhan, Ekim Onur; Irmak, Özgür; Hür, Deniz; Yaman, Batu Can; Karabucak, Bekir

2016-03-01

Mixing sodium hypochlorite (NaOCl) with chlorhexidine (CHX) forms a brown-colored precipitate. Previous studies are not in agreement whether this precipitate contains para-chloroaniline (PCA). Tests used for analysis may demonstrate different outcomes. Purpose of this study was to determine whether PCA is formed through the reaction of mixing NaOCl and CHX by using high performance liquid chromatography, proton nuclear magnetic resonance spectroscopy, gas chromatography, thin layer chromatography, infrared spectroscopy, and gas chromatography/mass spectrometry. To obtain a brown precipitate, 4.99% NaOCl was mixed with 2.0% CHX. This brown precipitate was analyzed and compared with signals obtained from commercially available 4.99% NaOCl, 2% solutions, and 98% PCA in powder form. Chromatographic and spectroscopic analyses showed that brown precipitate does not contain free PCA. This study will be a cutoff proof for the argument on PCA formation from reaction of CHX and NaOCl. Copyright © 2016 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

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

PubMed

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.

Exosomes confer pro-survival signals to alter the phenotype of prostate cells in their surrounding environment

PubMed Central

Hosseini-Beheshti, Elham; Choi, Wendy; Weiswald, Louis-Bastien; Kharmate, Geetanjali; Ghaffari, Mazyar; Roshan-Moniri, Mani; Hassona, Mohamed D.; Chan, Leslie; Chin, Mei Yieng; Tai, Isabella T.; Rennie, Paul S.; Fazli, Ladan; Guns, Emma S. Tomlinson

2016-01-01

Prostate cancer (PCa) is the most frequently diagnosed cancer in men. Current research on tumour-related extracellular vesicles (EVs) suggests that exosomes play a significant role in paracrine signaling pathways, thus potentially influencing cancer progression via multiple mechanisms. In fact, during the last decade numerous studies have revealed the role of EVs in the progression of various pathological conditions including cancer. Moreover, differences in the proteomic, lipidomic, and cholesterol content of exosomes derived from PCa cell lines versus benign prostate cell lines confirm that exosomes could be excellent biomarker candidates. As such, as part of an extensive proteomic analysis using LCMS we previously described a potential role of exosomes as biomarkers for PCa. Current evidence suggests that uptake of EV's into the local tumour microenvironment encouraging us to further examine the role of these vesicles in distinct mechanisms involved in the progression of PCa and castration resistant PCa. For the purpose of this study, we hypothesized that exosomes play a pivotal role in cell-cell communication in the local tumour microenvironment, conferring activation of numerous survival mechanisms during PCa progression and development of therapeutic resistance. Our in vitro results demonstrate that PCa derived exosomes significantly reduce apoptosis, increase cancer cell proliferation and induce cell migration in LNCaP and RWPE-1 cells. In conjunction with our in vitro findings, we have also demonstrated that exosomes increased tumor volume and serum PSA levels in vivo when xenograft bearing mice were administered DU145 cell derived exosomes intravenously. This research suggests that, regardless of androgen receptor phenotype, exosomes derived from PCa cells significantly enhance multiple mechanisms that contribute to PCa progression. PMID:26840259

A three-gene panel on urine increases PSA specificity in the detection of prostate cancer.

PubMed

Rigau, Marina; Ortega, Israel; Mir, Maria Carmen; Ballesteros, Carlos; Garcia, Marta; Llauradó, Marta; Colás, Eva; Pedrola, Núria; Montes, Melania; Sequeiros, Tamara; Ertekin, Tugce; Majem, Blanca; Planas, Jacques; Ruiz, Anna; Abal, Miguel; Sánchez, Alex; Morote, Juan; Reventós, Jaume; Doll, Andreas

2011-12-01

Several studies have demonstrated the usefulness of monitoring an RNA transcript, such as PCA3, in post-prostate massage (PM) urine for increasing the specificity of prostate-specific antigen (PSA) in the detection of prostate cancer (PCa). However, a single marker may not necessarily reflect the multifactorial nature of PCa. We analyzed post-PM urine samples from 154 consecutive patients, who presented for prostate biopsies because of elevated serum PSA (>4 ng/ml) and/or abnormal digital rectal exam. We tested whether the putative PCa biomarkers PSMA, PSGR, and PCA3 could be detected by quantitative real-time PCR in post-PM urine sediment. We combined these findings to test if a combination of these biomarkers could improve the specificity of actual diagnosis. Afterwards, we specifically tested our model for clinical usefulness in the PSA diagnostic "gray zone" (4-10 ng/ml) on a target subset of 82 men with no prior biopsy. By univariate analysis, we found that the PSMA, PSGR, and PCA3 scores were significant predictors of PCa. Using a multiplex model, the area under the multi receiver-operating characteristic curve was 0.74 versus 0.82 in the diagnostic "gray zone." Fixing the sensitivity at 96%, we obtained a specificity of 34% and 50% in the gray zone. Taken together, these results provide a strategy for the development of a more accurate model for PCa diagnosis. In the future, a multiplexed, urine-based diagnostic test for PCa with a higher specificity, but the same sensitivity as the serum-PSA test, could be used to determine better which patients should undergo biopsy. Copyright © 2011 Wiley Periodicals, Inc.

Inhibition of prostate cancer osteoblastic progression with VEGF121/rGel, a single agent targeting osteoblasts, osteoclasts, and tumor neovasculature.

PubMed

Mohamedali, Khalid A; Li, Zhi Gang; Starbuck, Michael W; Wan, Xinhai; Yang, Jun; Kim, Sehoon; Zhang, Wendy; Rosenblum, Michael G; Navone, Nora M

2011-04-15

A hallmark of prostate cancer (PCa) progression is the development of osteoblastic bone metastases, which respond poorly to available therapies. We previously reported that VEGF(121)/rGel targets osteoclast precursors and tumor neovasculature. Here we tested the hypothesis that targeting nontumor cells expressing these receptors can inhibit tumor progression in a clinically relevant model of osteoblastic PCa. Cells from MDA PCa 118b, a PCa xenograft obtained from a bone metastasis in a patient with castrate-resistant PCa, were injected into the femurs of mice. Osteoblastic progression was monitored following systemic administration of VEGF(121)/rGel. VEGF(121)/rGel was cytotoxic in vitro to osteoblast precursor cells. This cytotoxicity was specific as VEGF(121)/rGel internalization into osteoblasts was VEGF(121) receptor driven. Furthermore, VEGF(121)/rGel significantly inhibited PCa-induced bone formation in a mouse calvaria culture assay. In vivo, VEGF(121)/rGel significantly inhibited the osteoblastic progression of PCa cells in the femurs of nude mice. Microcomputed tomographic analysis revealed that VEGF(121)/rGel restored the bone volume fraction of tumor-bearing femurs to values similar to those of the contralateral (non-tumor-bearing) femurs. VEGF(121)/rGel significantly reduced the number of tumor-associated osteoclasts but did not change the numbers of peritumoral osteoblasts. Importantly, VEGF(121)/rGel-treated mice had significantly less tumor burden than control mice. Our results thus indicate that VEGF(121)/rGel inhibits osteoblastic tumor progression by targeting angiogenesis, osteoclastogenesis, and bone formation. Targeting VEGF receptor (VEGFR)-1- or VEGFR-2-expressing cells is effective in controlling the osteoblastic progression of PCa in bone. These findings provide the basis for an effective multitargeted approach for metastatic PCa. ©2011 AACR.

Effect of oxygen on volatile and sensory characteristics of Cabernet Sauvignon during secondary shelf life.

PubMed

Lee, Dong-Hyun; Kang, Bo-Sik; Park, Hyun-Jin

2011-11-09

The oxidation of Cabernet Sauvignon wines during secondary shelf life was studied by headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography-quadrupole mass spectrometry (GC-qMS) and sensory tests, with the support of multivariate statistical analyses such as OPLS-DA loading plot and PCA score plot. Four different oxidation conditions were established during a 1-week secondary shelf life. Samples collected on a regular basis were analyzed to determine the changes of volatile chemicals, with sensory characteristics evaluated through pattern recognition models. During secondary shelf life the separation among collected samples depended on the degree of oxidation in wine. Isoamyl acetate, ethyl decanoate, nonanoic acid, n-decanoic acid, undecanoic acid, 2-furancarboxylic acid, dodecanoic acid, and phenylacetaldehyde were determined to be associated with the oxidation of the wine. PCA sensory evaluation revealed that least oxidized wine and fresh wine was well-separated from more oxidized wines, demonstrating that sensory characteristics of less oxidized wines tend toward "fruity", "citrous", and "sweetness", while those of more oxidized wines are positively correlated with "animal", "bitterness", and "dairy". The study also demonstrates that OPLS-DA and PCA are very useful statistical tools for the understanding of wine oxidation.

Metabolic fingerprint of Brazilian maize landraces silk (stigma/styles) using NMR spectroscopy and chemometric methods.

PubMed

Kuhnen, Shirley; Bernardi Ogliari, Juliana; Dias, Paulo Fernando; da Silva Santos, Maiara; Ferreira, Antônio Gilberto; Bonham, Connie C; Wood, Karl Vernon; Maraschin, Marcelo

2010-02-24

Aqueous extract from maize silks is used by traditional medicine for the treatment of several ailments, mainly related to the urinary system. This work focuses on the application of NMR spectroscopy and chemometric analysis for the determination of metabolic fingerprint and pattern recognition of silk extracts from seven maize landraces cultivated in southern Brazil. Principal component analysis (PCA) of the (1)H NMR data set showed clear discrimination among the maize varieties by PC1 and PC2, pointing out three distinct metabolic profiles. Target compounds analysis showed significant differences (p < 0.05) in the contents of protocatechuic acid, gallic acid, t-cinnamic acid, and anthocyanins, corroborating the discrimination of the genotypes in this study as revealed by PCA analysis. Thus the combination of (1)H NMR and PCA is a useful tool for the discrimination of maize silks in respect to their chemical composition, including rapid authentication of the raw material of current pharmacological interest.

Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods.

PubMed

Liu, Changhong; Liu, Wei; Lu, Xuzhong; Chen, Wei; Yang, Jianbo; Zheng, Lei

2014-06-15

Crop-to-crop transgene flow may affect the seed purity of non-transgenic rice varieties, resulting in unwanted biosafety consequences. The feasibility of a rapid and nondestructive determination of transgenic rice seeds from its non-transgenic counterparts was examined by using multispectral imaging system combined with chemometric data analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM), and PCA-back propagation neural network (PCA-BPNN) methods were applied to classify rice seeds according to their genetic origins. The results demonstrated that clear differences between non-transgenic and transgenic rice seeds could be easily visualized with the nondestructive determination method developed through this study and an excellent classification (up to 100% with LS-SVM model) can be achieved. It is concluded that multispectral imaging together with chemometric data analysis is a promising technique to identify transgenic rice seeds with high efficiency, providing bright prospects for future applications. Copyright © 2013 Elsevier Ltd. All rights reserved.

Analysis of antique bronze coins by Laser Induced Breakdown Spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Bachler, M. Orlić; Bišćan, M.; Kregar, Z.; Jelovica Badovinac, I.; Dobrinić, J.; Milošević, S.

2016-09-01

This work presents a feasibility study of applying the Principal Component Analysis (PCA) to data obtained by Laser-Induced Breakdown Spectroscopy (LIBS) with the aim of determining correlation between different samples. The samples were antique bronze coins coated in silver (follis) dated in the Roman Empire period and were made during different rulers in different mints. While raw LIBS data revealed that in the period from the year 286 to 383 CE content of silver was constantly decreasing, the PCA showed that the samples can be somewhat grouped together based on their place of origin, which could be a useful hint when analysing unknown samples. It was also found that PCA can help in discriminating spectra corresponding to ablation from the surface and from the bulk. Furthermore, Partial Least Squares method (PLS) was used to obtain, based on a set of samples with known composition, an estimation of relative copper concentration in studied ancient coins. This analysis showed that copper concentration in surface layers ranged from 83% to 90%.

Multivariate Analysis of Electron Detachment Dissociation and Infrared Multiphoton Dissociation Mass Spectra of Heparan Sulfate Tetrasaccharides Differing Only in Hexuronic acid Stereochemistry

NASA Astrophysics Data System (ADS)

Oh, Han Bin; Leach, Franklin E.; Arungundram, Sailaja; Al-Mafraji, Kanar; Venot, Andre; Boons, Geert-Jan; Amster, I. Jonathan

2011-03-01

The structural characterization of glycosaminoglycan (GAG) carbohydrates by mass spectrometry has been a long-standing analytical challenge due to the inherent heterogeneity of these biomolecules, specifically polydispersity, variability in sulfation, and hexuronic acid stereochemistry. Recent advances in tandem mass spectrometry methods employing threshold and electron-based ion activation have resulted in the ability to determine the location of the labile sulfate modification as well as assign the stereochemistry of hexuronic acid residues. To facilitate the analysis of complex electron detachment dissociation (EDD) spectra, principal component analysis (PCA) is employed to differentiate the hexuronic acid stereochemistry of four synthetic GAG epimers whose EDD spectra are nearly identical upon visual inspection. For comparison, PCA is also applied to infrared multiphoton dissociation spectra (IRMPD) of the examined epimers. To assess the applicability of multivariate methods in GAG mixture analysis, PCA is utilized to identify the relative content of two epimers in a binary mixture.

Label free aggressive prostate cancer identification with ultraviolet photoacoustic spectral analysis (Conference Presentation)

NASA Astrophysics Data System (ADS)

Xu, Guan; Davis, Mandy A.; Siddiqui, Javed; Chao, Wan-yu; Tomlins, Scott A.; Wei, John T.; Wang, Xueding

2017-03-01

Prostate cancer (PCa) is the most commonly diagnosed cancer in American men for the past decades. PCa has a relatively low progression rate but the 5 year survival rate decreases dramatically once the cancer has metastasized. Differentiating aggressive from indolent PCa is critical for improving PCa patient outcomes and preventing metastasis and death. Prostate biopsy is the standard procedure for evaluating the presence and aggressiveness of PCa. The microarchitecture of the biopsied tissues visualized by histology process is evaluated by pathologists and assigned a Gleason score as a quantification of the aggressiveness. In our previous study, we have shown that photoacoustic spectral analysis (PASA) is capable of quantifying the Gleason scores of the H&E stained human prostate tissues. In this study, we attempt to assess the Gleason scores without any staining by taking advantage of the strong optical absorption of nucleic acid at ultraviolet wavelengths. PA signals were generated by wide field illumination at 266 nm and received by a hydrophone with a bandwidth of 0-20 MHz. DU145 prostate cancer cells at the concentrations of 0.8, 0.4, 0.05, 0.025 and 0.0125 million per cm3 simulating those in cancerous and normal tissues were first attempted. The measurements were repeated for 10 times at each concentration. A correlation of 0.86 was observed between the PA signal intensities and the cell concentrations. Human PCa tissues with Gleason score 6, 7 and 8 and normal tissues were assessed. With 11 samples, a correlation of 0.89 was found between the Gleason scores and PASA slopes.

Epidural Analgesia Versus Patient-Controlled Analgesia for Pain Relief in Uterine Artery Embolization for Uterine Fibroids: A Decision Analysis

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

Kooij, Sanne M. van der, E-mail: s.m.vanderkooij@amc.uva.nl; Moolenaar, Lobke M.; Ankum, Willem M.

Purpose: This study was designed to compare the costs and effects of epidural analgesia (EDA) to those of patient-controlled intravenous analgesia (PCA) for postintervention pain relief in women having uterine artery embolization (UAE) for systematic uterine fibroids. Methods: Cost-effectiveness analysis (CEA) based on data from the literature by constructing a decision tree to model the clinical pathways for estimating the effects and costs of treatment with EDA and PCA. Literature on EDA for pain-relief after UAE was missing, and therefore, data on EDA for abdominal surgery were used. Outcome measures were compared costs to reduce one point in visual analoguemore » score (VAS) or numeric rating scale (NRS) for pain 6 and 24 h after UAE and risk for complications. Results: Six hours after the intervention, the VAS was 3.56 when using PCA and 2.0 when using EDA. The costs for pain relief in women undergoing UAE with PCA and EDA were Euro-Sign 191 and Euro-Sign 355, respectively. The costs for EDA to reduce the VAS score 6 h after the intervention with one point compared with PCA were Euro-Sign 105 and Euro-Sign 179 after 24 h. The risk of having a complication was 2.45 times higher when using EDA. Conclusions: The results of this indirect comparison of EDA for abdominal surgery with PCA for UAE show that EDA would provide superior analgesia for post UAE pain at 6 and 24 h but with higher costs and an increased risk of complications.« less

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

NASA Astrophysics Data System (ADS)

Selle, B.; Schwientek, M.

2012-04-01

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

Identification of Surface Water Quality along the Coast of Sanya, South China Sea

PubMed Central

Wu, Zhen-Zhen; Che, Zhi-Wei; Wang, You-Shao; Dong, Jun-De; Wu, Mei-Lin

2015-01-01

Principal component analysis (PCA) and cluster analysis (CA) are utilized to identify the effects caused by human activities on water quality along the coast of Sanya, South China Sea. PCA and CA identify the seasonality of water quality (dry and wet seasons) and polluted status (polluted area). The seasonality of water quality is related to climate change and Southeast monsoons. Spatial pattern is mainly related to anthropogenic activities (especially land input of pollutions). PCA reveals the characteristics underlying the generation of coastal water quality. The temporal and spatial variation of the trophic status along the coast of Sanya is governed by hydrodynamics and human activities. The results provide a novel typological understanding of seasonal trophic status in a shallow, tropical, open marine bay. PMID:25894980

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

NASA Astrophysics Data System (ADS)

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.

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

PubMed

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

2017-07-01

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

Low-Dimensional Feature Representation for Instrument Identification

NASA Astrophysics Data System (ADS)

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

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

Guided filter and principal component analysis hybrid method for hyperspectral pansharpening

NASA Astrophysics Data System (ADS)

Qu, Jiahui; Li, Yunsong; Dong, Wenqian

2018-01-01

Hyperspectral (HS) pansharpening aims to generate a fused HS image with high spectral and spatial resolution through integrating an HS image with a panchromatic (PAN) image. A guided filter (GF) and principal component analysis (PCA) hybrid HS pansharpening method is proposed. First, the HS image is interpolated and the PCA transformation is performed on the interpolated HS image. The first principal component (PC1) channel concentrates on the spatial information of the HS image. Different from the traditional PCA method, the proposed method sharpens the PAN image and utilizes the GF to obtain the spatial information difference between the HS image and the enhanced PAN image. Then, in order to reduce spectral and spatial distortion, an appropriate tradeoff parameter is defined and the spatial information difference is injected into the PC1 channel through multiplying by this tradeoff parameter. Once the new PC1 channel is obtained, the fused image is finally generated by the inverse PCA transformation. Experiments performed on both synthetic and real datasets show that the proposed method outperforms other several state-of-the-art HS pansharpening methods in both subjective and objective evaluations.

Performance analysis of robust road sign identification

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

EDRN Pre-Validation of Multiplex Biomarker in Urine — EDRN Public Portal

Cancer.gov

The goal of this proposal is to begin to establish an EDRN “pre-validation” trial of a multiplex set of transcripts, including the ETS gene fusions, in post-DRE urine sediments. As can be evidenced by our preliminary data, we have established the utility of this multiplex urine test (which includes TMPRSS-ERG, SPINK1, PCA3 and GOLPH2) in a cohort of prospectively collected urine sediments from the University of Michigan EDRN CEVC site (collected by co-I, Dr. John Wei). In this proposal, we will run this multiplex assay on prospectively collected post-DRE urines collected from other EDRN sites. The idea is to couple this “pre-validation” study with an EDRN validation trial under consideration for the Gen-Probe PCA3 urine test (directed by Drs. John Wei and Harry Rittenhouse).

Pseudo-scalar pi N coupling and relativistic proton-nucleus scattering

NASA Technical Reports Server (NTRS)

Gross, Franz; Maung, Khin Maung; Tjon, J. A.; Townsend, L. W.; Wallace, S. J.

1988-01-01

Relativistic p-Ca-40 elastic scattering observables are calculated using relativistic NN amplitudes obtained from the solution of a two-body relativistic equation in which one particle is kept on its mass-shell. Results at 200 MeV are presented for two sets of NN amplitudes, one with pure pseudo-vector coupling for the pion and another with a 25 percent admixture of pseudo-scaling coupling. Both give a very good fit to the positive energy on-shell NN data. Differences between the predictions of these two models (which are shown to be due only to the differences in their corresponding negative energy amplitudes) provide a measure of the uncertainty in contructing Dirac optical potentials from NN amplitudes.

A novel nonparametric item response theory approach to measuring socioeconomic position: a comparison using household expenditure data from a Vietnam health survey, 2003

PubMed Central

2014-01-01

Background Measures of household socio-economic position (SEP) are widely used in health research. There exist a number of approaches to their measurement, with Principal Components Analysis (PCA) applied to a basket of household assets being one of the most common. PCA, however, carries a number of assumptions about the distribution of the data which may be untenable, and alternative, non-parametric, approaches may be preferred. Mokken scale analysis is a non-parametric, item response theory approach to scale development which appears never to have been applied to household asset data. A Mokken scale can be used to rank order items (measures of wealth) as well as households. Using data on household asset ownership from a national sample of 4,154 consenting households in the World Health Survey from Vietnam, 2003, we construct two measures of household SEP. Seventeen items asking about assets, and utility and infrastructure use were used. Mokken Scaling and PCA were applied to the data. A single item measure of total household expenditure is used as a point of contrast. Results An 11 item scale, out of the 17 items, was identified that conformed to the assumptions of a Mokken Scale. All the items in the scale were identified as strong items (Hi > .5). Two PCA measures of SEP were developed as a point of contrast. One PCA measure was developed using all 17 available asset items, the other used the reduced set of 11 items identified in the Mokken scale analaysis. The Mokken Scale measure of SEP and the 17 item PCA measure had a very high correlation (r = .98), and they both correlated moderately with total household expenditure: r = .59 and r = .57 respectively. In contrast the 11 item PCA measure correlated moderately with the Mokken scale (r = .68), and weakly with the total household expenditure (r = .18). Conclusion The Mokken scale measure of household SEP performed at least as well as PCA, and outperformed the PCA measure developed with the 11 items used in the Mokken scale. Unlike PCA, Mokken scaling carries no assumptions about the underlying shape of the distribution of the data, and can be used simultaneous to order household SEP and items. The approach, however, has not been tested with data from other countries and remains an interesting, but under researched approach. PMID:25126103

A novel nonparametric item response theory approach to measuring socioeconomic position: a comparison using household expenditure data from a Vietnam health survey, 2003.

PubMed

Reidpath, Daniel D; Ahmadi, Keivan

2014-01-01

Measures of household socio-economic position (SEP) are widely used in health research. There exist a number of approaches to their measurement, with Principal Components Analysis (PCA) applied to a basket of household assets being one of the most common. PCA, however, carries a number of assumptions about the distribution of the data which may be untenable, and alternative, non-parametric, approaches may be preferred. Mokken scale analysis is a non-parametric, item response theory approach to scale development which appears never to have been applied to household asset data. A Mokken scale can be used to rank order items (measures of wealth) as well as households. Using data on household asset ownership from a national sample of 4,154 consenting households in the World Health Survey from Vietnam, 2003, we construct two measures of household SEP. Seventeen items asking about assets, and utility and infrastructure use were used. Mokken Scaling and PCA were applied to the data. A single item measure of total household expenditure is used as a point of contrast. An 11 item scale, out of the 17 items, was identified that conformed to the assumptions of a Mokken Scale. All the items in the scale were identified as strong items (Hi > .5). Two PCA measures of SEP were developed as a point of contrast. One PCA measure was developed using all 17 available asset items, the other used the reduced set of 11 items identified in the Mokken scale analaysis. The Mokken Scale measure of SEP and the 17 item PCA measure had a very high correlation (r = .98), and they both correlated moderately with total household expenditure: r = .59 and r = .57 respectively. In contrast the 11 item PCA measure correlated moderately with the Mokken scale (r = .68), and weakly with the total household expenditure (r = .18). The Mokken scale measure of household SEP performed at least as well as PCA, and outperformed the PCA measure developed with the 11 items used in the Mokken scale. Unlike PCA, Mokken scaling carries no assumptions about the underlying shape of the distribution of the data, and can be used simultaneous to order household SEP and items. The approach, however, has not been tested with data from other countries and remains an interesting, but under researched approach.

Reaction Kinetics for the Biocatalytic Conversion of Phenazine-1-Carboxylic Acid to 2-Hydroxyphenazine

PubMed Central

Chen, Mingmin; Cao, Hongxia; Peng, Huasong; Hu, Hongbo; Wang, Wei; Zhang, Xuehong

2014-01-01

The phenazine derivative 2-hydroxyphenazine (2-OH-PHZ) plays an important role in the biocontrol of plant diseases, and exhibits stronger bacteriostatic and fungistatic activity than phenazine-1-carboxylic acid (PCA) toward some pathogens. PhzO has been shown to be responsible for the conversion of PCA to 2-OH-PHZ, however the kinetics of the reaction have not been systematically studied. Further, the yield of 2-OH-PHZ in fermentation culture is quite low and enhancement in our understanding of the reaction kinetics may contribute to improvements in large-scale, high-yield production of 2-OH-PHZ for biological control and other applications. In this study we confirmed previous reports that free PCA is converted to 2-hydroxy-phenazine-1-carboxylic acid (2-OH-PCA) by the action of a single enzyme PhzO, and particularly demonstrate that this reaction is dependent on NADP(H) and Fe3+. Fe3+ enhanced the conversion from PCA to 2-OH-PHZ and 28°C was a optimum temperature for the conversion. However, PCA added in excess to the culture inhibited the production of 2-OH-PHZ. 2-OH-PCA was extracted and purified from the broth, and it was confirmed that the decarboxylation of 2-OH-PCA could occur without the involvement of any enzyme. A kinetic analysis of the conversion of 2-OH-PCA to 2-OH-PHZ in the absence of enzyme and under different temperatures and pHs in vitro, revealed that the conversion followed first-order reaction kinetics. In the fermentation, the concentration of 2-OH-PCA increased to about 90 mg/L within a red precipitate fraction, as compared to 37 mg/L within the supernatant. The results of this study elucidate the reaction kinetics involved in the biosynthesis of 2-OH-PHZ and provide insights into in vitro methods to enhance yields of 2-OH-PHZ. PMID:24905009

Mortality among men with locally advanced prostate cancer managed with noncurative intent: a nationwide study in PCBaSe Sweden.

PubMed

Akre, Olof; Garmo, Hans; Adolfsson, Jan; Lambe, Mats; Bratt, Ola; Stattin, Pär

2011-09-01

There are limited prognostic data for locally advanced prostate cancer PCa to guide in the choice of treatment. To assess mortality in different prognostic categories among men with locally advanced PCa managed with noncurative intent. We conducted a register-based nationwide cohort study within the Prostate Cancer DataBase Sweden. The entire cohort of locally advanced PCa included 14 908 men. After the exclusion of 2724 (18%) men treated with curative intent, 12 184 men with locally advanced PCa either with local clinical stage T3 or T4 or with T2 with serum levels of prostate-specific antigen (PSA) between 50 and 99 ng/ml and without signs of metastases remained for analysis. We followed up the patient cohort in the Cause of Death Register for ≤ 11 yr and assessed cumulative incidence of PCa -specific death stratified by age and clinical characteristics. The PCa -specific mortality at 8 yr of follow-up was 28% (95% confidence interval [CI], 25-32%) for Gleason score (GS) 2-6, 41% (95% CI, 38-44%) for GS 7, 52% (95% CI, 47-57%) for GS 8, and 64% (95% CI, 59-69%) for GS 9-10. Even for men aged >85 yr at diagnosis with GS 8-10, PCa was a major cause of death: 42% (95% CI, 37-47%). Men with locally advanced disease and a PSA<4 ng/ml at diagnosis were at particularly increased risk of dying from PCa. One important limitation is the lack of bone scans in 42% of the patient cohort, but results remained after exclusion of patients with unknown metastasis status. The PCa-specific mortality within 8 yr of diagnosis is high in locally advanced PCa, suggesting undertreatment, particularly among men in older age groups. Our results underscore the need for more studies of treatment with curative intent for locally advanced tumors. Copyright © 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Lee, Kyunghoon

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

Application of a voltammetric electronic tongue and near infrared spectroscopy for a rapid umami taste assessment.

PubMed

Bagnasco, Lucia; Cosulich, M Elisabetta; Speranza, Giovanna; Medini, Luca; Oliveri, Paolo; Lanteri, Silvia

2014-08-15

The relationships between sensory attribute and analytical measurements, performed by electronic tongue (ET) and near-infrared spectroscopy (NIRS), were investigated in order to develop a rapid method for the assessment of umami taste. Commercially available umami products and some aminoacids were submitted to sensory analysis. Results were analysed in comparison with the outcomes of analytical measurements. Multivariate exploratory analysis was performed by principal component analysis (PCA). Calibration models for prediction of the umami taste on the basis of ET and NIR signals were obtained using partial least squares (PLS) regression. Different approaches for merging data from the two different analytical instruments were considered. Both of the techniques demonstrated to provide information related with umami taste. In particular, ET signals showed the higher correlation with umami attribute. Data fusion was found to be slightly beneficial - not so significantly as to justify the coupled use of the two analytical techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Classification of java tea (Orthosiphon aristatus) quality using FTIR spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Heryanto, R.; Pradono, D. I.; Marlina, E.; Darusman, L. K.

2017-05-01

Java tea (Orthosiphon aristatus) is a plant that widely used as a medicinal herb in Indonesia. Its quality is varying depends on various factors, such as cultivating area, climate and harvesting time. This study aimed to investigate the effectiveness of FTIR spectroscopy coupled with chemometrics for discriminating the quality of java tea from different cultivating area. FTIR spectra of ethanolic extracts were collected from five different regions of origin of java tea. Prior to chemometrics evaluation, spectra were pre-processed by using baselining, normalization and derivatization. Principal Components Analysis (PCA) was used to reduce the spectra to two PCs, which explained 73% of the total variance. Score plot of two PCs showed groupings of the samples according to their regions of origin. Furthermore, Partial Least Squares-Discriminant Analysis (PLSDA) was applied to the pre-processed data. The approach produced an external validation success rate of 100%. This study shows that FTIR analysis and chemometrics has discriminatory power to classify java tea based on its quality related to the region of origin.

High wavenumber Raman spectroscopic characterization of normal and oral cancer using blood plasma

NASA Astrophysics Data System (ADS)

Pachaiappan, Rekha; Prakasarao, Aruna; Suresh Kumar, Murugesan; Singaravelu, Ganesan

2017-02-01

Blood plasma possesses the biomolecules released from cells/tissues after metabolism and reflects the pathological conditions of the subjects. The analysis of biofluids for disease diagnosis becomes very attractive in the diagnosis of cancers due to the ease in the collection of samples, easy to transport, multiple sampling for regular screening of the disease and being less invasive to the patients. Hence, the intention of this study was to apply near-infrared (NIR) Raman spectroscopy in the high wavenumber (HW) region (2500-3400 cm-1) for the diagnosis of oral malignancy using blood plasma. From the Raman spectra it is observed that the biomolecules protein and lipid played a major role in the discrimination between groups. The diagnostic algorithms based on principal components analysis coupled with linear discriminant analysis (PCA-LDA) with the leave-one-patient-out cross-validation method on HW Raman spectra yielded a promising results in the identification of oral malignancy. The details of results will be discussed.

Study of archaeological coins of different dynasties using libs coupled with multivariate analysis

NASA Astrophysics Data System (ADS)

Awasthi, Shikha; Kumar, Rohit; Rai, G. K.; Rai, A. K.

2016-04-01

Laser Induced Breakdown Spectroscopy (LIBS) is an atomic emission spectroscopic technique having unique capability of an in-situ monitoring tool for detection and quantification of elements present in different artifacts. Archaeological coins collected form G.R. Sharma Memorial Museum; University of Allahabad, India has been analyzed using LIBS technique. These coins were obtained from excavation of Kausambi, Uttar Pradesh, India. LIBS system assembled in the laboratory (laser Nd:YAG 532 nm, 4 ns pulse width FWHM with Ocean Optics LIBS 2000+ spectrometer) is employed for spectral acquisition. The spectral lines of Ag, Cu, Ca, Sn, Si, Fe and Mg are identified in the LIBS spectra of different coins. LIBS along with Multivariate Analysis play an effective role for classification and contribution of spectral lines in different coins. The discrimination between five coins with Archaeological interest has been carried out using Principal Component Analysis (PCA). The results show the potential relevancy of the methodology used in the elemental identification and classification of artifacts with high accuracy and robustness.

Principal component analysis for fermionic critical points

NASA Astrophysics Data System (ADS)

Costa, Natanael C.; Hu, Wenjian; Bai, Z. J.; Scalettar, Richard T.; Singh, Rajiv R. P.

2017-11-01

We use determinant quantum Monte Carlo (DQMC), in combination with the principal component analysis (PCA) approach to unsupervised learning, to extract information about phase transitions in several of the most fundamental Hamiltonians describing strongly correlated materials. We first explore the zero-temperature antiferromagnet to singlet transition in the periodic Anderson model, the Mott insulating transition in the Hubbard model on a honeycomb lattice, and the magnetic transition in the 1/6-filled Lieb lattice. We then discuss the prospects for learning finite temperature superconducting transitions in the attractive Hubbard model, for which there is no sign problem. Finally, we investigate finite temperature charge density wave (CDW) transitions in the Holstein model, where the electrons are coupled to phonon degrees of freedom, and carry out a finite size scaling analysis to determine Tc. We examine the different behaviors associated with Hubbard-Stratonovich auxiliary field configurations on both the entire space-time lattice and on a single imaginary time slice, or other quantities, such as equal-time Green's and pair-pair correlation functions.

Risk Stratification Among Men With Prostate Imaging Reporting and Data System Version 2 Category 3 Transition Zone Lesions: Is Biopsy Always Necessary?

PubMed Central

Felker, Ely R.; Raman, Steven S.; Margolis, Daniel J.; Lu, David S. K.; Shaheen, Nicholas; Natarajan, Shyam; Sharma, Devi; Huang, Jiaoti; Dorey, Fred; Marks, Leonard S.

2017-01-01

OBJECTIVE The objective of our study was to determine the clinical and MRI characteristics of clinically significant prostate cancer (PCA) (Gleason score ≥ 3 + 4) in men with Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) category 3 transition zone (TZ) lesions. MATERIALS AND METHODS From 2014 to 2016, 865 men underwent prostate MRI and MRI/ultrasound (US) fusion biopsy (FB). A subset of 90 FB-naïve men with 96 PI-RADSv2 category 3 TZ lesions was identified. Patients were imaged at 3 T using a body coil. Images were assigned a PI-RADSv2 category by an experienced radiologist. Using clinical data and imaging features, we performed univariate and multivariate analyses to identify predictors of clinically significant PCA. RESULTS The mean patient age was 66 years, and the mean prostate-specific antigen density (PSAD) was 0.13 ng/mL2. PCA was detected in 34 of 96 (35%) lesions, 14 of which (15%) harbored clinically significant PCA. In univariate analysis, DWI score, prostate volume, and PSAD were significant predictors (p < 0.05) of clinically significant PCA with a suggested significance for apparent diffusion coefficient (ADC) and prostate-specific antigen value (p < 0.10). On multivariate analysis, PSAD and lesion ADC were the most important covariates. The combination of both PSAD of 0.15 ng/mL2 or greater and an ADC value of less than 1000 mm2/s yielded an AUC of 0.91 for clinically significant PCA (p < 0.001). If FB had been restricted to these criteria, only 10 of 90 men would have undergone biopsy, resulting in diagnosis of clinically significant PCA in 60% with eight men (9%) misdiagnosed (false-negative). CONCLUSION The yield of FB in men with PI-RADSv2 category 3 TZ lesions for clinically significant PCA is 15% but significantly improves to 60% (AUC > 0.9) among men with PSAD of 0.15 ng/mL2 or greater and lesion ADC value of less than 1000 mm2/s. PMID:28858541

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

PubMed

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 is an excellent approach to establishing a personalized model for patients, which is the mainstream direction of modern medicine in the following development, as well as improving the global monitoring performance. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Ubiquitin carboxyl-terminal hydrolase 1 (UCHL1) is a potential tumour suppressor in prostate cancer and is frequently silenced by promoter methylation

PubMed Central

2011-01-01

Background We have previously reported significant downregulation of ubiquitin carboxyl-terminal hydrolase 1 (UCHL1) in prostate cancer (PCa) compared to the surrounding benign tissue. UCHL1 plays an important role in ubiquitin system and different cellular processes such as cell proliferation and differentiation. We now show that the underlying mechanism of UCHL1 downregulation in PCa is linked to its promoter hypermethylation. Furthermore, we present evidences that UCHL1 expression can affect the behavior of prostate cancer cells in different ways. Results Methylation specific PCR analysis results showed a highly methylated promoter region for UCHL1 in 90% (18/20) of tumor tissue compared to 15% (3/20) of normal tissues from PCa patients. Pyrosequencing results confirmed a mean methylation of 41.4% in PCa whereas only 8.6% in normal tissues. To conduct functional analysis of UCHL1 in PCa, UCHL1 is overexpressed in LNCaP cells whose UCHL1 expression is normally suppressed by promoter methylation and found that UCHL1 has the ability to decrease the rate of cell proliferation and suppresses anchorage-independent growth of these cells. In further analysis, we found evidence that exogenous expression of UCHL1 suppress LNCaP cells growth probably via p53-mediated inhibition of Akt/PKB phosphorylation and also via accumulation of p27kip1 a cyclin dependant kinase inhibitor of cell cycle regulating proteins. Notably, we also observed that exogenous expression of UCHL1 induced a senescent phenotype that was detected by using the SA-ß-gal assay and might be due to increased p14ARF, p53, p27kip1 and decreased MDM2. Conclusion From these results, we propose that UCHL1 downregulation via promoter hypermethylation plays an important role in various molecular aspects of PCa biology, such as morphological diversification and regulation of proliferation. PMID:21999842

Prostate cancer mortality reduction by prostate-specific antigen-based screening adjusted for nonattendance and contamination in the European Randomised Study of Screening for Prostate Cancer (ERSPC).

PubMed

Roobol, Monique J; Kerkhof, Melissa; Schröder, Fritz H; Cuzick, Jack; Sasieni, Peter; Hakama, Matti; Stenman, Ulf Hakan; Ciatto, Stefano; Nelen, Vera; Kwiatkowski, Maciej; Lujan, Marcos; Lilja, Hans; Zappa, Marco; Denis, Louis; Recker, Franz; Berenguer, Antonio; Ruutu, Mirja; Kujala, Paula; Bangma, Chris H; Aus, Gunnar; Tammela, Teuvo L J; Villers, Arnauld; Rebillard, Xavier; Moss, Sue M; de Koning, Harry J; Hugosson, Jonas; Auvinen, Anssi

2009-10-01

Prostate-specific antigen (PSA) based screening for prostate cancer (PCa) has been shown to reduce prostate specific mortality by 20% in an intention to screen (ITS) analysis in a randomised trial (European Randomised Study of Screening for Prostate Cancer [ERSPC]). This effect may be diluted by nonattendance in men randomised to the screening arm and contamination in men randomised to the control arm. To assess the magnitude of the PCa-specific mortality reduction after adjustment for nonattendance and contamination. We analysed the occurrence of PCa deaths during an average follow-up of 9 yr in 162,243 men 55-69 yr of age randomised in seven participating centres of the ERSPC. Centres were also grouped according to the type of randomisation (ie, before or after informed written consent). Nonattendance was defined as nonattending the initial screening round in ERSPC. The estimate of contamination was based on PSA use in controls in ERSPC Rotterdam. Relative risks (RRs) with 95% confidence intervals (CIs) were compared between an ITS analysis and analyses adjusting for nonattendance and contamination using a statistical method developed for this purpose. In the ITS analysis, the RR of PCa death in men allocated to the intervention arm relative to the control arm was 0.80 (95% CI, 0.68-0.96). Adjustment for nonattendance resulted in a RR of 0.73 (95% CI, 0.58-0.93), and additional adjustment for contamination using two different estimates led to estimated reductions of 0.69 (95% CI, 0.51-0.92) to 0.71 (95% CI, 0.55-0.93), respectively. Contamination data were obtained through extrapolation of single-centre data. No heterogeneity was found between the groups of centres. PSA screening reduces the risk of dying of PCa by up to 31% in men actually screened. This benefit should be weighed against a degree of overdiagnosis and overtreatment inherent in PCa screening.

A comparison of the usefulness of canonical analysis, principal components analysis, and band selection for extraction of features from TMS data for landcover analysis

NASA Technical Reports Server (NTRS)

Boyd, R. K.; Brumfield, J. O.; Campbell, W. J.

1984-01-01

Three feature extraction methods, canonical analysis (CA), principal component analysis (PCA), and band selection, have been applied to Thematic Mapper Simulator (TMS) data in order to evaluate the relative performance of the methods. The results obtained show that CA is capable of providing a transformation of TMS data which leads to better classification results than provided by all seven bands, by PCA, or by band selection. A second conclusion drawn from the study is that TMS bands 2, 3, 4, and 7 (thermal) are most important for landcover classification.

Prostate calculi in cancer and BPH in a cohort of Korean men: presence of calculi did not correlate with cancer risk

PubMed Central

Hwang, Eu-Chang; Choi, Hyang-Sik; Im, Chang-Min; Jung, Seung-Il; Kim, Sun-Ouck; Kang, Taek-Won; Kwon, Dong-Deuk; Park, Kwang-Sung; Ryu, Soo-Bang

2010-01-01

Prostatic calculi are common and are associated with inflammation of the prostate. Recently, it has been suggested that this inflammation may be associated with prostate carcinogenesis. The aim of this study was to investigate the relationship between prostatic calculi and prostate cancer (PCa) in prostate biopsy specimens. We retrospectively analyzed 417 consecutive patients who underwent transrectal ultrasonography (TRUS) and prostate biopsies between January 2005 and January 2008. Based on the biopsy findings, patients were divided into benign prostatic hyperplasia and PCa groups. TRUS was used to detect prostatic calculi and to measure prostate volume. The correlations between PCa risk and age, serum total PSA levels, prostate volume, and prostatic calculi were analyzed. Patient age and PSA, as well as the frequency of prostatic calculi in the biopsy specimens, differed significantly between both the groups (P < 0.05). In the PCa group, the Gleason scores (GSs) were higher in patients with prostatic calculi than in patients without prostatic calculi (P = 0.023). Using multivariate logistic regression analysis, we found that patient age, serum total PSA and prostate volume were risk factors for PCa (P = 0.001), but that the presence of prostatic calculi was not associated with an increased risk of PCa (P = 0.13). In conclusion, although the presence of prostatic calculi was not shown to be a risk factor for PCa, prostatic calculi were more common in patients with PCa and were associated with a higher GS among these men. PMID:20037598

Substantial utilization of Facebook, Twitter, YouTube, and Instagram in the prostate cancer community.

PubMed

Struck, J P; Siegel, F; Kramer, M W; Tsaur, I; Heidenreich, A; Haferkamp, A; Merseburger, A S; Salem, J; Borgmann, H

2018-03-09

To measure the usage rate of social media (SoMe) resources in the prostate cancer community, we performed a comprehensive quantitative and qualitative assessment of SoMe activity on the topic of PCa on the four most frequented platforms. We scanned the SoMe platforms Facebook, Twitter, YouTube, and Instagram for "prostate cancer" as a cross-sectional analysis or during a defined time period. Sources were included if their communication centered on PCa by title and content. We assessed activity measurements for each SoMe source and classified the sources into six functional categories. We identified 99 PCa-related Facebook groups that amassed 31,262 members and 90 Facebook pages with 283,996 "likes". On YouTube, we found 536 PCa videos accounting for 43,966,634 views, 52,655 likes, 8597 dislikes, and 12,393 comments. During a 1-year time period, 32,537 users generated 110,971 tweets on #ProstateCancer on Twitter, providing over 544 million impressions. During a 1-month time period, 638 contributors posted 1081 posts on Instagram, generating over 22,000 likes and 4,748,159 impressions. Among six functional categories, general information/support dominated the SoMe landscape on all SoMe platforms. SoMe activity on the topic of PCa on the four most frequented platforms is high. Facebook groups, YouTube videos, and Twitter tweets are mainly used for giving general information on PCa and education. High SoMe utilization in the PCa community underlines its future role for communication of PCa.

Liquid chromatography tandem mass spectrometry determination of chemical markers and principal component analysis of Vitex agnus-castus L. fruits (Verbenaceae) and derived food supplements.

PubMed

Mari, Angela; Montoro, Paola; Pizza, Cosimo; Piacente, Sonia

2012-11-01

A validated analytical method for the quantitative determination of seven chemical markers occurring in a hydroalcoholic extract of Vitex agnus-castus fruits by liquid chromatography electrospray triple quadrupole tandem mass spectrometry (LC/ESI/(QqQ)MSMS) is reported. To carry out a comparative study, five commercial food supplements corresponding to hydroalcoholic extracts of V. agnus-castus fruits were analysed under the same chromatographic conditions of the crude extract. Principal component analysis (PCA), based only on the variation of the amount of the seven chemical markers, was applied in order to find similarities between the hydroalcoholic extract and the food supplements. A second PCA analysis was carried out considering the whole spectroscopic data deriving from liquid chromatography electrospray linear ion trap mass spectrometry (LC/ESI/(LIT)MS) analysis. High similarity between the two PCA was observed, showing the possibility to select one of these two approaches for future applications in the field of comparative analysis of food supplements and quality control procedures. Copyright © 2012 Elsevier B.V. All rights reserved.

Optimized principal component analysis on coronagraphic images of the fomalhaut system

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

Meshkat, Tiffany; Kenworthy, Matthew A.; Quanz, Sascha P.

We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing angular differential imaging and locally optimized combination of images (LOCI) for increasing the contrast achievable next to a bright star. The stellar point spread function (PSF) is constructed by removing linear combinations of principal components, allowing the flux from an extrasolar planet to shine through. The number of principal components used determines how well the stellar PSF is globally modeled. Using more principal components may decrease the number of speckles in the final image, but also increases themore » background noise. We apply PCA to Fomalhaut Very Large Telescope NaCo images acquired at 4.05 μm with an apodized phase plate. We do not detect any companions, with a model dependent upper mass limit of 13-18 M {sub Jup} from 4-10 AU. PCA achieves greater sensitivity than the LOCI algorithm for the Fomalhaut coronagraphic data by up to 1 mag. We make several adaptations to the PCA code and determine which of these prove the most effective at maximizing the signal-to-noise from a planet very close to its parent star. We demonstrate that optimizing the number of principal components used in PCA proves most effective for pulling out a planet signal.« less

An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

NASA Astrophysics Data System (ADS)

Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

2018-05-01

Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

Fluorescence spectroscopy for rapid detection and classification of bacterial pathogens.

PubMed

Sohn, Miryeong; Himmelsbach, David S; Barton, Franklin E; Fedorka-Cray, Paula J

2009-11-01

This study deals with the rapid detection and differentiation of Escherichia coli, Salmonella, and Campylobacter, which are the most commonly identified commensal and pathogenic bacteria in foods, using fluorescence spectroscopy and multivariate analysis. Each bacterial sample cultured under controlled conditions was diluted in physiologic saline for analysis. Fluorescence spectra were collected over a range of 200-700 nm with 0.5 nm intervals on the PerkinElmer Fluorescence Spectrometer. The synchronous scan technique was employed to find the optimum excitation (lambda(ex)) and emission (lambda(em)) wavelengths for individual bacteria with the wavelength interval (Deltalambda) being varied from 10 to 200 nm. The synchronous spectra and two-dimensional plots showed two maximum lambda(ex) values at 225 nm and 280 nm and one maximum lambda(em) at 335-345 nm (lambda(em) = lambda(ex) + Deltalambda), which correspond to the lambda(ex) = 225 nm, Deltalambda = 110-120 nm, and lambda(ex) = 280 nm, Deltalambda = 60-65 nm. For all three bacterial genera, the same synchronous scan results were obtained. The emission spectra from the three bacteria groups were very similar, creating difficulty in classification. However, the application of principal component analysis (PCA) to the fluorescence spectra resulted in successful classification of the bacteria by their genus as well as determining their concentration. The detection limit was approximately 10(3)-10(4) cells/mL for each bacterial sample. These results demonstrated that fluorescence spectroscopy, when coupled with PCA processing, has the potential to detect and to classify bacterial pathogens in liquids. The methodology is rapid (>10 min), inexpensive, and requires minimal sample preparation compared to standard analytical methods for bacterial detection.

In-line near-infrared (NIR) and Raman spectroscopy coupled with principal component analysis (PCA) for in situ evaluation of the transesterification reaction.

PubMed

Fontalvo-Gómez, Miriam; Colucci, José A; Velez, Natasha; Romañach, Rodolfo J

2013-10-01

Biodiesel was synthesized from different commercially available oils while in-line Raman and near-infrared (NIR) spectra were obtained simultaneously, and the spectral changes that occurred during the reaction were evaluated with principal component analysis (PCA). Raman and NIR spectra were acquired every 30 s with fiber optic probes inserted into the reaction vessel. The reaction was performed at 60-70 °C using magnetic stirring. The time of reaction was 90 min, and during this time, 180 Raman and NIR spectra were collected. NIR spectra were collected using a transflectance probe and an optical path length of 1 mm at 8 cm(-1) spectral resolution and averaging 32 scans; for Raman spectra a 3 s exposure time and three accumulations were adequate for the analysis. Raman spectroscopy showed the ester conversion as evidenced by the displacement of the C=O band from 1747 to 1744 cm(-1) and the decrease in the intensity of the 1000-1050 cm(-1) band and the 1405 cm(-1) band as methanol was consumed in the reaction. NIR spectra also showed the decrease in methanol concentration with the band in the 4750-5000 cm(-1) region; this signal is present in the spectra of the transesterification reaction but not in the neat oils. The variations in the intensity of the methanol band were a main factor in the in-line monitoring of the transesterification reaction using Raman and NIR spectroscopy. The score plot of the first principal component showed the progress of the reaction. The final product was analyzed using (1)H nuclear magnetic resonance ((1)H NMR) spectroscopy and using mid-infrared spectroscopy, confirming the conversion of the oils to biodiesel.

Accurate Structural Correlations from Maximum Likelihood Superpositions

PubMed Central

Theobald, Douglas L; Wuttke, Deborah S

2008-01-01

The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology. PMID:18282091

Clinical Features and Risk Factors of Skeletal-Related Events in Genitourinary Cancer Patients with Bone Metastasis: A Retrospective Analysis of Prostate Cancer, Renal Cell Carcinoma, and Urothelial Carcinoma.

PubMed

Owari, Takuya; Miyake, Makito; Nakai, Yasushi; Morizawa, Yosuke; Itami, Yoshitaka; Hori, Shunta; Anai, Satoshi; Tanaka, Nobumichi; Fujimoto, Kiyohide

2018-06-06

The objective of the present study was to report the incidence of skeletal-related events (SREs) and identify risk factors for SREs in patients with genitourinary cancer with newly diagnosed bone metastasis. This retrospective study included 180 patients with bone metastasis from prostate cancer (PCa; n = 111), renal cell carcinoma (RCC; n = 43), and urothelial carcinoma (UC; n = 26). Clinical factors at the time of diagnosis of bone metastasis were evaluated with Cox proportional hazards regression analysis to identify independent risk factors for SREs. During follow-up, 29 (26%) patients with PCa, 30 (70%) with RCC, and 15 (58%) with UC developed SREs. Treatment with bone-modifying agents (BMAs) before the development of SREs and within 6 months from the diagnosis of bone metastasis significantly delayed the time to first SRE as compared to nonuse of BMAs. Multivariate analysis identified type of primary cancer (PCa vs. RCC, PCa vs. UC), performance status, and bone pain as significant independent predictive risk factors for SREs. Treatment with BMAs significantly delayed the development of first SREs. The identified predictors of SREs might be useful to select patients who would benefit most from early treatment with BMAs. © 2018 S. Karger AG, Basel.

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

PubMed

Roopwani, Rahul; Buckner, Ira S

2011-10-14

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

Forensic analysis of Salvia divinorum using multivariate statistical procedures. Part I: discrimination from related Salvia species.

PubMed

Willard, Melissa A Bodnar; McGuffin, Victoria L; Smith, Ruth Waddell

2012-01-01

Salvia divinorum is a hallucinogenic herb that is internationally regulated. In this study, salvinorin A, the active compound in S. divinorum, was extracted from S. divinorum plant leaves using a 5-min extraction with dichloromethane. Four additional Salvia species (Salvia officinalis, Salvia guaranitica, Salvia splendens, and Salvia nemorosa) were extracted using this procedure, and all extracts were analyzed by gas chromatography-mass spectrometry. Differentiation of S. divinorum from other Salvia species was successful based on visual assessment of the resulting chromatograms. To provide a more objective comparison, the total ion chromatograms (TICs) were subjected to principal components analysis (PCA). Prior to PCA, the TICs were subjected to a series of data pretreatment procedures to minimize non-chemical sources of variance in the data set. Successful discrimination of S. divinorum from the other four Salvia species was possible based on visual assessment of the PCA scores plot. To provide a numerical assessment of the discrimination, a series of statistical procedures such as Euclidean distance measurement, hierarchical cluster analysis, Student's t tests, Wilcoxon rank-sum tests, and Pearson product moment correlation were also applied to the PCA scores. The statistical procedures were then compared to determine the advantages and disadvantages for forensic applications.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

PubMed Central

Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio

2017-01-01

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. PMID:28287094

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

PubMed Central

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

2017-01-01

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

Principal Components Analysis Studies of Martian Clouds

NASA Astrophysics Data System (ADS)

Klassen, D. R.; Bell, J. F., III

2001-11-01

We present the principal components analysis (PCA) of absolutely calibrated multi-spectral images of Mars as a function of Martian season. The PCA technique is a mathematical rotation and translation of the data from a brightness/wavelength space to a vector space of principal ``traits'' that lie along the directions of maximal variance. The first of these traits, accounting for over 90% of the data variance, is overall brightness and represented by an average Mars spectrum. Interpretation of the remaining traits, which account for the remaining ~10% of the variance, is not always the same and depends upon what other components are in the scene and thus, varies with Martian season. For example, during seasons with large amounts of water ice in the scene, the second trait correlates with the ice and anti-corrlates with temperature. We will investigate the interpretation of the second, and successive important PCA traits. Although these PCA traits are orthogonal in their own vector space, it is unlikely that any one trait represents a singular, mineralogic, spectral end-member. It is more likely that there are many spectral endmembers that vary identically to within the noise level, that the PCA technique will not be able to distinguish them. Another possibility is that similar absorption features among spectral endmembers may be tied to one PCA trait, for example ''amount of 2 \\micron\\ absorption''. We thus attempt to extract spectral endmembers by matching linear combinations of the PCA traits to USGS, JHU, and JPL spectral libraries as aquired through the JPL Aster project. The recovered spectral endmembers are then linearly combined to model the multi-spectral image set. We present here the spectral abundance maps of the water ice/frost endmember which allow us to track Martian clouds and ground frosts. This work supported in part through NASA Planetary Astronomy Grant NAG5-6776. All data gathered at the NASA Infrared Telescope Facility in collaboration with the telescope operators and with thanks to the support staff and day crew.

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

PubMed

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

2017-07-01

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

EVALUATION OF THE I-STAT PORTABLE CLINICAL ANALYZER FOR MEASUREMENT OF IONIZED CALCIUM AND SELECTED BLOOD CHEMISTRY VALUES IN ASIAN ELEPHANTS (ELEPHAS MAXIMUS).

PubMed

Tarbert, Danielle K; Behling-Kelly, Erica; Priest, Heather; Childs-Sanford, Sara

2017-06-01

Thei-STAT® portable clinical analyzer (PCA) provides patient-side results for hematologic, biochemical, and blood gas values when immediate results are desired. This analyzer is commonly used in nondomestic animals; however, validation of this method in comparison with traditional benchtop methods should be performed for each species. In this study, the i-STAT PCA was compared with the Radiometer ABL 800 Flex benchtop analyzer using 24 heparinized whole blood samples obtained from healthy E. maximus . In addition, the effect of sample storage was evaluated on the i-STAT PCA. Analytes evaluated were hydrogen ion concentration (pH), glucose, potassium (K + ), sodium (Na + ), bicarbonate (HCO 3 - ), total carbon dioxide (TCO 2 ), partial pressure of carbon dioxide (PCO 2 ), and ionized calcium (iCa 2+ ). Statistical analysis using correlation coefficients, Passing-Bablok regression analysis, and Bland-Altman plots found good agreement between results from samples run immediately after phlebotomy and 4 hr postsampling on the i-STAT PCA with the exception of K + , which is known to change with sample storage. Comparison of the results from the two analyzers at 4 hr postsampling found very strong or strong correlation in all values except K + , with statistically significant bias in all values except glucose and PCO 2 . Despite bias, mean differences assessed via Bland-Altman plots were clinically acceptable for all analytes excluding K + . Within the reference range for iCa 2+ , the iCa 2+ values obtained by the i-STAT PCA and Radiometer ABL 800 Flex were close in value, however in light of the constant and proportionate biases detected, overestimation at higher values and underestimation at lower values of iCa 2+ by the i-STAT PCA would be of potential concern. This study supports the use of the i-STAT PCA for the evaluation of these analytes, with the exception of K + , in the Asian elephant.

Fluorescence, electrophoretic and chromatographic fingerprints of herbal medicines and their comparative chemometric analysis.

PubMed

Mazina, Jekaterina; Vaher, Merike; Kuhtinskaja, Maria; Poryvkina, Larisa; Kaljurand, Mihkel

2015-07-01

The aim of the present study was to compare the polyphenolic compositions of 47 medicinal herbs (HM) and four herbal tea mixtures from Central Estonia by rapid, reliable and sensitive Spectral Fluorescence Signature (SFS) method in a front face mode. The SFS method was validated for the main identified HM representatives including detection limits (0.037mgL(-1) for catechin, 0.052mgL(-1) for protocatechuic acid, 0.136mgL(-1) for chlorogenic acid, 0.058mgL(-1) for syringic acid and 0.256mgL(-1) for ferulic acid), linearity (up to 5.0-15mgL(-1)), intra-day precision (RSDs=6.6-10.6%), inter-day precision (RSDs=6.4-13.8%), matrix effect (-15.8 to +5.5) and recovery (85-107%). The phytochemical fingerprints were differentiated by parallel factor analysis (PARAFAC) combined with hierarchical cluster analysis (CA) and principal component analysis (PCA). HM were clustered into four main clusters (catechin-like, hydroxycinnamic acid-like, dihydrobenzoic acid-like derivatives containing HM and HM with low/very low content of fluorescent constituents) and 14 subclusters (rich, medium, low/very low contents). The average accuracy and precision of CA for validation HM set were 97.4% (within 85.2-100%) and 89.6%, (within 66.7-100%), respectively. PARAFAC-PCA/CA has improved the analysis of HM by the SFS method. The results were verified by two separation methods CE-DAD and HPLC-DAD-MS also combined with PARAFAC-PCA/CA. The SFS-PARAFAC-PCA/CA method has potential as a rapid and reliable tool for investigating the fingerprints and predicting the composition of HM or evaluating the quality and authenticity of different standardised formulas. Moreover, SFS-PARAFAC-PCA/CA can be implemented as a laboratory and/or an onsite method. Copyright © 2015 Elsevier B.V. All rights reserved.

Differentiation of Chinese rice wines from different wineries based on mineral elemental fingerprinting.

PubMed

Shen, Fei; Wu, Jian; Ying, Yibin; Li, Bobin; Jiang, Tao

2013-12-15

Discrimination of Chinese rice wines from three well-known wineries ("Guyuelongshan", "Kuaijishan", and "Pagoda") in China has been carried out according to mineral element contents in this study. Nineteen macro and trace mineral elements (Na, Mg, Al, K, Ca, Mn, Fe, Cu, Zn, V, Cr, Co, Ni, As, Se, Mo, Cd, Ba and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in 117 samples. Then the experimental data were subjected to analysis of variance (ANOVA) and principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Stepwise linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA) were applied to develop classification models and achieved correct classified rates of 100% and 97.4% for the prediction sample set, respectively. The discrimination could be attributed to different raw materials (mainly water) and elaboration processes employed. The results indicate that the element compositions combined with multivariate analysis can be used as fingerprinting techniques to protect prestigious wineries and enable the authenticity of Chinese rice wine. Copyright © 2013 Elsevier Ltd. All rights reserved.

Using principal component analysis and annual seasonal trend analysis to assess karst rocky desertification in southwestern China.

PubMed

Zhang, Zhiming; Ouyang, Zhiyun; Xiao, Yi; Xiao, Yang; Xu, Weihua

2017-06-01

Increasing exploitation of karst resources is causing severe environmental degradation because of the fragility and vulnerability of karst areas. By integrating principal component analysis (PCA) with annual seasonal trend analysis (ASTA), this study assessed karst rocky desertification (KRD) within a spatial context. We first produced fractional vegetation cover (FVC) data from a moderate-resolution imaging spectroradiometer normalized difference vegetation index using a dimidiate pixel model. Then, we generated three main components of the annual FVC data using PCA. Subsequently, we generated the slope image of the annual seasonal trends of FVC using median trend analysis. Finally, we combined the three PCA components and annual seasonal trends of FVC with the incidence of KRD for each type of carbonate rock to classify KRD into one of four categories based on K-means cluster analysis: high, moderate, low, and none. The results of accuracy assessments indicated that this combination approach produced greater accuracy and more reasonable KRD mapping than the average FVC based on the vegetation coverage standard. The KRD map for 2010 indicated that the total area of KRD was 78.76 × 10 3  km 2 , which constitutes about 4.06% of the eight southwest provinces of China. The largest KRD areas were found in Yunnan province. The combined PCA and ASTA approach was demonstrated to be an easily implemented, robust, and flexible method for the mapping and assessment of KRD, which can be used to enhance regional KRD management schemes or to address assessment of other environmental issues.

Clinical performance of serum prostate-specific antigen isoform [-2]proPSA (p2PSA) and its derivatives, %p2PSA and the prostate health index (PHI), in men with a family history of prostate cancer: results from a multicentre European study, the PROMEtheuS project.

PubMed

Lazzeri, Massimo; Haese, Alexander; Abrate, Alberto; de la Taille, Alexandre; Redorta, Joan Palou; McNicholas, Thomas; Lughezzani, Giovanni; Lista, Giuliana; Larcher, Alessandro; Bini, Vittorio; Cestari, Andrea; Buffi, Nicolòmaria; Graefen, Markus; Bosset, Olivier; Le Corvoisier, Philippe; Breda, Alberto; de la Torre, Pablo; Fowler, Linda; Roux, Jacques; Guazzoni, Giorgio

2013-08-01

To test the sensitivity, specificity and accuracy of serum prostate-specific antigen isoform [-2]proPSA (p2PSA), %p2PSA and the prostate health index (PHI), in men with a family history of prostate cancer (PCa) undergoing prostate biopsy for suspected PCa. To evaluate the potential reduction in unnecessary biopsies and the characteristics of potentially missed cases of PCa that would result from using serum p2PSA, %p2PSA and PHI. The analysis consisted of a nested case-control study from the PRO-PSA Multicentric European Study, the PROMEtheuS project. All patients had a first-degree relative (father, brother, son) with PCa. Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis. Of the 1026 patients included in the PROMEtheuS cohort, 158 (15.4%) had a first-degree relative with PCa. p2PSA, %p2PSA and PHI values were significantly higher (P < 0.001), and free/total PSA (%fPSA) values significantly lower (P < 0.001) in the 71 patients with PCa (44.9%) than in patients without PCa. Univariable accuracy analysis showed %p2PSA (area under the receiver-operating characteristic curve [AUC]: 0.733) and PHI (AUC: 0.733) to be the most accurate predictors of PCa at biopsy, significantly outperforming total PSA ([tPSA] AUC: 0.549), free PSA ([fPSA] AUC: 0.489) and %fPSA (AUC: 0.600) (P ≤ 0.001). For %p2PSA a threshold of 1.66 was found to have the best balance between sensitivity and specificity (70.4 and 70.1%; 95% confidence interval [CI]: 58.4-80.7 and 59.4-79.5 respectively). A PHI threshold of 40 was found to have the best balance between sensitivity and specificity (64.8 and 71.3%, respectively; 95% CI 52.5-75.8 and 60.6-80.5). At 90% sensitivity, the thresholds for %p2PSA and PHI were 1.20 and 25.5, with a specificity of 37.9 and 25.5%, respectively. At a %p2PSA threshold of 1.20, a total of 39 (24.8%) biopsies could have been avoided, but two cancers with a Gleason score (GS) of 7 would have been missed. At a PHI threshold of 25.5 a total of 27 (17.2%) biopsies could have been avoided and two (3.8%) cancers with a GS of 7 would have been missed. In multivariable logistic regression models, %p2PSA and PHI achieved independent predictor status and significantly increased the accuracy of multivariable models including PSA and prostate volume by 8.7 and 10%, respectively (P ≤ 0.001). p2PSA, %p2PSA and PHI were directly correlated with Gleason score (ρ: 0.247, P = 0.038; ρ: 0.366, P = 0.002; ρ: 0.464, P < 0.001, respectively). %p2PSA and PHI are more accurate than tPSA, fPSA and %fPSA in predicting PCa in men with a family history of PCa. Consideration of %p2PSA and PHI results in the avoidance of several unnecessary biopsies. p2PSA, %p2PSA and PHI correlate with cancer aggressiveness. © 2013 BJU International.

Effect of transforming growth factor beta (TGF-β) receptor I kinase inhibitor on prostate cancer bone growth.

PubMed

Wan, Xinhai; Li, Zhi-Gang; Yingling, Jonathan M; Yang, Jun; Starbuck, Michael W; Ravoori, Murali K; Kundra, Vikas; Vazquez, Elba; Navone, Nora M

2012-03-01

Transforming growth factor beta 1 (TGF-β1) has been implicated in the pathogenesis of prostate cancer (PCa) bone metastasis. In this study, we tested the antitumor efficacy of a selective TGF-β receptor I kinase inhibitor, LY2109761, in preclinical models. The effect of LY2109761 on the growth of MDA PCa 2b and PC-3 human PCa cells and primary mouse osteoblasts (PMOs) was assessed in vitro by measuring radiolabeled thymidine incorporation into DNA. In vivo, the right femurs of male SCID mice were injected with PCa cells. We monitored the tumor burden in control- and LY2109761-treated mice with MRI analysis and the PCa-induced bone response with X-ray and micro-CT analyses. Histologic changes in bone were studied by performing bone histomorphometric evaluations. PCa cells and PMOs expressed TGF-β receptor I. TGF-β1 induced pathway activation (as assessed by induced expression of p-Smad2) and inhibited cell growth in PC-3 cells and PMOs but not in MDA PCa 2b cells. LY2109761 had no effect on PCa cells but induced PMO proliferation in vitro. As expected, LY2109761 reversed the TGF-β1-induced pathway activation and growth inhibition in PC-3 cells and PMOs. In vivo, LY2109761 treatment for 6weeks resulted in increased volume in normal bone and increased osteoblast and osteoclast parameters. In addition, LY2109761 treatment significantly inhibited the growth of MDA PCa 2b and PC-3 in the bone of SCID mice (p<0.05); moreover, it resulted in significantly less bone loss and change in osteoclast-associated parameters in the PC-3 tumor-bearing bones than in the untreated mice. In summary, we report for the first time that targeting TGF-β receptors with LY2109761 can control PCa bone growth while increasing the mass of normal bone. This increased bone mass in nontumorous bone may be a desirable side effect of LY2109761 treatment for men with osteopenia or osteoporosis secondary to androgen-ablation therapy, reinforcing the benefit of effectively controlling PCa growth in bone. Thus, targeting TGF-β receptor I is a valuable intervention in men with advanced PCa. Copyright © 2011 Elsevier Inc. All rights reserved.

Effect of transforming growth factor beta (TGF-β) receptor I kinase inhibitor on prostate cancer bone growth

PubMed Central

Wan, Xinhai; Li, Zhi-Gang; Yingling, Jonathan M.; Yang, Jun; Starbuck, Michael W.; Ravoori, Murali K.; Kundra, Vikas; Vazquez, Elba; Navone, Nora M.

2012-01-01

Transforming growth factor beta 1 (TGF-β1) has been implicated in the pathogenesis of prostate cancer (PCa) bone metastasis. In this study, we tested the antitumor efficacy of a selective TGF-β receptor I kinase inhibitor, LY2109761, in preclinical models. The effect of LY2109761 on the growth of MDA PCa 2b and PC-3 human PCa cells and primary mouse osteoblasts (PMOs) was assessed in vitro by measuring radiolabeled thymidine incorporation into DNA. In vivo, the right femurs of male SCID mice were injected with PCa cells. We monitored the tumor burden in control- and LY2109761-treated mice with MRI analysis and the PCa-induced bone response with x-ray and micro-CT analyses. Histologic changes in bone were studied by performing bone histomorphometric evaluations. PCa cells and PMOs expressed TGF-β receptor I. TGF-β1 induced pathway activation (as assessed by induced expression of p-Smad2) and inhibited cell growth in PC-3 cells and PMOs but not in MDA PCa 2b cells. LY2109761 had no effect on PCa cells but induced PMO proliferation in vitro. As expected, LY2109761 reversed the TGF-β1–induced pathway activation and growth inhibition in PC-3 cells and PMOs. In vivo, LY2109761 treatment for 6 weeks resulted in increased volume in normal bone and increased osteoblast and osteoclast parameters. In addition, LY2109761 treatment significantly inhibited the growth of MDA PCa 2b and PC-3 in the bone of SCID mice (p < 0.05); moreover, it resulted in significantly less bone loss and change in osteoclast-associated parameters in the PC-3 tumor–bearing bones than in the untreated mice. In summary, we report for the first time that targeting TGF-β receptors with LY2109761 can control PCa bone growth while increasing the mass of normal bone. This increased bone mass in nontumorous bone may be a desirable side effect of LY2109761 treatment for men with osteopenia or osteoporosis secondary to androgen-ablation therapy, reinforcing the benefit of effectively controlling PCa growth in bone. Thus, targeting TGF-β receptor I is a valuable intervention in men with advanced PCa. PMID:22173053

Application of principal component analysis to multispectral imaging data for evaluation of pigmented skin lesions

NASA Astrophysics Data System (ADS)

Jakovels, Dainis; Lihacova, Ilze; Kuzmina, Ilona; Spigulis, Janis

2013-11-01

Non-invasive and fast primary diagnostics of pigmented skin lesions is required due to frequent incidence of skin cancer - melanoma. Diagnostic potential of principal component analysis (PCA) for distant skin melanoma recognition is discussed. Processing of the measured clinical multi-spectral images (31 melanomas and 94 nonmalignant pigmented lesions) in the wavelength range of 450-950 nm by means of PCA resulted in 87 % sensitivity and 78 % specificity for separation between malignant melanomas and pigmented nevi.

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Association of THADA, FOXP4, GPRC6A/RFX6 genes and 8q24 risk alleles with prostate cancer in Northern Chinese men.

PubMed

Li, Xing-Hui; Xu, Yong; Yang, Kuo; Shi, Jian-Jian; Zhang, Xiao; Yang, Fang; Yuan, Huiping; Zhu, Xiaoquan; Zhang, Yu-Hong; Wang, Jian-Ye; Yang, Ze

2015-01-01

Prostate cancer (PCa) is one of the most common malignancies in males, and multiple genetic studies have confirmed association with susceptibility to PCa. However, the risk conferred in men living in China is unkown. We selected 6 previously identified variants as candidates to define their association with PCa in Chinese men. We genotyped 6 single nucleotide polymorphisms (SNPs) (rs1465618, rs1983891, rs339331, rs16901966, rs1447295 and rs10090154) using high resolution melting (HRM) analysis and assessed their association with PCa risk in a case-control study of 481 patients and 480 controls in a Chinese population. In addition, the individual and cumulative contribution for the risk of PCa and clinical covariates were analysed. We found that 5 of the 6 genetic variants were associated with PCa risk. The T allele of rs339331 and the G allele of rs16901966 showed a significant association with PCa susceptibility: OR (95%CI)= 0.78 (0.64-0.94), p<0.009 and OR (95%CI)= 0.66 (0.54-0.81), p<0.0001, as well as A allele of rs1447295 (OR [95%CI]=1.46 (1.17-1.84), p<0.001) and T allele of rs10090154 (OR [95%CI]= 0.58 (0.46-0.74), p<0.0001). rs339331(T) was associated with a 0.71-fold and 1.42-fold increase of PCa risk by dominant model (p=0.007) and recessive model (p=0.007). rs16901966 (G) was associated with a 0.51-fold and 1.98-fold increase of PCa risk by dominant model (p=0.006) and recessive model (p=0.0058). rs10090154 (T) was associated with a 1.89-fold and 0.53-fold increase of PCa risk by dominant model (p=0.000006) and recessive model (p=0.000006). And, rs1983891(C) was associated with a 0.77-fold increase of PCa risk by recessive model (p=0.045). rs1447295 was associated with a 1.57-fold increase of PCa risk by dominant model (p=0.008). rs1465618 showed no significant association with PCa. The cumulative effects test of risk alleles (rs rs1983891, rs339331, rs16901966, rs1447295 and rs10090154) showed an increasing risk to PCa in a frequency-dependent manner (ptrend=0.001), and men with more than 3 risk alleles had the most significant susceptibility to PCa (OR=1.99, p=0.001), compared with those who had one risk allele (OR=1.17, p=0.486). Our results provide further support for association of the THADA, FOXP4, GPRC6A/RFX6 and 8q24 genes with Pca in Asian populations. Further work is still required to determine the functional variations and finally clarify the underlying biological mechanisms.

Differential principal component analysis of ChIP-seq.

PubMed

Ji, Hongkai; Li, Xia; Wang, Qian-fei; Ning, Yang

2013-04-23

We propose differential principal component analysis (dPCA) for analyzing multiple ChIP-sequencing datasets to identify differential protein-DNA interactions between two biological conditions. dPCA integrates unsupervised pattern discovery, dimension reduction, and statistical inference into a single framework. It uses a small number of principal components to summarize concisely the major multiprotein synergistic differential patterns between the two conditions. For each pattern, it detects and prioritizes differential genomic loci by comparing the between-condition differences with the within-condition variation among replicate samples. dPCA provides a unique tool for efficiently analyzing large amounts of ChIP-sequencing data to study dynamic changes of gene regulation across different biological conditions. We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein-DNA interactions.

Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults.

PubMed

Batis, Carolina; Mendez, Michelle A; Gordon-Larsen, Penny; Sotres-Alvarez, Daniela; Adair, Linda; Popkin, Barry

2016-02-01

We examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in glycated Hb (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and fasting glucose. We measured diet over a 3 d period with 24 h recalls and a household food inventory in 2006 and used it to derive PCA and RRR dietary patterns. The outcomes were measured in 2009. Adults (n 4316) from the China Health and Nutrition Survey. The adjusted odds ratio for diabetes prevalence (HbA1c≥6·5 %), comparing the highest dietary pattern score quartile with the lowest, was 1·26 (95 % CI 0·76, 2·08) for a modern high-wheat pattern (PCA; wheat products, fruits, eggs, milk, instant noodles and frozen dumplings), 0·76 (95 % CI 0·49, 1·17) for a traditional southern pattern (PCA; rice, meat, poultry and fish) and 2·37 (95 % CI 1·56, 3·60) for the pattern derived with RRR. By comparing the dietary pattern structures of RRR and PCA, we found that the RRR pattern was also behaviourally meaningful. It combined the deleterious effects of the modern high-wheat pattern (high intakes of wheat buns and breads, deep-fried wheat and soya milk) with the deleterious effects of consuming the opposite of the traditional southern pattern (low intakes of rice, poultry and game, fish and seafood). Our findings suggest that using both PCA and RRR provided useful insights when studying the association of dietary patterns with diabetes.

Using both Principal Component Analysis and Reduced Rank Regression to Study Dietary Patterns and Diabetes in Chinese Adults

PubMed Central

Batis, Carolina; Mendez, Michelle A.; Gordon-Larsen, Penny; Sotres-Alvarez, Daniela; Adair, Linda; Popkin, Barry

2014-01-01

Objective We examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in hemoglobin A1c (HbA1c), homeostasis model of insulin resistance (HOMA-IR), and fasting glucose. Design We measured diet over a 3-day period with 24-hour recalls and a household food inventory in 2006 and used it to derive PCA and RRR dietary patterns. The outcomes were measured in 2009. Setting Adults (n = 4,316) from the China Health and Nutrition Survey. Results The adjusted odds ratio for diabetes prevalence (HbA1c ≥ 6.5%), comparing the highest dietary pattern score quartile to the lowest, was 1.26 (0.76, 2.08) for a modern high-wheat pattern (PCA; wheat products, fruits, eggs, milk, instant noodles and frozen dumplings), 0.76 (0.49, 1.17) for a traditional southern pattern (PCA; rice, meat, poultry, and fish), and 2.37 (1.56, 3.60) for the pattern derived with RRR. By comparing the dietary pattern structures of RRR and PCA, we found that the RRR pattern was also behaviorally meaningful. It combined the deleterious effects of the modern high-wheat (high intake of wheat buns and breads, deep-fried wheat, and soy milk) with the deleterious effects of consuming the opposite of the traditional southern (low intake of rice, poultry and game, fish and seafood). Conclusions Our findings suggest that using both PCA and RRR provided useful insights when studying the association of dietary patterns with diabetes. PMID:26784586

Liposome bupivacaine for improvement in economic outcomes and opioid burden in GI surgery: IMPROVE Study pooled analysis.

PubMed

Cohen, Stephen M; Vogel, Jon D; Marcet, Jorge E; Candiotti, Keith A

2014-01-01

Postsurgical pain management remains a significant challenge. Liposome bupivacaine, as part of a multimodal analgesic regimen, has been shown to significantly reduce postsurgical opioid consumption, hospital length of stay (LOS), and hospitalization costs in gastrointestinal (GI) surgery, compared with intravenous (IV) opioid-based patient-controlled analgesia (PCA). Pooled results from open-label studies comparing a liposome bupivacaine-based multimodal analgesic regimen with IV opioid PCA were analyzed. Patients (n=191) who underwent planned surgery and received study drug (IV opioid PCA, n=105; multimodal analgesia, n=86) were included. Liposome bupivacaine-based multimodal analgesia compared with IV opioid PCA significantly reduced mean (standard deviation [SD]) postsurgical opioid consumption (38 [55] mg versus [vs] 96 [85] mg; P<0.0001), postsurgical LOS (median 2.9 vs 4.3 days; P<0.0001), and mean hospitalization costs (US$8,271 vs US$10,726; P=0.0109). The multimodal analgesia group reported significantly fewer patients with opioid-related adverse events (AEs) than the IV opioid PCA group (P=0.0027); there were no significant between-group differences in patient satisfaction scores at 30 days. A liposome bupivacaine-based multimodal analgesic regimen was associated with significantly less opioid consumption, opioid-related AEs, and better health economic outcomes compared with an IV opioid PCA-based regimen in patients undergoing GI surgery. This pooled analysis is based on data from Phase IV clinical trials registered on the US National Institutes of Health www.ClinicalTrials.gov database under study identifiers NCT01460485, NCT01507220, NCT01507233, NCT01509638, NCT01509807, NCT01509820, NCT01461122, NCT01461135, NCT01534988, and NCT01507246.