Real-space analysis of radiation-induced specific changes with independent component analysis

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

Borek, Dominika; Bromberg, Raquel; Hattne, Johan

A method of analysis is presented that allows for the separation of specific radiation-induced changes into distinct components in real space. The method relies on independent component analysis (ICA) and can be effectively applied to electron density maps and other types of maps, provided that they can be represented as sets of numbers on a grid. Here, for glucose isomerase crystals, ICA was used in a proof-of-concept analysis to separate temperature-dependent and temperature-independent components of specific radiation-induced changes for data sets acquired from multiple crystals across multiple temperatures. ICA identified two components, with the temperature-independent component being responsible for themore » majority of specific radiation-induced changes at temperatures below 130 K. The patterns of specific temperature-independent radiation-induced changes suggest a contribution from the tunnelling of electron holes as a possible explanation. In the second case, where a group of 22 data sets was collected on a single thaumatin crystal, ICA was used in another type of analysis to separate specific radiation-induced effects happening on different exposure-level scales. Here, ICA identified two components of specific radiation-induced changes that likely result from radiation-induced chemical reactions progressing with different rates at different locations in the structure. In addition, ICA unexpectedly identified the radiation-damage state corresponding to reduced disulfide bridges rather than the zero-dose extrapolated state as the highest contrast structure. The application of ICA to the analysis of specific radiation-induced changes in real space and the data pre-processing for ICA that relies on singular value decomposition, which was used previously in data space to validate a two-component physical model of X-ray radiation-induced changes, are discussed in detail. This work lays a foundation for a better understanding of protein-specific radiation chemistries and provides a framework for analysing effects of specific radiation damage in crystallographic and cryo-EM experiments.« less

System diagnostics using qualitative analysis and component functional classification

DOEpatents

Reifman, J.; Wei, T.Y.C.

1993-11-23

A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system. 5 figures.

System diagnostics using qualitative analysis and component functional classification

DOEpatents

Reifman, Jaques; Wei, Thomas Y. C.

1993-01-01

A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system.

Analysis of metabolic syndrome components in >15 000 african americans identifies pleiotropic variants: results from the population architecture using genomics and epidemiology study.

PubMed

Carty, Cara L; Bhattacharjee, Samsiddhi; Haessler, Jeff; Cheng, Iona; Hindorff, Lucia A; Aroda, Vanita; Carlson, Christopher S; Hsu, Chun-Nan; Wilkens, Lynne; Liu, Simin; Selvin, Elizabeth; Jackson, Rebecca; North, Kari E; Peters, Ulrike; Pankow, James S; Chatterjee, Nilanjan; Kooperberg, Charles

2014-08-01

Metabolic syndrome (MetS) refers to the clustering of cardiometabolic risk factors, including dyslipidemia, central adiposity, hypertension, and hyperglycemia, in individuals. Identification of pleiotropic genetic factors associated with MetS traits may shed light on key pathways or mediators underlying MetS. Using the Metabochip array in 15 148 African Americans from the Population Architecture using Genomics and Epidemiology (PAGE) study, we identify susceptibility loci and investigate pleiotropy among genetic variants using a subset-based meta-analysis method, ASsociation-analysis-based-on-subSETs (ASSET). Unlike conventional models that lack power when associations for MetS components are null or have opposite effects, Association-analysis-based-on-subsets uses 1-sided tests to detect positive and negative associations for components separately and combines tests accounting for correlations among components. With Association-analysis-based-on-subsets, we identify 27 single nucleotide polymorphisms in 1 glucose and 4 lipids loci (TCF7L2, LPL, APOA5, CETP, and APOC1/APOE/TOMM40) significantly associated with MetS components overall, all P<2.5e-7, the Bonferroni adjusted P value. Three loci replicate in a Hispanic population, n=5172. A novel African American-specific variant, rs12721054/APOC1, and rs10096633/LPL are associated with ≥3 MetS components. We find additional evidence of pleiotropy for APOE, TOMM40, TCF7L2, and CETP variants, many with opposing effects (eg, the same rs7901695/TCF7L2 allele is associated with increased odds of high glucose and decreased odds of central adiposity). We highlight a method to increase power in large-scale genomic association analyses and report a novel variant associated with all MetS components in African Americans. We also identify pleiotropic associations that may be clinically useful in patient risk profiling and for informing translational research of potential gene targets and medications. © 2014 American Heart Association, Inc.

Probabilistic Structural Analysis Methods for select space propulsion system components (PSAM). Volume 2: Literature surveys of critical Space Shuttle main engine components

NASA Technical Reports Server (NTRS)

Rajagopal, K. R.

1992-01-01

The technical effort and computer code development is summarized. Several formulations for Probabilistic Finite Element Analysis (PFEA) are described with emphasis on the selected formulation. The strategies being implemented in the first-version computer code to perform linear, elastic PFEA is described. The results of a series of select Space Shuttle Main Engine (SSME) component surveys are presented. These results identify the critical components and provide the information necessary for probabilistic structural analysis. Volume 2 is a summary of critical SSME components.

Management Accounting in School Food Service.

ERIC Educational Resources Information Center

Bryan, E. Lewis; Friedlob, G. Thomas

1982-01-01

Describes a model for establishing control of school food services through analysis of the aggregate variances of quantity, collection, and price, and of their separate components. The separable component variances are identified, measured, and compared monthly to help supervisors identify exactly where plans and operations vary. (Author/MLF)

Independent Orbiter Assessment (IOA): Weibull analysis report

NASA Technical Reports Server (NTRS)

Raffaelli, Gary G.

1987-01-01

The Auxiliary Power Unit (APU) and Hydraulic Power Unit (HPU) Space Shuttle Subsystems were reviewed as candidates for demonstrating the Weibull analysis methodology. Three hardware components were identified as analysis candidates: the turbine wheel, the gearbox, and the gas generator. Detailed review of subsystem level wearout and failure history revealed the lack of actual component failure data. In addition, component wearout data were not readily available or would require a separate data accumulation effort by the vendor. Without adequate component history data being available, the Weibull analysis methodology application to the APU and HPU subsystem group was terminated.

The first report on transcriptome analysis of the venom gland of Iranian scorpion, Hemiscorpius lepturus.

PubMed

Kazemi-Lomedasht, Fatemeh; Khalaj, Vahid; Bagheri, Kamran Pooshang; Behdani, Mahdi; Shahbazzadeh, Delavar

2017-01-01

Hemiscorpius lepturus scorpion is one of the most venomous members of the Hemiscorpiidae family. H. lepturus is distributed in Iran, Iraq and Yemen. The prevalence and severity of scorpionism is high and health services are not able to control it. Scorpionism in Iran especially in the southern regions (Khuzestan, Sistan and Baluchestan, Hormozgan, Ilam) is one of the main health challenges. Due to the medical and health importance of scorpionism, the focus of various studies has been on the identification of H. lepturus venom components. Nevertheless, until now, only a few percent of H. lepturus venom components have been identified and there is no complete information about the venom components of H. lepturus. The current study reports transcriptome analysis of the venom gland of H. lepturus scorpion. Illumina Next Generation Sequencing results identified venom components of H. lepturus. When compared with other scorpion's venom, the venom of H. lepturus consists of mixtures of peptides, proteins and enzymes such as; phospholipases, metalloproteases, hyaluronidases, potassium channel toxins, calcium channel toxins, antimicrobial peptides (AMPs), venom proteins, venom toxins, allergens, La1-like peptides, proteases and scorpine-like peptides. Comparison of identified components of H. lepturus venom was carried out with venom components of reported scorpions and various identities and similarities between them were observed. With transcriptome analysis of H. lepturus venom unique sequences, coding venom components were investigated. Moreover, our study confirmed transcript expression of previously reported peptides; Hemitoxin, Hemicalcin and Hemilipin. The gene sequences of venom components were investigated employing transcriptome analysis of venom gland of H. lepturus. In summary, new bioactive molecules identified in this study, provide basis for venomics studies of scorpions of Hemiscorpiidae family and promises development of novel biotherapeutics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis and Evaluation of the Characteristic Taste Components in Portobello Mushroom.

PubMed

Wang, Jinbin; Li, Wen; Li, Zhengpeng; Wu, Wenhui; Tang, Xueming

2018-05-10

To identify the characteristic taste components of the common cultivated mushroom (brown; Portobello), Agaricus bisporus, taste components in the stipe and pileus of Portobello mushroom harvested at different growth stages were extracted and identified, and principal component analysis (PCA) and taste active value (TAV) were used to reveal the characteristic taste components during the each of the growth stages of Portobello mushroom. In the stipe and pileus, 20 and 14 different principal taste components were identified, respectively, and they were considered as the principal taste components of Portobello mushroom fruit bodies, which included most amino acids and 5'-nucleotides. Some taste components that were found at high levels, such as lactic acid and citric acid, were not detected as Portobello mushroom principal taste components through PCA. However, due to their high content, Portobello mushroom could be used as a source of organic acids. The PCA and TAV results revealed that 5'-GMP, glutamic acid, malic acid, alanine, proline, leucine, and aspartic acid were the characteristic taste components of Portobello mushroom fruit bodies. Portobello mushroom was also found to be rich in protein and amino acids, so it might also be useful in the formulation of nutraceuticals and functional food. The results in this article could provide a theoretical basis for understanding and regulating the characteristic flavor components synthesis process of Portobello mushroom. © 2018 Institute of Food Technologists®.

[Methods of a posteriori identification of food patterns in Brazilian children: a systematic review].

PubMed

Carvalho, Carolina Abreu de; Fonsêca, Poliana Cristina de Almeida; Nobre, Luciana Neri; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2016-01-01

The objective of this study is to provide guidance for identifying dietary patterns using the a posteriori approach, and analyze the methodological aspects of the studies conducted in Brazil that identified the dietary patterns of children. Articles were selected from the Latin American and Caribbean Literature on Health Sciences, Scientific Electronic Library Online and Pubmed databases. The key words were: Dietary pattern; Food pattern; Principal Components Analysis; Factor analysis; Cluster analysis; Reduced rank regression. We included studies that identified dietary patterns of children using the a posteriori approach. Seven studies published between 2007 and 2014 were selected, six of which were cross-sectional and one cohort, Five studies used the food frequency questionnaire for dietary assessment; one used a 24-hour dietary recall and the other a food list. The method of exploratory approach used in most publications was principal components factor analysis, followed by cluster analysis. The sample size of the studies ranged from 232 to 4231, the values of the Kaiser-Meyer-Olkin test from 0.524 to 0.873, and Cronbach's alpha from 0.51 to 0.69. Few Brazilian studies identified dietary patterns of children using the a posteriori approach and principal components factor analysis was the technique most used.

An automated method for identifying artifact in independent component analysis of resting-state FMRI.

PubMed

Bhaganagarapu, Kaushik; Jackson, Graeme D; Abbott, David F

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available.

An Automated Method for Identifying Artifact in Independent Component Analysis of Resting-State fMRI

PubMed Central

Bhaganagarapu, Kaushik; Jackson, Graeme D.; Abbott, David F.

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available. PMID:23847511

Identification and apportionment of hazardous elements in the sediments in the Yangtze River estuary.

PubMed

Wang, Jiawei; Liu, Ruimin; Wang, Haotian; Yu, Wenwen; Xu, Fei; Shen, Zhenyao

2015-12-01

In this study, positive matrix factorization (PMF) and principal components analysis (PCA) were combined to identify and apportion pollution-based sources of hazardous elements in the surface sediments in the Yangtze River estuary (YRE). Source identification analysis indicated that PC1, including Al, Fe, Mn, Cr, Ni, As, Cu, and Zn, can be defined as a sewage component; PC2, including Pb and Sb, can be considered as an atmospheric deposition component; and PC3, containing Cd and Hg, can be considered as an agricultural nonpoint component. To better identify the sources and quantitatively apportion the concentrations to their sources, eight sources were identified with PMF: agricultural/industrial sewage mixed (18.6 %), mining wastewater (15.9 %), agricultural fertilizer (14.5 %), atmospheric deposition (12.8 %), agricultural nonpoint (10.6 %), industrial wastewater (9.8 %), marine activity (9.0 %), and nickel plating industry (8.8 %). Overall, the hazardous element content seems to be more connected to anthropogenic activity instead of natural sources. The PCA results laid the foundation for the PMF analysis by providing a general classification of sources. PMF resolves more factors with a higher explained variance than PCA; PMF provided both the internal analysis and the quantitative analysis. The combination of the two methods can provide more reasonable and reliable results.

Comparing sugar components of cereal and pseudocereal flour by GC-MS analysis.

PubMed

Ačanski, Marijana M; Vujić, Djura N

2014-02-15

Gas chromatography with mass spectrometry was used for carrying out a qualitative analysis of the ethanol soluble flour extract of different types of cereals bread wheat and spelt and pseudocereals (amaranth and buckwheat). TMSI (trimethylsilylimidazole) was used as a reagent for the derivatisation of carbohydrates into trimethylsilyl ethers. All samples were first defatted with hexane. (In our earlier investigations, hexane extracts were used for the analysis of fatty acid of lipid components.) Many components of pentoses, hexoses and disaccharides were identified using 73 and 217 Da mass and the Wiley Online Library search. The aim of this paper is not to identify and find new components, but to compare sugar components of tested samples of flour of cereals bread wheat and spelt and pseudocereals (amarnath and buckwheat). Results were analysed using descriptive statistics (dendrograms and PCA). The results show that this method can be used for making a distinction among different types of flour. Copyright © 2013 Elsevier Ltd. All rights reserved.

Differentially Variable Component Analysis (dVCA): Identifying Multiple Evoked Components using Trial-to-Trial Variability

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.; Shah, Ankoor S.; Truccolo, Wilson; Ding, Ming-Zhou; Bressler, Steven L.; Schroeder, Charles E.

2003-01-01

Electric potentials and magnetic fields generated by ensembles of synchronously active neurons in response to external stimuli provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult as each detector records signals simultaneously generated by various regions throughout the brain. We introduce the differentially Variable Component Analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we evaluate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. Finally, we evaluate the technique using visually evoked field potentials recorded at incremental depths across the layers of cortical area VI, in an awake, behaving macaque monkey.

[Preliminary study on effective components of Tripterygium wilfordii for liver toxicity based on spectrum-effect correlation analysis].

PubMed

Zhao, Xiao-Mei; Pu, Shi-Biao; Zhao, Qing-Guo; Gong, Man; Wang, Jia-Bo; Ma, Zhi-Jie; Xiao, Xiao-He; Zhao, Kui-Jun

2016-08-01

In this paper, the spectrum-effect correlation analysis method was used to explore the main effective components of Tripterygium wilfordii for liver toxicity, and provide reference for promoting the quality control of T. wilfordii. Chinese medicine T.wilfordii was taken as the study object, and LC-Q-TOF-MS was used to characterize the chemical components in T. wilfordii samples from different areas, and their main components were initially identified after referring to the literature. With the normal human hepatocytes (LO2 cell line)as the carrier, acetaminophen as positive medicine, and cell inhibition rate as testing index, the simple correlation analysis and multivariate linear correlation analysis methods were used to screen the main components of T. wilfordii for liver toxicity. As a result, 10 kinds of main components were identified, and the spectrum-effect correlation analysis showed that triptolide may be the toxic component, which was consistent with previous results of traditional literature. Meanwhile it was found that tripterine and demethylzeylasteral may greatly contribute to liver toxicity in multivariate linear correlation analysis. T. wilfordii samples of different varieties or different origins showed large difference in quality, and the T. wilfordii from southwest China showed lower liver toxicity, while those from Hunan and Anhui province showed higher liver toxicity. This study will provide data support for further rational use of T. wilfordii and research on its liver toxicity ingredients. Copyright© by the Chinese Pharmaceutical Association.

Fault Tree Analysis Application for Safety and Reliability

NASA Technical Reports Server (NTRS)

Wallace, Dolores R.

2003-01-01

Many commercial software tools exist for fault tree analysis (FTA), an accepted method for mitigating risk in systems. The method embedded in the tools identifies a root as use in system components, but when software is identified as a root cause, it does not build trees into the software component. No commercial software tools have been built specifically for development and analysis of software fault trees. Research indicates that the methods of FTA could be applied to software, but the method is not practical without automated tool support. With appropriate automated tool support, software fault tree analysis (SFTA) may be a practical technique for identifying the underlying cause of software faults that may lead to critical system failures. We strive to demonstrate that existing commercial tools for FTA can be adapted for use with SFTA, and that applied to a safety-critical system, SFTA can be used to identify serious potential problems long before integrator and system testing.

Attention without intention: explicit processing and implicit goal-setting in family medicine residents' written reflections.

PubMed

Shaughnessy, Allen F; Allen, Lucas; Duggan, Ashley

2017-05-01

Reflection, a process of self-analysis to promote learning through better understanding of one's experiences, is often used to assess learners' metacognitive ability. However, writing reflective exercises, not submitted for assessment, may allow learners to explore their experiences and indicate learning and professional growth without explicitly connecting to intentional sense-making. To identify core components of learning about medicine or medical education from family medicine residents' written reflections. Family medicine residents' wrote reflections about their experiences throughout an academic year. Qualitative thematic analysis to identify core components in 767 reflections written by 33 residents. We identified four themes of learning: 'Elaborated reporting' and 'metacognitive monitoring' represent explicit, purposeful self-analysis that typically would be characterised as reflective learning about medicine. 'Simple reporting' and 'goal setting' signal an analysis of experience that indicates learning and professional growth but that is overlooked as a component of learning. Identified themes elucidate the explicit and implicit forms of written reflection as sense-making and learning. An expanded theoretical understanding of reflection as inclusive of conscious sense-making as well as implicit discovery better enables the art of physician self-development.

Selection of independent components based on cortical mapping of electromagnetic activity

NASA Astrophysics Data System (ADS)

Chan, Hui-Ling; Chen, Yong-Sheng; Chen, Li-Fen

2012-10-01

Independent component analysis (ICA) has been widely used to attenuate interference caused by noise components from the electromagnetic recordings of brain activity. However, the scalp topographies and associated temporal waveforms provided by ICA may be insufficient to distinguish functional components from artifactual ones. In this work, we proposed two component selection methods, both of which first estimate the cortical distribution of the brain activity for each component, and then determine the functional components based on the parcellation of brain activity mapped onto the cortical surface. Among all independent components, the first method can identify the dominant components, which have strong activity in the selected dominant brain regions, whereas the second method can identify those inter-regional associating components, which have similar component spectra between a pair of regions. For a targeted region, its component spectrum enumerates the amplitudes of its parceled brain activity across all components. The selected functional components can be remixed to reconstruct the focused electromagnetic signals for further analysis, such as source estimation. Moreover, the inter-regional associating components can be used to estimate the functional brain network. The accuracy of the cortical activation estimation was evaluated on the data from simulation studies, whereas the usefulness and feasibility of the component selection methods were demonstrated on the magnetoencephalography data recorded from a gender discrimination study.

Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis

NASA Astrophysics Data System (ADS)

Aouabdi, Salim; Taibi, Mahmoud; Bouras, Slimane; Boutasseta, Nadir

2017-06-01

This paper describes an approach for identifying localized gear tooth defects, such as pitting, using phase currents measured from an induction machine driving the gearbox. A new tool of anomaly detection based on multi-scale entropy (MSE) algorithm SampEn which allows correlations in signals to be identified over multiple time scales. The motor current signature analysis (MCSA) in conjunction with principal component analysis (PCA) and the comparison of observed values with those predicted from a model built using nominally healthy data. The Simulation results show that the proposed method is able to detect gear tooth pitting in current signals.

Identifying geochemical processes using End Member Mixing Analysis to decouple chemical components for mixing ratio calculations

NASA Astrophysics Data System (ADS)

Pelizardi, Flavia; Bea, Sergio A.; Carrera, Jesús; Vives, Luis

2017-07-01

Mixing calculations (i.e., the calculation of the proportions in which end-members are mixed in a sample) are essential for hydrological research and water management. However, they typically require the use of conservative species, a condition that may be difficult to meet due to chemical reactions. Mixing calculation also require identifying end-member waters, which is usually achieved through End Member Mixing Analysis (EMMA). We present a methodology to help in the identification of both end-members and such reactions, so as to improve mixing ratio calculations. The proposed approach consists of: (1) identifying the potential chemical reactions with the help of EMMA; (2) defining decoupled conservative chemical components consistent with those reactions; (3) repeat EMMA with the decoupled (i.e., conservative) components, so as to identify end-members waters; and (4) computing mixing ratios using the new set of components and end-members. The approach is illustrated by application to two synthetic mixing examples involving mineral dissolution and cation exchange reactions. Results confirm that the methodology can be successfully used to identify geochemical processes affecting the mixtures, thus improving the accuracy of mixing ratios calculations and relaxing the need for conservative species.

The assessment of facial variation in 4747 British school children.

PubMed

Toma, Arshed M; Zhurov, Alexei I; Playle, Rebecca; Marshall, David; Rosin, Paul L; Richmond, Stephen

2012-12-01

The aim of this study is to identify key components contributing to facial variation in a large population-based sample of 15.5-year-old children (2514 females and 2233 males). The subjects were recruited from the Avon Longitudinal Study of Parents and Children. Three-dimensional facial images were obtained for each subject using two high-resolution Konica Minolta laser scanners. Twenty-one reproducible facial landmarks were identified and their coordinates were recorded. The facial images were registered using Procrustes analysis. Principal component analysis was then employed to identify independent groups of correlated coordinates. For the total data set, 14 principal components (PCs) were identified which explained 82 per cent of the total variance, with the first three components accounting for 46 per cent of the variance. Similar results were obtained for males and females separately with only subtle gender differences in some PCs. Facial features may be treated as a multidimensional statistical continuum with respect to the PCs. The first three PCs characterize the face in terms of height, width, and prominence of the nose. The derived PCs may be useful to identify and classify faces according to a scale of normality.

Global Analysis Reveals the Complexity of the Human Glomerular Extracellular Matrix

PubMed Central

Byron, Adam; Humphries, Jonathan D.; Randles, Michael J.; Carisey, Alex; Murphy, Stephanie; Knight, David; Brenchley, Paul E.; Zent, Roy; Humphries, Martin J.

2014-01-01

The glomerulus contains unique cellular and extracellular matrix (ECM) components, which are required for intact barrier function. Studies of the cellular components have helped to build understanding of glomerular disease; however, the full composition and regulation of glomerular ECM remains poorly understood. We used mass spectrometry-based proteomics of enriched ECM extracts for a global analysis of human glomerular ECM in vivo and identified a tissue-specific proteome of 144 structural and regulatory ECM proteins. This catalog includes all previously identified glomerular components plus many new and abundant components. Relative protein quantification showed a dominance of collagen IV, collagen I, and laminin isoforms in the glomerular ECM together with abundant collagen VI and TINAGL1. Protein network analysis enabled the creation of a glomerular ECM interactome, which revealed a core of highly connected structural components. More than one half of the glomerular ECM proteome was validated using colocalization studies and data from the Human Protein Atlas. This study yields the greatest number of ECM proteins relative to previous investigations of whole glomerular extracts, highlighting the importance of sample enrichment. It also shows that the composition of glomerular ECM is far more complex than previously appreciated and suggests that many more ECM components may contribute to glomerular development and disease processes. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000456. PMID:24436468

Classification and identification of Rhodobryum roseum Limpr. and its adulterants based on fourier-transform infrared spectroscopy (FTIR) and chemometrics.

PubMed

Cao, Zhen; Wang, Zhenjie; Shang, Zhonglin; Zhao, Jiancheng

2017-01-01

Fourier-transform infrared spectroscopy (FTIR) with the attenuated total reflectance technique was used to identify Rhodobryum roseum from its four adulterants. The FTIR spectra of six samples in the range from 4000 cm-1 to 600 cm-1 were obtained. The second-derivative transformation test was used to identify the small and nearby absorption peaks. A cluster analysis was performed to classify the spectra in a dendrogram based on the spectral similarity. Principal component analysis (PCA) was used to classify the species of six moss samples. A cluster analysis with PCA was used to identify different genera. However, some species of the same genus exhibited highly similar chemical components and FTIR spectra. Fourier self-deconvolution and discrete wavelet transform (DWT) were used to enhance the differences among the species with similar chemical components and FTIR spectra. Three scales were selected as the feature-extracting space in the DWT domain. The results show that FTIR spectroscopy with chemometrics is suitable for identifying Rhodobryum roseum and its adulterants.

Probabilistic Structural Analysis Methods for select space propulsion system components (PSAM). Volume 3: Literature surveys and technical reports

NASA Technical Reports Server (NTRS)

1992-01-01

The technical effort and computer code developed during the first year are summarized. Several formulations for Probabilistic Finite Element Analysis (PFEA) are described with emphasis on the selected formulation. The strategies being implemented in the first-version computer code to perform linear, elastic PFEA is described. The results of a series of select Space Shuttle Main Engine (SSME) component surveys are presented. These results identify the critical components and provide the information necessary for probabilistic structural analysis.

Development and application of a time-history analysis for rotorcraft dynamics based on a component approach

NASA Technical Reports Server (NTRS)

Sopher, R.; Hallock, D. W.

1985-01-01

A time history analysis for rotorcraft dynamics based on dynamical substructures, and nonstructural mathematical and aerodynamic components is described. The analysis is applied to predict helicopter ground resonance and response to rotor damage. Other applications illustrate the stability and steady vibratory response of stopped and gimballed rotors, representative of new technology. Desirable attributes expected from modern codes are realized, although the analysis does not employ a complete set of techniques identified for advanced software. The analysis is able to handle a comprehensive set of steady state and stability problems with a small library of components.

Wavelet decomposition based principal component analysis for face recognition using MATLAB

NASA Astrophysics Data System (ADS)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Exergo-Economic Analysis of an Experimental Aircraft Turboprop Engine Under Low Torque Condition

NASA Astrophysics Data System (ADS)

Atilgan, Ramazan; Turan, Onder; Aydin, Hakan

Exergo-economic analysis is an unique combination of exergy analysis and cost analysis conducted at the component level. In exergo-economic analysis, cost of each exergy stream is determined. Inlet and outlet exergy streams of the each component are associated to a monetary cost. This is essential to detect cost-ineffective processes and identify technical options which could improve the cost effectiveness of the overall energy system. In this study, exergo-economic analysis is applied to an aircraft turboprop engine. Analysis is based on experimental values at low torque condition (240 N m). Main components of investigated turboprop engine are the compressor, the combustor, the gas generator turbine, the free power turbine and the exhaust. Cost balance equations have been formed for all components individually and exergo-economic parameters including cost rates and unit exergy costs have been calculated for each component.

Using qualitative comparative analysis in a systematic review of a complex intervention.

PubMed

Kahwati, Leila; Jacobs, Sara; Kane, Heather; Lewis, Megan; Viswanathan, Meera; Golin, Carol E

2016-05-04

Systematic reviews evaluating complex interventions often encounter substantial clinical heterogeneity in intervention components and implementation features making synthesis challenging. Qualitative comparative analysis (QCA) is a non-probabilistic method that uses mathematical set theory to study complex phenomena; it has been proposed as a potential method to complement traditional evidence synthesis in reviews of complex interventions to identify key intervention components or implementation features that might explain effectiveness or ineffectiveness. The objective of this study was to describe our approach in detail and examine the suitability of using QCA within the context of a systematic review. We used data from a completed systematic review of behavioral interventions to improve medication adherence to conduct two substantive analyses using QCA. The first analysis sought to identify combinations of nine behavior change techniques/components (BCTs) found among effective interventions, and the second analysis sought to identify combinations of five implementation features (e.g., agent, target, mode, time span, exposure) found among effective interventions. For each substantive analysis, we reframed the review's research questions to be designed for use with QCA, calibrated sets (i.e., transformed raw data into data used in analysis), and identified the necessary and/or sufficient combinations of BCTs and implementation features found in effective interventions. Our application of QCA for each substantive analysis is described in detail. We extended the original review findings by identifying seven combinations of BCTs and four combinations of implementation features that were sufficient for improving adherence. We found reasonable alignment between several systematic review steps and processes used in QCA except that typical approaches to study abstraction for some intervention components and features did not support a robust calibration for QCA. QCA was suitable for use within a systematic review of medication adherence interventions and offered insights beyond the single dimension stratifications used in the original completed review. Future prospective use of QCA during a review is needed to determine the optimal way to efficiently integrate QCA into existing approaches to evidence synthesis of complex interventions.

Key components of financial-analysis education for clinical nurses.

PubMed

Lim, Ji Young; Noh, Wonjung

2015-09-01

In this study, we identified key components of financial-analysis education for clinical nurses. We used a literature review, focus group discussions, and a content validity index survey to develop key components of financial-analysis education. First, a wide range of references were reviewed, and 55 financial-analysis education components were gathered. Second, two focus group discussions were performed; the participants were 11 nurses who had worked for more than 3 years in a hospital, and nine components were agreed upon. Third, 12 professionals, including professors, nurse executive, nurse managers, and an accountant, participated in the content validity index. Finally, six key components of financial-analysis education were selected. These key components were as follows: understanding the need for financial analysis, introduction to financial analysis, reading and implementing balance sheets, reading and implementing income statements, understanding the concepts of financial ratios, and interpretation and practice of financial ratio analysis. The results of this study will be used to develop an education program to increase financial-management competency among clinical nurses. © 2015 Wiley Publishing Asia Pty Ltd.

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.

Exploring the molecular mechanisms of Traditional Chinese Medicine components using gene expression signatures and connectivity map.

PubMed

Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kim, Jihye; Kang, Jaewoo; Tan, Aik Choon

2018-04-04

Traditional Chinese Medicine (TCM) has been practiced over thousands of years in China and other Asian countries for treating various symptoms and diseases. However, the underlying molecular mechanisms of TCM are poorly understood, partly due to the "multi-component, multi-target" nature of TCM. To uncover the molecular mechanisms of TCM, we perform comprehensive gene expression analysis using connectivity map. We interrogated gene expression signatures obtained 102 TCM components using the next generation Connectivity Map (CMap) resource. We performed systematic data mining and analysis on the mechanism of action (MoA) of these TCM components based on the CMap results. We clustered the 102 TCM components into four groups based on their MoAs using next generation CMap resource. We performed gene set enrichment analysis on these components to provide additional supports for explaining these molecular mechanisms. We also provided literature evidence to validate the MoAs identified through this bioinformatics analysis. Finally, we developed the Traditional Chinese Medicine Drug Repurposing Hub (TCM Hub) - a connectivity map resource to facilitate the elucidation of TCM MoA for drug repurposing research. TCMHub is freely available in http://tanlab.ucdenver.edu/TCMHub. Molecular mechanisms of TCM could be uncovered by using gene expression signatures and connectivity map. Through this analysis, we identified many of the TCM components possess diverse MoAs, this may explain the applications of TCM in treating various symptoms and diseases. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Stress Analysis of B-52B and B-52H Air-Launching Systems Failure-Critical Structural Components

NASA Technical Reports Server (NTRS)

Ko, William L.

2005-01-01

The operational life analysis of any airborne failure-critical structural component requires the stress-load equation, which relates the applied load to the maximum tangential tensile stress at the critical stress point. The failure-critical structural components identified are the B-52B Pegasus pylon adapter shackles, B-52B Pegasus pylon hooks, B-52H airplane pylon hooks, B-52H airplane front fittings, B-52H airplane rear pylon fitting, and the B-52H airplane pylon lower sway brace. Finite-element stress analysis was performed on the said structural components, and the critical stress point was located and the stress-load equation was established for each failure-critical structural component. The ultimate load, yield load, and proof load needed for operational life analysis were established for each failure-critical structural component.

[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.

Identification of periodical components in a signal. Role of the chronological arrangement of observations (French Title: Recherche des composantes périodiques dans un signal. Importance de la répartition chronologique des observations)

NASA Astrophysics Data System (ADS)

Hoynant, G.

2007-12-01

Fourier analysis allows to identify periodical components in a time series of measurements under the form of a spectrum of the periodical components mathematically included in the series. The reading of a spectrum is often delicate and contradictory interpretations can be presented in some cases as for the luminosity of Seyfert galaxy NGC 4151 despite the very large number of observations since 1968. The present study identifies the causes of these difficulties thanks to an experimental approach based on analysis of synthetic series with one periodic component only. The total duration of the campaign must be long as compared to the periods to be identified: this ratio governs the separation capability of the spectral analysis. A large number of observations is obviously favourable but the intervals between measurements are not critical : the analysis can accommodate intervals significantly longer than the periods to be identified. But interruptions along the campaign, with separate sessions of observations, make the physical understanding of the analysis difficult and sometimes impossible. An analysis performed on an imperfect series shows peaks which are not significant of the signal itself but of the chronological distribution of the measurements. These chronological peaks are becoming numerous and important when there are vacancy periods in the campaign. A method for authentication of a peak as a peak of the signal is to cut the chronological series in pieces with the same length than the period to identify and to superpose all these pieces. The present study shows that some chronological peaks can exhibit superposition graphics almost as clear as those for the signal peaks. Practically, the search for periodical components necessitates to organise the campaign specifically with a neutral chronological distribution of measurements without vacancies and the authentication of a peak as a peak of the signal requires a dominating amplitude or a graphic of periodical superposition significantly clearer than for any peak with a comparable or bigger amplitude.

Oil spill source identification by principal component analysis of electrospray ionization Fourier transform ion cyclotron resonance mass spectra.

PubMed

Corilo, Yuri E; Podgorski, David C; McKenna, Amy M; Lemkau, Karin L; Reddy, Christopher M; Marshall, Alan G; Rodgers, Ryan P

2013-10-01

One fundamental challenge with either acute or chronic oil spills is to identify the source, especially in highly polluted areas, near natural oil seeps, when the source contains more than one petroleum product or when extensive weathering has occurred. Here we focus on heavy fuel oil that spilled (~200,000 L) from two suspected fuel tanks that were ruptured on the motor vessel (M/V) Cosco Busan when it struck the San Francisco-Oakland Bay Bridge in November 2007. We highlight the utility of principal component analysis (PCA) of elemental composition data obtained by high resolution FT-ICR mass spectrometry to correctly identify the source of environmental contamination caused by the unintended release of heavy fuel oil (HFO). Using ultrahigh resolution electrospray ionization (ESI) Fourier transform ion cyclotron resonance mass spectrometry, we uniquely assigned thousands of elemental compositions of heteroatom-containing species in neat samples from both tanks and then applied principal component analysis. The components were based on double bond equivalents for constituents of elemental composition, CcHhN1S1. To determine if the fidelity of our source identification was affected by weathering, field samples were collected at various intervals up to two years after the spill. We are able to identify a suite of polar petroleum markers that are environmentally persistent, enabling us to confidently identify that only one tank was the source of the spilled oil: in fact, a single principal component could account for 98% of the variance. Although identification is unaffected by the presence of higher polarity, petrogenic oxidation (weathering) products, future studies may require removal of such species by anion exchange chromatography prior to mass spectral analysis due to their preferential ionization by ESI.

[A study of Boletus bicolor from different areas using Fourier transform infrared spectrometry].

PubMed

Zhou, Zai-Jin; Liu, Gang; Ren, Xian-Pei

2010-04-01

It is hard to differentiate the same species of wild growing mushrooms from different areas by macromorphological features. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis was used to identify 58 samples of boletus bicolor from five different areas. Based on the fingerprint infrared spectrum of boletus bicolor samples, principal component analysis was conducted on 58 boletus bicolor spectra in the range of 1 350-750 cm(-1) using the statistical software SPSS 13.0. According to the result, the accumulated contributing ratio of the first three principal components accounts for 88.87%. They included almost all the information of samples. The two-dimensional projection plot using first and second principal component is a satisfactory clustering effect for the classification and discrimination of boletus bicolor. All boletus bicolor samples were divided into five groups with a classification accuracy of 98.3%. The study demonstrated that wild growing boletus bicolor at species level from different areas can be identified by FTIR spectra combined with principal components analysis.

Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures.

PubMed

González-Vidal, Juan José; Pérez-Pueyo, Rosanna; Soneira, María José; Ruiz-Moreno, Sergio

2015-03-01

A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.

Determining Component Probability using Problem Report Data for Ground Systems used in Manned Space Flight

NASA Technical Reports Server (NTRS)

Monaghan, Mark W.; Gillespie, Amanda M.

2013-01-01

During the shuttle era NASA utilized a failure reporting system called the Problem Reporting and Corrective Action (PRACA) it purpose was to identify and track system non-conformance. The PRACA system over the years evolved from a relatively nominal way to identify system problems to a very complex tracking and report generating data base. The PRACA system became the primary method to categorize any and all anomalies from corrosion to catastrophic failure. The systems documented in the PRACA system range from flight hardware to ground or facility support equipment. While the PRACA system is complex, it does possess all the failure modes, times of occurrence, length of system delay, parts repaired or replaced, and corrective action performed. The difficulty is mining the data then to utilize that data in order to estimate component, Line Replaceable Unit (LRU), and system reliability analysis metrics. In this paper, we identify a methodology to categorize qualitative data from the ground system PRACA data base for common ground or facility support equipment. Then utilizing a heuristic developed for review of the PRACA data determine what reports identify a credible failure. These data are the used to determine inter-arrival times to perform an estimation of a metric for repairable component-or LRU reliability. This analysis is used to determine failure modes of the equipment, determine the probability of the component failure mode, and support various quantitative differing techniques for performing repairable system analysis. The result is that an effective and concise estimate of components used in manned space flight operations. The advantage is the components or LRU's are evaluated in the same environment and condition that occurs during the launch process.

What is reflection? A conceptual analysis of major definitions and a proposal of a five-component model.

PubMed

Nguyen, Quoc Dinh; Fernandez, Nicolas; Karsenti, Thierry; Charlin, Bernard

2014-12-01

Although reflection is considered a significant component of medical education and practice, the literature does not provide a consensual definition or model for it. Because reflection has taken on multiple meanings, it remains difficult to operationalise. A standard definition and model are needed to improve the development of practical applications of reflection. This study was conducted in order to identify, explore and analyse the most influential conceptualisations of reflection, and to develop a new theory-informed and unified definition and model of reflection. A systematic review was conducted to identify the 15 most cited authors in papers on reflection published during the period from 2008 to 2012. The authors' definitions and models were extracted. An exploratory thematic analysis was carried out and identified seven initial categories. Categories were clustered and reworded to develop an integrative definition and model of reflection, which feature core components that define reflection and extrinsic elements that influence instances of reflection. Following our review and analysis, five core components of reflection and two extrinsic elements were identified as characteristics of the reflective thinking process. Reflection is defined as the process of engaging the self (S) in attentive, critical, exploratory and iterative (ACEI) interactions with one's thoughts and actions (TA), and their underlying conceptual frame (CF), with a view to changing them and a view on the change itself (VC). Our conceptual model consists of the defining core components, supplemented with the extrinsic elements that influence reflection. This article presents a new theory-informed, five-component definition and model of reflection. We believe these have advantages over previous models in terms of helping to guide the further study, learning, assessment and teaching of reflection. © 2014 John Wiley & Sons Ltd.

16S rRNA analysis provides evidence of biofilms on all components of three infected periprosthetic knees including permanent braided suture.

PubMed

Swearingen, Matthew C; DiBartola, Alex C; Dusane, Devendra; Granger, Jeffrey; Stoodley, Paul

2016-10-01

Bacterial biofilms are the main etiological agent of periprosthetic joint infections (PJI); however, it is unclear if biofilms colonize one or multiple components. Because biofilms can colonize a variety of surfaces, we hypothesized that biofilms would be present on all components. 16S ribosomal RNA (rRNA) gene sequencing analysis was used to identify bacteria recovered from individual components and non-absorbable suture material recovered from three PJI total knee revision cases. Bray-Curtis non-metric multidimensional scaling analysis revealed no significant differences in similarity when factoring component, material type, or suture versus non-suture material, but did reveal significant differences in organism profile between patients (P < 0.001) and negative controls (P < 0.001). Confocal microscopy and a novel agar encasement culturing method also confirmed biofilm growth on a subset of components. While 16S sequencing suggested that the microbiology was more complex than revealed by culture contaminating, bacterial DNA generates a risk of false positives. This report highlights that biofilm bacteria may colonize all infected prosthetic components including braided suture material, and provides further evidence that clinical culture can fail to sufficiently identify the full pathogen profile in PJI cases. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A case study in nonconformance and performance trend analysis

NASA Technical Reports Server (NTRS)

Maloy, Joseph E.; Newton, Coy P.

1990-01-01

As part of NASA's effort to develop an agency-wide approach to trend analysis, a pilot nonconformance and performance trending analysis study was conducted on the Space Shuttle auxiliary power unit (APU). The purpose of the study was to (1) demonstrate that nonconformance analysis can be used to identify repeating failures of a specific item (and the associated failure modes and causes) and (2) determine whether performance parameters could be analyzed and monitored to provide an indication of component or system degradation prior to failure. The nonconformance analysis of the APU did identify repeating component failures, which possibly could be reduced if key performance parameters were monitored and analyzed. The performance-trending analysis verified that the characteristics of hardware parameters can be effective in detecting degradation of hardware performance prior to failure.

Laser-induced breakdown spectroscopy is a reliable method for urinary stone analysis

PubMed Central

Mutlu, Nazım; Çiftçi, Seyfettin; Gülecen, Turgay; Öztoprak, Belgin Genç; Demir, Arif

2016-01-01

Objective We compared laser-induced breakdown spectroscopy (LIBS) with the traditionally used and recommended X-ray diffraction technique (XRD) for urinary stone analysis. Material and methods In total, 65 patients with urinary calculi were enrolled in this prospective study. Stones were obtained after surgical or extracorporeal shockwave lithotripsy procedures. All stones were divided into two equal pieces. One sample was analyzed by XRD and the other by LIBS. The results were compared by the kappa (κ) and Spearman’s correlation coefficient (rho) tests. Results Using LIBS, 95 components were identified from 65 stones, while XRD identified 88 components. LIBS identified 40 stones with a single pure component, 20 stones with two different components, and 5 stones with three components. XRD demonstrated 42 stones with a single component, 22 stones with two different components, and only 1 stone with three different components. There was a strong relationship in the detection of stone types between LIBS and XRD for stones components (Spearman rho, 0.866; p<0.001). There was excellent agreement between the two techniques among 38 patients with pure stones (κ index, 0.910; Spearman rho, 0.916; p<0.001). Conclusion Our study indicates that LIBS is a valid and reliable technique for determining urinary stone composition. Moreover, it is a simple, low-cost, and nondestructive technique. LIBS can be safely used in routine daily practice if our results are supported by studies with larger numbers of patients. PMID:27011877

Genome-wide selection components analysis in a fish with male pregnancy.

PubMed

Flanagan, Sarah P; Jones, Adam G

2017-04-01

A major goal of evolutionary biology is to identify the genome-level targets of natural and sexual selection. With the advent of next-generation sequencing, whole-genome selection components analysis provides a promising avenue in the search for loci affected by selection in nature. Here, we implement a genome-wide selection components analysis in the sex role reversed Gulf pipefish, Syngnathus scovelli. Our approach involves a double-digest restriction-site associated DNA sequencing (ddRAD-seq) technique, applied to adult females, nonpregnant males, pregnant males, and their offspring. An F ST comparison of allele frequencies among these groups reveals 47 genomic regions putatively experiencing sexual selection, as well as 468 regions showing a signature of differential viability selection between males and females. A complementary likelihood ratio test identifies similar patterns in the data as the F ST analysis. Sexual selection and viability selection both tend to favor the rare alleles in the population. Ultimately, we conclude that genome-wide selection components analysis can be a useful tool to complement other approaches in the effort to pinpoint genome-level targets of selection in the wild. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Quantitative descriptive analysis and principal component analysis for sensory characterization of Indian milk product cham-cham.

PubMed

Puri, Ritika; Khamrui, Kaushik; Khetra, Yogesh; Malhotra, Ravinder; Devraja, H C

2016-02-01

Promising development and expansion in the market of cham-cham, a traditional Indian dairy product is expected in the coming future with the organized production of this milk product by some large dairies. The objective of this study was to document the extent of variation in sensory properties of market samples of cham-cham collected from four different locations known for their excellence in cham-cham production and to find out the attributes that govern much of variation in sensory scores of this product using quantitative descriptive analysis (QDA) and principal component analysis (PCA). QDA revealed significant (p < 0.05) difference in sensory attributes of cham-cham among the market samples. PCA identified four significant principal components that accounted for 72.4 % of the variation in the sensory data. Factor scores of each of the four principal components which primarily correspond to sweetness/shape/dryness of interior, surface appearance/surface dryness, rancid and firmness attributes specify the location of each market sample along each of the axes in 3-D graphs. These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring attributes of cham-cham that contribute most to its sensory acceptability.

Conformational states and folding pathways of peptides revealed by principal-independent component analyses.

PubMed

Nguyen, Phuong H

2007-05-15

Principal component analysis is a powerful method for projecting multidimensional conformational space of peptides or proteins onto lower dimensional subspaces in which the main conformations are present, making it easier to reveal the structures of molecules from e.g. molecular dynamics simulation trajectories. However, the identification of all conformational states is still difficult if the subspaces consist of more than two dimensions. This is mainly due to the fact that the principal components are not independent with each other, and states in the subspaces cannot be visualized. In this work, we propose a simple and fast scheme that allows one to obtain all conformational states in the subspaces. The basic idea is that instead of directly identifying the states in the subspace spanned by principal components, we first transform this subspace into another subspace formed by components that are independent of one other. These independent components are obtained from the principal components by employing the independent component analysis method. Because of independence between components, all states in this new subspace are defined as all possible combinations of the states obtained from each single independent component. This makes the conformational analysis much simpler. We test the performance of the method by analyzing the conformations of the glycine tripeptide and the alanine hexapeptide. The analyses show that our method is simple and quickly reveal all conformational states in the subspaces. The folding pathways between the identified states of the alanine hexapeptide are analyzed and discussed in some detail. 2007 Wiley-Liss, Inc.

Perturbation analyses of intermolecular interactions

NASA Astrophysics Data System (ADS)

Koyama, Yohei M.; Kobayashi, Tetsuya J.; Ueda, Hiroki R.

2011-08-01

Conformational fluctuations of a protein molecule are important to its function, and it is known that environmental molecules, such as water molecules, ions, and ligand molecules, significantly affect the function by changing the conformational fluctuations. However, it is difficult to systematically understand the role of environmental molecules because intermolecular interactions related to the conformational fluctuations are complicated. To identify important intermolecular interactions with regard to the conformational fluctuations, we develop herein (i) distance-independent and (ii) distance-dependent perturbation analyses of the intermolecular interactions. We show that these perturbation analyses can be realized by performing (i) a principal component analysis using conditional expectations of truncated and shifted intermolecular potential energy terms and (ii) a functional principal component analysis using products of intermolecular forces and conditional cumulative densities. We refer to these analyses as intermolecular perturbation analysis (IPA) and distance-dependent intermolecular perturbation analysis (DIPA), respectively. For comparison of the IPA and the DIPA, we apply them to the alanine dipeptide isomerization in explicit water. Although the first IPA principal components discriminate two states (the α state and PPII (polyproline II) + β states) for larger cutoff length, the separation between the PPII state and the β state is unclear in the second IPA principal components. On the other hand, in the large cutoff value, DIPA eigenvalues converge faster than that for IPA and the top two DIPA principal components clearly identify the three states. By using the DIPA biplot, the contributions of the dipeptide-water interactions to each state are analyzed systematically. Since the DIPA improves the state identification and the convergence rate with retaining distance information, we conclude that the DIPA is a more practical method compared with the IPA. To test the feasibility of the DIPA for larger molecules, we apply the DIPA to the ten-residue chignolin folding in explicit water. The top three principal components identify the four states (native state, two misfolded states, and unfolded state) and their corresponding eigenfunctions identify important chignolin-water interactions to each state. Thus, the DIPA provides the practical method to identify conformational states and their corresponding important intermolecular interactions with distance information.

Perturbation analyses of intermolecular interactions.

PubMed

Koyama, Yohei M; Kobayashi, Tetsuya J; Ueda, Hiroki R

2011-08-01

Conformational fluctuations of a protein molecule are important to its function, and it is known that environmental molecules, such as water molecules, ions, and ligand molecules, significantly affect the function by changing the conformational fluctuations. However, it is difficult to systematically understand the role of environmental molecules because intermolecular interactions related to the conformational fluctuations are complicated. To identify important intermolecular interactions with regard to the conformational fluctuations, we develop herein (i) distance-independent and (ii) distance-dependent perturbation analyses of the intermolecular interactions. We show that these perturbation analyses can be realized by performing (i) a principal component analysis using conditional expectations of truncated and shifted intermolecular potential energy terms and (ii) a functional principal component analysis using products of intermolecular forces and conditional cumulative densities. We refer to these analyses as intermolecular perturbation analysis (IPA) and distance-dependent intermolecular perturbation analysis (DIPA), respectively. For comparison of the IPA and the DIPA, we apply them to the alanine dipeptide isomerization in explicit water. Although the first IPA principal components discriminate two states (the α state and PPII (polyproline II) + β states) for larger cutoff length, the separation between the PPII state and the β state is unclear in the second IPA principal components. On the other hand, in the large cutoff value, DIPA eigenvalues converge faster than that for IPA and the top two DIPA principal components clearly identify the three states. By using the DIPA biplot, the contributions of the dipeptide-water interactions to each state are analyzed systematically. Since the DIPA improves the state identification and the convergence rate with retaining distance information, we conclude that the DIPA is a more practical method compared with the IPA. To test the feasibility of the DIPA for larger molecules, we apply the DIPA to the ten-residue chignolin folding in explicit water. The top three principal components identify the four states (native state, two misfolded states, and unfolded state) and their corresponding eigenfunctions identify important chignolin-water interactions to each state. Thus, the DIPA provides the practical method to identify conformational states and their corresponding important intermolecular interactions with distance information.

Use of multivariate statistics to identify unreliable data obtained using CASA.

PubMed

Martínez, Luis Becerril; Crispín, Rubén Huerta; Mendoza, Maximino Méndez; Gallegos, Oswaldo Hernández; Martínez, Andrés Aragón

2013-06-01

In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering realized on two first principal components produced three clusters. Cluster 1 contained evaluations of the two first samples in each treatment, each one of a different boar. With the exception of one individual measurement, all other measurements in cluster 1 were the same as observed in abnormal Chernoff faces. Unreliable data in cluster 1 are probably related to the operator inexperience with a CASA system. These findings could be used to objectively evaluate the skill level of an operator of a CASA system. This may be particularly useful in the quality control of semen analysis using CASA systems.

A Bayesian Network Based Global Sensitivity Analysis Method for Identifying Dominant Processes in a Multi-physics Model

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2016-12-01

Sensitivity analysis has been an important tool in groundwater modeling to identify the influential parameters. Among various sensitivity analysis methods, the variance-based global sensitivity analysis has gained popularity for its model independence characteristic and capability of providing accurate sensitivity measurements. However, the conventional variance-based method only considers uncertainty contribution of single model parameters. In this research, we extended the variance-based method to consider more uncertainty sources and developed a new framework to allow flexible combinations of different uncertainty components. We decompose the uncertainty sources into a hierarchical three-layer structure: scenario, model and parametric. Furthermore, each layer of uncertainty source is capable of containing multiple components. An uncertainty and sensitivity analysis framework was then constructed following this three-layer structure using Bayesian network. Different uncertainty components are represented as uncertain nodes in this network. Through the framework, variance-based sensitivity analysis can be implemented with great flexibility of using different grouping strategies for uncertainty components. The variance-based sensitivity analysis thus is improved to be able to investigate the importance of an extended range of uncertainty sources: scenario, model, and other different combinations of uncertainty components which can represent certain key model system processes (e.g., groundwater recharge process, flow reactive transport process). For test and demonstration purposes, the developed methodology was implemented into a test case of real-world groundwater reactive transport modeling with various uncertainty sources. The results demonstrate that the new sensitivity analysis method is able to estimate accurate importance measurements for any uncertainty sources which were formed by different combinations of uncertainty components. The new methodology can provide useful information for environmental management and decision-makers to formulate policies and strategies.

Automatic removal of eye-movement and blink artifacts from EEG signals.

PubMed

Gao, Jun Feng; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chong Xun

2010-03-01

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification.

PubMed

Bou Assi, Elie; Rihana, Sandy; Sawan, Mohamad

2014-01-01

Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG) to identify the ocular artifact components. In this study, artifactual components were identified using an adaptive thresholding by means of Kmeans clustering. The denoised EEG signals have been fed into a feature extraction algorithm extracting the band power, the coherence and the phase locking value and inserted into a linear discriminant analysis classifier for a motor imagery classification.

An Inquiry-Based Project Focused on the X-Ray Powder Diffraction Analysis of Common Household Solids

ERIC Educational Resources Information Center

Hulien, Molly L.; Lekse, Jonathan W.; Rosmus, Kimberly A.; Devlin, Kasey P.; Glenn, Jennifer R.; Wisneski, Stephen D.; Wildfong, Peter; Lake, Charles H.; MacNeil, Joseph H.; Aitken, Jennifer A.

2015-01-01

While X-ray powder diffraction (XRPD) is a fundamental analytical technique used by solid-state laboratories across a breadth of disciplines, it is still underrepresented in most undergraduate curricula. In this work, we incorporate XRPD analysis into an inquiry-based project that requires students to identify the crystalline component(s) of…

Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

ERIC Educational Resources Information Center

Brusco, Michael J.; Singh, Renu; Steinley, Douglas

2009-01-01

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the…

A comparative study of volatile components in Dianhong teas from fresh leaves of four tea cultivars by using chromatography-mass spectrometry, multivariate data analysis, and descriptive sensory analysis.

PubMed

Wang, Chao; Zhang, Chenxia; Kong, Yawen; Peng, Xiaopei; Li, Changwen; Liu, Shunhang; Du, Liping; Xiao, Dongguang; Xu, Yongquan

2017-10-01

Dianhong teas produced from fresh leaves of different tea cultivars (YK is Yunkang No. 10, XY is Xueya 100, CY is Changyebaihao, SS is Shishengmiao), were compared in terms of volatile compounds and descriptive sensory analysis. A total of 73 volatile compounds in 16 tea samples were tentatively identified. YK, XY, CY, and SS contained 55, 53, 49, and 51 volatile compounds, respectively. Partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) were used to classify the samples, and 40 key components were selected based on variable importance in the projection. Moreover, 11 flavor attributes, namely, floral, fruity, grass/green, woody, sweet, roasty, caramel, mellow and thick, bitter, astringent, and sweet aftertaste were identified through descriptive sensory analysis (DSA). In generally, innate differences among the tea varieties significantly affected the intensities of most of the key sensory attributes of Dianhong teas possibly because of the different amounts of aroma-active and taste components in Dianhong teas. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identification of atmospheric organic sources using the carbon hollow tube-gas chromatography method and factor analysis

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

Cobb, G.P.; Braman, R.S.; Gilbert, R.A.

Atmospheric organics were sampled and analyzed by using the carbon hollow tube-gas chromatography method. Chromatograms from spice mixtures, cigarettes, and ambient air were analyzed. Principal factor analysis of row order chromatographic data produces factors which are eigenchromatograms of the components in the samples. Component sources are identified from the eigenchromatograms in all experiments and the individual eigenchromatogram corresponding to a particular source is determined in most cases. Organic sources in ambient air and in cigaretts are identified with 87% certainty. Analysis of clove cigarettes allows the determination of the relative amount of clove in different cigarettes. A new nondestructive qualitymore » control method using the hollow tube-gas chromatography analysis is discussed.« less

Comparative Analysis of Metabolic Syndrome Components in over 15,000 African Americans Identifies Pleiotropic Variants: Results from the PAGE Study

PubMed Central

Carty, Cara L.; Bhattacharjee, Samsiddhi; Haessler, Jeff; Cheng, Iona; Hindorff, Lucia A.; Aroda, Vanita; Carlson, Christopher S.; Hsu, Chun-Nan; Wilkens, Lynne; Liu, Simin; Selvin, Elizabeth; Jackson, Rebecca; North, Kari E.; Peters, Ulrike; Pankow, James S.; Chatterjee, Nilanjan; Kooperberg, Charles

2014-01-01

Background Metabolic syndrome (MetS) refers to the clustering of cardio-metabolic risk factors including dyslipidemia, central adiposity, hypertension and hyperglycemia in individuals. Identification of pleiotropic genetic factors associated with MetS traits may shed light on key pathways or mediators underlying MetS. Methods and Results Using the Metabochip array in 15,148 African Americans (AA) from the PAGE Study, we identify susceptibility loci and investigate pleiotropy among genetic variants using a subset-based meta-analysis method, ASsociation-analysis-based-on-subSETs (ASSET). Unlike conventional models which lack power when associations for MetS components are null or have opposite effects, ASSET uses one-sided tests to detect positive and negative associations for components separately and combines tests accounting for correlations among components. With ASSET, we identify 27 SNPs in 1 glucose and 4 lipids loci (TCF7L2, LPL, APOA5, CETP, LPL, APOC1/APOE/TOMM40) significantly associated with MetS components overall, all P< 2.5e-7, the Bonferroni adjusted P-value. Three loci replicate in a Hispanic population, n=5172. A novel AA-specific variant, rs12721054/APOC1, and rs10096633/LPL are associated with ≥3 MetS components. We find additional evidence of pleiotropy for APOE, TOMM40, TCF7L2 and CETP variants, many with opposing effects; e.g. the same rs7901695/TCF7L2 allele is associated with increased odds of high glucose and decreased odds of central adiposity. Conclusions We highlight a method to increase power in large-scale genomic association analyses, and report a novel variant associated with all MetS components in AA. We also identify pleiotropic associations that may be clinically useful in patient risk profiling and for informing translational research of potential gene targets and medications. PMID:25023634

Toward the Design of Evidence-Based Mental Health Information Systems for People With Depression: A Systematic Literature Review and Meta-Analysis.

PubMed

Wahle, Fabian; Bollhalder, Lea; Kowatsch, Tobias; Fleisch, Elgar

2017-05-31

Existing research postulates a variety of components that show an impact on utilization of technology-mediated mental health information systems (MHIS) and treatment outcome. Although researchers assessed the effect of isolated design elements on the results of Web-based interventions and the associations between symptom reduction and use of components across computer and mobile phone platforms, there remains uncertainty with regard to which components of technology-mediated interventions for mental health exert the greatest therapeutic gain. Until now, no studies have presented results on the therapeutic benefit associated with specific service components of technology-mediated MHIS for depression. This systematic review aims at identifying components of technology-mediated MHIS for patients with depression. Consequently, all randomized controlled trials comparing technology-mediated treatments for depression to either waiting-list control, treatment as usual, or any other form of treatment for depression were reviewed. Updating prior reviews, this study aims to (1) assess the effectiveness of technology-supported interventions for the treatment of depression and (2) add to the debate on what components in technology-mediated MHIS for the treatment of depression should be standard of care. Systematic searches in MEDLINE, PsycINFO, and the Cochrane Library were conducted. Effect sizes for each comparison between a technology-enabled intervention and a control condition were computed using the standard mean difference (SMD). Chi-square tests were used to test for heterogeneity. Using subgroup analysis, potential sources of heterogeneity were analyzed. Publication bias was examined using visual inspection of funnel plots and Begg's test. Qualitative data analysis was also used. In an explorative approach, a list of relevant components was extracted from the body of literature by consensus between two researchers. Of 6387 studies initially identified, 45 met all inclusion criteria. Programs analyzed showed a significant trend toward reduced depressive symptoms (SMD -0.58, 95% CI -0.71 to -0.45, P<.001). Heterogeneity was large (I2≥76). A total of 15 components were identified. Technology-mediated MHIS for the treatment of depression has a consistent positive overall effect compared to controls. A total of 15 components have been identified. Further studies are needed to quantify the impact of individual components on treatment effects and to identify further components that are relevant for the design of future technology-mediated interventions for the treatment of depression and other mental disorders. ©Fabian Wahle, Lea Bollhalder, Tobias Kowatsch, Elgar Fleisch. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 31.05.2017.

Toward the Design of Evidence-Based Mental Health Information Systems for People With Depression: A Systematic Literature Review and Meta-Analysis

PubMed Central

Fleisch, Elgar

2017-01-01

Background Existing research postulates a variety of components that show an impact on utilization of technology-mediated mental health information systems (MHIS) and treatment outcome. Although researchers assessed the effect of isolated design elements on the results of Web-based interventions and the associations between symptom reduction and use of components across computer and mobile phone platforms, there remains uncertainty with regard to which components of technology-mediated interventions for mental health exert the greatest therapeutic gain. Until now, no studies have presented results on the therapeutic benefit associated with specific service components of technology-mediated MHIS for depression. Objective This systematic review aims at identifying components of technology-mediated MHIS for patients with depression. Consequently, all randomized controlled trials comparing technology-mediated treatments for depression to either waiting-list control, treatment as usual, or any other form of treatment for depression were reviewed. Updating prior reviews, this study aims to (1) assess the effectiveness of technology-supported interventions for the treatment of depression and (2) add to the debate on what components in technology-mediated MHIS for the treatment of depression should be standard of care. Methods Systematic searches in MEDLINE, PsycINFO, and the Cochrane Library were conducted. Effect sizes for each comparison between a technology-enabled intervention and a control condition were computed using the standard mean difference (SMD). Chi-square tests were used to test for heterogeneity. Using subgroup analysis, potential sources of heterogeneity were analyzed. Publication bias was examined using visual inspection of funnel plots and Begg’s test. Qualitative data analysis was also used. In an explorative approach, a list of relevant components was extracted from the body of literature by consensus between two researchers. Results Of 6387 studies initially identified, 45 met all inclusion criteria. Programs analyzed showed a significant trend toward reduced depressive symptoms (SMD –0.58, 95% CI –0.71 to –0.45, P<.001). Heterogeneity was large (I2≥76). A total of 15 components were identified. Conclusions Technology-mediated MHIS for the treatment of depression has a consistent positive overall effect compared to controls. A total of 15 components have been identified. Further studies are needed to quantify the impact of individual components on treatment effects and to identify further components that are relevant for the design of future technology-mediated interventions for the treatment of depression and other mental disorders. PMID:28566267

Comprehensive Proteomic Analysis of Human Milk-derived Extracellular Vesicles Unveils a Novel Functional Proteome Distinct from Other Milk Components*

PubMed Central

van Herwijnen, Martijn J.C.; Zonneveld, Marijke I.; Goerdayal, Soenita; Nolte – 't Hoen, Esther N.M.; Garssen, Johan; Stahl, Bernd; Maarten Altelaar, A.F.; Redegeld, Frank A.; Wauben, Marca H.M.

2016-01-01

Breast milk contains several macromolecular components with distinctive functions, whereby milk fat globules and casein micelles mainly provide nutrition to the newborn, and whey contains molecules that can stimulate the newborn's developing immune system and gastrointestinal tract. Although extracellular vesicles (EV) have been identified in breast milk, their physiological function and composition has not been addressed in detail. EV are submicron sized vehicles released by cells for intercellular communication via selectively incorporated lipids, nucleic acids, and proteins. Because of the difficulty in separating EV from other milk components, an in-depth analysis of the proteome of human milk-derived EV is lacking. In this study, an extensive LC-MS/MS proteomic analysis was performed of EV that had been purified from breast milk of seven individual donors using a recently established, optimized density-gradient-based EV isolation protocol. A total of 1963 proteins were identified in milk-derived EV, including EV-associated proteins like CD9, Annexin A5, and Flotillin-1, with a remarkable overlap between the different donors. Interestingly, 198 of the identified proteins are not present in the human EV database Vesiclepedia, indicating that milk-derived EV harbor proteins not yet identified in EV of different origin. Similarly, the proteome of milk-derived EV was compared with that of other milk components. For this, data from 38 published milk proteomic studies were combined in order to construct the total milk proteome, which consists of 2698 unique proteins. Remarkably, 633 proteins identified in milk-derived EV have not yet been identified in human milk to date. Interestingly, these novel proteins include proteins involved in regulation of cell growth and controlling inflammatory signaling pathways, suggesting that milk-derived EVs could support the newborn's developing gastrointestinal tract and immune system. Overall, this study provides an expansion of the whole milk proteome and illustrates that milk-derived EV are macromolecular components with a unique functional proteome. PMID:27601599

Liquid chromatography-diode array detection-mass spectrometry for compositional analysis of low molecular weight heparins.

PubMed

Wang, Zhangjie; Li, Daoyuan; Sun, Xiaojun; Bai, Xue; Jin, Lan; Chi, Lianli

2014-04-15

Low molecular weight heparins (LMWHs) are important artificial preparations from heparin polysaccharide and are widely used as anticoagulant drugs. To analyze the structure and composition of LMWHs, identification and quantitation of their natural and modified building blocks are indispensable. We have established a novel reversed-phase high-performance liquid chromatography-diode array detection-electrospray ionization-mass spectrometry approach for compositional analysis of LMWHs. After being exhaustively digested and labeled with 2-aminoacridone, the structural motifs constructing LMWHs, including 17 components from dalteparin and 15 components from enoxaparin, were well separated, identified, and quantified. Besides the eight natural heparin disaccharides, many characteristic structures from dalteparin and enoxaparin, such as modified structures from the reducing end and nonreducing end, 3-O-sulfated tetrasaccharides, and trisaccharides, have been unambiguously identified based on their retention time and mass spectra. Compared with the traditional heparin compositional analysis methods, the approach described here is not only robust but also comprehensive because it is capable of identifying and quantifying nearly all components from lyase digests of LMWHs. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Rapid differentiation of Chinese hop varieties (Humulus lupulus) using volatile fingerprinting by HS-SPME-GC-MS combined with multivariate statistical analysis.

PubMed

Liu, Zechang; Wang, Liping; Liu, Yumei

2018-01-18

Hops impart flavor to beer, with the volatile components characterizing the various hop varieties and qualities. Fingerprinting, especially flavor fingerprinting, is often used to identify 'flavor products' because inconsistencies in the description of flavor may lead to an incorrect definition of beer quality. Compared to flavor fingerprinting, volatile fingerprinting is simpler and easier. We performed volatile fingerprinting using head space-solid phase micro-extraction gas chromatography-mass spectrometry combined with similarity analysis and principal component analysis (PCA) for evaluating and distinguishing between three major Chinese hops. Eighty-four volatiles were identified, which were classified into seven categories. Volatile fingerprinting based on similarity analysis did not yield any obvious result. By contrast, hop varieties and qualities were identified using volatile fingerprinting based on PCA. The potential variables explained the variance in the three hop varieties. In addition, the dendrogram and principal component score plot described the differences and classifications of hops. Volatile fingerprinting plus multivariate statistical analysis can rapidly differentiate between the different varieties and qualities of the three major Chinese hops. Furthermore, this method can be used as a reference in other fields. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Optimizing of MALDI-ToF-based low-molecular-weight serum proteome pattern analysis in detection of breast cancer patients; the effect of albumin removal on classification performance.

PubMed

Pietrowska, M; Marczak, L; Polanska, J; Nowicka, E; Behrent, K; Tarnawski, R; Stobiecki, M; Polanski, A; Widlak, P

2010-01-01

Mass spectrometry-based analysis of the serum proteome allows identifying multi-peptide patterns/signatures specific for blood of cancer patients, thus having high potential value for cancer diagnostics. However, because of problems with optimization and standardization of experimental and computational design, none of identified proteome patterns/signatures was approved for diagnostics in clinical practice as yet. Here we compared two methods of serum sample preparation for mass spectrometry-based proteome pattern analysis aimed to identify biomarkers that could be used in early detection of breast cancer patients. Blood samples were collected in a group of 92 patients diagnosed at early (I and II) stages of the disease before the start of therapy, and in a group of age-matched healthy controls (104 women). Serum specimens were purified and analyzed using MALDI-ToF spectrometry, either directly or after membrane filtration (50 kDa cut-off) to remove albumin and other large serum proteins. Mass spectra of the low-molecular-weight fraction (2-10 kDa) of the serum proteome were resolved using the Gaussian mixture decomposition, and identified spectral components were used to build classifiers that differentiated samples from breast cancer patients and healthy persons. Mass spectra of complete serum and membrane-filtered albumin-depleted samples have apparently different structure and peaks specific for both types of samples could be identified. The optimal classifier built for the complete serum specimens consisted of 8 spectral components, and had 81% specificity and 72% sensitivity, while that built for the membrane-filtered samples consisted of 4 components, and had 80% specificity and 81% sensitivity. We concluded that pre-processing of samples to remove albumin might be recommended before MALDI-ToF mass spectrometric analysis of the low-molecular-weight components of human serum Keywords: albumin removal; breast cancer; clinical proteomics; mass spectrometry; pattern analysis; serum proteome.

Identification of faulty sensor using relative partial decomposition via independent component analysis

NASA Astrophysics Data System (ADS)

Wang, Z.; Quek, S. T.

2015-07-01

Performance of any structural health monitoring algorithm relies heavily on good measurement data. Hence, it is necessary to employ robust faulty sensor detection approaches to isolate sensors with abnormal behaviour and exclude the highly inaccurate data in the subsequent analysis. The independent component analysis (ICA) is implemented to detect the presence of sensors showing abnormal behaviour. A normalized form of the relative partial decomposition contribution (rPDC) is proposed to identify the faulty sensor. Both additive and multiplicative types of faults are addressed and the detectability illustrated using a numerical and an experimental example. An empirical method to establish control limits for detecting and identifying the type of fault is also proposed. The results show the effectiveness of the ICA and rPDC method in identifying faulty sensor assuming that baseline cases are available.

Identifying apple surface defects using principal components analysis and artifical neural networks

USDA-ARS?s Scientific Manuscript database

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

Support for life-cycle product reuse in NASA's SSE

NASA Technical Reports Server (NTRS)

Shotton, Charles

1989-01-01

The Software Support Environment (SSE) is a software factory for the production of Space Station Freedom Program operational software. The SSE is to be centrally developed and maintained and used to configure software production facilities in the field. The PRC product TTCQF provides for an automated qualification process and analysis of existing code that can be used for software reuse. The interrogation subsystem permits user queries of the reusable data and components which have been identified by an analyzer and qualified with associated metrics. The concept includes reuse of non-code life-cycle components such as requirements and designs. Possible types of reusable life-cycle components include templates, generics, and as-is items. Qualification of reusable elements requires analysis (separation of candidate components into primitives), qualification (evaluation of primitives for reusability according to reusability criteria) and loading (placing qualified elements into appropriate libraries). There can be different qualifications for different installations, methodologies, applications and components. Identifying reusable software and related components is labor-intensive and is best carried out as an integrated function of an SSE.

Use of Raman microscopy and band-target entropy minimization analysis to identify dyes in a commercial stamp. Implications for authentication and counterfeit detection.

PubMed

Widjaja, Effendi; Garland, Marc

2008-02-01

Raman microscopy was used in mapping mode to collect more than 1000 spectra in a 100 microm x 100 microm area from a commercial stamp. Band-target entropy minimization (BTEM) was then employed to unmix the mixture spectra in order to extract the pure component spectra of the samples. Three pure component spectral patterns with good signal-to-noise ratios were recovered, and their spatial distributions were determined. The three pure component spectral patterns were then identified as copper phthalocyanine blue, calcite-like material, and yellow organic dye material by comparison to known spectral libraries. The present investigation, consisting of (1) advanced curve resolution (blind-source separation) followed by (2) spectral data base matching, readily suggests extensions to authenticity and counterfeit studies of other types of commercial objects. The presence or absence of specific observable components form the basis for assessment. The present spectral analysis (BTEM) is applicable to highly overlapping spectral information. Since a priori information such as the number of components present and spectral libraries are not needed in BTEM, and since minor signals arising from trace components can be reconstructed, this analysis offers a robust approach to a wide variety of material problems involving authenticity and counterfeit issues.

The method of trend analysis of parameters time series of gas-turbine engine state

NASA Astrophysics Data System (ADS)

Hvozdeva, I.; Myrhorod, V.; Derenh, Y.

2017-10-01

This research substantiates an approach to interval estimation of time series trend component. The well-known methods of spectral and trend analysis are used for multidimensional data arrays. The interval estimation of trend component is proposed for the time series whose autocorrelation matrix possesses a prevailing eigenvalue. The properties of time series autocorrelation matrix are identified.

Transportation of Large Wind Components: A Review of Existing Geospatial Data

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

Mooney, Meghan; Maclaurin, Galen

2016-09-01

This report features the geospatial data component of a larger project evaluating logistical and infrastructure requirements for transporting oversized and overweight (OSOW) wind components. The goal of the larger project was to assess the status and opportunities for improving the infrastructure and regulatory practices necessary to transport wind turbine towers, blades, and nacelles from current and potential manufacturing facilities to end-use markets. The purpose of this report is to summarize existing geospatial data on wind component transportation infrastructure and to provide a data gap analysis, identifying areas for further analysis and data collection.

Respiratory protective device design using control system techniques

NASA Technical Reports Server (NTRS)

Burgess, W. A.; Yankovich, D.

1972-01-01

The feasibility of a control system analysis approach to provide a design base for respiratory protective devices is considered. A system design approach requires that all functions and components of the system be mathematically identified in a model of the RPD. The mathematical notations describe the operation of the components as closely as possible. The individual component mathematical descriptions are then combined to describe the complete RPD. Finally, analysis of the mathematical notation by control system theory is used to derive compensating component values that force the system to operate in a stable and predictable manner.

HepG2 cells biospecific extraction and HPLC-ESI-MS analysis for screening potential antiatherosclerotic active components in Bupeuri radix.

PubMed

Liu, Shuqiang; Tan, Zhibin; Li, Pingting; Gao, Xiaoling; Zeng, Yuaner; Wang, Shuling

2016-03-20

HepG2 cells biospecific extraction method and high performance liquid chromatography-electrospray ionization-mass spectrometry (HPLC-ESI-MS) analysis was proposed for screening of potential antiatherosclerotic active components in Bupeuri radix, a well-known Traditional Chinese Medicine (TCM). The hypothesis suggested that when cells are incubated together with the extracts of TCM, the potential bioactive components in the TCM should selectively combine with the receptor or channel of HepG2 cells, then the eluate which contained biospecific component binding to HepG2 cells was identified using HPLC-ESI-MS analysis. The potential bioactive components of Bupeuri radix were investigated using the proposed approach. Five compounds in the saikosaponins of Bupeuri radix were detected as these components selectively combined with HepG2 cells, among these compounds, two potentially bioactive compounds namely saikosaponin b1 and saikosaponin b2 (SSb2) were identified by comparing with the chromatography of the standard sample and analysis of the structural clearance characterization of MS. Then SSb2 was used to assess the uptake of DiI-high density lipoprotein (HDL) in HepG2 cells for antiatherosclerotic activity. The results have showed that SSb2, with indicated concentrations (5, 15, 25, and 40 μM) could remarkably uptake dioctadecylindocarbocyanine labeled- (DiI) -HDL in HepG2 cells (Vs control group, *P<0.01). In conclusion, the application of HepG2 biospecific extraction coupled with HPLC-ESI-MS analysis is a rapid, convenient, and reliable method for screening potential bioactive components in TCM and SSb2 may be a valuable novel drug agent for the treatment of atherosclerosis. Copyright © 2016 Elsevier B.V. All rights reserved.

The rate of change in declining steroid hormones: a new parameter of healthy aging in men?

PubMed

Walther, Andreas; Philipp, Michel; Lozza, Niclà; Ehlert, Ulrike

2016-09-20

Research on healthy aging in men has increasingly focused on age-related hormonal changes. Testosterone (T) decline is primarily investigated, while age-related changes in other sex steroids (dehydroepiandrosterone [DHEA], estradiol [E2], progesterone [P]) are mostly neglected. An integrated hormone parameter reflecting aging processes in men has yet to be identified. 271 self-reporting healthy men between 40 and 75 provided both psychometric data and saliva samples for hormone analysis. Correlation analysis between age and sex steroids revealed negative associations for the four sex steroids (T, DHEA, E2, and P). Principal component analysis including ten salivary analytes identified a principal component mainly unifying the variance of the four sex steroid hormones. Subsequent principal component analysis including the four sex steroids extracted the principal component of declining steroid hormones (DSH). Moderation analysis of the association between age and DSH revealed significant moderation effects for psychosocial factors such as depression, chronic stress and perceived general health. In conclusion, these results provide further evidence that sex steroids decline in aging men and that the integrated hormone parameter DSH and its rate of change can be used as biomarkers for healthy aging in men. Furthermore, the negative association of age and DSH is moderated by psychosocial factors.

The rate of change in declining steroid hormones: a new parameter of healthy aging in men?

PubMed Central

Walther, Andreas; Philipp, Michel; Lozza, Niclà; Ehlert, Ulrike

2016-01-01

Research on healthy aging in men has increasingly focused on age-related hormonal changes. Testosterone (T) decline is primarily investigated, while age-related changes in other sex steroids (dehydroepiandrosterone [DHEA], estradiol [E2], progesterone [P]) are mostly neglected. An integrated hormone parameter reflecting aging processes in men has yet to be identified. 271 self-reporting healthy men between 40 and 75 provided both psychometric data and saliva samples for hormone analysis. Correlation analysis between age and sex steroids revealed negative associations for the four sex steroids (T, DHEA, E2, and P). Principal component analysis including ten salivary analytes identified a principal component mainly unifying the variance of the four sex steroid hormones. Subsequent principal component analysis including the four sex steroids extracted the principal component of declining steroid hormones (DSH). Moderation analysis of the association between age and DSH revealed significant moderation effects for psychosocial factors such as depression, chronic stress and perceived general health. In conclusion, these results provide further evidence that sex steroids decline in aging men and that the integrated hormone parameter DSH and its rate of change can be used as biomarkers for healthy aging in men. Furthermore, the negative association of age and DSH is moderated by psychosocial factors. PMID:27589836

Clustering analysis of water distribution systems: identifying critical components and community impacts.

PubMed

Diao, K; Farmani, R; Fu, G; Astaraie-Imani, M; Ward, S; Butler, D

2014-01-01

Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.

[Using 2-DCOS to identify the molecular spectrum peaks for the isomer in the multi-component mixture gases Fourier transform infrared analysis].

PubMed

Zhao, An-Xin; Tang, Xiao-Jun; Zhang, Zhong-Hua; Liu, Jun-Hua

2014-10-01

The generalized two-dimensional correlation spectroscopy and Fourier transform infrared were used to identify hydrocarbon isomers in the mixed gases for absorption spectra resolution enhancement. The Fourier transform infrared spectrum of n-butane and iso-butane and the two-dimensional correlation infrared spectrum of concentration perturbation were used for analysis as an example. The all band and the main absorption peak wavelengths of Fourier transform infrared spectrum for single component gas showed that the spectra are similar, and if they were mixed together, absorption peaks overlap and peak is difficult to identify. The synchronous and asynchronous spectrum of two-dimensional correlation spectrum can clearly identify the iso-butane and normal butane and their respective characteristic absorption peak intensity. Iso-butane has strong absorption characteristics spectrum lines at 2,893, 2,954 and 2,893 cm(-1), and n-butane at 2,895 and 2,965 cm(-1). The analysis result in this paper preliminary verified that the two-dimensional infrared correlation spectroscopy can be used for resolution enhancement in Fourier transform infrared spectrum quantitative analysis.

Life-cycle assessment of Nebraska bridges.

DOT National Transportation Integrated Search

2013-05-01

Life-cycle cost analysis (LCCA) is a necessary component in bridge management systems (BMSs) for : assessing investment decisions and identifying the most cost-effective improvement alternatives. The : LCCA helps to identify the lowest cost alternati...

Novel presentational approaches were developed for reporting network meta-analysis.

PubMed

Tan, Sze Huey; Cooper, Nicola J; Bujkiewicz, Sylwia; Welton, Nicky J; Caldwell, Deborah M; Sutton, Alexander J

2014-06-01

To present graphical tools for reporting network meta-analysis (NMA) results aiming to increase the accessibility, transparency, interpretability, and acceptability of NMA analyses. The key components of NMA results were identified based on recommendations by agencies such as the National Institute for Health and Care Excellence (United Kingdom). Three novel graphs were designed to amalgamate the identified components using familiar graphical tools such as the bar, line, or pie charts and adhering to good graphical design principles. Three key components for presentation of NMA results were identified, namely relative effects and their uncertainty, probability of an intervention being best, and between-study heterogeneity. Two of the three graphs developed present results (for each pairwise comparison of interventions in the network) obtained from both NMA and standard pairwise meta-analysis for easy comparison. They also include options to display the probability best, ranking statistics, heterogeneity, and prediction intervals. The third graph presents rankings of interventions in terms of their effectiveness to enable clinicians to easily identify "top-ranking" interventions. The graphical tools presented can display results tailored to the research question of interest, and targeted at a whole spectrum of users from the technical analyst to the nontechnical clinician. Copyright © 2014 Elsevier Inc. All rights reserved.

Temporal trends and bioavailability assessment of heavy metals in the sediments of Deception Bay, Queensland, Australia.

PubMed

Brady, James P; Ayoko, Godwin A; Martens, Wayde N; Goonetilleke, Ashantha

2014-12-15

Thirteen sites in Deception Bay, Queensland, Australia were sampled three times over a period of 7 months and assessed for contamination by a range of heavy metals, primarily As, Cd, Cr, Cu, Pb and Hg. Fraction analysis, enrichment factors and Principal Components Analysis-Absolute Principal Component Scores (PCA-APCS) analysis were conducted in order to identify the potential bioavailability of these elements of concern and their sources. Hg and Te were identified as the elements of highest enrichment in Deception Bay while marine sediments, shipping and antifouling agents were identified as the sources of the Weak Acid Extractable Metals (WE-M), with antifouling agents showing long residence time for mercury contamination. This has significant implications for the future of monitoring and regulation of heavy metal contamination within Deception Bay. Copyright © 2014 Elsevier Ltd. All rights reserved.

Spatiotemporal analysis of single-trial EEG of emotional pictures based on independent component analysis and source location

NASA Astrophysics Data System (ADS)

Liu, Jiangang; Tian, Jie

2007-03-01

The present study combined the Independent Component Analysis (ICA) and low-resolution brain electromagnetic tomography (LORETA) algorithms to identify the spatial distribution and time course of single-trial EEG record differences between neural responses to emotional stimuli vs. the neutral. Single-trial multichannel (129-sensor) EEG records were collected from 21 healthy, right-handed subjects viewing the emotion emotional (pleasant/unpleasant) and neutral pictures selected from International Affective Picture System (IAPS). For each subject, the single-trial EEG records of each emotional pictures were concatenated with the neutral, and a three-step analysis was applied to each of them in the same way. First, the ICA was performed to decompose each concatenated single-trial EEG records into temporally independent and spatially fixed components, namely independent components (ICs). The IC associated with artifacts were isolated. Second, the clustering analysis classified, across subjects, the temporally and spatially similar ICs into the same clusters, in which nonparametric permutation test for Global Field Power (GFP) of IC projection scalp maps identified significantly different temporal segments of each emotional condition vs. neutral. Third, the brain regions accounted for those significant segments were localized spatially with LORETA analysis. In each cluster, a voxel-by-voxel randomization test identified significantly different brain regions between each emotional condition vs. the neutral. Compared to the neutral, both emotional pictures elicited activation in the visual, temporal, ventromedial and dorsomedial prefrontal cortex and anterior cingulated gyrus. In addition, the pleasant pictures activated the left middle prefrontal cortex and the posterior precuneus, while the unpleasant pictures activated the right orbitofrontal cortex, posterior cingulated gyrus and somatosensory region. Our results were well consistent with other functional imaging studies, while revealed temporal dynamics of emotional processing of specific brain structure with high temporal resolution.

Principal components analysis of the photoresponse nonuniformity of a matrix detector.

PubMed

Ferrero, Alejandro; Alda, Javier; Campos, Joaquín; López-Alonso, Jose Manuel; Pons, Alicia

2007-01-01

The principal component analysis is used to identify and quantify spatial distributions of relative photoresponse as a function of the exposure time for a visible CCD array. The analysis shows a simple way to define an invariant photoresponse nonuniformity and compare it with the definition of this invariant pattern as the one obtained for long exposure times. Experimental data of radiant exposure from levels of irradiance obtained in a stable and well-controlled environment are used.

A novel approach to analyzing fMRI and SNP data via parallel independent component analysis

NASA Astrophysics Data System (ADS)

Liu, Jingyu; Pearlson, Godfrey; Calhoun, Vince; Windemuth, Andreas

2007-03-01

There is current interest in understanding genetic influences on brain function in both the healthy and the disordered brain. Parallel independent component analysis, a new method for analyzing multimodal data, is proposed in this paper and applied to functional magnetic resonance imaging (fMRI) and a single nucleotide polymorphism (SNP) array. The method aims to identify the independent components of each modality and the relationship between the two modalities. We analyzed 92 participants, including 29 schizophrenia (SZ) patients, 13 unaffected SZ relatives, and 50 healthy controls. We found a correlation of 0.79 between one fMRI component and one SNP component. The fMRI component consists of activations in cingulate gyrus, multiple frontal gyri, and superior temporal gyrus. The related SNP component is contributed to significantly by 9 SNPs located in sets of genes, including those coding for apolipoprotein A-I, and C-III, malate dehydrogenase 1 and the gamma-aminobutyric acid alpha-2 receptor. A significant difference in the presences of this SNP component is found between the SZ group (SZ patients and their relatives) and the control group. In summary, we constructed a framework to identify the interactions between brain functional and genetic information; our findings provide new insight into understanding genetic influences on brain function in a common mental disorder.

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci

PubMed Central

Ju, Jin Hyun; Crystal, Ronald G.

2017-01-01

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL. PMID:28505156

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.

PubMed

Ju, Jin Hyun; Shenoy, Sushila A; Crystal, Ronald G; Mezey, Jason G

2017-05-01

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.

Web-ware bioinformatical analysis and structure modelling of N-terminus of human multisynthetase complex auxiliary component protein p43.

PubMed

Deineko, Viktor

2006-01-01

Human multisynthetase complex auxiliary component, protein p43 is an endothelial monocyte-activating polypeptide II precursor. In this study, comprehensive sequence analysis of N-terminus has been performed to identify structural domains, motifs, sites of post-translation modification and other functionally important parameters. The spatial structure model of full-chain protein p43 is obtained.

Model Performance Evaluation and Scenario Analysis ...

EPA Pesticide Factsheets

This tool consists of two parts: model performance evaluation and scenario analysis (MPESA). The model performance evaluation consists of two components: model performance evaluation metrics and model diagnostics. These metrics provides modelers with statistical goodness-of-fit measures that capture magnitude only, sequence only, and combined magnitude and sequence errors. The performance measures include error analysis, coefficient of determination, Nash-Sutcliffe efficiency, and a new weighted rank method. These performance metrics only provide useful information about the overall model performance. Note that MPESA is based on the separation of observed and simulated time series into magnitude and sequence components. The separation of time series into magnitude and sequence components and the reconstruction back to time series provides diagnostic insights to modelers. For example, traditional approaches lack the capability to identify if the source of uncertainty in the simulated data is due to the quality of the input data or the way the analyst adjusted the model parameters. This report presents a suite of model diagnostics that identify if mismatches between observed and simulated data result from magnitude or sequence related errors. MPESA offers graphical and statistical options that allow HSPF users to compare observed and simulated time series and identify the parameter values to adjust or the input data to modify. The scenario analysis part of the too

Candidate gene analyses of 3-dimensional dentoalveolar phenotypes in subjects with malocclusion

PubMed Central

Weaver, Cole A.; Miller, Steven F.; da Fontoura, Clarissa S. G.; Wehby, George L.; Amendt, Brad A.; Holton, Nathan E.; Allareddy, Veeratrishul; Southard, Thomas E.; Moreno Uribe, Lina M.

2017-01-01

Introduction Genetic studies of malocclusion etiology have identified 4 deleterious mutations in genes, DUSP6, ARHGAP21, FGF23, and ADAMTS1 in familial Class III cases. Although these variants may have large impacts on Class III phenotypic expression, their low frequency (<1%) makes them unlikely to explain most malocclusions. Thus, much of the genetic variation underlying the dentofacial phenotypic variation associated with malocclusion remains unknown. In this study, we evaluated associations between common genetic variations in craniofacial candidate genes and 3-dimensional dentoalveolar phenotypes in patients with malocclusion. Methods Pretreatment dental casts or cone-beam computed tomographic images from 300 healthy subjects were digitized with 48 landmarks. The 3-dimensional coordinate data were submitted to a geometric morphometric approach along with principal component analysis to generate continuous phenotypes including symmetric and asymmetric components of dentoalveolar shape variation, fluctuating asymmetry, and size. The subjects were genotyped for 222 single-nucleotide polymorphisms in 82 genes/loci, and phenotpye-genotype associations were tested via multivariate linear regression. Results Principal component analysis of symmetric variation identified 4 components that explained 68% of the total variance and depicted anteroposterior, vertical, and transverse dentoalveolar discrepancies. Suggestive associations (P < 0.05) were identified with PITX2, SNAI3, 11q22.2-q22.3, 4p16.1, ISL1, and FGF8. Principal component analysis for asymmetric variations identified 4 components that explained 51% of the total variations and captured left-to-right discrepancies resulting in midline deviations, unilateral crossbites, and ectopic eruptions. Suggestive associations were found with TBX1 AJUBA, SNAI3 SATB2, TP63, and 1p22.1. Fluctuating asymmetry was associated with BMP3 and LATS1. Associations for SATB2 and BMP3 with asymmetric variations remained significant after the Bonferroni correction (P <0.00022). Suggestive associations were found for centroid size, a proxy for dentoalveolar size variation with 4p16.1 and SNAI1. Conclusions Specific genetic pathways associated with 3-dimensional dentoalveolar phenotypic variation in malocclusions were identified. PMID:28257739

Three-way parallel independent component analysis for imaging genetics using multi-objective optimization.

PubMed

Ulloa, Alvaro; Jingyu Liu; Vergara, Victor; Jiayu Chen; Calhoun, Vince; Pattichis, Marios

2014-01-01

In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging and genetic data. This paper describes a new algorithm, three-way parallel independent component analysis (3pICA), for jointly identifying genomic loci associated with brain function and structure. The proposed algorithm relies on the use of multi-objective optimization methods to identify correlations among the modalities and maximally independent sources within modality. We test the robustness of the proposed approach by varying the effect size, cross-modality correlation, noise level, and dimensionality of the data. Simulation results suggest that 3p-ICA is robust to data with SNR levels from 0 to 10 dB and effect-sizes from 0 to 3, while presenting its best performance with high cross-modality correlations, and more than one subject per 1,000 variables. In an experimental study with 112 human subjects, the method identified links between a genetic component (pointing to brain function and mental disorder associated genes, including PPP3CC, KCNQ5, and CYP7B1), a functional component related to signal decreases in the default mode network during the task, and a brain structure component indicating increases of gray matter in brain regions of the default mode region. Although such findings need further replication, the simulation and in-vivo results validate the three-way parallel ICA algorithm presented here as a useful tool in biomedical data decomposition applications.

Sensitivity analysis by approximation formulas - Illustrative examples. [reliability analysis of six-component architectures

NASA Technical Reports Server (NTRS)

White, A. L.

1983-01-01

This paper examines the reliability of three architectures for six components. For each architecture, the probabilities of the failure states are given by algebraic formulas involving the component fault rate, the system recovery rate, and the operating time. The dominant failure modes are identified, and the change in reliability is considered with respect to changes in fault rate, recovery rate, and operating time. The major conclusions concern the influence of system architecture on failure modes and parameter requirements. Without this knowledge, a system designer may pick an inappropriate structure.

The use of principal component and cluster analysis to differentiate banana peel flours based on their starch and dietary fibre components.

PubMed

Ramli, Saifullah; Ismail, Noryati; Alkarkhi, Abbas Fadhl Mubarek; Easa, Azhar Mat

2010-08-01

Banana peel flour (BPF) prepared from green or ripe Cavendish and Dream banana fruits were assessed for their total starch (TS), digestible starch (DS), resistant starch (RS), total dietary fibre (TDF), soluble dietary fibre (SDF) and insoluble dietary fibre (IDF). Principal component analysis (PCA) identified that only 1 component was responsible for 93.74% of the total variance in the starch and dietary fibre components that differentiated ripe and green banana flours. Cluster analysis (CA) applied to similar data obtained two statistically significant clusters (green and ripe bananas) to indicate difference in behaviours according to the stages of ripeness based on starch and dietary fibre components. We concluded that the starch and dietary fibre components could be used to discriminate between flours prepared from peels obtained from fruits of different ripeness. The results were also suggestive of the potential of green and ripe BPF as functional ingredients in food.

The Use of Principal Component and Cluster Analysis to Differentiate Banana Peel Flours Based on Their Starch and Dietary Fibre Components

PubMed Central

Ramli, Saifullah; Ismail, Noryati; Alkarkhi, Abbas Fadhl Mubarek; Easa, Azhar Mat

2010-01-01

Banana peel flour (BPF) prepared from green or ripe Cavendish and Dream banana fruits were assessed for their total starch (TS), digestible starch (DS), resistant starch (RS), total dietary fibre (TDF), soluble dietary fibre (SDF) and insoluble dietary fibre (IDF). Principal component analysis (PCA) identified that only 1 component was responsible for 93.74% of the total variance in the starch and dietary fibre components that differentiated ripe and green banana flours. Cluster analysis (CA) applied to similar data obtained two statistically significant clusters (green and ripe bananas) to indicate difference in behaviours according to the stages of ripeness based on starch and dietary fibre components. We concluded that the starch and dietary fibre components could be used to discriminate between flours prepared from peels obtained from fruits of different ripeness. The results were also suggestive of the potential of green and ripe BPF as functional ingredients in food. PMID:24575193

Measurement analysis of two radials with a common-origin point and its application.

PubMed

Liu, Zhenyao; Yang, Jidong; Zhu, Weiwei; Zhou, Shang; Tan, Xuanping

2017-08-01

In spectral analysis, a chemical component is usually identified by its characteristic spectra, especially the peaks. If two components have overlapping spectral peaks, they are generally considered to be indiscriminate in current analytical chemistry textbooks and related literature. However, if the intensities of the overlapping major spectral peaks are additive, and have different rates of change with respect to variations in the concentration of the individual components, a simple method, named the 'common-origin ray', for the simultaneous determination of two components can be established. Several case studies highlighting its applications are presented. Copyright © 2017 John Wiley & Sons, Ltd.

Identification of Volatile Flavor Components by Headspace Analysis: A Quick and Easy Experiment for Introducing GC/MS

NASA Astrophysics Data System (ADS)

Kjonaas, Richard; Soller, Jean L.; McCoy, Leslee A.

1997-09-01

By placing a piece of chewing gum (Wrigley's) or a crushed piece of hard candy (LifeSavers or Runts) into a vial, followed by GC/MS analysis of a five microliter sample of the headspace, students are able to identify several of the volatile flavoring components which are present. The experiment has been used successfully with sophomore organic chemistry students, and with visiting groups of talented high school students over a three year period. Identification is simplified by handing out a list of the structural formulas of some likely candidates. Some of the components that these students easily identity include ethyl acetate, isobutyl acetate, isoamyl acetate, ethyl butyrate, benzaldehyde, benzyl alcohol, limonene, and cinnamaldehyde. Some of the more difficult to identify components include menthol, menthone, carvone, cineole, myrcene, alpha-pinene, beta-pinene, para-cymene, and gama-terpinene. Most of the major headspace components give signals whose size is comparable to that of the carbon dioxide which is present in each injection. Even with split injection, the background noise is trivial compared to the signals from the major components. The experiments were carried out with a commercially available tabletop GC/MS (Varian 3400 with Saturn MS).

Audio-visual speech perception in adult readers with dyslexia: an fMRI study.

PubMed

Rüsseler, Jascha; Ye, Zheng; Gerth, Ivonne; Szycik, Gregor R; Münte, Thomas F

2018-04-01

Developmental dyslexia is a specific deficit in reading and spelling that often persists into adulthood. In the present study, we used slow event-related fMRI and independent component analysis to identify brain networks involved in perception of audio-visual speech in a group of adult readers with dyslexia (RD) and a group of fluent readers (FR). Participants saw a video of a female speaker saying a disyllabic word. In the congruent condition, audio and video input were identical whereas in the incongruent condition, the two inputs differed. Participants had to respond to occasionally occurring animal names. The independent components analysis (ICA) identified several components that were differently modulated in FR and RD. Two of these components including fusiform gyrus and occipital gyrus showed less activation in RD compared to FR possibly indicating a deficit to extract face information that is needed to integrate auditory and visual information in natural speech perception. A further component centered on the superior temporal sulcus (STS) also exhibited less activation in RD compared to FR. This finding is corroborated in the univariate analysis that shows less activation in STS for RD compared to FR. These findings suggest a general impairment in recruitment of audiovisual processing areas in dyslexia during the perception of natural speech.

Identification of the isomers using principal component analysis (PCA) method

NASA Astrophysics Data System (ADS)

Kepceoǧlu, Abdullah; Gündoǧdu, Yasemin; Ledingham, Kenneth William David; Kilic, Hamdi Sukur

2016-03-01

In this work, we have carried out a detailed statistical analysis for experimental data of mass spectra from xylene isomers. Principle Component Analysis (PCA) was used to identify the isomers which cannot be distinguished using conventional statistical methods for interpretation of their mass spectra. Experiments have been carried out using a linear TOF-MS coupled to a femtosecond laser system as an energy source for the ionisation processes. We have performed experiments and collected data which has been analysed and interpreted using PCA as a multivariate analysis of these spectra. This demonstrates the strength of the method to get an insight for distinguishing the isomers which cannot be identified using conventional mass analysis obtained through dissociative ionisation processes on these molecules. The PCA results dependending on the laser pulse energy and the background pressure in the spectrometers have been presented in this work.

Principal component similarity analysis of Raman spectra to study the effects of pH, heating, and kappa-carrageenan on whey protein structure.

PubMed

Alizadeh-Pasdar, Nooshin; Nakai, Shuryo; Li-Chan, Eunice C Y

2002-10-09

Raman spectroscopy was used to elucidate structural changes of beta-lactoglobulin (BLG), whey protein isolate (WPI), and bovine serum albumin (BSA), at 15% concentration, as a function of pH (5.0, 7.0, and 9.0), heating (80 degrees C, 30 min), and presence of 0.24% kappa-carrageenan. Three data-processing techniques were used to assist in identifying significant changes in Raman spectral data. Analysis of variance showed that of 12 characteristics examined in the Raman spectra, only a few were significantly affected by pH, heating, kappa-carrageenan, and their interactions. These included amide I (1658 cm(-1)) for WPI and BLG, alpha-helix for BLG and BSA, beta-sheet for BSA, CH stretching (2880 cm(-1)) for BLG and BSA, and CH stretching (2930 cm(-1)) for BSA. Principal component analysis reduced dimensionality of the characteristics. Heating and its interaction with kappa-carrageenan were identified as the most influential in overall structure of the whey proteins, using principal component similarity analysis.

Source localization of temporal lobe epilepsy using PCA-LORETA analysis on ictal EEG recordings.

PubMed

Stern, Yaki; Neufeld, Miriam Y; Kipervasser, Svetlana; Zilberstein, Amir; Fried, Itzhak; Teicher, Mina; Adi-Japha, Esther

2009-04-01

Localizing the source of an epileptic seizure using noninvasive EEG suffers from inaccuracies produced by other generators not related to the epileptic source. The authors isolated the ictal epileptic activity, and applied a source localization algorithm to identify its estimated location. Ten ictal EEG scalp recordings from five different patients were analyzed. The patients were known to have temporal lobe epilepsy with a single epileptic focus that had a concordant MRI lesion. The patients had become seizure-free following partial temporal lobectomy. A midinterval (approximately 5 seconds) period of ictal activity was used for Principal Component Analysis starting at ictal onset. The level of epileptic activity at each electrode (i.e., the eigenvector of the component that manifest epileptic characteristic), was used as an input for low-resolution tomography analysis for EEG inverse solution (Zilberstain et al., 2004). The algorithm accurately and robustly identified the epileptic focus in these patients. Principal component analysis and source localization methods can be used in the future to monitor the progression of an epileptic seizure and its expansion to other areas.

Correlation between the pattern volatiles and the overall aroma of wild edible mushrooms.

PubMed

de Pinho, P Guedes; Ribeiro, Bárbara; Gonçalves, Rui F; Baptista, Paula; Valentão, Patrícia; Seabra, Rosa M; Andrade, Paula B

2008-03-12

Volatile and semivolatile components of 11 wild edible mushrooms, Suillus bellini, Suillus luteus, Suillus granulatus, Tricholomopsis rutilans, Hygrophorus agathosmus, Amanita rubescens, Russula cyanoxantha, Boletus edulis, Tricholoma equestre, Fistulina hepatica, and Cantharellus cibarius, were determined by headspace solid-phase microextraction (HS-SPME) and by liquid extraction combined with gas chromatography-mass spectrometry (GC-MS). Fifty volatiles and nonvolatiles components were formally identified and 13 others were tentatively identified. Using sensorial analysis, the descriptors "mushroomlike", "farm-feed", "floral", "honeylike", "hay-herb", and "nutty" were obtained. A correlation between sensory descriptors and volatiles was observed by applying multivariate analysis (principal component analysis and agglomerative hierarchic cluster analysis) to the sensorial and chemical data. The studied edible mushrooms can be divided in three groups. One of them is rich in C8 derivatives, such as 3-octanol, 1-octen-3-ol, trans-2-octen-1-ol, 3-octanone, and 1-octen-3-one; another one is rich in terpenic volatile compounds; and the last one is rich in methional. The presence and contents of these compounds give a considerable contribution to the sensory characteristics of the analyzed species.

Comprehensive Proteomic Analysis of Human Milk-derived Extracellular Vesicles Unveils a Novel Functional Proteome Distinct from Other Milk Components.

PubMed

van Herwijnen, Martijn J C; Zonneveld, Marijke I; Goerdayal, Soenita; Nolte-'t Hoen, Esther N M; Garssen, Johan; Stahl, Bernd; Maarten Altelaar, A F; Redegeld, Frank A; Wauben, Marca H M

2016-11-01

Breast milk contains several macromolecular components with distinctive functions, whereby milk fat globules and casein micelles mainly provide nutrition to the newborn, and whey contains molecules that can stimulate the newborn's developing immune system and gastrointestinal tract. Although extracellular vesicles (EV) have been identified in breast milk, their physiological function and composition has not been addressed in detail. EV are submicron sized vehicles released by cells for intercellular communication via selectively incorporated lipids, nucleic acids, and proteins. Because of the difficulty in separating EV from other milk components, an in-depth analysis of the proteome of human milk-derived EV is lacking. In this study, an extensive LC-MS/MS proteomic analysis was performed of EV that had been purified from breast milk of seven individual donors using a recently established, optimized density-gradient-based EV isolation protocol. A total of 1963 proteins were identified in milk-derived EV, including EV-associated proteins like CD9, Annexin A5, and Flotillin-1, with a remarkable overlap between the different donors. Interestingly, 198 of the identified proteins are not present in the human EV database Vesiclepedia, indicating that milk-derived EV harbor proteins not yet identified in EV of different origin. Similarly, the proteome of milk-derived EV was compared with that of other milk components. For this, data from 38 published milk proteomic studies were combined in order to construct the total milk proteome, which consists of 2698 unique proteins. Remarkably, 633 proteins identified in milk-derived EV have not yet been identified in human milk to date. Interestingly, these novel proteins include proteins involved in regulation of cell growth and controlling inflammatory signaling pathways, suggesting that milk-derived EVs could support the newborn's developing gastrointestinal tract and immune system. Overall, this study provides an expansion of the whole milk proteome and illustrates that milk-derived EV are macromolecular components with a unique functional proteome. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Identification and visualization of dominant patterns and anomalies in remotely sensed vegetation phenology using a parallel tool for principal components analysis

Treesearch

Richard Tran Mills; Jitendra Kumar; Forrest M. Hoffman; William W. Hargrove; Joseph P. Spruce; Steven P. Norman

2013-01-01

We investigated the use of principal components analysis (PCA) to visualize dominant patterns and identify anomalies in a multi-year land surface phenology data set (231 m × 231 m normalized difference vegetation index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)) used for detecting threats to forest health in the conterminous...

Specialized data analysis of SSME and advanced propulsion system vibration measurements

NASA Technical Reports Server (NTRS)

Coffin, Thomas; Swanson, Wayne L.; Jong, Yen-Yi

1993-01-01

The basic objectives of this contract were to perform detailed analysis and evaluation of dynamic data obtained during Space Shuttle Main Engine (SSME) test and flight operations, including analytical/statistical assessment of component dynamic performance, and to continue the development and implementation of analytical/statistical models to effectively define nominal component dynamic characteristics, detect anomalous behavior, and assess machinery operational conditions. This study was to provide timely assessment of engine component operational status, identify probable causes of malfunction, and define feasible engineering solutions. The work was performed under three broad tasks: (1) Analysis, Evaluation, and Documentation of SSME Dynamic Test Results; (2) Data Base and Analytical Model Development and Application; and (3) Development and Application of Vibration Signature Analysis Techniques.

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

Comparative Analysis of the Volatile Components of Agrimonia eupatoria from Leaves and Roots by Gas Chromatography-Mass Spectrometry and Multivariate Curve Resolution

PubMed Central

Feng, Xiao-Liang; He, Yun-biao; Liang, Yi-Zeng; Wang, Yu-Lin; Huang, Lan-Fang; Xie, Jian-Wei

2013-01-01

Gas chromatography-mass spectrometry and multivariate curve resolution were applied to the differential analysis of the volatile components in Agrimonia eupatoria specimens from different plant parts. After extracted with water distillation method, the volatile components in Agrimonia eupatoria from leaves and roots were detected by GC-MS. Then the qualitative and quantitative analysis of the volatile components in the main root of Agrimonia eupatoria was completed with the help of subwindow factor analysis resolving two-dimensional original data into mass spectra and chromatograms. 68 of 87 separated constituents in the total ion chromatogram of the volatile components were identified and quantified, accounting for about 87.03% of the total content. Then, the common peaks in leaf were extracted with orthogonal projection resolution method. Among the components determined, there were 52 components coexisting in the studied samples although the relative content of each component showed difference to some extent. The results showed a fair consistency in their GC-MS fingerprint. It was the first time to apply orthogonal projection method to compare different plant parts of Agrimonia eupatoria, and it reduced the burden of qualitative analysis as well as the subjectivity. The obtained results proved the combined approach powerful for the analysis of complex Agrimonia eupatoria samples. The developed method can be used to further study and quality control of Agrimonia eupatoria. PMID:24286016

Comparative Analysis of the Volatile Components of Agrimonia eupatoria from Leaves and Roots by Gas Chromatography-Mass Spectrometry and Multivariate Curve Resolution.

PubMed

Feng, Xiao-Liang; He, Yun-Biao; Liang, Yi-Zeng; Wang, Yu-Lin; Huang, Lan-Fang; Xie, Jian-Wei

2013-01-01

Gas chromatography-mass spectrometry and multivariate curve resolution were applied to the differential analysis of the volatile components in Agrimonia eupatoria specimens from different plant parts. After extracted with water distillation method, the volatile components in Agrimonia eupatoria from leaves and roots were detected by GC-MS. Then the qualitative and quantitative analysis of the volatile components in the main root of Agrimonia eupatoria was completed with the help of subwindow factor analysis resolving two-dimensional original data into mass spectra and chromatograms. 68 of 87 separated constituents in the total ion chromatogram of the volatile components were identified and quantified, accounting for about 87.03% of the total content. Then, the common peaks in leaf were extracted with orthogonal projection resolution method. Among the components determined, there were 52 components coexisting in the studied samples although the relative content of each component showed difference to some extent. The results showed a fair consistency in their GC-MS fingerprint. It was the first time to apply orthogonal projection method to compare different plant parts of Agrimonia eupatoria, and it reduced the burden of qualitative analysis as well as the subjectivity. The obtained results proved the combined approach powerful for the analysis of complex Agrimonia eupatoria samples. The developed method can be used to further study and quality control of Agrimonia eupatoria.

[Determination of the Plant Origin of Licorice Oil Extract, a Natural Food Additive, by Principal Component Analysis Based on Chemical Components].

PubMed

Tada, Atsuko; Ishizuki, Kyoko; Sugimoto, Naoki; Yoshimatsu, Kayo; Kawahara, Nobuo; Suematsu, Takako; Arifuku, Kazunori; Fukai, Toshio; Tamura, Yukiyoshi; Ohtsuki, Takashi; Tahara, Maiko; Yamazaki, Takeshi; Akiyama, Hiroshi

2015-01-01

"Licorice oil extract" (LOE) (antioxidant agent) is described in the notice of Japanese food additive regulations as a material obtained from the roots and/or rhizomes of Glycyrrhiza uralensis, G. inflata or G. glabra. In this study, we aimed to identify the original Glycyrrhiza species of eight food additive products using LC/MS. Glabridin, a characteristic compound in G. glabra, was specifically detected in seven products, and licochalcone A, a characteristic compound in G. inflata, was detected in one product. In addition, Principal Component Analysis (PCA) (a kind of multivariate analysis) using the data of LC/MS or (1)H-NMR analysis was performed. The data of thirty-one samples, including LOE products used as food additives, ethanol extracts of various Glycyrrhiza species and commercially available Glycyrrhiza species-derived products were assessed. Based on the PCA results, the majority of LOE products was confirmed to be derived from G. glabra. This study suggests that PCA using (1)H-NMR analysis data is a simple and useful method to identify the plant species of origin of natural food additive products.

[Identification of two varieties of Citri Fructus by fingerprint and chemometrics].

PubMed

Su, Jing-hua; Zhang, Chao; Sun, Lei; Gu, Bing-ren; Ma, Shuang-cheng

2015-06-01

Citri Fructus identification by fingerprint and chemometrics was investigated in this paper. Twenty-three Citri Fructus samples were collected which referred to two varieties as Cirtus wilsonii and C. medica recorded in Chinese Pharmacopoeia. HPLC chromatograms were obtained. The components were partly identified by reference substances, and then common pattern was established for chemometrics analysis. Similarity analysis, principal component analysis (PCA) , partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis heatmap were applied. The results indicated that C. wilsonii and C. medica could be ideally classified with common pattern contained twenty-five characteristic peaks. Besides, preliminary pattern recognition had verified the chemometrics analytical results. Absolute peak area (APA) was used for relevant quantitative analysis, results showed the differences between two varieties and it was valuable for further quality control as selection of characteristic components.

A Network Pharmacology Approach to Determine the Active Components and Potential Targets of Curculigo Orchioides in the Treatment of Osteoporosis.

PubMed

Wang, Nani; Zhao, Guizhi; Zhang, Yang; Wang, Xuping; Zhao, Lisha; Xu, Pingcui; Shou, Dan

2017-10-27

BACKGROUND Osteoporosis is a complex bone disorder with a genetic predisposition, and is a cause of health problems worldwide. In China, Curculigo orchioides (CO) has been widely used as a herbal medicine in the prevention and treatment of osteoporosis. However, research on the mechanism of action of CO is still lacking. The aim of this study was to identify the absorbable components, potential targets, and associated treatment pathways of CO using a network pharmacology approach. MATERIAL AND METHODS We explored the chemical components of CO and used the five main principles of drug absorption to identify absorbable components. Targets for the therapeutic actions of CO were obtained from the PharmMapper server database. Pathway enrichment analysis was performed using the Comparative Toxicogenomics Database (CTD). Cytoscape was used to visualize the multiple components-multiple target-multiple pathways-multiple disease network for CO. RESULTS We identified 77 chemical components of CO, of which 32 components could be absorbed in the blood. These potential active components of CO regulated 83 targets and affected 58 pathways. Data analysis showed that the genes for estrogen receptor alpha (ESR1) and beta (ESR2), and the gene for 11 beta-hydroxysteroid dehydrogenase type 1, or cortisone reductase (HSD11B1) were the main targets of CO. Endocrine regulatory factors and factors regulating calcium reabsorption, steroid hormone biosynthesis, and metabolic pathways were related to these main targets and to ten corresponding compounds. CONCLUSIONS The network pharmacology approach used in our study has attempted to explain the mechanisms for the effects of CO in the prevention and treatment of osteoporosis, and provides an alternative approach to the investigation of the effects of this complex compound.

Method of detecting leakage of reactor core components of liquid metal cooled fast reactors

DOEpatents

Holt, Fred E.; Cash, Robert J.; Schenter, Robert E.

1977-01-01

A method of detecting the failure of a sealed non-fueled core component of a liquid-metal cooled fast reactor having an inert cover gas. A gas mixture is incorporated in the component which includes Xenon-124; under neutron irradiation, Xenon-124 is converted to radioactive Xenon-125. The cover gas is scanned by a radiation detector. The occurrence of 188 Kev gamma radiation and/or other identifying gamma radiation-energy level indicates the presence of Xenon-125 and therefore leakage of a component. Similarly, Xe-126, which transmutes to Xe-127 and Kr-84, which produces Kr-85.sup.m can be used for detection of leakage. Different components are charged with mixtures including different ratios of isotopes other than Xenon-124. On detection of the identifying radiation, the cover gas is subjected to mass spectroscopic analysis to locate the leaking component.

Application of linear mixed-effects model with LASSO to identify metal components associated with cardiac autonomic responses among welders: a repeated measures study

PubMed Central

Zhang, Jinming; Cavallari, Jennifer M; Fang, Shona C; Weisskopf, Marc G; Lin, Xihong; Mittleman, Murray A; Christiani, David C

2017-01-01

Background Environmental and occupational exposure to metals is ubiquitous worldwide, and understanding the hazardous metal components in this complex mixture is essential for environmental and occupational regulations. Objective To identify hazardous components from metal mixtures that are associated with alterations in cardiac autonomic responses. Methods Urinary concentrations of 16 types of metals were examined and ‘acceleration capacity’ (AC) and ‘deceleration capacity’ (DC), indicators of cardiac autonomic effects, were quantified from ECG recordings among 54 welders. We fitted linear mixed-effects models with least absolute shrinkage and selection operator (LASSO) to identify metal components that are associated with AC and DC. The Bayesian Information Criterion was used as the criterion for model selection procedures. Results Mercury and chromium were selected for DC analysis, whereas mercury, chromium and manganese were selected for AC analysis through the LASSO approach. When we fitted the linear mixed-effects models with ‘selected’ metal components only, the effect of mercury remained significant. Every 1 µg/L increase in urinary mercury was associated with −0.58 ms (−1.03, –0.13) changes in DC and 0.67 ms (0.25, 1.10) changes in AC. Conclusion Our study suggests that exposure to several metals is associated with impaired cardiac autonomic functions. Our findings should be replicated in future studies with larger sample sizes. PMID:28663305

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.

The Cuban scorpion Rhopalurus junceus (Scorpiones, Buthidae): component variations in venom samples collected in different geographical areas

PubMed Central

2013-01-01

Backgound The venom of the Cuban scorpion Rhopalurus junceus is poorly study from the point of view of their components at molecular level and the functions associated. The purpose of this article was to conduct a proteomic analysis of venom components from scorpions collected in different geographical areas of the country. Results Venom from the blue scorpion, as it is called, was collected separately from specimens of five distinct Cuban towns (Moa, La Poa, Limonar, El Chote and Farallones) of the Nipe-Sagua-Baracoa mountain massif and fractionated by high performance liquid chromatography (HPLC); the molecular masses of each fraction were ascertained by mass spectrometry analysis. At least 153 different molecular mass components were identified among the five samples analyzed. Molecular masses varied from 466 to 19755 Da. Scorpion HPLC profiles differed among these different geographical locations and the predominant molecular masses of their components. The most evident differences are in the relative concentration of the venom components. The most abundant components presented molecular weights around 4 kDa, known to be K+-channel specific peptides, and 7 kDa, known to be Na+-channel specific peptides, but with small molecular weight differences. Approximately 30 peptides found in venom samples from the different geographical areas are identical, supporting the idea that they all probably belong to the same species, with some interpopulational variations. Differences were also found in the presence of phospholipase, found in venoms from the Poa area (molecular weights on the order of 14 to 19 kDa). The only ubiquitous enzyme identified in the venoms from all five localities studied (hyaluronidase) presented the same 45 kD molecular mass, identified by gel electrophoresis analysis. Conclusions The venom of these scorpions from different geographical areas seem to be similar, and are rich in peptides that have of the same molecular masses of the peptides purified from other scorpions that affect ion-channel functions. PMID:23849540

Determination and fingerprint analysis of steroidal saponins in roots of Liriope muscari (Decne.) L. H. Bailey by ultra high performance liquid chromatography coupled with ion trap time-of-flight mass spectrometry.

PubMed

Li, Yong-Wei; Qi, Jin; Wen-Zhang; Zhou, Shui-Ping; Yan-Wu; Yu, Bo-Yang

2014-07-01

Liriope muscari (Decne.) L. H. Bailey is a well-known traditional Chinese medicine used for treating cough and insomnia. There are few reports on the quality evaluation of this herb partly because the major steroid saponins are not readily identified by UV detectors and are not easily isolated due to the existence of many similar isomers. In this study, a qualitative and quantitative method was developed to analyze the major components in L. muscari (Decne.) L. H. Bailey roots. Sixteen components were deduced and identified primarily by the information obtained from ultra high performance liquid chromatography with ion-trap time-of-flight mass spectrometry. The method demonstrated the desired specificity, linearity, stability, precision, and accuracy for simultaneous determination of 15 constituents (13 steroidal glycosides, 25(R)-ruscogenin, and pentylbenzoate) in 26 samples from different origins. The fingerprint was established, and the evaluation was achieved using similarity analysis and principal component analysis of 15 fingerprint peaks from 26 samples by ultra high performance liquid chromatography. The results from similarity analysis were consistent with those of principal component analysis. All results suggest that the established method could be applied effectively to the determination of multi-ingredients and fingerprint analysis of steroid saponins for quality assessment and control of L. muscari (Decne.) L. H. Bailey. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Differentiation of essential oils in Atractylodes lancea and Atractylodes koreana by gas chromatography with mass spectrometry.

PubMed

Liu, Qiutao; Zhang, Shanshan; Yang, Xihui; Wang, Ruilin; Guo, Weiying; Kong, Weijun; Yang, Meihua

2016-12-01

Atractylodes rhizome is a valuable traditional Chinese medicinal herb that comprises complex several species whose essential oils are the primary pharmacologically active component. Essential oils of Atractylodes lancea and Atractylodes koreana were extracted by hydrodistillation, and the yield was determined. The average yield of essential oil obtained from A. lancea (2.91%) was higher than that from A. koreana (2.42%). The volatile components of the essential oils were then identified by a gas chromatography with mass spectrometry method that demonstrated good precision. The method showed clear differences in the numbers and contents of volatile components between the two species. 41 and 45 volatile components were identified in A. lancea and A. koreana, respectively. Atractylon (48.68%) was the primary volatile component in A. lancea, while eudesma-4(14)-en-11-ol (11.81%) was major in A. koreana. However, the most significant difference between A. lancea and A. koreana was the major component of atractylon and atractydin. Principal component analysis was utilized to reveal the correlation between volatile components and species, and the analysis was used to successfully discriminate between A. lancea and A. koreana samples. These results suggest that different species of Atractylodes rhizome may yield essential oils that differ significantly in content and composition. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Wind Turbine Control Design to Reduce Capital Costs: 7 January 2009 - 31 August 2009

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

Darrow, P. J.

2010-01-01

This report first discusses and identifies which wind turbine components can benefit from advanced control algorithms and also presents results from a preliminary loads case analysis using a baseline controller. Next, it describes the design, implementation, and simulation-based testing of an advanced controller to reduce loads on those components. The case-by-case loads analysis and advanced controller design will help guide future control research.

10 CFR 63.112 - Requirements for preclosure safety analysis of the geologic repository operations area.

Code of Federal Regulations, 2014 CFR

2014-01-01

... emergency power to instruments, utility service systems, and operating systems important to safety if there... include: (a) A general description of the structures, systems, components, equipment, and process... of the performance of the structures, systems, and components to identify those that are important to...

10 CFR 63.112 - Requirements for preclosure safety analysis of the geologic repository operations area.

Code of Federal Regulations, 2013 CFR

2013-01-01

... emergency power to instruments, utility service systems, and operating systems important to safety if there... include: (a) A general description of the structures, systems, components, equipment, and process... of the performance of the structures, systems, and components to identify those that are important to...

10 CFR 63.112 - Requirements for preclosure safety analysis of the geologic repository operations area.

Code of Federal Regulations, 2012 CFR

2012-01-01

... emergency power to instruments, utility service systems, and operating systems important to safety if there... include: (a) A general description of the structures, systems, components, equipment, and process... of the performance of the structures, systems, and components to identify those that are important to...

10 CFR 63.112 - Requirements for preclosure safety analysis of the geologic repository operations area.

Code of Federal Regulations, 2011 CFR

2011-01-01

... emergency power to instruments, utility service systems, and operating systems important to safety if there... include: (a) A general description of the structures, systems, components, equipment, and process... of the performance of the structures, systems, and components to identify those that are important to...

A method to identify aperiodic disturbances in the ionosphere

NASA Astrophysics Data System (ADS)

Wang, J.-S.; Chen, Z.; Huang, C.-M.

2014-05-01

In this paper, variations in the ionospheric F2 layer's critical frequency are decomposed into their periodic and aperiodic components. The latter include disturbances caused both by geophysical impacts on the ionosphere and random noise. The spectral whitening method (SWM), a signal-processing technique used in statistical estimation and/or detection, was used to identify aperiodic components in the ionosphere. The whitening algorithm adopted herein is used to divide the Fourier transform of the observed data series by a real envelope function. As a result, periodic components are suppressed and aperiodic components emerge as the dominant contributors. Application to a synthetic data set based on significant simulated periodic features of ionospheric observations containing artificial (and, hence, controllable) disturbances was used to validate the SWM for identification of aperiodic components. Although the random noise was somewhat enhanced by post-processing, the artificial disturbances could still be clearly identified. The SWM was then applied to real ionospheric observations. It was found to be more sensitive than the often-used monthly median method to identify geomagnetic effects. In addition, disturbances detected by the SWM were characterized by a Gaussian-type probability density function over all timescales, which further simplifies statistical analysis and suggests that the disturbances thus identified can be compared regardless of timescale.

Operation and maintenance results from ISFOC CPV plants

NASA Astrophysics Data System (ADS)

Gil, Eduardo; Martinez, María; de la Rubia, Oscar

2017-09-01

The analysis of field operation and maintenance data collected during a period of over eight years, from CPV installations consisting of three different CPV technologies (including second generation of one of these technologies), has allowed us to get valuable information about the long-term degradation of the CPV systems. Through the study of the maintenance control ratio previously defined and by applying the root cause analysis methodology, the components responsible for the most unplanned interventions for each technology were identified. Focusing maintenance efforts on these components, a reduction of the unplanned interventions and the total cost of maintenance has been achieved over the years. Therefore, the deployment of an effective maintenance plan, identifying critical components, is essential to minimize the risk for investors and maximize the CPV power plants lifetime and energy output, increasing the availability of CPV installations, boosting market confidence in CPV systems.

Molecular diversity of toxic components from the scorpion Heterometrus petersii venom revealed by proteomic and transcriptome analysis.

PubMed

Ma, Yibao; Zhao, Yong; Zhao, Ruiming; Zhang, Weiping; He, Yawen; Wu, Yingliang; Cao, Zhijian; Guo, Lin; Li, Wenxin

2010-07-01

Scorpion venoms contain a vast untapped reservoir of natural products, which have the potential for medicinal value in drug discovery. In this study, toxin components from the scorpion Heterometrus petersii venom were evaluated by transcriptome and proteome analysis.Ten known families of venom peptides and proteins were identified, which include: two families of potassium channel toxins, four families of antimicrobial and cytolytic peptides,and one family from each of the calcium channel toxins, La1-like peptides, phospholipase A2,and the serine proteases. In addition, we also identified 12 atypical families, which include the acid phosphatases, diuretic peptides, and ten orphan families. From the data presented here, the extreme diversity and convergence of toxic components in scorpion venom was uncovered. Our work demonstrates the power of combining transcriptomic and proteomic approaches in the study of animal venoms.

Principal component analysis and analysis of variance on the effects of Entellan New on the Raman spectra of fibers.

PubMed

Yu, Marcia M L; Sandercock, P Mark L

2012-01-01

During the forensic examination of textile fibers, fibers are usually mounted on glass slides for visual inspection and identification under the microscope. One method that has the capability to accurately identify single textile fibers without subsequent demounting is Raman microspectroscopy. The effect of the mountant Entellan New on the Raman spectra of fibers was investigated to determine if it is suitable for fiber analysis. Raman spectra of synthetic fibers mounted in three different ways were collected and subjected to multivariate analysis. Principal component analysis score plots revealed that while spectra from different fiber classes formed distinct groups, fibers of the same class formed a single group regardless of the mounting method. The spectra of bare fibers and those mounted in Entellan New were found to be statistically indistinguishable by analysis of variance calculations. These results demonstrate that fibers mounted in Entellan New may be identified directly by Raman microspectroscopy without further sample preparation. © 2011 American Academy of Forensic Sciences.

Localization of spontaneous magnetoencephalographic activity of neonates and fetuses using independent component and Hilbert phase analysis.

PubMed

Vairavan, Srinivasan; Eswaran, Hari; Preissl, Hubert; Wilson, James D; Haddad, Naim; Lowery, Curtis L; Govindan, Rathinaswamy B

2010-01-01

The fetal magnetoencephalogram (fMEG) is measured in the presence of large interference from maternal and fetal magnetocardiograms (mMCG and fMCG). These cardiac interferences can be attenuated by orthogonal projection (OP) technique of the corresponding spatial vectors. However, the OP technique redistributes the fMEG signal among the channels and also leaves some cardiac residuals (partially attenuated mMCG and fMCG) due to loss of stationarity in the signal. In this paper, we propose a novel way to extract and localize the neonatal and fetal spontaneous brain activity by using independent component analysis (ICA) technique. In this approach, we perform ICA on a small subset of sensors for 1-min duration. The independent components obtained are further investigated for the presence of discontinuous patterns as identified by the Hilbert phase analysis and are used as decision criteria for localizing the spontaneous brain activity. In order to locate the region of highest spontaneous brain activity content, this analysis is performed on the sensor subsets, which are traversed across the entire sensor space. The region of the spontaneous brain activity as identified by the proposed approach correlated well with the neonatal and fetal head location. In addition, the burst duration and the inter-burst interval computed for the identified discontinuous brain patterns are in agreement with the reported values.

Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein.

PubMed

Steindl, Theodora M; Crump, Carolyn E; Hayden, Frederick G; Langer, Thierry

2005-10-06

The development and application of a sophisticated virtual screening and selection protocol to identify potential, novel inhibitors of the human rhinovirus coat protein employing various computer-assisted strategies are described. A large commercially available database of compounds was screened using a highly selective, structure-based pharmacophore model generated with the program Catalyst. A docking study and a principal component analysis were carried out within the software package Cerius and served to validate and further refine the obtained results. These combined efforts led to the selection of six candidate structures, for which in vitro anti-rhinoviral activity could be shown in a biological assay.

Empirical mode decomposition for analyzing acoustical signals

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2005-01-01

The present invention discloses a computer implemented signal analysis method through the Hilbert-Huang Transformation (HHT) for analyzing acoustical signals, which are assumed to be nonlinear and nonstationary. The Empirical Decomposition Method (EMD) and the Hilbert Spectral Analysis (HSA) are used to obtain the HHT. Essentially, the acoustical signal will be decomposed into the Intrinsic Mode Function Components (IMFs). Once the invention decomposes the acoustic signal into its constituting components, all operations such as analyzing, identifying, and removing unwanted signals can be performed on these components. Upon transforming the IMFs into Hilbert spectrum, the acoustical signal may be compared with other acoustical signals.

An economic analysis methodology for project evaluation and programming.

DOT National Transportation Integrated Search

2013-08-01

Economic analysis is a critical component of a comprehensive project or program evaluation methodology that considers all key : quantitative and qualitative impacts of highway investments. It allows highway agencies to identify, quantify, and value t...

Biochemometrics for Natural Products Research: Comparison of Data Analysis Approaches and Application to Identification of Bioactive Compounds.

PubMed

Kellogg, Joshua J; Todd, Daniel A; Egan, Joseph M; Raja, Huzefa A; Oberlies, Nicholas H; Kvalheim, Olav M; Cech, Nadja B

2016-02-26

A central challenge of natural products research is assigning bioactive compounds from complex mixtures. The gold standard approach to address this challenge, bioassay-guided fractionation, is often biased toward abundant, rather than bioactive, mixture components. This study evaluated the combination of bioassay-guided fractionation with untargeted metabolite profiling to improve active component identification early in the fractionation process. Key to this methodology was statistical modeling of the integrated biological and chemical data sets (biochemometric analysis). Three data analysis approaches for biochemometric analysis were compared, namely, partial least-squares loading vectors, S-plots, and the selectivity ratio. Extracts from the endophytic fungi Alternaria sp. and Pyrenochaeta sp. with antimicrobial activity against Staphylococcus aureus served as test cases. Biochemometric analysis incorporating the selectivity ratio performed best in identifying bioactive ions from these extracts early in the fractionation process, yielding altersetin (3, MIC 0.23 μg/mL) and macrosphelide A (4, MIC 75 μg/mL) as antibacterial constituents from Alternaria sp. and Pyrenochaeta sp., respectively. This study demonstrates the potential of biochemometrics coupled with bioassay-guided fractionation to identify bioactive mixture components. A benefit of this approach is the ability to integrate multiple stages of fractionation and bioassay data into a single analysis.

Serum Biochemical Phenotypes in the Domestic Dog

PubMed Central

Chang, Yu-Mei; Hadox, Erin; Szladovits, Balazs; Garden, Oliver A.

2016-01-01

The serum or plasma biochemical profile is essential in the diagnosis and monitoring of systemic disease in veterinary medicine, but current reference intervals typically take no account of breed-specific differences. Breed-specific hematological phenotypes have been documented in the domestic dog, but little has been published on serum biochemical phenotypes in this species. Serum biochemical profiles of dogs in which all measurements fell within the existing reference intervals were retrieved from a large veterinary database. Serum biochemical profiles from 3045 dogs were retrieved, of which 1495 had an accompanying normal glucose concentration. Sixty pure breeds plus a mixed breed control group were represented by at least 10 individuals. All analytes, except for sodium, chloride and glucose, showed variation with age. Total protein, globulin, potassium, chloride, creatinine, cholesterol, total bilirubin, ALT, CK, amylase, and lipase varied between sexes. Neutering status significantly impacted all analytes except albumin, sodium, calcium, urea, and glucose. Principal component analysis of serum biochemical data revealed 36 pure breeds with distinctive phenotypes. Furthermore, comparative analysis identified 23 breeds with significant differences from the mixed breed group in all biochemical analytes except urea and glucose. Eighteen breeds were identified by both principal component and comparative analysis. Tentative reference intervals were generated for breeds with a distinctive phenotype identified by comparative analysis and represented by at least 120 individuals. This is the first large-scale analysis of breed-specific serum biochemical phenotypes in the domestic dog and highlights potential genetic components of biochemical traits in this species. PMID:26919479

Analysis of model Titan atmospheric components using ion mobility spectrometry

NASA Technical Reports Server (NTRS)

Kojiro, D. R.; Cohen, M. J.; Wernlund, R. F.; Stimac, R. M.; Humphry, D. E.; Takeuchi, N.

1991-01-01

The Gas Chromatograph-Ion Mobility Spectrometer (GC-IMS) was proposed as an analytical technique for the analysis of Titan's atmosphere during the Cassini Mission. The IMS is an atmospheric pressure, chemical detector that produces an identifying spectrum of each chemical species measured. When the IMS is combined with a GC as a GC-IMS, the GC is used to separate the sample into its individual components, or perhaps small groups of components. The IMS is then used to detect, quantify, and identify each sample component. Conventional IMS detection and identification of sample components depends upon a source of energetic radiation, such as beta radiation, which ionizes the atmospheric pressure host gas. This primary ionization initiates a sequence of ion-molecule reactions leading to the formation of sufficiently energetic positive or negative ions, which in turn ionize most constituents in the sample. In conventional IMS, this reaction sequence is dominated by the water cluster ion. However, many of the light hydrocarbons expected in Titan's atmosphere cannot be analyzed by IMS using this mechanism at the concentrations expected. Research at NASA Ames and PCP Inc., has demonstrated IMS analysis of expected Titan atmospheric components, including saturated aliphatic hydrocarbons, using two alternate sample ionizations mechanisms. The sensitivity of the IMS to hydrocarbons such as propane and butane was increased by several orders of magnitude. Both ultra dry (waterless) IMS sample ionization and metastable ionization were successfully used to analyze a model Titan atmospheric gas mixture.

Developing a framework for transferring knowledge into action: a thematic analysis of the literature

PubMed Central

Ward, Vicky; House, Allan; Hamer, Susan

2010-01-01

Objectives Although there is widespread agreement about the importance of transferring knowledge into action, we still lack high quality information about what works, in which settings and with whom. Whilst there are a large number of models and theories for knowledge transfer interventions, they are untested meaning that their applicability and relevance is largely unknown. This paper describes the development of a conceptual framework of translating knowledge into action and discusses how it can be used for developing a useful model of the knowledge transfer process. Methods A narrative review of the knowledge transfer literature identified 28 different models which explained all or part of the knowledge transfer process. The models were subjected to a thematic analysis to identify individual components and the types of processes used when transferring knowledge into action. The results were used to build a conceptual framework of the process. Results Five common components of the knowledge transfer process were identified: problem identification and communication; knowledge/research development and selection; analysis of context; knowledge transfer activities or interventions; and knowledge/research utilization. We also identified three types of knowledge transfer processes: a linear process; a cyclical process; and a dynamic multidirectional process. From these results a conceptual framework of knowledge transfer was developed. The framework illustrates the five common components of the knowledge transfer process and shows that they are connected via a complex, multidirectional set of interactions. As such the framework allows for the individual components to occur simultaneously or in any given order and to occur more than once during the knowledge transfer process. Conclusion Our framework provides a foundation for gathering evidence from case studies of knowledge transfer interventions. We propose that future empirical work is designed to test and refine the relevant importance and applicability of each of the components in order to build more useful models of knowledge transfer which can serve as a practical checklist for planning or evaluating knowledge transfer activities. PMID:19541874

The structure of binding curves and practical identifiability of equilibrium ligand-binding parameters

PubMed Central

Middendorf, Thomas R.

2017-01-01

A critical but often overlooked question in the study of ligands binding to proteins is whether the parameters obtained from analyzing binding data are practically identifiable (PI), i.e., whether the estimates obtained from fitting models to noisy data are accurate and unique. Here we report a general approach to assess and understand binding parameter identifiability, which provides a toolkit to assist experimentalists in the design of binding studies and in the analysis of binding data. The partial fraction (PF) expansion technique is used to decompose binding curves for proteins with n ligand-binding sites exactly and uniquely into n components, each of which has the form of a one-site binding curve. The association constants of the PF component curves, being the roots of an n-th order polynomial, may be real or complex. We demonstrate a fundamental connection between binding parameter identifiability and the nature of these one-site association constants: all binding parameters are identifiable if the constants are all real and distinct; otherwise, at least some of the parameters are not identifiable. The theory is used to construct identifiability maps from which the practical identifiability of binding parameters for any two-, three-, or four-site binding curve can be assessed. Instructions for extending the method to generate identifiability maps for proteins with more than four binding sites are also given. Further analysis of the identifiability maps leads to the simple rule that the maximum number of structurally identifiable binding parameters (shown in the previous paper to be equal to n) will also be PI only if the binding curve line shape contains n resolved components. PMID:27993951

Orbital transfer rocket engine technology 7.5K-LB thrust rocket engine preliminary design

NASA Technical Reports Server (NTRS)

Harmon, T. J.; Roschak, E.

1993-01-01

A preliminary design of an advanced LOX/LH2 expander cycle rocket engine producing 7,500 lbf thrust for Orbital Transfer vehicle missions was completed. Engine system, component and turbomachinery analysis at both on design and off design conditions were completed. The preliminary design analysis results showed engine requirements and performance goals were met. Computer models are described and model outputs are presented. Engine system assembly layouts, component layouts and valve and control system analysis are presented. Major design technologies were identified and remaining issues and concerns were listed.

Structural dynamic analysis of the Space Shuttle Main Engine

NASA Technical Reports Server (NTRS)

Scott, L. P.; Jamison, G. T.; Mccutcheon, W. A.; Price, J. M.

1981-01-01

This structural dynamic analysis supports development of the SSME by evaluating components subjected to critical dynamic loads, identifying significant parameters, and evaluating solution methods. Engine operating parameters at both rated and full power levels are considered. Detailed structural dynamic analyses of operationally critical and life limited components support the assessment of engine design modifications and environmental changes. Engine system test results are utilized to verify analytic model simulations. The SSME main chamber injector assembly is an assembly of 600 injector elements which are called LOX posts. The overall LOX post analysis procedure is shown.

Proteomic analysis of the venom from the fish eating coral snake Micrurus surinamensis: novel toxins, their function and phylogeny.

PubMed

Olamendi-Portugal, Timoteo; Batista, Cesar V F; Restano-Cassulini, Rita; Pando, Victoria; Villa-Hernandez, Oscar; Zavaleta-Martínez-Vargas, Alfonso; Salas-Arruz, Maria C; Rodríguez de la Vega, Ricardo C; Becerril, Baltazar; Possani, Lourival D

2008-05-01

The protein composition of the soluble venom from the South American fish-eating coral snake Micrurus surinamensis surinamensis, here abbreviated M. surinamensis, was separated by RP-HPLC and 2-DE, and their components were analyzed by automatic Edman degradation, MALDI-TOF and ESI-MS/MS. Approximately 100 different molecules were identified. Sixty-two components possess molecular masses between 6 and 8 kDa, are basically charged molecules, among which are cytotoxins and neurotoxins lethal to fish (Brachidanios rerio). Six new toxins (abbreviated Ms1-Ms5 and Ms11) were fully sequenced. Amino acid sequences similar to the enzymes phospholipase A2 and amino acid oxidase were identified. Over 20 additional peptides were identified by sequencing minor components of the HPLC separation and from 2-DE gels. A functional assessment of the physiological activity of the six toxins was also performed by patch clamp using muscular nicotinic acetylcholine receptor assays. Variable degrees of blockade were observed, most of them reversible. The structural and functional data obtained were used for phylogenetic analysis, providing information on some evolutionary aspects of the venom components of this snake. This contribution increases by a factor of two the total number of alpha-neurotoxins sequenced from the Micrurus genus in currently available literature.

Principal component analysis of TOF-SIMS spectra, images and depth profiles: an industrial perspective

NASA Astrophysics Data System (ADS)

Pacholski, Michaeleen L.

2004-06-01

Principal component analysis (PCA) has been successfully applied to time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra, images and depth profiles. Although SIMS spectral data sets can be small (in comparison to datasets typically discussed in literature from other analytical techniques such as gas or liquid chromatography), each spectrum has thousands of ions resulting in what can be a difficult comparison of samples. Analysis of industrially-derived samples means the identity of most surface species are unknown a priori and samples must be analyzed rapidly to satisfy customer demands. PCA enables rapid assessment of spectral differences (or lack there of) between samples and identification of chemically different areas on sample surfaces for images. Depth profile analysis helps define interfaces and identify low-level components in the system.

Robust Principal Component Analysis Regularized by Truncated Nuclear Norm for Identifying Differentially Expressed Genes.

PubMed

Wang, Ya-Xuan; Gao, Ying-Lian; Liu, Jin-Xing; Kong, Xiang-Zhen; Li, Hai-Jun

2017-09-01

Identifying differentially expressed genes from the thousands of genes is a challenging task. Robust principal component analysis (RPCA) is an efficient method in the identification of differentially expressed genes. RPCA method uses nuclear norm to approximate the rank function. However, theoretical studies showed that the nuclear norm minimizes all singular values, so it may not be the best solution to approximate the rank function. The truncated nuclear norm is defined as the sum of some smaller singular values, which may achieve a better approximation of the rank function than nuclear norm. In this paper, a novel method is proposed by replacing nuclear norm of RPCA with the truncated nuclear norm, which is named robust principal component analysis regularized by truncated nuclear norm (TRPCA). The method decomposes the observation matrix of genomic data into a low-rank matrix and a sparse matrix. Because the significant genes can be considered as sparse signals, the differentially expressed genes are viewed as the sparse perturbation signals. Thus, the differentially expressed genes can be identified according to the sparse matrix. The experimental results on The Cancer Genome Atlas data illustrate that the TRPCA method outperforms other state-of-the-art methods in the identification of differentially expressed genes.

The Use of Propensity Scores in Mediation Analysis

ERIC Educational Resources Information Center

Jo, Booil; Stuart, Elizabeth A.; MacKinnon, David P.; Vinokur, Amiram D.

2011-01-01

Mediation analysis uses measures of hypothesized mediating variables to test theory for how a treatment achieves effects on outcomes and to improve subsequent treatments by identifying the most efficient treatment components. Most current mediation analysis methods rely on untested distributional and functional form assumptions for valid…

Multivariate classification of small order watersheds in the Quabbin Reservoir Basin, Massachusetts

USGS Publications Warehouse

Lent, R.M.; Waldron, M.C.; Rader, J.C.

1998-01-01

A multivariate approach was used to analyze hydrologic, geologic, geographic, and water-chemistry data from small order watersheds in the Quabbin Reservoir Basin in central Massachusetts. Eighty three small order watersheds were delineated and landscape attributes defining hydrologic, geologic, and geographic features of the watersheds were compiled from geographic information system data layers. Principal components analysis was used to evaluate 11 chemical constituents collected bi-weekly for 1 year at 15 surface-water stations in order to subdivide the basin into subbasins comprised of watersheds with similar water quality characteristics. Three principal components accounted for about 90 percent of the variance in water chemistry data. The principal components were defined as a biogeochemical variable related to wetland density, an acid-neutralization variable, and a road-salt variable related to density of primary roads. Three subbasins were identified. Analysis of variance and multiple comparisons of means were used to identify significant differences in stream water chemistry and landscape attributes among subbasins. All stream water constituents were significantly different among subbasins. Multiple regression techniques were used to relate stream water chemistry to landscape attributes. Important differences in landscape attributes were related to wetlands, slope, and soil type.A multivariate approach was used to analyze hydrologic, geologic, geographic, and water-chemistry data from small order watersheds in the Quabbin Reservoir Basin in central Massachusetts. Eighty three small order watersheds were delineated and landscape attributes defining hydrologic, geologic, and geographic features of the watersheds were compiled from geographic information system data layers. Principal components analysis was used to evaluate 11 chemical constituents collected bi-weekly for 1 year at 15 surface-water stations in order to subdivide the basin into subbasins comprised of watersheds with similar water quality characteristics. Three principal components accounted for about 90 percent of the variance in water chemistry data. The principal components were defined as a biogeochemical variable related to wetland density, an acid-neutralization variable, and a road-salt variable related to density of primary roads. Three subbasins were identified. Analysis of variance and multiple comparisons of means were used to identify significant differences in stream water chemistry and landscape attributes among subbasins. All stream water constituents were significantly different among subbasins. Multiple regression techniques were used to relate stream water chemistry to landscape attributes. Important differences in landscape attributes were related to wetlands, slope, and soil type.

Macrophage biospecific extraction and HPLC-ESI-MSn analysis for screening immunological active components in Smilacis Glabrae Rhizoma.

PubMed

Zheng, Zhao-Guang; Duan, Ting-Ting; He, Bao; Tang, Dan; Jia, Xiao-Bin; Wang, Ru-Shang; Zhu, Jia-Xiao; Xu, You-Hua; Zhu, Quan; Feng, Liang

2013-04-15

A cell-permeable membrane, as typified by Transwell insert Permeable Supports, permit accurate repeatable invasion assays, has been developed as a tool for screening immunological active components in Smilacis Glabrae Rhizoma (SGR). In this research, components in the water extract of SGR (ESGR) might conjugate with the receptors or other targets on macrophages which invaded Transwell inserts, and then the eluate which contained components biospecific binding to macrophages was identified by HPLC-ESI-MS(n) analysis. Six compounds, which could interact with macrophages, were detected and identified. Among these compounds, taxifolin (2) and astilbin (4) were identified by comparing with the chromatography of standards, while the four others including 5-O-caffeoylshikimic acid (1), neoastilbin (3), neoisoastilbin (5) and isoastilbin (6), were elucidated by their structure clearage characterizations of tandem mass spectrometry. Then compound 1 was isolated and purified from SGR, along with 2 and 4, was applied to the macrophage migration and adhesion assay in HUVEC (Human Umbilical Vein Endothelial Cells) -macrophages co-incultured Transwell system for immunological activity assessment. The results showed that compounds 1, 2 and 4 with concentration of 5μM (H), 500nM (M) and 50nM (L) could remarkably inhibit the macrophage migration and adhesion (Vs AGEs (Advanced Glycation End Produces) group, 1-L, 2-H and 4-L groups: p<0.05; other groups: p<0.01). Moreover, 1 and 4 showed satisfactory dose-effect relationship. In conclusion, the application of macrophage biospecific extraction coupled with HPLC-ESI-MS(n) analysis is a rapid, simple and reliable method for screening immunological active components from Traditional Chinese Medicine. Copyright © 2013 Elsevier B.V. All rights reserved.

Early warning reporting categories analysis of recall and complaints data.

DOT National Transportation Integrated Search

2001-12-31

This analysis was performed to assist the National Highway Traffic Safety Administration (NHTSA) in identifying components and systems to be included in early warning reporting (EWR) categories that would be based upon historical safety-related recal...

Evaluation of Parallel Analysis Methods for Determining the Number of Factors

ERIC Educational Resources Information Center

Crawford, Aaron V.; Green, Samuel B.; Levy, Roy; Lo, Wen-Juo; Scott, Lietta; Svetina, Dubravka; Thompson, Marilyn S.

2010-01-01

Population and sample simulation approaches were used to compare the performance of parallel analysis using principal component analysis (PA-PCA) and parallel analysis using principal axis factoring (PA-PAF) to identify the number of underlying factors. Additionally, the accuracies of the mean eigenvalue and the 95th percentile eigenvalue criteria…

Identification of the chemical components of Saussurea involucrata by high-resolution mass spectrometry and the mass spectral trees similarity filter technique.

PubMed

Jia, Zhixin; Wu, Caisheng; Jin, Hongtao; Zhang, Jinlan

2014-11-15

Saussurea involucrata is a rare traditional Chinese medicine (TCM) that displays anti-fatigue, anti-inflammatory and anti-tumor effects. In this paper, the different chemical components of Saussurea involucrata were characterized and identified over a wide dynamic range by high-performance liquid chromatography coupled with high-resolution hybrid mass spectrometry (HPLC/HRMS/MS(n)) and the mass spectral trees similarity filter (MTSF) technique. The aerial parts of Saussurea involucrata were extracted with 75% ethanol. The partial extract was separated on a chromatography column to concentrate the low-concentration compounds. Mass data were acquired using full-scan mass analysis (resolving power 50,000) with data-dependent incorporation of dynamic exclusion analysis. The identified compounds were used as templates to construct a database of mass spectral trees. Data for the unknown compounds were matched with those templates and matching candidate structures were obtained. The detected compounds were characterized based on matching to candidate structures by the MTSF technique and were further identified by their accurate mass weight, multiple-stage analysis and fragmentation patterns and through comparison with literature data. A total of 38 compounds were identified including 19 flavones, 11 phenylpropanoids and 8 sphingolipids. Among them, 7 flavonoids, 8 phenylpropanoids and 8 sphingolipids were identified for the first time in Saussurea involucrata. HPLC/HRMS/MS(n) combined with MTSF was successfully used to discover and identify the chemical compounds in Saussurea involucrata. The results indicated that this combined technique was extremely useful for the rapid detection and identification of the chemical components in TCMs. Copyright © 2014 John Wiley & Sons, Ltd.

Reliability and Productivity Modeling for the Optimization of Separated Spacecraft Interferometers

NASA Technical Reports Server (NTRS)

Kenny, Sean (Technical Monitor); Wertz, Julie

2002-01-01

As technological systems grow in capability, they also grow in complexity. Due to this complexity, it is no longer possible for a designer to use engineering judgement to identify the components that have the largest impact on system life cycle metrics, such as reliability, productivity, cost, and cost effectiveness. One way of identifying these key components is to build quantitative models and analysis tools that can be used to aid the designer in making high level architecture decisions. Once these key components have been identified, two main approaches to improving a system using these components exist: add redundancy or improve the reliability of the component. In reality, the most effective approach to almost any system will be some combination of these two approaches, in varying orders of magnitude for each component. Therefore, this research tries to answer the question of how to divide funds, between adding redundancy and improving the reliability of components, to most cost effectively improve the life cycle metrics of a system. While this question is relevant to any complex system, this research focuses on one type of system in particular: Separate Spacecraft Interferometers (SSI). Quantitative models are developed to analyze the key life cycle metrics of different SSI system architectures. Next, tools are developed to compare a given set of architectures in terms of total performance, by coupling different life cycle metrics together into one performance metric. Optimization tools, such as simulated annealing and genetic algorithms, are then used to search the entire design space to find the "optimal" architecture design. Sensitivity analysis tools have been developed to determine how sensitive the results of these analyses are to uncertain user defined parameters. Finally, several possibilities for the future work that could be done in this area of research are presented.

Research on Air Quality Evaluation based on Principal Component Analysis

NASA Astrophysics Data System (ADS)

Wang, Xing; Wang, Zilin; Guo, Min; Chen, Wei; Zhang, Huan

2018-01-01

Economic growth has led to environmental capacity decline and the deterioration of air quality. Air quality evaluation as a fundamental of environmental monitoring and air pollution control has become increasingly important. Based on the principal component analysis (PCA), this paper evaluates the air quality of a large city in Beijing-Tianjin-Hebei Area in recent 10 years and identifies influencing factors, in order to provide reference to air quality management and air pollution control.

Analysing Normal and Partial Glossectomee Tongues Using Ultrasound

ERIC Educational Resources Information Center

Bressmann, Tim; Uy, Catherine; Irish, Jonathan C.

2005-01-01

The present study aimed at identifying underlying parameters that govern the shape of the tongue. A functional topography of the tongue surface was developed based on three-dimensional ultrasound scans of sustained speech sounds in ten normal subjects. A principal component analysis extracted three components that explained 89.2% of the variance…

Response of the small hive beetle (Aethina tumida) to a blend of chemicals identified from honeybee (Apis mellifera) volatiles

USDA-ARS?s Scientific Manuscript database

Coupled gas chromatographic-electroantennographic detection (GC-EAD) analyses of Super Q collected worker honey bee volatiles revealed several components that elicited antennal responses by the small hive beetle Aethina tumida. However, GC-MS analysis showed that eight of these EAD-active components...

Teachers Learning to Prepare Future Engineers: A Systemic Analysis Through Five Components of Development and Transfer

ERIC Educational Resources Information Center

Hardré, Patricia L.; Ling, Chen; Shehab, Randa L.; Nanny, Mark A.; Refai, Hazem; Nollert, Matthias U.; Ramseyer, Christopher; Wollega, Ebisa D.; Huang, Su-Min; Herron, Jason

2018-01-01

This study used a systemic perspective to examine a five-component experiential process of perceptual and developmental growth, and transfer-to-teaching. Nineteen secondary math and science teachers participated in a year-long, engineering immersion and support experience, with university faculty mentors. Teachers identified critical shifts in…

Callings in Career: A Typological Approach to Essential and Optional Components

ERIC Educational Resources Information Center

Hirschi, Andreas

2011-01-01

A sense of calling in career is supposed to have positive implications for individuals and organizations but current theoretical development is plagued with incongruent conceptualizations of what does or does not constitute a calling. The present study used cluster analysis to identify essential and optional components of a presence of calling…

Mental health stigmatisation in deployed UK Armed Forces: a principal components analysis.

PubMed

Fertout, Mohammed; Jones, N; Keeling, M; Greenberg, N

2015-12-01

UK military research suggests that there is a significant link between current psychological symptoms, mental health stigmatisation and perceived barriers to care (stigma/BTC). Few studies have explored the construct of stigma/BTC in depth amongst deployed UK military personnel. Three survey datasets containing a stigma/BTC scale obtained during UK deployments to Iraq and Afghanistan were combined (n=3405 personnel). Principal component analysis was used to identify the key components of stigma/BTC. The relationship between psychological symptoms, the stigma/BTC components and help seeking were examined. Two components were identified: 'potential loss of personal military credibility and trust' (stigma Component 1, five items, 49.4% total model variance) and 'negative perceptions of mental health services and barriers to help seeking' (Component 2, six items, 11.2% total model variance). Component 1 was endorsed by 37.8% and Component 2 by 9.4% of personnel. Component 1 was associated with both assessed and subjective mental health, medical appointments and admission to hospital. Stigma Component 2 was associated with subjective and assessed mental health but not with medical appointments. Neither component was associated with help-seeking for subjective psycho-social problems. Potential loss of credibility and trust appeared to be associated with help-seeking for medical reasons but not for help-seeking for subjective psychosocial problems. Those experiencing psychological symptoms appeared to minimise the effects of stigma by seeking out a socially acceptable route into care, such as the medical consultation, whereas those who experienced a subjective mental health problem appeared willing to seek help from any source. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Tipping point analysis of ocean acoustic noise

NASA Astrophysics Data System (ADS)

Livina, Valerie N.; Brouwer, Albert; Harris, Peter; Wang, Lian; Sotirakopoulos, Kostas; Robinson, Stephen

2018-02-01

We apply tipping point analysis to a large record of ocean acoustic data to identify the main components of the acoustic dynamical system and study possible bifurcations and transitions of the system. The analysis is based on a statistical physics framework with stochastic modelling, where we represent the observed data as a composition of deterministic and stochastic components estimated from the data using time-series techniques. We analyse long-term and seasonal trends, system states and acoustic fluctuations to reconstruct a one-dimensional stochastic equation to approximate the acoustic dynamical system. We apply potential analysis to acoustic fluctuations and detect several changes in the system states in the past 14 years. These are most likely caused by climatic phenomena. We analyse trends in sound pressure level within different frequency bands and hypothesize a possible anthropogenic impact on the acoustic environment. The tipping point analysis framework provides insight into the structure of the acoustic data and helps identify its dynamic phenomena, correctly reproducing the probability distribution and scaling properties (power-law correlations) of the time series.

Ultra-high-performance liquid chromatography/tandem high-resolution mass spectrometry analysis of sixteen red beverages containing carminic acid: identification of degradation products by using principal component analysis/discriminant analysis.

PubMed

Gosetti, Fabio; Chiuminatto, Ugo; Mazzucco, Eleonora; Mastroianni, Rita; Marengo, Emilio

2015-01-15

The study investigates the sunlight photodegradation process of carminic acid, a natural red colourant used in beverages. For this purpose, both carminic acid aqueous standard solutions and sixteen different commercial beverages, ten containing carminic acid and six containing E120 dye, were subjected to photoirradiation. The results show different patterns of degradation, not only between the standard solutions and the beverages, but also from beverage to beverage. Due to the different beverage recipes, unpredictable reactions take place between the dye and the other ingredients. To identify the dye degradation products in a very complex scenario, a methodology was used, based on the combined use of principal component analysis with discriminant analysis and ultra-high-performance liquid chromatography coupled with tandem high resolution mass spectrometry. The methodology is unaffected by beverage composition and allows the degradation products of carminic acid dye to be identified for each beverage. Copyright © 2014 Elsevier Ltd. All rights reserved.

Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Gulgundi, Mohammad Shahid; Shetty, Amba

2018-03-01

Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

Personality in domestic cats.

PubMed

Lee, Christina M; Ryan, Joseph J; Kreiner, David S

2007-02-01

Personality ratings of 196 cats were made by their owners using a 5-point Likert scale anchored by 1: not at all and 5: a great deal with 12 items: timid, friendly, curious, sociable, obedient, clever, protective, active, independent, aggressive, bad-tempered, and emotional. A principal components analysis with varimax rotation identified three intepretable components. Component I had high loadings by active, clever, curious, and sociable. Component II had high loadings by emotional, friendly, and protective, Component III by aggressive and bad-tempered, and Component IV by timid. Sex was not associated with any component, but age showed a weak negative correlation with Component I. Older animals were rated less social and curious than younger animals.

6-C polarization analysis using point measurements of translational and rotational ground-motion: theory and applications

NASA Astrophysics Data System (ADS)

Sollberger, David; Greenhalgh, Stewart A.; Schmelzbach, Cedric; Van Renterghem, Cédéric; Robertsson, Johan O. A.

2018-04-01

We provide a six-component (6-C) polarization model for P-, SV-, SH-, Rayleigh-, and Love-waves both inside an elastic medium as well as at the free surface. It is shown that single-station 6-C data comprised of three components of rotational motion and three components of translational motion provide the opportunity to unambiguously identify the wave type, propagation direction, and local P- and S-wave velocities at the receiver location by use of polarization analysis. To extract such information by conventional processing of three-component (3-C) translational data would require large and dense receiver arrays. The additional rotational components allow the extension of the rank of the coherency matrix used for polarization analysis. This enables us to accurately determine the wave type and wave parameters (propagation direction and velocity) of seismic phases, even if more than one wave is present in the analysis time window. This is not possible with standard, pure-translational 3-C recordings. In order to identify modes of vibration and to extract the accompanying wave parameters, we adapt the multiple signal classification algorithm (MUSIC). Due to the strong nonlinearity of the MUSIC estimator function, it can be used to detect the presence of specific wave types within the analysis time window at very high resolution. We show how the extracted wavefield properties can be used, in a fully automated way, to separate the wavefield into its different wave modes using only a single 6-C recording station. As an example, we apply the method to remove surface wave energy while preserving the underlying reflection signal and to suppress energy originating from undesired directions, such as side-scattered waves.

Minimum number of measurements for evaluating soursop (Annona muricata L.) yield.

PubMed

Sánchez, C F B; Teodoro, P E; Londoño, S; Silva, L A; Peixoto, L A; Bhering, L L

2017-05-31

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of soursop (Annona muricata L.) genotypes based on fruit yield. Sixteen measurements of fruit yield from 71 soursop genotypes were carried out between 2000 and 2016. In order to estimate r with the best accuracy, four procedures were used: analysis of variance, principal component analysis based on the correlation matrix, principal component analysis based on the phenotypic variance and covariance matrix, and structural analysis based on the correlation matrix. The minimum number of measurements needed to predict the actual value of individuals was estimated. Principal component analysis using the phenotypic variance and covariance matrix provided the most accurate estimates of both r and the number of measurements required for accurate evaluation of fruit yield in soursop. Our results indicate that selection of soursop genotypes with high fruit yield can be performed based on the third and fourth measurements in the early years and/or based on the eighth and ninth measurements at more advanced stages.

Comparative proteomic analysis of male and female venoms from the Cuban scorpion Rhopalurus junceus.

PubMed

Rodríguez-Ravelo, Rodolfo; Batista, Cesar V F; Coronas, Fredy I V; Zamudio, Fernando Z; Hernández-Orihuela, Lorena; Espinosa-López, Georgina; Ruiz-Urquiola, Ariel; Possani, Lourival D

2015-12-01

A complete mass spectrometry analysis of venom components from male and female scorpions of the species Rhophalurus junceus of Cuba is reported. In the order of 200 individual molecular masses were identified in both venoms, from which 63 are identical in male and females genders. It means that a significant difference of venom components exists between individuals of different sexes, but the most abundant components are present in both sexes. The relative abundance of identical components is different among the genders. Three well defined groups of different peptides were separated and identified. The first group corresponds to peptides with molecular masses of 1000-2000 Da; the second to peptides with 3500-4500 Da molecular weight, and the third with 6500-8000 Da molecular weights. A total of 86 peptides rich in disulfide bridges were found in the venoms, 27 with three disulfide bridges and 59 with four disulfide bridges. LC-MS/MS analysis allowed the identification and amino acid sequence determination of 31 novel peptides in male venom. Two new putative K(+)-channel peptides were sequences by Edman degradation. They contain 37 amino acid residues, packed by three disulfide bridges and were assigned the systematic numbers: α-KTx 1.18 and α-KTx 2.15. Copyright © 2015 Elsevier Ltd. All rights reserved.

Novel entries in a fungal biofilm matrix encyclopedia.

PubMed

Zarnowski, Robert; Westler, William M; Lacmbouh, Ghislain Ade; Marita, Jane M; Bothe, Jameson R; Bernhardt, Jörg; Lounes-Hadj Sahraoui, Anissa; Fontaine, Joël; Sanchez, Hiram; Hatfield, Ronald D; Ntambi, James M; Nett, Jeniel E; Mitchell, Aaron P; Andes, David R

2014-08-05

Virulence of Candida is linked with its ability to form biofilms. Once established, biofilm infections are nearly impossible to eradicate. Biofilm cells live immersed in a self-produced matrix, a blend of extracellular biopolymers, many of which are uncharacterized. In this study, we provide a comprehensive analysis of the matrix manufactured by Candida albicans both in vitro and in a clinical niche animal model. We further explore the function of matrix components, including the impact on drug resistance. We uncovered components from each of the macromolecular classes (55% protein, 25% carbohydrate, 15% lipid, and 5% nucleic acid) in the C. albicans biofilm matrix. Three individual polysaccharides were identified and were suggested to interact physically. Surprisingly, a previously identified polysaccharide of functional importance, β-1,3-glucan, comprised only a small portion of the total matrix carbohydrate. Newly described, more abundant polysaccharides included α-1,2 branched α-1,6-mannans (87%) associated with unbranched β-1,6-glucans (13%) in an apparent mannan-glucan complex (MGCx). Functional matrix proteomic analysis revealed 458 distinct activities. The matrix lipids consisted of neutral glycerolipids (89.1%), polar glycerolipids (10.4%), and sphingolipids (0.5%). Examination of matrix nucleic acid identified DNA, primarily noncoding sequences. Several of the in vitro matrix components, including proteins and each of the polysaccharides, were also present in the matrix of a clinically relevant in vivo biofilm. Nuclear magnetic resonance (NMR) analysis demonstrated interaction of aggregate matrix with the antifungal fluconazole, consistent with a role in drug impedance and contribution of multiple matrix components. Importance: This report is the first to decipher the complex and unique macromolecular composition of the Candida biofilm matrix, demonstrate the clinical relevance of matrix components, and show that multiple matrix components are needed for protection from antifungal drugs. The availability of these biochemical analyses provides a unique resource for further functional investigation of the biofilm matrix, a defining trait of this lifestyle. Copyright © 2014 Zarnowski et al.

A cognitive task analysis of information management strategies in a computerized provider order entry environment.

PubMed

Weir, Charlene R; Nebeker, Jonathan J R; Hicken, Bret L; Campo, Rebecca; Drews, Frank; Lebar, Beth

2007-01-01

Computerized Provider Order Entry (CPOE) with electronic documentation, and computerized decision support dramatically changes the information environment of the practicing clinician. Prior work patterns based on paper, verbal exchange, and manual methods are replaced with automated, computerized, and potentially less flexible systems. The objective of this study is to explore the information management strategies that clinicians use in the process of adapting to a CPOE system using cognitive task analysis techniques. Observation and semi-structured interviews were conducted with 88 primary-care clinicians at 10 Veterans Administration Medical Centers. Interviews were taped, transcribed, and extensively analyzed to identify key information management goals, strategies, and tasks. Tasks were aggregated into groups, common components across tasks were clarified, and underlying goals and strategies identified. Nearly half of the identified tasks were not fully supported by the available technology. Six core components of tasks were identified. Four meta-cognitive information management goals emerged: 1) Relevance Screening; 2) Ensuring Accuracy; 3) Minimizing memory load; and 4) Negotiating Responsibility. Strategies used to support these goals are presented. Users develop a wide array of information management strategies that allow them to successfully adapt to new technology. Supporting the ability of users to develop adaptive strategies to support meta-cognitive goals is a key component of a successful system.

Characterization of the chemical composition of white chrysanthemum flowers of Hangzhou by using high-performance ion trap mass spectrometry.

PubMed

Zhou, Xiahui; Chen, Xiaocheng; Wu, Xin; Cao, Gang; Zhang, Junjie

2016-04-01

In this study, high-performance liquid chromatography coupled with amaZon SL high-performance ion trap mass spectrometry was used to analyze the target components in white chrysanthemum flowers of Hangzhou. Twenty-one components were detected and identified in both white chrysanthemum flowers of Hangzhou samples by using target compound analysis. Furthermore, seven new compounds in white chrysanthemum flowers of Hangzhou were found and identified by analyzing the fragment ion behavior in the mass spectra. The established method can be expedient for the global quality investigation of complex components in herbal medicines and food. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A novel principal component analysis for spatially misaligned multivariate air pollution data.

PubMed

Jandarov, Roman A; Sheppard, Lianne A; Sampson, Paul D; Szpiro, Adam A

2017-01-01

We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

Lay Worker Health Literacy: A Concept Analysis and Operational Definition.

PubMed

Cadman, Kathleen Paco

2017-10-01

The concept of lay worker health literacy is created by concurrently analyzing and synthesizing two intersecting concepts, lay workers and health literacy. Articulation of this unique intersection is the result of implementing a simplified Wilson's Concept Analysis Procedure. This process incorporates the following components: a) selecting a concept, b) determining the aims/purposes of analysis, c) identifying all uses of the concept, d) determining defining attributes, e) identifying a model case, f) identifying borderline, related, contrary, and illegitimate cases, g) identifying antecedents and consequences, and h) defining empirical referents. Furthermore, as current literature provides no operational definition for lay worker health literacy, one is created to contribute cohesion to the concept. © 2017 Wiley Periodicals, Inc.

Geographic distribution of suicide and railway suicide in Belgium, 2008-2013: a principal component analysis.

PubMed

Strale, Mathieu; Krysinska, Karolina; Overmeiren, Gaëtan Van; Andriessen, Karl

2017-06-01

This study investigated the geographic distribution of suicide and railway suicide in Belgium over 2008--2013 on local (i.e., district or arrondissement) level. There were differences in the regional distribution of suicide and railway suicides in Belgium over the study period. Principal component analysis identified three groups of correlations among population variables and socio-economic indicators, such as population density, unemployment, and age group distribution, on two components that helped explaining the variance of railway suicide at a local (arrondissement) level. This information is of particular importance to prevent suicides in high-risk areas on the Belgian railway network.

Space Shuttle critical function audit

NASA Technical Reports Server (NTRS)

Sacks, Ivan J.; Dipol, John; Su, Paul

1990-01-01

A large fault-tolerance model of the main propulsion system of the US space shuttle has been developed. This model is being used to identify single components and pairs of components that will cause loss of shuttle critical functions. In addition, this model is the basis for risk quantification of the shuttle. The process used to develop and analyze the model is digraph matrix analysis (DMA). The DMA modeling and analysis process is accessed via a graphics-based computer user interface. This interface provides coupled display of the integrated system schematics, the digraph models, the component database, and the results of the fault tolerance and risk analyses.

Parallel line analysis: multifunctional software for the biomedical sciences

NASA Technical Reports Server (NTRS)

Swank, P. R.; Lewis, M. L.; Damron, K. L.; Morrison, D. R.

1990-01-01

An easy to use, interactive FORTRAN program for analyzing the results of parallel line assays is described. The program is menu driven and consists of five major components: data entry, data editing, manual analysis, manual plotting, and automatic analysis and plotting. Data can be entered from the terminal or from previously created data files. The data editing portion of the program is used to inspect and modify data and to statistically identify outliers. The manual analysis component is used to test the assumptions necessary for parallel line assays using analysis of covariance techniques and to determine potency ratios with confidence limits. The manual plotting component provides a graphic display of the data on the terminal screen or on a standard line printer. The automatic portion runs through multiple analyses without operator input. Data may be saved in a special file to expedite input at a future time.

Chemical Composition Analysis of Extracts from Ficus Hirta Using Supercritical Fluid

NASA Astrophysics Data System (ADS)

Deng, S. B.; Chen, J. P.; Chen, Y. Z.; Yu, C. Q.; Yang, Y.; Wu, S. H.; Chen, C. Z.

2018-05-01

Ficus hirta was extracted by supercritical carbon dioxide. The volatile chemical components of extracts were analyzed using gas chromatography-mass spectrometry (GC-MS). The percentage of products extracted by Supercritical Fluid Extraction(SFE) was 2.5%. Nineteen volatile compounds were identified. The main volatile components were Elemicin, Psoralen, Palmitic acid, Bergapten, α-Linolenic acid, Medicarpin, Retinoic Acid, Maackiain, and Squalene. The method is simple and quick, and can be used for the preliminary analysis of chemical constituents of supercritical extracts of Ficus hirta.

Minimum number of measurements for evaluating Bertholletia excelsa.

PubMed

Baldoni, A B; Tonini, H; Tardin, F D; Botelho, S C C; Teodoro, P E

2017-09-27

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of Brazil nut tree (Bertholletia excelsa) genotypes based on fruit yield. For this, we assessed the number of fruits and dry mass of seeds of 75 Brazil nut genotypes, from native forest, located in the municipality of Itaúba, MT, for 5 years. To better estimate r, four procedures were used: analysis of variance (ANOVA), principal component analysis based on the correlation matrix (CPCOR), principal component analysis based on the phenotypic variance and covariance matrix (CPCOV), and structural analysis based on the correlation matrix (mean r - AECOR). There was a significant effect of genotypes and measurements, which reveals the need to study the minimum number of measurements for selecting superior Brazil nut genotypes for a production increase. Estimates of r by ANOVA were lower than those observed with the principal component methodology and close to AECOR. The CPCOV methodology provided the highest estimate of r, which resulted in a lower number of measurements needed to identify superior Brazil nut genotypes for the number of fruits and dry mass of seeds. Based on this methodology, three measurements are necessary to predict the true value of the Brazil nut genotypes with a minimum accuracy of 85%.

A Deeper Examination of Thorellius atrox Scorpion Venom Components with Omic Techonologies.

PubMed

Romero-Gutierrez, Teresa; Peguero-Sanchez, Esteban; Cevallos, Miguel A; Batista, Cesar V F; Ortiz, Ernesto; Possani, Lourival D

2017-12-12

This communication reports a further examination of venom gland transcripts and venom composition of the Mexican scorpion Thorellius atrox using RNA-seq and tandem mass spectrometry. The RNA-seq, which was performed with the Illumina protocol, yielded more than 20,000 assembled transcripts. Following a database search and annotation strategy, 160 transcripts were identified, potentially coding for venom components. A novel sequence was identified that potentially codes for a peptide with similarity to spider ω-agatoxins, which act on voltage-gated calcium channels, not known before to exist in scorpion venoms. Analogous transcripts were found in other scorpion species. They could represent members of a new scorpion toxin family, here named omegascorpins. The mass fingerprint by LC-MS identified 135 individual venom components, five of which matched with the theoretical masses of putative peptides translated from the transcriptome. The LC-MS/MS de novo sequencing allowed to reconstruct and identify 42 proteins encoded by assembled transcripts, thus validating the transcriptome analysis. Earlier studies conducted with this scorpion venom permitted the identification of only twenty putative venom components. The present work performed with more powerful and modern omic technologies demonstrates the capacity of accomplishing a deeper characterization of scorpion venom components and the identification of novel molecules with potential applications in biomedicine and the study of ion channel physiology.

Mapping the sources of the seismic wave field at Kilauea volcano, Hawaii, using data recorded on multiple seismic Antennas

USGS Publications Warehouse

Almendros, J.; Chouet, B.; Dawson, P.; Huber, Caleb G.

2002-01-01

Seismic antennas constitute a powerful tool for the analysis of complex wave fields. Well-designed antennas can identify and separate components of a complex wave field based on their distinct propagation properties. The combination of several antennas provides the basis for a more complete understanding of volcanic wave fields, including an estimate of the location of each individual wave-field component identified simultaneously by at least two antennas. We used frequency-slowness analyses of data from three antennas to identify and locate the different components contributing to the wave fields recorded at Kilauea volcano, Hawaii, in February 1997. The wave-field components identified are (1) a sustained background volcanic tremor in the form of body waves generated in a shallow hydrothermal system located below the northeastern edge of the Halemaumau pit crater; (2) surface waves generated along the path between this hydrothermal source and the antennas; (3) back-scattered surface wave energy from a shallow reflector located near the southeastern rim of Kilauea caldera; (4) evidence for diffracted wave components originating at the southeastern edge of Halemaumau; and (5) body waves reflecting the activation of a deeper tremor source between 02 hr 00 min and 16 hr 00 min Hawaii Standard Time on 11 February.

Novel Entries in a Fungal Biofilm Matrix Encyclopedia

PubMed Central

Zarnowski, Robert; Westler, William M.; Lacmbouh, Ghislain Ade; Marita, Jane M.; Bothe, Jameson R.; Bernhardt, Jörg; Lounes-Hadj Sahraoui, Anissa; Fontaine, Joël; Sanchez, Hiram; Hatfield, Ronald D.; Ntambi, James M.; Nett, Jeniel E.; Mitchell, Aaron P.

2014-01-01

ABSTRACT Virulence of Candida is linked with its ability to form biofilms. Once established, biofilm infections are nearly impossible to eradicate. Biofilm cells live immersed in a self-produced matrix, a blend of extracellular biopolymers, many of which are uncharacterized. In this study, we provide a comprehensive analysis of the matrix manufactured by Candida albicans both in vitro and in a clinical niche animal model. We further explore the function of matrix components, including the impact on drug resistance. We uncovered components from each of the macromolecular classes (55% protein, 25% carbohydrate, 15% lipid, and 5% nucleic acid) in the C. albicans biofilm matrix. Three individual polysaccharides were identified and were suggested to interact physically. Surprisingly, a previously identified polysaccharide of functional importance, β-1,3-glucan, comprised only a small portion of the total matrix carbohydrate. Newly described, more abundant polysaccharides included α-1,2 branched α-1,6-mannans (87%) associated with unbranched β-1,6-glucans (13%) in an apparent mannan-glucan complex (MGCx). Functional matrix proteomic analysis revealed 458 distinct activities. The matrix lipids consisted of neutral glycerolipids (89.1%), polar glycerolipids (10.4%), and sphingolipids (0.5%). Examination of matrix nucleic acid identified DNA, primarily noncoding sequences. Several of the in vitro matrix components, including proteins and each of the polysaccharides, were also present in the matrix of a clinically relevant in vivo biofilm. Nuclear magnetic resonance (NMR) analysis demonstrated interaction of aggregate matrix with the antifungal fluconazole, consistent with a role in drug impedance and contribution of multiple matrix components. PMID:25096878

Multi-component determination and chemometric analysis of Paris polyphylla by ultra high performance liquid chromatography with photodiode array detection.

PubMed

Chen, Pei; Jin, Hong-Yu; Sun, Lei; Ma, Shuang-Cheng

2016-09-01

Multi-source analysis of traditional Chinese medicine is key to ensuring its safety and efficacy. Compared with traditional experimental differentiation, chemometric analysis is a simpler strategy to identify traditional Chinese medicines. Multi-component analysis plays an increasingly vital role in the quality control of traditional Chinese medicines. A novel strategy, based on chemometric analysis and quantitative analysis of multiple components, was proposed to easily and effectively control the quality of traditional Chinese medicines such as Chonglou. Ultra high performance liquid chromatography was more convenient and efficient. Five species of Chonglou were distinguished by chemometric analysis and nine saponins, including Chonglou saponins I, II, V, VI, VII, D, and H, as well as dioscin and gracillin, were determined in 18 min. The method is feasible and credible, and enables to improve quality control of traditional Chinese medicines and natural products. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An Electro-Physiological Temporal Principal Component Analysis of Processing Stages of Number Comparison in Developmental Dyscalculia

ERIC Educational Resources Information Center

Soltesz, Fruzsina; Szucs, Denes

2009-01-01

Developmental dyscalculia (DD) still lacks a generally accepted definition. A major problem is that the cognitive component processes contributing to arithmetic performance are still poorly defined. By a reanalysis of our previous event-related brain potential (ERP) data (Soltesz et al., 2007) here our objective was to identify and compare…

Subgroups of Adult Basic Education Learners with Different Profiles of Reading Skills

ERIC Educational Resources Information Center

MacArthur, Charles A.; Konold, Timothy R.; Glutting, Joseph J.; Alamprese, Judith A.

2012-01-01

The purpose of this study was to identify subgroups of adult basic education (ABE) learners with different profiles of skills in the core reading components of decoding, word recognition, spelling, fluency, and comprehension. The analysis uses factor scores of those 5 reading components from on a prior investigation of the reliability and…

Screening for frailty in older adults using a self-reported instrument.

PubMed

Nunes, Daniella Pires; Duarte, Yeda Aparecida de Oliveira; Santos, Jair Lício Ferreira; Lebrão, Maria Lúcia

2015-01-01

OBJECTIVE To validate a screening instrument using self-reported assessment of frailty syndrome in older adults. METHODS This cross-sectional study used data from the Saúde, Bem-estar e Envelhecimento study conducted in Sao Paulo, SP, Southeastern Brazil. The sample consisted of 433 older adult individuals (≥ 75 years) assessed in 2009. The self-reported instrument can be applied to older adults or their proxy respondents and consists of dichotomous questions directly related to each component of the frailty phenotype, which is considered the gold standard model: unintentional weight loss, fatigue, low physical activity, decreased physical strength, and decreased walking speed. The same classification proposed in the phenotype was utilized: not frail (no component identified); pre-frail (presence of one or two components), and frail (presence of three or more components). Because this is a screening instrument, "process of frailty" was included as a category (pre-frail and frail). Cronbach's α was used in psychometric analysis to evaluate the reliability and validity of the criterion, the sensitivity, the specificity, as well as positive and negative predictive values. Factor analysis was used to assess the suitability of the proposed number of components. RESULTS Decreased walking speed and decreased physical strength showed good internal consistency (α = 0.77 and 0.72, respectively); however, low physical activity was less satisfactory (α = 0.63). The sensitivity and specificity for identifying pre-frail individuals were 89.7% and 24.3%, respectively, while those for identifying frail individuals were 63.2% and 71.6%, respectively. In addition, 89.7% of the individuals from both the evaluations were identified in the "process of frailty" category. CONCLUSIONS The self-reported assessment of frailty can identify the syndrome among older adults and can be used as a screening tool. Its advantages include simplicity, rapidity, low cost, and ability to be used by different professionals.

Screening for frailty in older adults using a self-reported instrument

PubMed Central

Nunes, Daniella Pires; Duarte, Yeda Aparecida de Oliveira; Santos, Jair Lício Ferreira; Lebrão, Maria Lúcia

2015-01-01

OBJECTIVE To validate a screening instrument using self-reported assessment of frailty syndrome in older adults. METHODS This cross-sectional study used data from the Saúde, Bem-estar e Envelhecimento study conducted in Sao Paulo, SP, Southeastern Brazil. The sample consisted of 433 older adult individuals (≥ 75 years) assessed in 2009. The self-reported instrument can be applied to older adults or their proxy respondents and consists of dichotomous questions directly related to each component of the frailty phenotype, which is considered the gold standard model: unintentional weight loss, fatigue, low physical activity, decreased physical strength, and decreased walking speed. The same classification proposed in the phenotype was utilized: not frail (no component identified); pre-frail (presence of one or two components), and frail (presence of three or more components). Because this is a screening instrument, “process of frailty” was included as a category (pre-frail and frail). Cronbach’s α was used in psychometric analysis to evaluate the reliability and validity of the criterion, the sensitivity, the specificity, as well as positive and negative predictive values. Factor analysis was used to assess the suitability of the proposed number of components. RESULTS Decreased walking speed and decreased physical strength showed good internal consistency (α = 0.77 and 0.72, respectively); however, low physical activity was less satisfactory (α = 0.63). The sensitivity and specificity for identifying pre-frail individuals were 89.7% and 24.3%, respectively, while those for identifying frail individuals were 63.2% and 71.6%, respectively. In addition, 89.7% of the individuals from both the evaluations were identified in the “process of frailty” category. CONCLUSIONS The self-reported assessment of frailty can identify the syndrome among older adults and can be used as a screening tool. Its advantages include simplicity, rapidity, low cost, and ability to be used by different professionals. PMID:25741658

Characterization of Chromophoric Dissolved Organic Matter across the Eastern and the Central Arctic Regions using PARAFAC Modelling

NASA Astrophysics Data System (ADS)

Molodtsova, T.; Amon, R. M. W.

2016-12-01

In this study the optical properties (absorption and fluorescence intensity) of chromophoric dissolved organic matter (CDOM) were investigated in water samples collected during the cruise conducted in August and September 2007 across the Eastern and Central Arctic regions. The fluorescence spectroscopy analysis was complimented with the parallel factor analysis (PARAFAC) and the identified six components were compared to other water properties including salinity, in situ fluorescence, dissolved organic carbon, and specific ultraviolet absorbance at 254 nm. The principal component analysis was conducted to distinguish between the water masses and identify the features such as the Trans Polar Drift and the North Atlantic Current. The preliminary results indicate that investigation of the optical properties of CDOM are able to provide better understanding of Arctic Ocean circulation and environmental changes such as the loss of the perennial sea ice and more light penetrating the water column.

Temporal evolution of financial-market correlations.

PubMed

Fenn, Daniel J; Porter, Mason A; Williams, Stacy; McDonald, Mark; Johnson, Neil F; Jones, Nick S

2011-08-01

We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.

Temporal evolution of financial-market correlations

NASA Astrophysics Data System (ADS)

Fenn, Daniel J.; Porter, Mason A.; Williams, Stacy; McDonald, Mark; Johnson, Neil F.; Jones, Nick S.

2011-08-01

We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.

A Method for Accurate Group Difference Detection by Constraining the Mixing Coefficients in an ICA Framework

PubMed Central

Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Clark, Vincent P.; Calhoun, Vince D.

2009-01-01

Independent component analysis (ICA) is a promising method that is increasingly used to analyze brain imaging data such as functional magnetic resonance imaging (fMRI), structural MRI, and electroencephalography and has also proved useful for group comparison, e.g., differentiating healthy controls from patients. An advantage of ICA is its ability to identify components that are mixed in an unknown manner. However, ICA is not necessarily robust and optimal in identifying between-group effects, especially in highly noisy situations. Here, we propose a modified ICA framework for multi-group data analysis that incorporates prior information regarding group membership as a constraint into the mixing coefficients. Our approach, called coefficient-constrained ICA (CC-ICA), prioritizes identification of components that show a significant group difference. The performance of CC-ICA via synthetic and hybrid data simulations is evaluated under different hypothesis testing assumptions and signal to noise ratios (SNRs). Group analysis is also conducted on real multitask fMRI data. Results show that CC-ICA improves the estimation accuracy of the independent components greatly, especially those that have different patterns for different groups (e.g., patients vs. controls); In addition, it enhances the data extraction sensitivity to group differences by ranking components with P value or J-divergence more consistently with the ground truth. The proposed algorithm performs quite well for both group-difference detection and multitask fMRI data fusion, which may prove especially important for the identification of relevant disease biomarkers. PMID:19172631

Identification and Analysis of Bioactive Components of Fruit and Vegetable Products

ERIC Educational Resources Information Center

Mann, Francis M.

2015-01-01

Many small-molecule antioxidants found in whole fruits and vegetables are analyzed and identified in this laboratory module for upper-division biochemistry courses. During this experiment, students develop their knowledge of the bioactivity of fruit and vegetable products while learning techniques to identify vitamins and nutritionally derived…

A Typology of Students Based on Academic Entitlement

ERIC Educational Resources Information Center

Luckett, Michael; Trocchia, Philip J.; Noel, Noel Mark; Marlin, Dan

2017-01-01

Two hundred ninety-three university business students were surveyed using an academic entitlement (AE) scale updated to include new technologies. Using factor analysis, three components of AE were identified: grade entitlement, behavioral entitlement, and service entitlement. A k-means clustering procedure was then applied to identify four groups…

Support vector machine based classification of fast Fourier transform spectroscopy of proteins

NASA Astrophysics Data System (ADS)

Lazarevic, Aleksandar; Pokrajac, Dragoljub; Marcano, Aristides; Melikechi, Noureddine

2009-02-01

Fast Fourier transform spectroscopy has proved to be a powerful method for study of the secondary structure of proteins since peak positions and their relative amplitude are affected by the number of hydrogen bridges that sustain this secondary structure. However, to our best knowledge, the method has not been used yet for identification of proteins within a complex matrix like a blood sample. The principal reason is the apparent similarity of protein infrared spectra with actual differences usually masked by the solvent contribution and other interactions. In this paper, we propose a novel machine learning based method that uses protein spectra for classification and identification of such proteins within a given sample. The proposed method uses principal component analysis (PCA) to identify most important linear combinations of original spectral components and then employs support vector machine (SVM) classification model applied on such identified combinations to categorize proteins into one of given groups. Our experiments have been performed on the set of four different proteins, namely: Bovine Serum Albumin, Leptin, Insulin-like Growth Factor 2 and Osteopontin. Our proposed method of applying principal component analysis along with support vector machines exhibits excellent classification accuracy when identifying proteins using their infrared spectra.

Determination of Flavonoids and Anthocyanins in Nitraria tangutorum by High Performance Liquid Chromatography Coupled with Tandem Mass Spectrometry.

PubMed

Zhe, Gao; Ying-Chun, Wang; Yan-Xu, Chang

2016-01-01

Using high-performance liquid chromatography coupled with diode array detection and electrospray ionization tandem mass spectrometry (HPLC-DAD-MSn) method, qualitative and quantitative analysis of flavonoids of stems, leaves, fruits and seeds, and anthocyanidin of fresh fruits in Nitraria tangutorum were performed. A total of 14 flavonoid components were identified from the seeds of N. tangutorum including three quercetin derivatives, three kaempferol derivatives, and eight isorhamnetin derivatives. A total of 12, 10, and 7 flavonoid components were identified from leaves, stems, and fruits of N. tangutorum, respectively; all were present in seeds also. The total content of flavonoids in leaves was the highest, up to 42.43 mg/g·dry weight. A total of 12 anthocyanidin components were identified from the fresh fruits of N. tangutorum, belonging to five anthocyanidin. The total content of anthocyanidin in fresh fruits was up to 45.83 mg/100 g· fresh weight, of which the acylated anthocyanidin accounted for 65.7%. The HPLC-DAD-MS(n) method can be operated easily, rapidly, and accurately, and is feasible for qualitative and quantitative analysis of flavone glycosides in N. tangutorum.

Photovoltaic solar panels of crystalline silicon: Characterization and separation.

PubMed

Dias, Pablo Ribeiro; Benevit, Mariana Gonçalves; Veit, Hugo Marcelo

2016-03-01

Photovoltaic panels have a limited lifespan and estimates show large amounts of solar modules will be discarded as electronic waste in a near future. In order to retrieve important raw materials, reduce production costs and environmental impacts, recycling such devices is important. Initially, this article investigates which silicon photovoltaic module's components are recyclable through their characterization using X-ray fluorescence, X-ray diffraction, energy dispersion spectroscopy and atomic absorption spectroscopy. Next, different separation methods are tested to favour further recycling processes. The glass was identified as soda-lime glass, the metallic filaments were identified as tin-lead coated copper, the panel cells were made of silicon and had silver filaments attached to it and the modules' frames were identified as aluminium, all of which are recyclable. Moreover, three different components segregation methods have been studied. Mechanical milling followed by sieving was able to separate silver from copper while chemical separation using sulphuric acid was able to detach the semiconductor material. A thermo gravimetric analysis was performed to evaluate the use of a pyrolysis step prior to the component's removal. The analysis showed all polymeric fractions present degrade at 500 °C. © The Author(s) 2016.

Reduction of PM emissions from specific sources reflected on key components concentrations of ambient PM10

NASA Astrophysics Data System (ADS)

Minguillon, M. C.; Querol, X.; Monfort, E.; Alastuey, A.; Escrig, A.; Celades, I.; Miro, J. V.

2009-04-01

The relationship between specific particulate emission control and ambient levels of some PM10 components (Zn, As, Pb, Cs, Tl) was evaluated. To this end, the industrial area of Castellón (Eastern Spain) was selected, where around 40% of the EU glazed ceramic tiles and a high proportion of EU ceramic frits (middle product for the manufacture of ceramic glaze) are produced. The PM10 emissions from the ceramic processes were calculated over the period 2000 to 2007 taking into account the degree of implementation of corrective measures throughout the study period. Abatement systems (mainly bag filters) were implemented in the majority of the fusion kilns for frit manufacture in the area as a result of the application of the Directive 1996/61/CE, leading to a marked decrease in PM10 emissions. On the other hand, ambient PM10 sampling was carried out from April 2002 to July 2008 at three urban sites and one suburban site of the area and a complete chemical analysis was made for about 35 % of the collected samples, by means of different techniques (ICP-AES, ICP-MS, Ion Chromatography, selective electrode and elemental analyser). The series of chemical composition of PM10 allowed us to apply a source contribution model (Principal Component Analysis), followed by a multilinear regression analysis, so that PM10 sources were identified and their contribution to bulk ambient PM10 was quantified on a daily basis, as well as the contribution to bulk ambient concentrations of the identified key components (Zn, As, Pb, Cs, Tl). The contribution of the sources identified as the manufacture and use of ceramic glaze components, including the manufacture of ceramic frits, accounted for more than 65, 75, 58, 53, and 53% of ambient Zn, As, Pb, Cs and Tl levels, respectively (with the exception of Tl contribution at one of the sites). The important emission reductions of these sources during the study period had an impact on ambient key components levels, such that there was a high correlation between PM10 emissions from these sources and ambient key components levels (R2= 0.61-0.98).

Comparison of three-dimensional fluorescence analysis methods for predicting formation of trihalomethanes and haloacetic acids.

PubMed

Peleato, Nicolás M; Andrews, Robert C

2015-01-01

This work investigated the application of several fluorescence excitation-emission matrix analysis methods as natural organic matter (NOM) indicators for use in predicting the formation of trihalomethanes (THMs) and haloacetic acids (HAAs). Waters from four different sources (two rivers and two lakes) were subjected to jar testing followed by 24hr disinfection by-product formation tests using chlorine. NOM was quantified using three common measures: dissolved organic carbon, ultraviolet absorbance at 254 nm, and specific ultraviolet absorbance as well as by principal component analysis, peak picking, and parallel factor analysis of fluorescence spectra. Based on multi-linear modeling of THMs and HAAs, principle component (PC) scores resulted in the lowest mean squared prediction error of cross-folded test sets (THMs: 43.7 (μg/L)(2), HAAs: 233.3 (μg/L)(2)). Inclusion of principle components representative of protein-like material significantly decreased prediction error for both THMs and HAAs. Parallel factor analysis did not identify a protein-like component and resulted in prediction errors similar to traditional NOM surrogates as well as fluorescence peak picking. These results support the value of fluorescence excitation-emission matrix-principal component analysis as a suitable NOM indicator in predicting the formation of THMs and HAAs for the water sources studied. Copyright © 2014. Published by Elsevier B.V.

Relation between aerosol sources and meteorological parameters for inhalable atmospheric particles in Sao Paulo City, Brazil

NASA Astrophysics Data System (ADS)

Andrade, Fatima; Orsini, Celso; Maenhaut, Willy

Stacked filter units were used to collect atmospheric particles in separate coarse and fine fractions at the Sao Paulo University Campus during the winter of 1989. The samples were analysed by particle-induced X-ray emission (PIXE) and the data were subjected to an absolute principal component analysis (APCA). Five sources were identified for the fine particles: industrial emissions, which accounted for 13% of the fine mass; emissions from residual oil and diesel, explaining 41%; resuspended soil dust, with 28%; and emissions of Cu and of Mg, together with 18%. For the coarse particles, four sources were identified: soil dust, accounting for 59% of the coarse mass; industrial emissions, with 19%; oil burning, with 8%; and sea salt aerosol, with 14% of the coarse mass. A data set with various meteorological parameters was also subjected to APCA, and a correlation analysis was performed between the meteorological "absolute principal component scores" (APCS) and the APCS from the fine and coarse particle data sets. The soil dust sources for the fine and coarse aerosol were highly correlated with each other and were anticorrelated with the sea breeze component. The industrial components in the fine and coarse size fractions were also highly positively correlated. Furthermore, the industrial component was related with the northeasterly wind direction and, to a lesser extent, with the sea breeze component.

The use of the sexual function questionnaire as a screening tool for women with sexual dysfunction.

PubMed

Quirk, Frances; Haughie, Scott; Symonds, Tara

2005-07-01

To determine if the validated Sexual Function Questionnaire (SFQ), developed to assess efficacy in female sexual dysfunction (FSD) clinical trials, may also have utility in identifying target populations for such studies. Data from five clinical trials and two general population surveys were used to analyze the utility of the SFQ as a tool to discriminate between the presence of specific components of FSD (i.e., hypoactive sexual desire disorder, female sexual arousal disorder, female orgasmic disorder, and dyspareunia). Sensitivity/specificity analysis and logistic regression analysis, using data from all five clinical studies and the general population surveys, confirmed that the SFQ domains have utility in detecting the presence of specific components of FSD and provide scores indicative of the presence of a specific sexual disorder. The SFQ is a valuable new tool for detecting the presence of FSD and identifying the specific components of sexual functions affected (desire, arousal, orgasm, or dyspareunia).

Facile hyphenation of gas chromatography and a microcantilever array sensor for enhanced selectivity.

PubMed

Chapman, Peter J; Vogt, Frank; Dutta, Pampa; Datskos, Panos G; Devault, Gerald L; Sepaniak, Michael J

2007-01-01

The very simple coupling of a standard, packed-column gas chromatograph with a microcantilever array (MCA) is demonstrated for enhanced selectivity and potential analyte identification in the analysis of volatile organic compounds (VOCs). The cantilevers in MCAs are differentially coated on one side with responsive phases (RPs) and produce bending responses of the cantilevers due to analyte-induced surface stresses. Generally, individual components are difficult to elucidate when introduced to MCA systems as mixtures, although pattern recognition techniques are helpful in identifying single components, binary mixtures, or composite responses of distinct mixtures (e.g., fragrances). In the present work, simple test VOC mixtures composed of acetone, ethanol, and trichloroethylene (TCE) in pentane and methanol and acetonitrile in pentane are first separated using a standard gas chromatograph and then introduced into a MCA flow cell. Significant amounts of response diversity to the analytes in the mixtures are demonstrated across the RP-coated cantilevers of the array. Principal component analysis is used to demonstrate that only three components of a four-component VOC mixture could be identified without mixture separation. Calibration studies are performed, demonstrating a good linear response over 2 orders of magnitude for each component in the primary study mixture. Studies of operational parameters including column temperature, column flow rate, and array cell temperature are conducted. Reproducibility studies of VOC peak areas and peak heights are also carried out showing RSDs of less than 4 and 3%, respectively, for intra-assay studies. Of practical significance is the facile manner by which the hyphenation of a mature separation technique and the burgeoning sensing approach is accomplished, and the potential to use pattern recognition techniques with MCAs as a new type of detector for chromatography with analyte-identifying capabilities.

Characterization of CDOM from urban waters in Northern-Northeastern China using excitation-emission matrix fluorescence and parallel factor analysis.

PubMed

Zhao, Ying; Song, Kaishan; Li, Sijia; Ma, Jianhang; Wen, Zhidan

2016-08-01

Chromophoric dissolved organic matter (CDOM) plays an important role in aquatic systems, but high concentrations of organic materials are considered pollutants. The fluorescent component characteristics of CDOM in urban waters sampled from Northern and Northeastern China were examined by excitation-emission matrix fluorescence and parallel factor analysis (EEM-PARAFAC) to investigate the source and compositional changes of CDOM on both space and pollution levels. One humic-like (C1), one tryptophan-like component (C2), and one tyrosine-like component (C3) were identified by PARAFAC. Mean fluorescence intensities of the three CDOM components varied spatially and by pollution level in cities of Northern and Northeastern China during July-August, 2013 and 2014. Principal components analysis (PCA) was conducted to identify the relative distribution of all water samples. Cluster analysis (CA) was also used to categorize the samples into groups of similar pollution levels within a study area. Strong positive linear relationships were revealed between the CDOM absorption coefficients a(254) (R (2) = 0.89, p < 0.01); a(355) (R (2) = 0.94, p < 0.01); and the fluorescence intensity (F max) for the humic-like C1 component. A positive linear relationship (R (2) = 0.77) was also exhibited between dissolved organic carbon (DOC) and the F max for the humic-like C1 component, but a relatively weak correlation (R (2) = 0.56) was detected between DOC and the F max for the tryptophan-like component (C2). A strong positive correlation was observed between the F max for the tryptophan-like component (C2) and total nitrogen (TN) (R (2) = 0.78), but moderate correlations were observed with ammonium-N (NH4-N) (R (2) = 0.68), and chemical oxygen demand (CODMn) (R (2) = 0.52). Therefore, the fluorescence intensities of CDOM components can be applied to monitor water quality in real time compared to that of traditional approaches. These results demonstrate that EEM-PARAFAC is useful to evaluate the dynamics of CDOM fluorescent components in urban waters from Northern and Northeastern China and this method has potential applications for monitoring urban water quality in different regions with various hydrological conditions and pollution levels.

NHEXAS PHASE I MARYLAND STUDY--LIST OF AVAILABLE DOCUMENTS: PROTOCOLS AND SOPS

EPA Science Inventory

This document lists available protocols and SOPs for the NHEXAS Phase I Maryland study. It identifies protocols and SOPs for the following study components: (1) Sample collection and field operations, (2) Sample analysis and general laboratory procedures, (3) Data Analysis Proced...

Moche CAPE Formula: Cost Analysis of Public Education.

ERIC Educational Resources Information Center

Moche, Joanne Spiers

The Moche Cost Analysis of Public Education (CAPE) formula was developed to identify total and per pupil costs of regular elementary education, regular secondary education, elementary special education, and secondary special education. Costs are analyzed across five components: (1) comprehensive costs (including transportation and supplemental…

Factor structure of DSM-IV criteria for obsessive compulsive personality disorder in patients with binge eating disorder.

PubMed

Grilo, C M

2004-01-01

To examine the factor structure of DSM-IV criteria for obsessive compulsive personality disorder (OCPD) in patients with binge eating disorder (BED). Two hundred and eleven consecutive out-patients with axis I diagnoses of BED were reliably assessed with semi-structured diagnostic interviews. The eight criteria for the OCPD diagnosis were examined with reliability and correlational analyses. Exploratory factor analysis was performed to identify potential components. Cronbach's coefficient alpha for the OCPD criteria was 0.77. Principal components factor analysis with varimax rotation revealed a three-factor solution (rigidity, perfectionism, and miserliness), which accounted for 65% of variance. The DSM-IV criteria for OCPD showed good internal consistency. Exploratory factor analysis, however, revealed three components that may reflect distinct interpersonal, intrapersonal (cognitive), and behavioral features.

Multivariate proteomic profiling identifies novel accessory proteins of coated vesicles

PubMed Central

Antrobus, Robin; Hirst, Jennifer; Bhumbra, Gary S.; Kozik, Patrycja; Jackson, Lauren P.; Sahlender, Daniela A.

2012-01-01

Despite recent advances in mass spectrometry, proteomic characterization of transport vesicles remains challenging. Here, we describe a multivariate proteomics approach to analyzing clathrin-coated vesicles (CCVs) from HeLa cells. siRNA knockdown of coat components and different fractionation protocols were used to obtain modified coated vesicle-enriched fractions, which were compared by stable isotope labeling of amino acids in cell culture (SILAC)-based quantitative mass spectrometry. 10 datasets were combined through principal component analysis into a “profiling” cluster analysis. Overall, 136 CCV-associated proteins were predicted, including 36 new proteins. The method identified >93% of established CCV coat proteins and assigned >91% correctly to intracellular or endocytic CCVs. Furthermore, the profiling analysis extends to less well characterized types of coated vesicles, and we identify and characterize the first AP-4 accessory protein, which we have named tepsin. Finally, our data explain how sequestration of TACC3 in cytosolic clathrin cages causes the severe mitotic defects observed in auxilin-depleted cells. The profiling approach can be adapted to address related cell and systems biological questions. PMID:22472443

Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.

PubMed

Rinaldi, Maurizio; Gindro, Roberto; Barbeni, Massimo; Allegrone, Gianna

2009-01-01

Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product. To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches. Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample. Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds. SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices.

Analysis of Molecular Diffusion by First-Passage Time Variance Identifies the Size of Confinement Zones

PubMed Central

Rajani, Vishaal; Carrero, Gustavo; Golan, David E.; de Vries, Gerda; Cairo, Christopher W.

2011-01-01

The diffusion of receptors within the two-dimensional environment of the plasma membrane is a complex process. Although certain components diffuse according to a random walk model (Brownian diffusion), an overwhelming body of work has found that membrane diffusion is nonideal (anomalous diffusion). One of the most powerful methods for studying membrane diffusion is single particle tracking (SPT), which records the trajectory of a label attached to a membrane component of interest. One of the outstanding problems in SPT is the analysis of data to identify the presence of heterogeneity. We have adapted a first-passage time (FPT) algorithm, originally developed for the interpretation of animal movement, for the analysis of SPT data. We discuss the general application of the FPT analysis to molecular diffusion, and use simulations to test the method against data containing known regions of confinement. We conclude that FPT can be used to identify the presence and size of confinement within trajectories of the receptor LFA-1, and these results are consistent with previous reports on the size of LFA-1 clusters. The analysis of trajectory data for cell surface receptors by FPT provides a robust method to determine the presence and size of confined regions of diffusion. PMID:21402028

Adulteration and cultivation region identification of American ginseng using HPLC coupled with multivariate analysis

PubMed Central

Yu, Chunhao; Wang, Chong-Zhi; Zhou, Chun-Jie; Wang, Bin; Han, Lide; Zhang, Chun-Feng; Wu, Xiao-Hui; Yuan, Chun-Su

2014-01-01

American ginseng (Panax quinquefolius) is originally grown in North America. Due to price difference and supply shortage, American ginseng recently has been cultivated in northern China. Further, in the market, some Asian ginsengs are labeled as American ginseng. In this study, forty-three American ginseng samples cultivated in the USA, Canada or China were collected and 14 ginseng saponins were determined using HPLC. HPLC coupled with hierarchical cluster analysis and principal component analysis was developed to identify the species. Subsequently, an HPLC-linear discriminant analysis was established to discriminate cultivation regions of American ginseng. This method was successfully applied to identify the sources of 6 commercial American ginseng samples. Two of them were identified as Asian ginseng, while 4 others were identified as American ginseng, which were cultivated in the USA (3) and China (1). Our newly developed method can be used to identify American ginseng with different cultivation regions. PMID:25044150

Development of a Prototype Simulation Executive with Zooming in the Numerical Propulsion System Simulation

NASA Technical Reports Server (NTRS)

Reed, John A.; Afjeh, Abdollah A.

1995-01-01

A major difficulty in designing aeropropulsion systems is that of identifying and understanding the interactions between the separate engine components and disciplines (e.g., fluid mechanics, structural mechanics, heat transfer, material properties, etc.). The traditional analysis approach is to decompose the system into separate components with the interaction between components being evaluated by the application of each of the single disciplines in a sequential manner. Here, one discipline uses information from the calculation of another discipline to determine the effects of component coupling. This approach, however, may not properly identify the consequences of these effects during the design phase, leaving the interactions to be discovered and evaluated during engine testing. This contributes to the time and cost of developing new propulsion systems as, typically, several design-build-test cycles are needed to fully identify multidisciplinary effects and reach the desired system performance. The alternative to sequential isolated component analysis is to use multidisciplinary coupling at a more fundamental level. This approach has been made more plausible due to recent advancements in computation simulation along with application of concurrent engineering concepts. Computer simulation systems designed to provide an environment which is capable of integrating the various disciplines into a single simulation system have been proposed and are currently being developed. One such system is being developed by the Numerical Propulsion System Simulation (NPSS) project. The NPSS project, being developed at the Interdisciplinary Technology Office at the NASA Lewis Research Center is a 'numerical test cell' designed to provide for comprehensive computational design and analysis of aerospace propulsion systems. It will provide multi-disciplinary analyses on a variety of computational platforms, and a user-interface consisting of expert systems, data base management and visualization tools, to allow the designer to investigate the complex interactions inherent in these systems. An interactive programming software system, known as the Application Visualization System (AVS), was utilized for the development of the propulsion system simulation. The modularity of this system provides the ability to couple propulsion system components, as well as disciplines, and provides for the ability to integrate existing, well established analysis codes into the overall system simulation. This feature allows the user to customize the simulation model by inserting desired analysis codes. The prototypical simulation environment for multidisciplinary analysis, called Turbofan Engine System Simulation (TESS), which incorporates many of the characteristics of the simulation environment proposed herein, is detailed.

Global Qualitative Flow-Path Modeling for Local State Determination in Simulation and Analysis

NASA Technical Reports Server (NTRS)

Malin, Jane T. (Inventor); Fleming, Land D. (Inventor)

1998-01-01

For qualitative modeling and analysis, a general qualitative abstraction of power transmission variables (flow and effort) for elements of flow paths includes information on resistance, net flow, permissible directions of flow, and qualitative potential is discussed. Each type of component model has flow-related variables and an associated internal flow map, connected into an overall flow network of the system. For storage devices, the implicit power transfer to the environment is represented by "virtual" circuits that include an environmental junction. A heterogeneous aggregation method simplifies the path structure. A method determines global flow-path changes during dynamic simulation and analysis, and identifies corresponding local flow state changes that are effects of global configuration changes. Flow-path determination is triggered by any change in a flow-related device variable in a simulation or analysis. Components (path elements) that may be affected are identified, and flow-related attributes favoring flow in the two possible directions are collected for each of them. Next, flow-related attributes are determined for each affected path element, based on possibly conflicting indications of flow direction. Spurious qualitative ambiguities are minimized by using relative magnitudes and permissible directions of flow, and by favoring flow sources over effort sources when comparing flow tendencies. The results are output to local flow states of affected components.

Morphological features (defects) in fuel cell membrane electrode assemblies

NASA Astrophysics Data System (ADS)

Kundu, S.; Fowler, M. W.; Simon, L. C.; Grot, S.

Reliability and durability issues in fuel cells are becoming more important as the technology and the industry matures. Although research in this area has increased, systematic failure analysis, such as a failure modes and effects analysis (FMEA), are very limited in the literature. This paper presents a categorization scheme of causes, modes, and effects related to fuel cell degradation and failure, with particular focus on the role of component quality, that can be used in FMEAs for polymer electrolyte membrane (PEM) fuel cells. The work also identifies component defects imparted on catalyst-coated membranes (CCM) by manufacturing and proposes mechanisms by which they can influence overall degradation and reliability. Six major defects have been identified on fresh CCM materials, i.e., cracks, orientation, delamination, electrolyte clusters, platinum clusters, and thickness variations.

Gene expression profiles in rainbow trout, Onchorynchus mykiss, exposed to a simple chemical mixture.

PubMed

Hook, Sharon E; Skillman, Ann D; Gopalan, Banu; Small, Jack A; Schultz, Irvin R

2008-03-01

Among proposed uses for microarrays in environmental toxiciology is the identification of key contributors to toxicity within a mixture. However, it remains uncertain whether the transcriptomic profiles resulting from exposure to a mixture have patterns of altered gene expression that contain identifiable contributions from each toxicant component. We exposed isogenic rainbow trout Onchorynchus mykiss, to sublethal levels of ethynylestradiol, 2,2,4,4-tetrabromodiphenyl ether, and chromium VI or to a mixture of all three toxicants Fluorescently labeled complementary DNA (cDNA) were generated and hybridized against a commercially available Salmonid array spotted with 16,000 cDNAs. Data were analyzed using analysis of variance (p<0.05) with a Benjamani-Hochberg multiple test correction (Genespring [Agilent] software package) to identify up and downregulated genes. Gene clustering patterns that can be used as "expression signatures" were determined using hierarchical cluster analysis. The gene ontology terms associated with significantly altered genes were also used to identify functional groups that were associated with toxicant exposure. Cross-ontological analytics approach was used to assign functional annotations to genes with "unknown" function. Our analysis indicates that transcriptomic profiles resulting from the mixture exposure resemble those of the individual contaminant exposures, but are not a simple additive list. However, patterns of altered genes representative of each component of the mixture are clearly discernible, and the functional classes of genes altered represent the individual components of the mixture. These findings indicate that the use of microarrays to identify transcriptomic profiles may aid in the identification of key stressors within a chemical mixture, ultimately improving environmental assessment.

[Study on material basis of essential oil from Yin Teng Gu Bi Kang prescription on activating blood circulation].

PubMed

Wang, Yuan-Qing; Yan, Jian-Ye; Gong, Li-Min; Luo, Kun; Li, Shun-Xiang; Yang, Yan-Tao; Xie, Yu

2014-08-01

To explore the component difference of the serum containing essential oil from Yin Teng Gu Bi Kang prescription in pathologic and physiologic rat models, and to reveal the material basis of its efficacy of activating blood circulation. The essential oils were obtained by CO2 supercritical fluid extraction and the ingredients of the essential oils in vitro and in vivo (under physiological and pathological status) were analyzed by GC-MS to compare differences of the essential oil under physiological and pathological status in rats. 32 components were identified with the main components of Z-ligustilide (39.23%) and d-limonene (21.7%) in the essential oil. In vivo analysis on the essential oil indicated that 16 components were identified, 7 existed originally in essential oil and 9 were metabolites under physiological status; while 22 components were identified, 10 existed originally in essential oil and 12 were metabolites under pathological status (acute blood stasis). There were 7 common prototypes and 8 common metabolites under different physiological status. The absorption and metabolism of essential oils were affected by blood stasis and the compounds migrating to blood may be the effective substance in activating blood circulation.

Identifying sources of emerging organic contaminants in a mixed use watershed using principal components analysis.

PubMed

Karpuzcu, M Ekrem; Fairbairn, David; Arnold, William A; Barber, Brian L; Kaufenberg, Elizabeth; Koskinen, William C; Novak, Paige J; Rice, Pamela J; Swackhamer, Deborah L

2014-01-01

Principal components analysis (PCA) was used to identify sources of emerging organic contaminants in the Zumbro River watershed in Southeastern Minnesota. Two main principal components (PCs) were identified, which together explained more than 50% of the variance in the data. Principal Component 1 (PC1) was attributed to urban wastewater-derived sources, including municipal wastewater and residential septic tank effluents, while Principal Component 2 (PC2) was attributed to agricultural sources. The variances of the concentrations of cotinine, DEET and the prescription drugs carbamazepine, erythromycin and sulfamethoxazole were best explained by PC1, while the variances of the concentrations of the agricultural pesticides atrazine, metolachlor and acetochlor were best explained by PC2. Mixed use compounds carbaryl, iprodione and daidzein did not specifically group with either PC1 or PC2. Furthermore, despite the fact that caffeine and acetaminophen have been historically associated with human use, they could not be attributed to a single dominant land use category (e.g., urban/residential or agricultural). Contributions from septic systems did not clarify the source for these two compounds, suggesting that additional sources, such as runoff from biosolid-amended soils, may exist. Based on these results, PCA may be a useful way to broadly categorize the sources of new and previously uncharacterized emerging contaminants or may help to clarify transport pathways in a given area. Acetaminophen and caffeine were not ideal markers for urban/residential contamination sources in the study area and may need to be reconsidered as such in other areas as well.

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

2014-01-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Application of principal component analysis in protein unfolding: an all-atom molecular dynamics simulation study.

PubMed

Das, Atanu; Mukhopadhyay, Chaitali

2007-10-28

We have performed molecular dynamics (MD) simulation of the thermal denaturation of one protein and one peptide-ubiquitin and melittin. To identify the correlation in dynamics among various secondary structural fragments and also the individual contribution of different residues towards thermal unfolding, principal component analysis method was applied in order to give a new insight to protein dynamics by analyzing the contribution of coefficients of principal components. The cross-correlation matrix obtained from MD simulation trajectory provided important information regarding the anisotropy of backbone dynamics that leads to unfolding. Unfolding of ubiquitin was found to be a three-state process, while that of melittin, though smaller and mostly helical, is more complicated.

Application of principal component analysis in protein unfolding: An all-atom molecular dynamics simulation study

NASA Astrophysics Data System (ADS)

Das, Atanu; Mukhopadhyay, Chaitali

2007-10-01

We have performed molecular dynamics (MD) simulation of the thermal denaturation of one protein and one peptide—ubiquitin and melittin. To identify the correlation in dynamics among various secondary structural fragments and also the individual contribution of different residues towards thermal unfolding, principal component analysis method was applied in order to give a new insight to protein dynamics by analyzing the contribution of coefficients of principal components. The cross-correlation matrix obtained from MD simulation trajectory provided important information regarding the anisotropy of backbone dynamics that leads to unfolding. Unfolding of ubiquitin was found to be a three-state process, while that of melittin, though smaller and mostly helical, is more complicated.

Proteomics reveals novel components of the Anopheles gambiae eggshell

PubMed Central

Amenya, Dolphine A.; Chou, Wayne; Li, Jianyong; Yan, Guiyun; Gershon, Paul D.; James, Anthony A.; Marinotti, Osvaldo

2010-01-01

While genome and transcriptome sequencing has revealed a large number and diversity of Anopheles gambiae predicted proteins, identifying their functions and biosynthetic pathways remains challenging. Applied mass spectrometry based proteomics in conjunction with mosquito genome and transcriptome databases were used to identify 44 proteins as putative components of the eggshell. Among the identified molecules are two vitelline membrane proteins and a group of seven putative chorion proteins. Enzymes with peroxidase, laccase and phenoloxidase activities, likely involved in cross-linking reactions that stabilize the eggshell structure, also were identified. Seven odorant binding proteins were found in association with the mosquito eggshell, although their role has yet to be demonstrated. This analysis fills a considerable gap of knowledge about proteins that build the eggshell of anopheline mosquitoes. PMID:20433845

Developmental Patterns of the Invasive Bramble (Rubus alceifolius Poiret, Rosaceae) in Réunion Island: an Architectural and Morphometric Analysis

PubMed Central

BARET, STÉPHANE; NICOLINI, ERIC; LE BOURGEOIS, THOMAS; STRASBERG, DOMINIQUE

2003-01-01

The aim of this study was to identify the developmental stages of Rubus alceifolius and to determine one or more characteristic morphological markers for each stage. The developmental reconstitution method used involved a detailed description of many individuals throughout the different stages of growth, from germination to the development of an adult shoot capable of fruiting. Results revealed that R. alceifolius passes through five developmental stages that can be distinguished by changes in several morphological markers such as internode length and diameter, pith diameter and plant shape. This analysis indicated that R. alceifolius has a heteroblastic developmental pattern, midway between that of a bush and a liana. Moreover, results showed that this species taps environmental resources early in its development, i.e. foliarization is high (the foliar component overrides the caulinary component) and an autotrophic stage is rapidly reached, whereas it ‘explores’ the environment during the adult stage, i.e. axialization is substantial (the caulinary component overrides the foliar component) and autotrophy occurs at a later stage. The morphological markers identified could benefit land‐use managers attempting to control this species before it reaches its optimum developmental stage. PMID:12495918

Bioactive components on immuno-enhancement effects in the traditional Chinese medicine Shenqi Fuzheng Injection based on relevance analysis between chemical HPLC fingerprints and in vivo biological effects.

PubMed

Wang, Jinxu; Tong, Xin; Li, Peibo; Liu, Menghua; Peng, Wei; Cao, Hui; Su, Weiwei

2014-08-08

Shenqi Fuzheng Injection (SFI) is an injectable traditional Chinese herbal formula comprised of two Chinese herbs, Radix codonopsis and Radix astragali, which were commonly used to improve immune functions against chronic diseases in an integrative and holistic way in China and other East Asian countries for thousands of years. This present study was designed to explore the bioactive components on immuno-enhancement effects in SFI using the relevance analysis between chemical fingerprints and biological effects in vivo. According to a four-factor, nine-level uniform design, SFI samples were prepared with different proportions of the four portions separated from SFI via high speed counter current chromatography (HSCCC). SFI samples were assessed with high performance liquid chromatography (HPLC) for 23 identified components. For the immunosuppressed murine experiments, biological effects in vivo were evaluated on spleen index (E1), peripheral white blood cell counts (E2), bone marrow cell counts (E3), splenic lymphocyte proliferation (E4), splenic natural killer cell activity (E5), peritoneal macrophage phagocytosis (E6) and the amount of interleukin-2 (E7). Based on the hypothesis that biological effects in vivo varied with differences in components, multivariate relevance analysis, including gray relational analysis (GRA), multi-linear regression analysis (MLRA) and principal component analysis (PCA), were performed to evaluate the contribution of each identified component. The results indicated that the bioactive components of SFI on immuno-enhancement activities were calycosin-7-O-β-d-glucopyranoside (P9), isomucronulatol-7,2'-di-O-glucoside (P11), biochanin-7-glucoside (P12), 9,10-dimethoxypterocarpan-3-O-xylosylglucoside (P15) and astragaloside IV (P20), which might have positive effects on spleen index (E1), splenic lymphocyte proliferation (E4), splenic natural killer cell activity (E5), peritoneal macrophage phagocytosis (E6) and the amount of interleukin-2 (E7), while 5-hydroxymethyl-furaldehyde (P5) and lobetyolin (P13) might have negative effects on E1, E4, E5, E6 and E7. Finally, the bioactive HPLC fingerprint of SFI based on its bioactive components on immuno-enhancement effects was established for quality control of SFI. In summary, this study provided a perspective to explore the bioactive components in a traditional Chinese herbal formula with a series of HPLC and animal experiments, which would be helpful to improve quality control and inspire further clinical studies of traditional Chinese medicines. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Rapid Characterization of Components in Bolbostemma paniculatum by UPLC/LTQ-Orbitrap MSn Analysis and Multivariate Statistical Analysis for Herb Discrimination.

PubMed

Zeng, Yanling; Lu, Yang; Chen, Zhao; Tan, Jiawei; Bai, Jie; Li, Pengyue; Wang, Zhixin; Du, Shouying

2018-05-11

Bolbostemma paniculatum is a traditional Chinese medicine (TCM) showed various therapeutic effects. Owing to its complex chemical composition, few investigations have acquired a comprehensive cognition for the chemical profiles of this herb and explicated the differences between samples collected from different places. In this study, a strategy based on UPLC tandem LTQ-Orbitrap MS n was established for characterizing chemical components of B. paniculatum . Through a systematic identification strategy, a total of 60 components in B. paniculatum were rapidly separated in 30 min and identified. Then based on peak intensities of all the characterized components, principle component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to classify 18 batches of B. paniculatum into four groups, which were highly consistent with the four climate types of their original places. And five compounds were finally screened out as chemical markers to discriminate the internal quality of B. paniculatum . As the first study to systematically characterize the chemical components of B. paniculatum by UPLC-MS n , the above results could offer essential data for its pharmacological research. And the current strategy could provide useful reference for future investigations on discovery of important chemical constituents in TCM, as well as establishment of quality control and evaluation method.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.

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.

Characterization of extracellular polymeric substances in biofilms under long-term exposure to ciprofloxacin antibiotic using fluorescence excitation-emission matrix and parallel factor analysis.

PubMed

Gu, Chaochao; Gao, Pin; Yang, Fan; An, Dongxuan; Munir, Mariya; Jia, Hanzhong; Xue, Gang; Ma, Chunyan

2017-05-01

The presence of antibiotic residues in the environment has been regarded as an emerging concern due to their potential adverse environmental consequences such as antibiotic resistance. However, the interaction between antibiotics and extracellular polymeric substances (EPSs) of biofilms in wastewater treatment systems is not entirely clear. In this study, the effect of ciprofloxacin (CIP) antibiotic on biofilm EPS matrix was investigated and characterized using fluorescence excitation-emission matrix (EEM) and parallel factor (PARAFAC) analysis. Physicochemical analysis showed that the proteins were the major EPS fraction, and their contents increased gradually with an increase in CIP concentration (0-300 μg/L). Based on the characterization of biofilm tightly bound EPS (TB-EPS) by EEM, three fluorescent components were identified by PARAFAC analysis. Component C1 was associated with protein-like substances, and components C2 and C3 belonged to humic-like substances. Component C1 exhibited an increasing trend as the CIP addition increased. Pearson's correlation results showed that CIP correlated significantly with the protein contents and component C1, while strong correlations were also found among UV 254 , dissolved organic carbon, humic acids, and component C3. A combined use of EEM-PARAFAC analysis and chemical measurements was demonstrated as a favorable approach for the characterization of variations in biofilm EPS in the presence of CIP antibiotic.

Partial Analysis of Insta-Foam

NASA Technical Reports Server (NTRS)

Chou, L. W.

1983-01-01

Insta-Foam, used as a thermal insulator for the non-critical area of the external tank during the prelaunch phase to minimize icing, is a two-component system. Component A has polyisocyanates, blowing agents, and stabilizers; Component B has the polyols, catalysts, blowing agents, stabilizers and fire retardant. The blowing agents are Freon 11 and Freon 12, the stabilizers are silicone surfactants, the catalysts are tertiary amines, and the fire retardant is tri-(beta-chloro-isopropyl) phosphate (PCF). High performance liquid chromatography (HPLC) was quantitatively identified polyols and PFC.

Recognition of units in coarse, unconsolidated braided-stream deposits from geophysical log data with principal components analysis

USGS Publications Warehouse

Morin, R.H.

1997-01-01

Returns from drilling in unconsolidated cobble and sand aquifers commonly do not identify lithologic changes that may be meaningful for Hydrogeologic investigations. Vertical resolution of saturated, Quaternary, coarse braided-slream deposits is significantly improved by interpreting natural gamma (G), epithermal neutron (N), and electromagnetically induced resistivity (IR) logs obtained from wells at the Capital Station site in Boise, Idaho. Interpretation of these geophysical logs is simplified because these sediments are derived largely from high-gamma-producing source rocks (granitics of the Boise River drainage), contain few clays, and have undergone little diagenesis. Analysis of G, N, and IR data from these deposits with principal components analysis provides an objective means to determine if units can be recognized within the braided-stream deposits. In particular, performing principal components analysis on G, N, and IR data from eight wells at Capital Station (1) allows the variable system dimensionality to be reduced from three to two by selecting the two eigenvectors with the greatest variance as axes for principal component scatterplots, (2) generates principal components with interpretable physical meanings, (3) distinguishes sand from cobble-dominated units, and (4) provides a means to distinguish between cobble-dominated units.

A Cognitive Task Analysis of Information Management Strategies in a Computerized Provider Order Entry Environment

PubMed Central

Weir, Charlene R.; Nebeker, Jonathan J.R.; Hicken, Bret L.; Campo, Rebecca; Drews, Frank; LeBar, Beth

2007-01-01

Objective Computerized Provider Order Entry (CPOE) with electronic documentation, and computerized decision support dramatically changes the information environment of the practicing clinician. Prior work patterns based on paper, verbal exchange, and manual methods are replaced with automated, computerized, and potentially less flexible systems. The objective of this study is to explore the information management strategies that clinicians use in the process of adapting to a CPOE system using cognitive task analysis techniques. Design Observation and semi-structured interviews were conducted with 88 primary-care clinicians at 10 Veterans Administration Medical Centers. Measurements Interviews were taped, transcribed, and extensively analyzed to identify key information management goals, strategies, and tasks. Tasks were aggregated into groups, common components across tasks were clarified, and underlying goals and strategies identified. Results Nearly half of the identified tasks were not fully supported by the available technology. Six core components of tasks were identified. Four meta-cognitive information management goals emerged: 1) Relevance Screening; 2) Ensuring Accuracy; 3) Minimizing memory load; and 4) Negotiating Responsibility. Strategies used to support these goals are presented. Conclusion Users develop a wide array of information management strategies that allow them to successfully adapt to new technology. Supporting the ability of users to develop adaptive strategies to support meta-cognitive goals is a key component of a successful system. PMID:17068345

Analysis of E. rutaecarpa Alkaloids Constituents In Vitro and In Vivo by UPLC-Q-TOF-MS Combined with Diagnostic Fragment

PubMed Central

Yang, Shenshen; Tian, Meng; Yuan, Lei; Deng, Haoyue; Wang, Lei; Li, Aizhu; Hou, Zhiguo; Li, Yubo

2016-01-01

Evodia rutaecarpa (Juss.) Benth. (Rutaceae) dried ripe fruit is used for dispelling colds, soothing liver, and analgesia. Pharmacological research has proved that alkaloids are the main active ingredients of E. rutaecarpa. This study aimed to rapidly classify and identify the alkaloids constituents of E. rutaecarpa by using UPLC-Q-TOF-MS coupled with diagnostic fragments. Furthermore, the effects of the material base of E. rutaecarpa bioactive ingredients in vivo were examined such that the transitional components in the blood of rats intragastrically given E. rutaecarpa were analyzed and identified. In this study, the type of alcohol extraction of E. rutaecarpa and the corresponding blood sample were used for the analysis by UPLC-Q-TOF-MS in positive ion mode. After reviewing much of the literature and collected information on the fragments, we obtained some diagnostic fragments of the alkaloids. Combining the diagnostic fragments with the technology of UPLC-Q-TOF-MS, we identified the compounds of E. rutaecarpa and blood samples and compared the ion fragment information with that of the alkaloids in E. rutaecarpa. A total of 17 alkaloids components and 6 blood components were identified. The proposed method was rapid, accurate, and sensitive. Therefore, this technique can reliably and practically analyze the chemical constituents in traditional Chinese medicine (TCM). PMID:27446630

Quantitative Trait Loci Mapping in Brassica rapa Revealed the Structural and Functional Conservation of Genetic Loci Governing Morphological and Yield Component Traits in the A, B, and C Subgenomes of Brassica Species

PubMed Central

Li, Xiaonan; Ramchiary, Nirala; Dhandapani, Vignesh; Choi, Su Ryun; Hur, Yoonkang; Nou, Ill-Sup; Yoon, Moo Kyoung; Lim, Yong Pyo

2013-01-01

Brassica rapa is an important crop species that produces vegetables, oilseed, and fodder. Although many studies reported quantitative trait loci (QTL) mapping, the genes governing most of its economically important traits are still unknown. In this study, we report QTL mapping for morphological and yield component traits in B. rapa and comparative map alignment between B. rapa, B. napus, B. juncea, and Arabidopsis thaliana to identify candidate genes and conserved QTL blocks between them. A total of 95 QTL were identified in different crucifer blocks of the B. rapa genome. Through synteny analysis with A. thaliana, B. rapa candidate genes and intronic and exonic single nucleotide polymorphisms in the parental lines were detected from whole genome resequenced data, a few of which were validated by mapping them to the QTL regions. Semi-quantitative reverse transcriptase PCR analysis showed differences in the expression levels of a few genes in parental lines. Comparative mapping identified five key major evolutionarily conserved crucifer blocks (R, J, F, E, and W) harbouring QTL for morphological and yield components traits between the A, B, and C subgenomes of B. rapa, B. juncea, and B. napus. The information of the identified candidate genes could be used for breeding B. rapa and other related Brassica species. PMID:23223793

The Lymantria dispar nucleopolyhedrovirus enhancins are components of occlusion-derived virus

Treesearch

James M. Slavicek; Holly J.R. Popham

2005-01-01

Enhancins are metalloproteinases, first identified in granuloviruses, that can enhance nucleopolyhedrovirus (NIPV) potency. We had previously identified two enhamin genes (El and E2) in the Lymantria dispar multinucleocapsid NPV (LdMNPV) and showed that both were functional. For this study, we have extended our analysis of LdMNPV...

Factors Related to the Recruitment and Retention of Professionals from Specialized Disciplines.

ERIC Educational Resources Information Center

Harrington, Deborah; And Others

A survey was developed to identify critical factors in job selection and retention for speech/language pathologists, physical therapists, and occupational therapists. The survey was completed by 455 New Mexico professionals in these disciplines. A principal-components analysis identified six factors that were important in career decisions: (1)…

A three-way parallel ICA approach to analyze links among genetics, brain structure and brain function.

PubMed

Vergara, Victor M; Ulloa, Alvaro; Calhoun, Vince D; Boutte, David; Chen, Jiayu; Liu, Jingyu

2014-09-01

Multi-modal data analysis techniques, such as the Parallel Independent Component Analysis (pICA), are essential in neuroscience, medical imaging and genetic studies. The pICA algorithm allows the simultaneous decomposition of up to two data modalities achieving better performance than separate ICA decompositions and enabling the discovery of links between modalities. However, advances in data acquisition techniques facilitate the collection of more than two data modalities from each subject. Examples of commonly measured modalities include genetic information, structural magnetic resonance imaging (MRI) and functional MRI. In order to take full advantage of the available data, this work extends the pICA approach to incorporate three modalities in one comprehensive analysis. Simulations demonstrate the three-way pICA performance in identifying pairwise links between modalities and estimating independent components which more closely resemble the true sources than components found by pICA or separate ICA analyses. In addition, the three-way pICA algorithm is applied to real experimental data obtained from a study that investigate genetic effects on alcohol dependence. Considered data modalities include functional MRI (contrast images during alcohol exposure paradigm), gray matter concentration images from structural MRI and genetic single nucleotide polymorphism (SNP). The three-way pICA approach identified links between a SNP component (pointing to brain function and mental disorder associated genes, including BDNF, GRIN2B and NRG1), a functional component related to increased activation in the precuneus area, and a gray matter component comprising part of the default mode network and the caudate. Although such findings need further verification, the simulation and in-vivo results validate the three-way pICA algorithm presented here as a useful tool in biomedical data fusion applications. Copyright © 2014 Elsevier Inc. All rights reserved.

Comparing Independent Component Analysis with Principle Component Analysis in Detecting Alterations of Porphyry Copper Deposit (case Study: Ardestan Area, Central Iran)

NASA Astrophysics Data System (ADS)

Mahmoudishadi, S.; Malian, A.; Hosseinali, F.

2017-09-01

The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) has been evaluated for the visible and near-infrared (VNIR) and Shortwave infrared (SWIR) subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6) were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.

Correspondence Analysis-Theory and Application in Management Accounting Research

NASA Astrophysics Data System (ADS)

Duller, Christine

2010-09-01

Correspondence analysis is an explanatory data analytic technique and is used to identify systematic relations between categorical variables. It is related to principal component analysis and the results provide information on the structure of categorical variables similar to the results given by a principal component analysis in case of metric variables. Classical correspondence analysis is designed two-dimensional, whereas multiple correspondence analysis is an extension to more than two variables. After an introductory overview of the idea and the implementation in standard software packages (PASW, SAS, R) an example in recent research is presented, which deals with strategic management accounting in family and non-family enterprises in Austria, where 70% to 80% of all enterprises can be classified as family firms. Although there is a growing body of literature focusing on various management issues in family firms, so far the state of the art of strategic management accounting in family firms is an empirically under-researched subject. In relevant literature only the (empirically untested) hypothesis can be found, that family firms tend to have less formalized management accounting systems than non-family enterprises. Creating a correspondence analysis will help to identify the underlying structure, which is responsible for differences in strategic management accounting.

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.

Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

NASA Astrophysics Data System (ADS)

Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

2016-04-01

Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.

Comparison of the phenolic composition of fruit juices by single step gradient HPLC analysis of multiple components versus multiple chromatographic runs optimised for individual families.

PubMed

Bremner, P D; Blacklock, C J; Paganga, G; Mullen, W; Rice-Evans, C A; Crozier, A

2000-06-01

After minimal sample preparation, two different HPLC methodologies, one based on a single gradient reversed-phase HPLC step, the other on multiple HPLC runs each optimised for specific components, were used to investigate the composition of flavonoids and phenolic acids in apple and tomato juices. The principal components in apple juice were identified as chlorogenic acid, phloridzin, caffeic acid and p-coumaric acid. Tomato juice was found to contain chlorogenic acid, caffeic acid, p-coumaric acid, naringenin and rutin. The quantitative estimates of the levels of these compounds, obtained with the two HPLC procedures, were very similar, demonstrating that either method can be used to analyse accurately the phenolic components of apple and tomato juices. Chlorogenic acid in tomato juice was the only component not fully resolved in the single run study and the multiple run analysis prior to enzyme treatment. The single run system of analysis is recommended for the initial investigation of plant phenolics and the multiple run approach for analyses where chromatographic resolution requires improvement.

Sublaminate analysis of interlaminar fracture in composites

NASA Technical Reports Server (NTRS)

Armanios, E. A.; Rehfield, L. W.

1986-01-01

A simple analysis method based upon a transverse shear deformation theory and a sublaminate approach is utilized to analyze a mixed-mode edge delamination specimen. The analysis provides closed form expressions for the interlaminar shear stresses ahead of the crack, the total energy release rate, and the energy release rate components. The parameters controlling the behavior are identified. The effect of specimen stacking sequence and delamination interface on the strain energy release rate components is investigated. Results are compared with a finite element simulation for reference. The simple nature of the method makes it suitable for preliminary design analyses which require a large number of configurations to be evaluated quickly and economically.

Screening of patients with bronchopulmonary diseases using methods of infrared laser photoacoustic spectroscopy and principal component analysis

NASA Astrophysics Data System (ADS)

Kistenev, Yury V.; Karapuzikov, Alexander I.; Kostyukova, Nadezhda Yu.; Starikova, Marina K.; Boyko, Andrey A.; Bukreeva, Ekaterina B.; Bulanova, Anna A.; Kolker, Dmitry B.; Kuzmin, Dmitry A.; Zenov, Konstantin G.; Karapuzikov, Alexey A.

2015-06-01

A human exhaled air analysis by means of infrared (IR) laser photoacoustic spectroscopy is presented. Eleven healthy nonsmoking volunteers (control group) and seven patients with chronic obstructive pulmonary disease (COPD, target group) were involved in the study. The principal component analysis method was used to select the most informative ranges of the absorption spectra of patients' exhaled air in terms of the separation of the studied groups. It is shown that the data of the profiles of exhaled air absorption spectrum in the informative ranges allow identifying COPD patients in comparison to the control group.

Sensory characteristics and consumer preference for chicken meat in Guinea.

PubMed

Sow, T M A; Grongnet, J F

2010-10-01

This study identified the sensory characteristics and consumer preference for chicken meat in Guinea. Five chicken samples [live village chicken, live broiler, live spent laying hen, ready-to-cook broiler, and ready-to-cook broiler (imported)] bought from different locations were assessed by 10 trained panelists using 19 sensory attributes. The ANOVA results showed that 3 chicken appearance attributes (brown, yellow, and white), 5 chicken odor attributes (oily, intense, medicine smell, roasted, and mouth persistent), 3 chicken flavor attributes (sweet, bitter, and astringent), and 8 chicken texture attributes (firm, tender, juicy, chew, smooth, springy, hard, and fibrous) were significantly discriminating between the chicken samples (P<0.05). Principal component analysis of the sensory data showed that the first 2 principal components explained 84% of the sensory data variance. The principal component analysis results showed that the live village chicken, the live spent laying hen, and the ready-to-cook broiler (imported) were very well represented and clearly distinguished from the live broiler and the ready-to-cook broiler. One hundred twenty consumers expressed their preferences for the chicken samples using a 5-point Likert scale. The hierarchical cluster analysis of the preference data identified 4 homogenous consumer clusters. The hierarchical cluster analysis results showed that the live village chicken was the most preferred chicken sample, whereas the ready-to-cook broiler was the least preferred one. The partial least squares regression (PLSR) type 1 showed that 72% of the sensory data for the first 2 principal components explained 83% of the chicken preference. The PLSR1 identified that the sensory characteristics juicy, oily, sweet, hard, mouth persistent, and yellow were the most relevant sensory drivers of the Guinean chicken preference. The PLSR2 (with multiple responses) identified the relationship between the chicken samples, their sensory attributes, and the consumer clusters. Our results showed that there was not a chicken category that was exclusively preferred from the other chicken samples and therefore highlight the existence of place for development of all chicken categories in the local market.

Multispectral Digital Image Analysis of Varved Sediments in Thin Sections

NASA Astrophysics Data System (ADS)

Jäger, K.; Rein, B.; Dietrich, S.

2006-12-01

An update of the recently developed method COMPONENTS (Rein, 2003, Rein & Jäger, subm.) for the discrimination of sediment components in thin sections is presented here. COMPONENTS uses a 6-band (multispectral) image analysis. To derive six-band spectral information of the sediments, thin sections are scanned with a digital camera mounted on a polarizing microscope. The thin sections are scanned twice, under polarized and under unpolarized plain light. During each run RGB images are acquired which are subsequently stacked to a six-band file. The first three bands (Blue=1, Green=2, Red=3) result from the spectral behaviour in the blue, green and red band with unpolarized light conditions, and the bands 4 to 6 (Blue=4, Green=5, Red=6) from the polarized light run. The next step is the discrimination of the sediment components by their transmission behaviour. Automatic classification algorithms broadly used in remote sensing applications cannot be used due to unavoidable variations of sediment particle or thin section thicknesses that change absolute grey values of the sediment components. Thus, we use an approach based on band ratios, also known as indices. By using band ratios, the grey values measured in different bands are normalized against each other and illumination variations (e.g. thickness variations) are eliminated. By combining specific ratios we are able to detect all seven major components in the investigated sediments (carbonates, diatoms, fine clastic material, plant rests, pyrite, quartz and resin). Then, the classification results (compositional maps) are validated. Although the automatic classification and the analogous classification show high concordances, some systematic errors could be identified. For example, the transition zone between the sediment and resin filled cracks is classified as fine clastic material and very coarse carbonates are partly classified as quartz because coarse carbonates can be very bright and spectra are partly saturated (grey value 255). With reduced illumination intensity "carbonate image pixels" get unsaturated and can be well distinguished from quartz grains. During the evaluation process we identify all falsely classified areas using neighbourhood matrices and reclassify them. Finally, we use filter techniques to derive downcore component frequencies from the classified thin section images for variable thick virtual samples. The filter conducts neighbourhood analyses. After filtering, each pixel of the filtered images carries the information about the frequency of any given component in a defined neighbourhood around (virtual sampling). References Rein, B. (2003) In-situ Reflektionsspektroskopie und digitale Bildanalyse Gewinnung hochauflösender Paläoumweltdaten mit fernerkundlichen Methoden, Habilitation Thesis, Univ. Mainz, 104 p. Jäger, K. and Rein, B. (2005): Identifying varve components using digital image analysis techniques. - in: Heidi Haas, Karl Ramseyer & Fritz Schlunegger (eds.): Sediment 2005 (18th-20th July 2005), Schriftenreihe der Deutschen Gesellschaft für Geowissenschaften, 38, p. 81 Rein, B. and Jäger, K. (subm.) COMPONENTS - Sediment component detection in thin sections by multispectral digital image analysis. Sedimentology.

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

PubMed

Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

2018-05-01

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

Cell Wall Composition and Candidate Biosynthesis Gene Expression During Rice Development

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

Lin, Fan; Manisseri, Chithra; Fagerström, Alexandra

Cell walls of grasses, including cereal crops and biofuel grasses, comprise the majority of plant biomass and intimately influence plant growth, development and physiology. However, the functions of many cell wall synthesis genes, and the relationships among and the functions of cell wall components remain obscure. To better understand the patterns of cell wall accumulation and identify genes that act in grass cell wall biosynthesis, we characterized 30 samples from aerial organs of rice (Oryza sativa cv. Kitaake) at 10 developmental time points, 3-100 d post-germination. Within these samples, we measured 15 cell wall chemical components, enzymatic digestibility and 18more » cell wall polysaccharide epitopes/ligands. We also used quantitative reverse transcription-PCR to measure expression of 50 glycosyltransferases, 15 acyltransferases and eight phenylpropanoid genes, many of which had previously been identified as being highly expressed in rice. Most cell wall components vary significantly during development, and correlations among them support current understanding of cell walls. We identified 92 significant correlations between cell wall components and gene expression and establish nine strong hypotheses for genes that synthesize xylans, mixed linkage glucan and pectin components. This work provides an extensive analysis of cell wall composition throughout rice development, identifies genes likely to synthesize grass cell walls, and provides a framework for development of genetically improved grasses for use in lignocellulosic biofuel production and agriculture.« less

A Deeper Examination of Thorellius atrox Scorpion Venom Components with Omic Techonologies

PubMed Central

Romero-Gutierrez, Teresa; Batista, Cesar V. F.

2017-01-01

This communication reports a further examination of venom gland transcripts and venom composition of the Mexican scorpion Thorellius atrox using RNA-seq and tandem mass spectrometry. The RNA-seq, which was performed with the Illumina protocol, yielded more than 20,000 assembled transcripts. Following a database search and annotation strategy, 160 transcripts were identified, potentially coding for venom components. A novel sequence was identified that potentially codes for a peptide with similarity to spider ω-agatoxins, which act on voltage-gated calcium channels, not known before to exist in scorpion venoms. Analogous transcripts were found in other scorpion species. They could represent members of a new scorpion toxin family, here named omegascorpins. The mass fingerprint by LC-MS identified 135 individual venom components, five of which matched with the theoretical masses of putative peptides translated from the transcriptome. The LC-MS/MS de novo sequencing allowed to reconstruct and identify 42 proteins encoded by assembled transcripts, thus validating the transcriptome analysis. Earlier studies conducted with this scorpion venom permitted the identification of only twenty putative venom components. The present work performed with more powerful and modern omic technologies demonstrates the capacity of accomplishing a deeper characterization of scorpion venom components and the identification of novel molecules with potential applications in biomedicine and the study of ion channel physiology. PMID:29231872

Estimating optical imaging system performance for space applications

NASA Technical Reports Server (NTRS)

Sinclair, K. F.

1972-01-01

The critical system elements of an optical imaging system are identified and a method for an initial assessment of system performance is presented. A generalized imaging system is defined. A system analysis is considered, followed by a component analysis. An example of the method is given using a film imaging system.

Opportunities for Applied Behavior Analysis in the Total Quality Movement.

ERIC Educational Resources Information Center

Redmon, William K.

1992-01-01

This paper identifies critical components of recent organizational quality improvement programs and specifies how applied behavior analysis can contribute to quality technology. Statistical Process Control and Total Quality Management approaches are compared, and behavior analysts are urged to build their research base and market behavior change…

A Review of Functional Analysis Methods Conducted in Public School Classroom Settings

ERIC Educational Resources Information Center

Lloyd, Blair P.; Weaver, Emily S.; Staubitz, Johanna L.

2016-01-01

The use of functional behavior assessments (FBAs) to address problem behavior in classroom settings has increased as a result of education legislation and long-standing evidence supporting function-based interventions. Although functional analysis remains the standard for identifying behavior--environment functional relations, this component is…

Genome-wide Association Analysis of Kernel Weight in Hard Winter Wheat

USDA-ARS?s Scientific Manuscript database

Wheat kernel weight is an important and heritable component of wheat grain yield and a key predictor of flour extraction. Genome-wide association analysis was conducted to identify genomic regions associated with kernel weight and kernel weight environmental response in 8 trials of 299 hard winter ...

School Health Promotion Policies and Adolescent Risk Behaviors in Israel: A Multilevel Analysis

ERIC Educational Resources Information Center

Tesler, Riki; Harel-Fisch, Yossi; Baron-Epel, Orna

2016-01-01

Background: Health promotion policies targeting risk-taking behaviors are being implemented across schools in Israel. This study identified the most effective components of these policies influencing cigarette smoking and alcohol consumption among adolescents. Methods: Logistic hierarchical linear model (HLM) analysis of data for 5279 students in…

An Analysis of the Perceptions and Resources of Large University Classes

PubMed Central

Cash, Ceilidh Barlow; Letargo, Jessa; Graether, Steffen P.; Jacobs, Shoshanah R.

2017-01-01

Large class learning is a reality that is not exclusive to the first-year experience at midsized, comprehensive universities; upper-year courses have similarly high enrollment, with many class sizes greater than 200 students. Research into the efficacy and deficiencies of large undergraduate classes has been ongoing for more than 100 years, with most research associating large classes with weak student engagement, decreased depth of learning, and ineffective interactions. This study used a multidimensional research approach to survey student and instructor perceptions of large biology classes and to characterize the courses offered by a department according to resources and course structure using a categorical principal components analysis. Both student and instructor survey results indicated that a large class begins around 240 students. Large classes were identified as impersonal and classified using extrinsic qualifiers; however, students did identify techniques that made the classes feel smaller. In addition to the qualitative survey, we also attempted to quantify courses by collecting data from course outlines and analyzed the data using categorical principal component analysis. The analysis maps institutional change in resource allocation and teaching structure from 2010 through 2014 and validates the use of categorical principal components analysis in educational research. We examine what perceptions and factors are involved in a large class that is perceived to feel small. Our analysis suggests that it is not the addition of resources or difference in the lecturing method, but it is the instructor that determines whether a large class can feel small. PMID:28495937

[Autoantibody formation against the antigens of the synaptonemal complex in the syngeneic immunization of male Mus musculus].

PubMed

Dadashev, S Ia; Gorach, G G; Kolomiets, O L

1994-01-01

Male mice were immunized with the suspension of synaptonemal complexes (SC) isolated from mouse spermatocytes nuclei. The indirect immunofluorescent analysis showed the active binding of sera obtained from immunized mice to SC of mouse spermatocyte spreads. At early and mid-pachytene, SC can be clearly identified in 19 autosome bivalents and in sex chromosome bivalent. According to the electron microscopic analysis, all structural elements of SC bind antibodies. Metaphase chromosomes were not stained with the immune sera. Specificity of interaction between SC components and antibodies was confirmed in a series of control experiments. Analysis of sera obtained from mice after their syngeneic immunization with isolated SC fraction suggested that certain mouse SC components induce the formation of autoantibodies. This, in turn, suggests that these SC components are meiosis-specific.

Comprehensive proteomic analysis of the human spliceosome

NASA Astrophysics Data System (ADS)

Zhou, Zhaolan; Licklider, Lawrence J.; Gygi, Steven P.; Reed, Robin

2002-09-01

The precise excision of introns from pre-messenger RNA is performed by the spliceosome, a macromolecular machine containing five small nuclear RNAs and numerous proteins. Much has been learned about the protein components of the spliceosome from analysis of individual purified small nuclear ribonucleoproteins and salt-stable spliceosome `core' particles. However, the complete set of proteins that constitutes intact functional spliceosomes has yet to be identified. Here we use maltose-binding protein affinity chromatography to isolate spliceosomes in highly purified and functional form. Using nanoscale microcapillary liquid chromatography tandem mass spectrometry, we identify ~145 distinct spliceosomal proteins, making the spliceosome the most complex cellular machine so far characterized. Our spliceosomes comprise all previously known splicing factors and 58 newly identified components. The spliceosome contains at least 30 proteins with known or putative roles in gene expression steps other than splicing. This complexity may be required not only for splicing multi-intronic metazoan pre-messenger RNAs, but also for mediating the extensive coupling between splicing and other steps in gene expression.

Stability analysis of an autocatalytic protein model

NASA Astrophysics Data System (ADS)

Lee, Julian

2016-05-01

A self-regulatory genetic circuit, where a protein acts as a positive regulator of its own production, is known to be the simplest biological network with a positive feedback loop. Although at least three components—DNA, RNA, and the protein—are required to form such a circuit, stability analysis of the fixed points of this self-regulatory circuit has been performed only after reducing the system to a two-component system, either by assuming a fast equilibration of the DNA component or by removing the RNA component. Here, stability of the fixed points of the three-component positive feedback loop is analyzed by obtaining eigenvalues of the full three-dimensional Hessian matrix. In addition to rigorously identifying the stable fixed points and saddle points, detailed information about the system can be obtained, such as the existence of complex eigenvalues near a fixed point.

Discriminant analysis of resting-state functional connectivity patterns on the Grassmann manifold

NASA Astrophysics Data System (ADS)

Fan, Yong; Liu, Yong; Jiang, Tianzi; Liu, Zhening; Hao, Yihui; Liu, Haihong

2010-03-01

The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional networks that are informative for schizophrenia diagnosis.

Combination of multivariate curve resolution and multivariate classification techniques for comprehensive high-performance liquid chromatography-diode array absorbance detection fingerprints analysis of Salvia reuterana extracts.

PubMed

Hakimzadeh, Neda; Parastar, Hadi; Fattahi, Mohammad

2014-01-24

In this study, multivariate curve resolution (MCR) and multivariate classification methods are proposed to develop a new chemometric strategy for comprehensive analysis of high-performance liquid chromatography-diode array absorbance detection (HPLC-DAD) fingerprints of sixty Salvia reuterana samples from five different geographical regions. Different chromatographic problems occurred during HPLC-DAD analysis of S. reuterana samples, such as baseline/background contribution and noise, low signal-to-noise ratio (S/N), asymmetric peaks, elution time shifts, and peak overlap are handled using the proposed strategy. In this way, chromatographic fingerprints of sixty samples are properly segmented to ten common chromatographic regions using local rank analysis and then, the corresponding segments are column-wise augmented for subsequent MCR analysis. Extended multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure component profiles in each segment. In general, thirty-one chemical components were resolved using MCR-ALS in sixty S. reuterana samples and the lack of fit (LOF) values of MCR-ALS models were below 10.0% in all cases. Pure spectral profiles are considered for identification of chemical components by comparing their resolved spectra with the standard ones and twenty-four components out of thirty-one components were identified. Additionally, pure elution profiles are used to obtain relative concentrations of chemical components in different samples for multivariate classification analysis by principal component analysis (PCA) and k-nearest neighbors (kNN). Inspection of the PCA score plot (explaining 76.1% of variance accounted for three PCs) showed that S. reuterana samples belong to four clusters. The degree of class separation (DCS) which quantifies the distance separating clusters in relation to the scatter within each cluster is calculated for four clusters and it was in the range of 1.6-5.8. These results are then confirmed by kNN. In addition, according to the PCA loading plot and kNN dendrogram of thirty-one variables, five chemical constituents of luteolin-7-o-glucoside, salvianolic acid D, rosmarinic acid, lithospermic acid and trijuganone A are identified as the most important variables (i.e., chemical markers) for clusters discrimination. Finally, the effect of different chemical markers on samples differentiation is investigated using counter-propagation artificial neural network (CP-ANN) method. It is concluded that the proposed strategy can be successfully applied for comprehensive analysis of chromatographic fingerprints of complex natural samples. Copyright © 2013 Elsevier B.V. All rights reserved.

PHOSPHOLIPID COMPONENTS OF SINDBIS VIRUS,

DTIC Science & Technology

values. In addition to sphingomyelin, phosphatidyl choline , and phosphatidyl ethanolamine, other phospholipids were tentatively identified as phosphatidyl... inositol , phosphatidyl serine, phosphatidyl glycerol, and diphosphatidyl glycerol. Gas chromatographic analysis of the total fatty acids from

Multimodal neural correlates of cognitive control in the Human Connectome Project.

PubMed

Lerman-Sinkoff, Dov B; Sui, Jing; Rachakonda, Srinivas; Kandala, Sridhar; Calhoun, Vince D; Barch, Deanna M

2017-12-01

Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function. Copyright © 2017 Elsevier Inc. All rights reserved.

Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods.

PubMed

Sun, Li-Li; Wang, Meng; Zhang, Hui-Jie; Liu, Ya-Nan; Ren, Xiao-Liang; Deng, Yan-Ru; Qi, Ai-Di

2018-01-01

Polygoni Multiflori Radix (PMR) is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, multivariate chemometric methods and mass spectrometry were combined to analyze the ultra-high-performance liquid chromatograph (UPLC) fingerprints of PMR from six different geographical origins. A chemometric strategy based on multivariate curve resolution-alternating least squares (MCR-ALS) and three classification methods is proposed to analyze the UPLC fingerprints obtained. Common chromatographic problems, including the background contribution, baseline contribution, and peak overlap, were handled by the established MCR-ALS model. A total of 22 components were resolved. Moreover, relative species concentrations were obtained from the MCR-ALS model, which was used for multivariate classification analysis. Principal component analysis (PCA) and Ward's method have been applied to classify 72 PMR samples from six different geographical regions. The PCA score plot showed that the PMR samples fell into four clusters, which related to the geographical location and climate of the source areas. The results were then corroborated by Ward's method. In addition, according to the variance-weighted distance between cluster centers obtained from Ward's method, five components were identified as the most significant variables (chemical markers) for cluster discrimination. A counter-propagation artificial neural network has been applied to confirm and predict the effects of chemical markers on different samples. Finally, the five chemical markers were identified by UPLC-quadrupole time-of-flight mass spectrometer. Components 3, 12, 16, 18, and 19 were identified as 2,3,5,4'-tetrahydroxy-stilbene-2-O-β-d-glucoside, emodin-8-O-β-d-glucopyranoside, emodin-8-O-(6'-O-acetyl)-β-d-glucopyranoside, emodin, and physcion, respectively. In conclusion, the proposed method can be applied for the comprehensive analysis of natural samples. Copyright © 2016. Published by Elsevier B.V.

Customer Service Analysis of Air Combat Command Vehicle Maintenance Support

DTIC Science & Technology

1993-09-01

the survey, the researchers categorized the services or variables into marketing mix components: product, price, promotion, and customer service...comparing and analyzing the variables identified in the previous three phases to determine a strategic marketing mix (46:9). After analyzing the data...service/physical distribution. Additionally, they found that customer service/physical distribution was an integral component of the marketing mix , and

A Developmental Model of Cross-Cultural Competence at the Tactical Level

DTIC Science & Technology

2010-11-01

components of 3C and describe how 3C develops in Soldiers. Five components of 3C were identified: Cultural Maturity , Cognitive Flexibility, Cultural...a result of the data analysis: Cultural Maturity , Cognitive Flexibility, Cultural Knowledge, Cultural Acuity, and Interpersonal Skills. These five...create regressions in the 3C development process. In short, KSAAs mature interdependently and simultaneously. Thus, development and transitions across

Modality-Spanning Deficits in Attention-Deficit/Hyperactivity Disorder in Functional Networks, Gray Matter, and White Matter

PubMed Central

Kessler, Daniel; Angstadt, Michael; Welsh, Robert C.

2014-01-01

Previous neuroimaging investigations in attention-deficit/hyperactivity disorder (ADHD) have separately identified distributed structural and functional deficits, but interconnections between these deficits have not been explored. To unite these modalities in a common model, we used joint independent component analysis, a multivariate, multimodal method that identifies cohesive components that span modalities. Based on recent network models of ADHD, we hypothesized that altered relationships between large-scale networks, in particular, default mode network (DMN) and task-positive networks (TPNs), would co-occur with structural abnormalities in cognitive regulation regions. For 756 human participants in the ADHD-200 sample, we produced gray and white matter volume maps with voxel-based morphometry, as well as whole-brain functional connectomes. Joint independent component analysis was performed, and the resulting transmodal components were tested for differential expression in ADHD versus healthy controls. Four components showed greater expression in ADHD. Consistent with our a priori hypothesis, we observed reduced DMN-TPN segregation co-occurring with structural abnormalities in dorsolateral prefrontal cortex and anterior cingulate cortex, two important cognitive control regions. We also observed altered intranetwork connectivity in DMN, dorsal attention network, and visual network, with co-occurring distributed structural deficits. There was strong evidence of spatial correspondence across modalities: For all four components, the impact of the respective component on gray matter at a region strongly predicted the impact on functional connectivity at that region. Overall, our results demonstrate that ADHD involves multiple, cohesive modality spanning deficits, each one of which exhibits strong spatial overlap in the pattern of structural and functional alterations. PMID:25505309

'The genetic analysis of functional connectomics in Drosophila'

PubMed Central

Meinertzhagen, Ian A.; Lee, Chi-Hon

2014-01-01

Fly and vertebrate nervous systems share many organization characteristics, such as layers, columns and glomeruli, and utilize similar synaptic components, such ion channels and receptors. Both also exhibit similar network features. Recent technological advances, especially in electron microscopy, now allow us to determine synaptic circuits and identify pathways cell-by-cell, as part of the fly’s connectome. Genetic tools provide the means to identify synaptic components, as well as to record and manipulate neuronal activity, adding function to the connectome. This review discusses technical advances in these emerging areas of functional connectomics, offering prognoses in each and identifying the challenges in bridging structural connectomics to molecular biology and synaptic physiology, thereby determining fundamental computation mechanisms that underlie behaviour. PMID:23084874

Differentiation of Aurantii Fructus Immaturus from Poniciri Trifoliatae Fructus Immaturus using Flow- injection Mass spectrometric (FIMS) Metabolic Fingerprinting Method Combined with Chemometrics

PubMed Central

Zhao, Yang; Chang, Yuan-Shiun; Chen, Pei

2015-01-01

A flow-injection mass spectrometric metabolic fingerprinting method in combination with chemometrics was used to differentiate Aurantii Fructus Immaturus from its counterfeit Poniciri Trifoliatae Fructus Immaturus. Flow-injection mass spectrometric (FIMS) fingerprints of 9 Aurantii Fructus Immaturus samples and 12 Poniciri Trifoliatae Fructus Immaturus samples were acquired and analyzed using principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). The authentic herbs were differentiated from their counterfeits easily. Eight characteristic components which were responsible for the difference between the samples were tentatively identified. Furthermore, three out of the eight components, naringin, hesperidin, and neohesperidin, were quantified. The results are useful to help identify the authenticity of Aurantii Fructus Immaturus. PMID:25622204

Identification of a xanthine oxidase-inhibitory component from Sophora flavescens using NMR-based metabolomics.

PubMed

Suzuki, Ryuichiro; Hasuike, Yuka; Hirabayashi, Moeka; Fukuda, Tatsuo; Okada, Yoshihito; Shirataki, Yoshiaki

2013-10-01

We demonstrate that NMR-based metabolomics studies can be used to identify xanthine oxidase-inhibitory compounds in the diethyl ether soluble fraction prepared from a methanolic extract of Sophora flavescens. Loading plot analysis, accompanied by direct comparison of 1H NMR spectraexhibiting characteristic signals, identified compounds exhibiting inhibitory activity. NMR analysis indicated that these characteristic signals were attributed to flavanones such as sophoraflavanone G and kurarinone. Sophoraflavanone G showed inhibitory activity towards xanthine oxidase in an in vitro assay.

Evaluating the efficacy of fully automated approaches for the selection of eye blink ICA components

PubMed Central

Pontifex, Matthew B.; Miskovic, Vladimir; Laszlo, Sarah

2017-01-01

Independent component analysis (ICA) offers a powerful approach for the isolation and removal of eye blink artifacts from EEG signals. Manual identification of the eye blink ICA component by inspection of scalp map projections, however, is prone to error, particularly when non-artifactual components exhibit topographic distributions similar to the blink. The aim of the present investigation was to determine the extent to which automated approaches for selecting eye blink related ICA components could be utilized to replace manual selection. We evaluated popular blink selection methods relying on spatial features [EyeCatch()], combined stereotypical spatial and temporal features [ADJUST()], and a novel method relying on time-series features alone [icablinkmetrics()] using both simulated and real EEG data. The results of this investigation suggest that all three methods of automatic component selection are able to accurately identify eye blink related ICA components at or above the level of trained human observers. However, icablinkmetrics(), in particular, appears to provide an effective means of automating ICA artifact rejection while at the same time eliminating human errors inevitable during manual component selection and false positive component identifications common in other automated approaches. Based upon these findings, best practices for 1) identifying artifactual components via automated means and 2) reducing the accidental removal of signal-related ICA components are discussed. PMID:28191627

Developing an Automated Science Analysis System for Mars Surface Exploration for MSL and Beyond

NASA Technical Reports Server (NTRS)

Gulick, V. C.; Hart, S. D.; Shi, X.; Siegel, V. L.

2004-01-01

We are developing an automated science analysis system that could be utilized by robotic or human explorers on Mars (or even in remote locations on Earth) to improve the quality and quantity of science data returned. Three components of this system (our rock, layer, and horizon detectors) [1] have been incorporated into the JPL CLARITY system for possible use by MSL and future Mars robotic missions. Two other components include a multi-spectral image compression (SPEC) algorithm for pancam-type images with multiple filters and image fusion algorithms that identify the in focus regions of individual images in an image focal series [2]. Recently, we have been working to combine image and spectral data, and other knowledge to identify both rocks and minerals. Here we present our progress on developing an igneous rock detection system.

Mass fingerprinting of the venom and transcriptome of venom gland of scorpion Centruroides tecomanus.

PubMed

Valdez-Velázquez, Laura L; Quintero-Hernández, Verónica; Romero-Gutiérrez, Maria Teresa; Coronas, Fredy I V; Possani, Lourival D

2013-01-01

Centruroides tecomanus is a Mexican scorpion endemic of the State of Colima, that causes human fatalities. This communication describes a proteome analysis obtained from milked venom and a transcriptome analysis from a cDNA library constructed from two pairs of venom glands of this scorpion. High perfomance liquid chromatography separation of soluble venom produced 80 fractions, from which at least 104 individual components were identified by mass spectrometry analysis, showing to contain molecular masses from 259 to 44,392 Da. Most of these components are within the expected molecular masses for Na(+)- and K(+)-channel specific toxic peptides, supporting the clinical findings of intoxication, when humans are stung by this scorpion. From the cDNA library 162 clones were randomly chosen, from which 130 sequences of good quality were identified and were clustered in 28 contigs containing, each, two or more expressed sequence tags (EST) and 49 singlets with only one EST. Deduced amino acid sequence analysis from 53% of the total ESTs showed that 81% (24 sequences) are similar to known toxic peptides that affect Na(+)-channel activity, and 19% (7 unique sequences) are similar to K(+)-channel especific toxins. Out of the 31 sequences, at least 8 peptides were confirmed by direct Edman degradation, using components isolated directly from the venom. The remaining 19%, 4%, 4%, 15% and 5% of the ESTs correspond respectively to proteins involved in cellular processes, antimicrobial peptides, venom components, proteins without defined function and sequences without similarity in databases. Among the cloned genes are those similar to metalloproteinases.

Space science technology: In-situ science. Sample Acquisition, Analysis, and Preservation Project summary

NASA Technical Reports Server (NTRS)

Aaron, Kim

1991-01-01

The Sample Acquisition, Analysis, and Preservation Project is summarized in outline and graphic form. The objective of the project is to develop component and system level technology to enable the unmanned collection, analysis and preservation of physical, chemical and mineralogical data from the surface of planetary bodies. Technology needs and challenges are identified and specific objectives are described.

A Mediation Analysis of a Tobacco Prevention Program for Adolescents in India: How Did Project MYTRI Work?

ERIC Educational Resources Information Center

Stigler, Melissa Harrell; Perry, Cheryl L.; Smolenski, Derek; Arora, Monika; Reddy, K. Srinath

2011-01-01

This article presents the results of a mediation analysis of Project MYTRI (Mobilizing Youth for Tobacco Related Initiatives in India), a randomized, controlled trial of a multiple-component, school-based tobacco prevention program for sixth- to ninth-graders (n = 14,085) in Delhi and Chennai, India. A mediation analysis identifies "how"…

Identification of human-to-human transmissibility factors in PB2 proteins of influenza A by large-scale mutual information analysis

PubMed Central

Miotto, Olivo; Heiny, AT; Tan, Tin Wee; August, J Thomas; Brusic, Vladimir

2008-01-01

Background The identification of mutations that confer unique properties to a pathogen, such as host range, is of fundamental importance in the fight against disease. This paper describes a novel method for identifying amino acid sites that distinguish specific sets of protein sequences, by comparative analysis of matched alignments. The use of mutual information to identify distinctive residues responsible for functional variants makes this approach highly suitable for analyzing large sets of sequences. To support mutual information analysis, we developed the AVANA software, which utilizes sequence annotations to select sets for comparison, according to user-specified criteria. The method presented was applied to an analysis of influenza A PB2 protein sequences, with the objective of identifying the components of adaptation to human-to-human transmission, and reconstructing the mutation history of these components. Results We compared over 3,000 PB2 protein sequences of human-transmissible and avian isolates, to produce a catalogue of sites involved in adaptation to human-to-human transmission. This analysis identified 17 characteristic sites, five of which have been present in human-transmissible strains since the 1918 Spanish flu pandemic. Sixteen of these sites are located in functional domains, suggesting they may play functional roles in host-range specificity. The catalogue of characteristic sites was used to derive sequence signatures from historical isolates. These signatures, arranged in chronological order, reveal an evolutionary timeline for the adaptation of the PB2 protein to human hosts. Conclusion By providing the most complete elucidation to date of the functional components participating in PB2 protein adaptation to humans, this study demonstrates that mutual information is a powerful tool for comparative characterization of sequence sets. In addition to confirming previously reported findings, several novel characteristic sites within PB2 are reported. Sequence signatures generated using the characteristic sites catalogue characterize concisely the adaptation characteristics of individual isolates. Evolutionary timelines derived from signatures of early human influenza isolates suggest that characteristic variants emerged rapidly, and remained remarkably stable through subsequent pandemics. In addition, the signatures of human-infecting H5N1 isolates suggest that this avian subtype has low pandemic potential at present, although it presents more human adaptation components than most avian subtypes. PMID:18315849

Use of Principal Components Analysis and Kriging to Predict Groundwater-Sourced Rural Drinking Water Quality in Saskatchewan

PubMed Central

McLeod, Lianne; Bharadwaj, Lalita; Epp, Tasha; Waldner, Cheryl L.

2017-01-01

Groundwater drinking water supply surveillance data were accessed to summarize water quality delivered as public and private water supplies in southern Saskatchewan as part of an exposure assessment for epidemiologic analyses of associations between water quality and type 2 diabetes or cardiovascular disease. Arsenic in drinking water has been linked to a variety of chronic diseases and previous studies have identified multiple wells with arsenic above the drinking water standard of 0.01 mg/L; therefore, arsenic concentrations were of specific interest. Principal components analysis was applied to obtain principal component (PC) scores to summarize mixtures of correlated parameters identified as health standards and those identified as aesthetic objectives in the Saskatchewan Drinking Water Quality Standards and Objective. Ordinary, universal, and empirical Bayesian kriging were used to interpolate arsenic concentrations and PC scores in southern Saskatchewan, and the results were compared. Empirical Bayesian kriging performed best across all analyses, based on having the greatest number of variables for which the root mean square error was lowest. While all of the kriging methods appeared to underestimate high values of arsenic and PC scores, empirical Bayesian kriging was chosen to summarize large scale geographic trends in groundwater-sourced drinking water quality and assess exposure to mixtures of trace metals and ions. PMID:28914824

Use of Principal Components Analysis and Kriging to Predict Groundwater-Sourced Rural Drinking Water Quality in Saskatchewan.

PubMed

McLeod, Lianne; Bharadwaj, Lalita; Epp, Tasha; Waldner, Cheryl L

2017-09-15

Groundwater drinking water supply surveillance data were accessed to summarize water quality delivered as public and private water supplies in southern Saskatchewan as part of an exposure assessment for epidemiologic analyses of associations between water quality and type 2 diabetes or cardiovascular disease. Arsenic in drinking water has been linked to a variety of chronic diseases and previous studies have identified multiple wells with arsenic above the drinking water standard of 0.01 mg/L; therefore, arsenic concentrations were of specific interest. Principal components analysis was applied to obtain principal component (PC) scores to summarize mixtures of correlated parameters identified as health standards and those identified as aesthetic objectives in the Saskatchewan Drinking Water Quality Standards and Objective. Ordinary, universal, and empirical Bayesian kriging were used to interpolate arsenic concentrations and PC scores in southern Saskatchewan, and the results were compared. Empirical Bayesian kriging performed best across all analyses, based on having the greatest number of variables for which the root mean square error was lowest. While all of the kriging methods appeared to underestimate high values of arsenic and PC scores, empirical Bayesian kriging was chosen to summarize large scale geographic trends in groundwater-sourced drinking water quality and assess exposure to mixtures of trace metals and ions.

Managing Complexity in Evidence Analysis: A Worked Example in Pediatric Weight Management.

PubMed

Parrott, James Scott; Henry, Beverly; Thompson, Kyle L; Ziegler, Jane; Handu, Deepa

2018-05-02

Nutrition interventions are often complex and multicomponent. Typical approaches to meta-analyses that focus on individual causal relationships to provide guideline recommendations are not sufficient to capture this complexity. The objective of this study is to describe the method of meta-analysis used for the Pediatric Weight Management (PWM) Guidelines update and provide a worked example that can be applied in other areas of dietetics practice. The effects of PWM interventions were examined for body mass index (BMI), body mass index z-score (BMIZ), and waist circumference at four different time periods. For intervention-level effects, intervention types were identified empirically using multiple correspondence analysis paired with cluster analysis. Pooled effects of identified types were examined using random effects meta-analysis models. Differences in effects among types were examined using meta-regression. Context-level effects are examined using qualitative comparative analysis. Three distinct types (or families) of PWM interventions were identified: medical nutrition, behavioral, and missing components. Medical nutrition and behavioral types showed statistically significant improvements in BMIZ across all time points. Results were less consistent for BMI and waist circumference, although four distinct patterns of weight status change were identified. These varied by intervention type as well as outcome measure. Meta-regression indicated statistically significant differences between the medical nutrition and behavioral types vs the missing component type for both BMIZ and BMI, although the pattern varied by time period and intervention type. Qualitative comparative analysis identified distinct configurations of context characteristics at each time point that were consistent with positive outcomes among the intervention types. Although analysis of individual causal relationships is invaluable, this approach is inadequate to capture the complexity of dietetics practice. An alternative approach that integrates intervention-level with context-level meta-analyses may provide deeper understanding in the development of practice guidelines. Copyright © 2018 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Effect of rotational alignment on outcome of total knee arthroplasty

PubMed Central

Breugem, Stefan J; van den Bekerom, Michel PJ; Tuinebreijer, Willem E; van Geenen, Rutger C I

2015-01-01

Background and purpose Poor outcomes have been linked to errors in rotational alignment of total knee arthroplasty components. The aims of this study were to determine the correlation between rotational alignment and outcome, to review the success of revision for malrotated total knee arthroplasty, and to determine whether evidence-based guidelines for malrotated total knee arthroplasty can be proposed. Patients and methods We conducted a systematic review including all studies reporting on both rotational alignment and functional outcome. Comparable studies were used in a correlation analysis and results of revision were analyzed separately. Results 846 studies were identified, 25 of which met the inclusion criteria. From this selection, 11 studies could be included in the correlation analysis. A medium positive correlation (ρ = 0.44, 95% CI: 0.27–0.59) and a large positive correlation (ρ = 0.68, 95% CI: 0.64–0.73) were found between external rotation of the tibial component and the femoral component, respectively, and the Knee Society score. Revision for malrotation gave positive results in all 6 studies in this field. Interpretation Medium and large positive correlations were found between tibial and femoral component rotational alignment on the one hand and better functional outcome on the other. Revision of malrotated total knee arthroplasty may be successful. However, a clear cutoff point for revision for malrotated total knee arthroplasty components could not be identified. PMID:25708694

A Partial Least-Squares Analysis of Health-Related Quality-of-Life Outcomes After Aneurysmal Subarachnoid Hemorrhage.

PubMed

Young, Julia M; Morgan, Benjamin R; Mišić, Bratislav; Schweizer, Tom A; Ibrahim, George M; Macdonald, R Loch

2015-12-01

Individuals who have aneurysmal subarachnoid hemorrhages (SAHs) experience decreased health-related qualities of life (HRQoLs) that persist after the primary insult. To identify clinical variables that concurrently associate with HRQoL outcomes by using a partial least-squares approach, which has the distinct advantage of explaining multidimensional variance where predictor variables may be highly collinear. Data collected from the CONSCIOUS-1 trial was used to extract 29 clinical variables including SAH presentation, hospital procedures, and demographic information in addition to 5 HRQoL outcome variables for 256 individuals. A partial least-squares analysis was performed by calculating a heterogeneous correlation matrix and applying singular value decomposition to determine components that best represent the correlations between the 2 sets of variables. Bootstrapping was used to estimate statistical significance. The first 2 components accounting for 81.6% and 7.8% of the total variance revealed significant associations between clinical predictors and HRQoL outcomes. The first component identified associations between disability in self-care with longer durations of critical care stay, invasive intracranial monitoring, ventricular drain time, poorer clinical grade on presentation, greater amounts of cerebral spinal fluid drainage, and a history of hypertension. The second component identified associations between disability due to pain and discomfort as well as anxiety and depression with greater body mass index, abnormal heart rate, longer durations of deep sedation and critical care, and higher World Federation of Neurosurgical Societies and Hijdra scores. By applying a data-driven, multivariate approach, we identified robust associations between SAH clinical presentations and HRQoL outcomes. EQ-VAS, EuroQoL visual analog scaleHRQoL, health-related quality of lifeICU, intensive care unitIVH, intraventricular hemorrhagePLS, partial least squaresSAH, subarachnoid hemorrhageSVD, singular value decompositionWFNS, World Federation of Neurosurgical Societies.

What Not To Do: Anti-patterns for Developing Scientific Workflow Software Components

NASA Astrophysics Data System (ADS)

Futrelle, J.; Maffei, A. R.; Sosik, H. M.; Gallager, S. M.; York, A.

2013-12-01

Scientific workflows promise to enable efficient scaling-up of researcher code to handle large datasets and workloads, as well as documentation of scientific processing via standardized provenance records, etc. Workflow systems and related frameworks for coordinating the execution of otherwise separate components are limited, however, in their ability to overcome software engineering design problems commonly encountered in pre-existing components, such as scripts developed externally by scientists in their laboratories. In practice, this often means that components must be rewritten or replaced in a time-consuming, expensive process. In the course of an extensive workflow development project involving large-scale oceanographic image processing, we have begun to identify and codify 'anti-patterns'--problematic design characteristics of software--that make components fit poorly into complex automated workflows. We have gone on to develop and document low-effort solutions and best practices that efficiently address the anti-patterns we have identified. The issues, solutions, and best practices can be used to evaluate and improve existing code, as well as guiding the development of new components. For example, we have identified a common anti-pattern we call 'batch-itis' in which a script fails and then cannot perform more work, even if that work is not precluded by the failure. The solution we have identified--removing unnecessary looping over independent units of work--is often easier to code than the anti-pattern, as it eliminates the need for complex control flow logic in the component. Other anti-patterns we have identified are similarly easy to identify and often easy to fix. We have drawn upon experience working with three science teams at Woods Hole Oceanographic Institution, each of which has designed novel imaging instruments and associated image analysis code. By developing use cases and prototypes within these teams, we have undertaken formal evaluations of software components developed by programmers with widely varying levels of expertise, and have been able to discover and characterize a number of anti-patterns. Our evaluation methodology and testbed have also enabled us to assess the efficacy of strategies to address these anti-patterns according to scientifically relevant metrics, such as ability of algorithms to perform faster than the rate of data acquisition and the accuracy of workflow component output relative to ground truth. The set of anti-patterns and solutions we have identified augments of the body of more well-known software engineering anti-patterns by addressing additional concerns that obtain when a software component has to function as part of a workflow assembled out of independently-developed codebases. Our experience shows that identifying and resolving these anti-patterns reduces development time and improves performance without reducing component reusability.

Toward the International Classification of Functioning, Disability and Health (ICF) Rehabilitation Set: A Minimal Generic Set of Domains for Rehabilitation as a Health Strategy.

PubMed

Prodinger, Birgit; Cieza, Alarcos; Oberhauser, Cornelia; Bickenbach, Jerome; Üstün, Tevfik Bedirhan; Chatterji, Somnath; Stucki, Gerold

2016-06-01

To develop a comprehensive set of the International Classification of Functioning, Disability and Health (ICF) categories as a minimal standard for reporting and assessing functioning and disability in clinical populations along the continuum of care. The specific aims were to specify the domains of functioning recommended for an ICF Rehabilitation Set and to identify a minimal set of environmental factors (EFs) to be used alongside the ICF Rehabilitation Set when describing disability across individuals and populations with various health conditions. Secondary analysis of existing data sets using regression methods (Random Forests and Group Lasso regression) and expert consultations. Along the continuum of care, including acute, early postacute, and long-term and community rehabilitation settings. Persons (N=9863) with various health conditions participated in primary studies. The number of respondents for whom the dependent variable data were available and used in this analysis was 9264. Not applicable. For regression analyses, self-reported general health was used as a dependent variable. The ICF categories from the functioning component and the EF component were used as independent variables for the development of the ICF Rehabilitation Set and the minimal set of EFs, respectively. Thirty ICF categories to be complemented with 12 EFs were identified as relevant to the identified ICF sets. The ICF Rehabilitation Set constitutes of 9 ICF categories from the component body functions and 21 from the component activities and participation. The minimal set of EFs contains 12 categories spanning all chapters of the EF component of the ICF. The identified sets proposed serve as minimal generic sets of aspects of functioning in clinical populations for reporting data within and across heath conditions, time, clinical settings including rehabilitation, and countries. These sets present a reference framework for harmonizing existing information on disability across general and clinical populations. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Identification of limit cycles in multi-nonlinearity, multiple path systems

NASA Technical Reports Server (NTRS)

Mitchell, J. R.; Barron, O. L.

1979-01-01

A method of analysis which identifies limit cycles in autonomous systems with multiple nonlinearities and multiple forward paths is presented. The FORTRAN code for implementing the Harmonic Balance Algorithm is reported. The FORTRAN code is used to identify limit cycles in multiple path and nonlinearity systems while retaining the effects of several harmonic components.

Interplanetary dust - Trace element analysis of individual particles by neutron activation

NASA Technical Reports Server (NTRS)

Ganapathy, R.; Brownlee, D. E.

1979-01-01

Although micrometeorites of cometary origin are thought to be the dominant component of interplanetary dust, it has never been possible to positively identify such micrometer-sized particles. Two such particles have been identified as definitely micrometeorites since their abundances of volatile and nonvolatile trace elements closely match those of primitive solar system material.

Selection into Medicine Using Interviews and Other Measures: Much Remains to Be Learned

ERIC Educational Resources Information Center

Ma, Colleen; Harris, Peter; Cole, Andrew; Jones, Phil; Shulruf, Boaz

2016-01-01

The objectives of this study were to identify the effectiveness of the panel admission interview as a selection tool for the medical program and identify improvements in the selection tools battery. Data from 1024 students, representing four cohorts of students were used in this study. Exploratory factor analysis using principal component analysis…

Clustering of immunological, metabolic and genetic features in latent autoimmune diabetes in adults: evidence from principal component analysis.

PubMed

Pes, Giovanni Mario; Delitala, Alessandro Palmerio; Errigo, Alessandra; Delitala, Giuseppe; Dore, Maria Pina

2016-06-01

Latent autoimmune diabetes in adults (LADA) which accounts for more than 10 % of all cases of diabetes is characterized by onset after age 30, absence of ketoacidosis, insulin independence for at least 6 months, and presence of circulating islet-cell antibodies. Its marked heterogeneity in clinical features and immunological markers suggests the existence of multiple mechanisms underlying its pathogenesis. The principal component (PC) analysis is a statistical approach used for finding patterns in data of high dimension. In this study the PC analysis was applied to a set of variables from a cohort of Sardinian LADA patients to identify a smaller number of latent patterns. A list of 11 variables including clinical (gender, BMI, lipid profile, systolic and diastolic blood pressure and insulin-free time period), immunological (anti-GAD65, anti-IA-2 and anti-TPO antibody titers) and genetic features (predisposing gene variants previously identified as risk factors for autoimmune diabetes) retrieved from clinical records of 238 LADA patients referred to the Internal Medicine Unit of University of Sassari, Italy, were analyzed by PC analysis. The predictive value of each PC on the further development of insulin dependence was evaluated using Kaplan-Meier curves. Overall 4 clusters were identified by PC analysis. In component PC-1, the dominant variables were: BMI, triglycerides, systolic and diastolic blood pressure and duration of insulin-free time period; in PC-2: genetic variables such as Class II HLA, CTLA-4 as well as anti-GAD65, anti-IA-2 and anti-TPO antibody titers, and the insulin-free time period predominated; in PC-3: gender and triglycerides; and in PC-4: total cholesterol. These components explained 18, 15, 12, and 12 %, respectively, of the total variance in the LADA cohort. The predictive power of insulin dependence of the four components was different. PC-2 (characterized mostly by high antibody titers and presence of predisposing genetic markers) showed a faster beta-cells failure and PC-3 (characterized mostly by gender and high triglycerides) and PC-4 (high cholesterol) showed a slower beta-cells failure. PC-1 (including dislipidemia and other metabolic dysfunctions), showed a mild beta-cells failure. In conclusion variable clustering might be consistent with different pathogenic pathways and/or distinct immune mechanisms in LADA and could potentially help physicians improve the clinical management of these patients.

Derivative component analysis for mass spectral serum proteomic profiles.

PubMed

Han, Henry

2014-01-01

As a promising way to transform medicine, mass spectrometry based proteomics technologies have seen a great progress in identifying disease biomarkers for clinical diagnosis and prognosis. However, there is a lack of effective feature selection methods that are able to capture essential data behaviors to achieve clinical level disease diagnosis. Moreover, it faces a challenge from data reproducibility, which means that no two independent studies have been found to produce same proteomic patterns. Such reproducibility issue causes the identified biomarker patterns to lose repeatability and prevents it from real clinical usage. In this work, we propose a novel machine-learning algorithm: derivative component analysis (DCA) for high-dimensional mass spectral proteomic profiles. As an implicit feature selection algorithm, derivative component analysis examines input proteomics data in a multi-resolution approach by seeking its derivatives to capture latent data characteristics and conduct de-noising. We further demonstrate DCA's advantages in disease diagnosis by viewing input proteomics data as a profile biomarker via integrating it with support vector machines to tackle the reproducibility issue, besides comparing it with state-of-the-art peers. Our results show that high-dimensional proteomics data are actually linearly separable under proposed derivative component analysis (DCA). As a novel multi-resolution feature selection algorithm, DCA not only overcomes the weakness of the traditional methods in subtle data behavior discovery, but also suggests an effective resolution to overcoming proteomics data's reproducibility problem and provides new techniques and insights in translational bioinformatics and machine learning. The DCA-based profile biomarker diagnosis makes clinical level diagnostic performances reproducible across different proteomic data, which is more robust and systematic than the existing biomarker discovery based diagnosis. Our findings demonstrate the feasibility and power of the proposed DCA-based profile biomarker diagnosis in achieving high sensitivity and conquering the data reproducibility issue in serum proteomics. Furthermore, our proposed derivative component analysis suggests the subtle data characteristics gleaning and de-noising are essential in separating true signals from red herrings for high-dimensional proteomic profiles, which can be more important than the conventional feature selection or dimension reduction. In particular, our profile biomarker diagnosis can be generalized to other omics data for derivative component analysis (DCA)'s nature of generic data analysis.

One size (never) fits all: segment differences observed following a school-based alcohol social marketing program.

PubMed

Dietrich, Timo; Rundle-Thiele, Sharyn; Leo, Cheryl; Connor, Jason

2015-04-01

According to commercial marketing theory, a market orientation leads to improved performance. Drawing on the social marketing principles of segmentation and audience research, the current study seeks to identify segments to examine responses to a school-based alcohol social marketing program. A sample of 371 year 10 students (aged: 14-16 years; 51.4% boys) participated in a prospective (pre-post) multisite alcohol social marketing program. Game On: Know Alcohol (GO:KA) program included 6, student-centered, and interactive lessons to teach adolescents about alcohol and strategies to abstain or moderate drinking. A repeated measures design was used. Baseline demographics, drinking attitudes, drinking intentions, and alcohol knowledge were cluster analyzed to identify segments. Change on key program outcome measures and satisfaction with program components were assessed by segment. Three segments were identified; (1) Skeptics, (2) Risky Males, (3) Good Females. Segments 2 and 3 showed greatest change in drinking attitudes and intentions. Good Females reported highest satisfaction with all program components and Skeptics lowest program satisfaction with all program components. Three segments, each differing on psychographic and demographic variables, exhibited different change patterns following participation in GO:KA. Post hoc analysis identified that satisfaction with program components differed by segment offering opportunities for further research. © 2015, American School Health Association.

An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques

PubMed Central

Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Calhoun, Vince D.

2013-01-01

Extraction of relevant features from multitask functional MRI (fMRI) data in order to identify potential biomarkers for disease, is an attractive goal. In this paper, we introduce a novel feature-based framework, which is sensitive and accurate in detecting group differences (e.g. controls vs. patients) by proposing three key ideas. First, we integrate two goal-directed techniques: coefficient-constrained independent component analysis (CC-ICA) and principal component analysis with reference (PCA-R), both of which improve sensitivity to group differences. Secondly, an automated artifact-removal method is developed for selecting components of interest derived from CC-ICA, with an average accuracy of 91%. Finally, we propose a strategy for optimal feature/component selection, aiming to identify optimal group-discriminative brain networks as well as the tasks within which these circuits are engaged. The group-discriminating performance is evaluated on 15 fMRI feature combinations (5 single features and 10 joint features) collected from 28 healthy control subjects and 25 schizophrenia patients. Results show that a feature from a sensorimotor task and a joint feature from a Sternberg working memory (probe) task and an auditory oddball (target) task are the top two feature combinations distinguishing groups. We identified three optimal features that best separate patients from controls, including brain networks consisting of temporal lobe, default mode and occipital lobe circuits, which when grouped together provide improved capability in classifying group membership. The proposed framework provides a general approach for selecting optimal brain networks which may serve as potential biomarkers of several brain diseases and thus has wide applicability in the neuroimaging research community. PMID:19457398

Molecular Characterization and Expression Analysis of Chloroplast Protein Import Components in Tomato (Solanum lycopersicum)

PubMed Central

Yan, Jianmin; Campbell, James H.; Glick, Bernard R.; Smith, Matthew D.; Liang, Yan

2014-01-01

The translocon at the outer envelope membrane of chloroplasts (Toc) mediates the recognition and initial import into the organelle of thousands of nucleus-encoded proteins. These proteins are translated in the cytosol as precursor proteins with cleavable amino-terminal targeting sequences called transit peptides. The majority of the known Toc components that mediate chloroplast protein import were originally identified in pea, and more recently have been studied most extensively in Arabidopsis. With the completion of the tomato genome sequencing project, it is now possible to identify putative homologues of the chloroplast import components in tomato. In the work reported here, the Toc GTPase cDNAs from tomato were identified, cloned and analyzed. The analysis revealed that there are four Toc159 homologues (slToc159-1, -2, -3 and -4) and two Toc34 homologues (slToc34-1 and -2) in tomato, and it was shown that tomato Toc159 and Toc34 homologues share high sequence similarity with the comparable import apparatus components from Arabidopsis and pea. Thus, tomato is a valid model for further study of this system. The expression level of Toc complex components was also investigated in different tissues during tomato development. The two tomato Toc34 homologues are expressed at higher levels in non-photosynthetic tissues, whereas, the expression of two tomato Toc159 homologues, slToc159-1 and slToc159-4, were higher in photosynthetic tissues, and the expression patterns of slToc159-2 was not significantly different in photosynthetic and non-photosynthetic tissues, and slToc159-3 expression was limited to a few select tissues. PMID:24751891

Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis.

PubMed

Huang, Huiyuan; Wang, Junjing; Seger, Carol; Lu, Min; Deng, Feng; Wu, Xiaoyan; He, Yuan; Niu, Chen; Wang, Jun; Huang, Ruiwang

2018-01-01

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

Fevers and Chills: Separating thermal and synchrotron components in SNR spectra

NASA Astrophysics Data System (ADS)

Fedor, Emily Elizabeth; Martina-Hood, Hyourin; Stage, Michael D.

2018-06-01

Spatially-resolved spectroscopy is an extremely powerful tool in X-ray analysis of extended sources, but can be computationally difficult if a source exhibits complex morphology. For example, high-resolution Chandra data of bright Galactic supernova remnants (Cas A, Tycho, etc.) allow extractions of high-quality spectra from tens to hundreds of thousands of regions, providing a rich laboratory for localizing emission from processes such as thermal line emission, bremsstrahlung, and synchrotron. This soft-band analysis informs our understanding of the typically nonthermal hard X-ray emission observed with other lower-resolution instruments. The analysis is complicated by both projection effects and the presence of multiple emission mechanisms in some regions. In particular, identifying regions with significant nonthermal emission is critical to understanding acceleration processes in remnants. Fitting tens of thousands of regions with complex, multi-component models can be time-consuming and involve so many free parameters that little constraint can be placed on the values. Previous work by Stage & Allen ('06, '07, '11) on Cas A used a technique to identify regions dominated by the highest-cutoff synchrotron emission by fitting with a simple thermal emission model and identifying regions with anomalously high apparent temperatures (caused by presence of the high-energy tail of the synchrotron emission component). Here, we present a similar technique. We verify the previous approach and, more importantly, expand it to include a method to identify regions containing strong lower-cutoff synchrotron radiation. Such regions might be associated with the reverse-shock of a supernova. Identification of a nonthermal electron population in the interior of an SNR would have significant implications for the energy balance and emission mechanisms producing the high-energy (> 10 keV) spectrum.

A new approach for computing a flood vulnerability index using cluster analysis

NASA Astrophysics Data System (ADS)

Fernandez, Paulo; Mourato, Sandra; Moreira, Madalena; Pereira, Luísa

2016-08-01

A Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes. FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores. The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified. The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI.

Component Analysis of Remanent Magnetization Curves: A Revisit with a New Model Distribution

NASA Astrophysics Data System (ADS)

Zhao, X.; Suganuma, Y.; Fujii, M.

2017-12-01

Geological samples often consist of several magnetic components that have distinct origins. As the magnetic components are often indicative of their underlying geological and environmental processes, it is therefore desirable to identify individual components to extract associated information. This component analysis can be achieved using the so-called unmixing method, which fits a mixture model of certain end-member model distribution to the measured remanent magnetization curve. In earlier studies, the lognormal, skew generalized Gaussian and skewed Gaussian distributions have been used as the end-member model distribution in previous studies, which are performed on the gradient curve of remanent magnetization curves. However, gradient curves are sensitive to measurement noise as the differentiation of the measured curve amplifies noise, which could deteriorate the component analysis. Though either smoothing or filtering can be applied to reduce the noise before differentiation, their effect on biasing component analysis is vaguely addressed. In this study, we investigated a new model function that can be directly applied to the remanent magnetization curves and therefore avoid the differentiation. The new model function can provide more flexible shape than the lognormal distribution, which is a merit for modeling the coercivity distribution of complex magnetic component. We applied the unmixing method both to model and measured data, and compared the results with those obtained using other model distributions to better understand their interchangeability, applicability and limitation. The analyses on model data suggest that unmixing methods are inherently sensitive to noise, especially when the number of component is over two. It is, therefore, recommended to verify the reliability of component analysis by running multiple analyses with synthetic noise. Marine sediments and seafloor rocks are analyzed with the new model distribution. Given the same component number, the new model distribution can provide closer fits than the lognormal distribution evidenced by reduced residuals. Moreover, the new unmixing protocol is automated so that the users are freed from the labor of providing initial guesses for the parameters, which is also helpful to improve the subjectivity of component analysis.

Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network.

PubMed

Liu, Ming; Zhao, Jing; Lu, XiaoZuo; Li, Gang; Wu, Taixia; Zhang, LiFu

2018-05-10

With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis results) participate. Reflectance spectra of subjects' tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity. Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation. To measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with 100 times Hold-out method and 67 times Leave-one-out method are 88.05% and 97.01%, respectively. Experimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.

Survey to Identify Substandard and Falsified Tablets in Several Asian Countries with Pharmacopeial Quality Control Tests and Principal Component Analysis of Handheld Raman Spectroscopy.

PubMed

Kakio, Tomoko; Nagase, Hitomi; Takaoka, Takashi; Yoshida, Naoko; Hirakawa, Junichi; Macha, Susan; Hiroshima, Takashi; Ikeda, Yukihiro; Tsuboi, Hirohito; Kimura, Kazuko

2018-06-01

The World Health Organization has warned that substandard and falsified medical products (SFs) can harm patients and fail to treat the diseases for which they were intended, and they affect every region of the world, leading to loss of confidence in medicines, health-care providers, and health systems. Therefore, development of analytical procedures to detect SFs is extremely important. In this study, we investigated the quality of pharmaceutical tablets containing the antihypertensive candesartan cilexetil, collected in China, Indonesia, Japan, and Myanmar, using the Japanese pharmacopeial analytical procedures for quality control, together with principal component analysis (PCA) of Raman spectrum obtained with handheld Raman spectrometer. Some samples showed delayed dissolution and failed to meet the pharmacopeial specification, whereas others failed the assay test. These products appeared to be substandard. Principal component analysis showed that all Raman spectra could be explained in terms of two components: the amount of the active pharmaceutical ingredient and the kinds of excipients. Principal component analysis score plot indicated one substandard, and the falsified tablets have similar principal components in Raman spectra, in contrast to authentic products. The locations of samples within the PCA score plot varied according to the source country, suggesting that manufacturers in different countries use different excipients. Our results indicate that the handheld Raman device will be useful for detection of SFs in the field. Principal component analysis of that Raman data clarify the difference in chemical properties between good quality products and SFs that circulate in the Asian market.

Tool development to assess the work related neck and upper limb musculoskeletal disorders among female garment workers in Sri-Lanka.

PubMed

Amarasinghe, Nirmalie Champika; De AlwisSenevirathne, Rohini

2016-10-17

Musculoskeletal disorders (MSDs) have been identified as a predisposing factor for lesser productivity, but no validated tool has been developed to assess them in the Sri- Lankan context. To develop a validated tool to assess the neck and upper limb MSDs. It comprises three components: item selections, item reduction using principal component analysis, and validation. A tentative self-administrated questionnaire was developed, translated, and pre-tested. Four important domains - neck, shoulder, elbow and wrist - were identified through principal component analysis. Prevalence of any MSDs was 38.1% and prevalence of neck, shoulder, elbow and wrist MSDs are 12.85%, 13.71%, 12%, 13.71% respectively. Content and criterion validity of the tool was assessed. Separate ROC curves were produced and sensitivity and specificity of neck (83.1%, 71.7%), shoulder (97.6%, 91.9%), elbow (98.2%, 87.2%), and wrist (97.6%, 94.9%) was determined. Cronbach's Alpha and correlation coefficient was above 0.7. The tool has high sensitivity, specificity, internal consistency, and test re-test reliability.

Work-related musculoskeletal disorders (WMDs) risk assessment at core assembly production of electronic components manufacturing company

NASA Astrophysics Data System (ADS)

Yahya, N. M.; Zahid, M. N. O.

2018-03-01

This study conducted to assess the work-related musculoskeletal disorders (WMDs) among the workers at core assembly production in an electronic components manufacturing company located in Pekan, Pahang, Malaysia. The study is to identify the WMDs risk factor and risk level. A set of questionnaires survey based on modified Nordic Musculoskeletal Disorder Questionnaires have been distributed to respective workers to acquire the WMDs risk factor identification. Then, postural analysis was conducted in order to measure the respective WMDs risk level. The analysis were based on two ergonomics assessment tools; Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA). The study found that 30 respondents out of 36 respondents suffered from WMDs especially at shoulder, wrists and lower back. The WMDs risk have been identified from unloading process, pressing process and winding process. In term of the WMDs risk level, REBA and RULA assessment tools have indicated high risk level to unloading and pressing process. Thus, this study had established the WMDs risk factor and risk level of core assembly production in an electronic components manufacturing company at Malaysia environment.

Science Alert Demonstration with a Rover Traverse Science Data Analysis System

NASA Technical Reports Server (NTRS)

Castano, R.; Estlin, T.; Gaines, D.; Castano, A.; Bornstein, B.; Anderson, R. C.; Judd, M.; Stough, T.; Wagstaff, K.

2005-01-01

The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify science opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity. OASIS is a NASA-funded research project that is currently being tested on the FIDO rover at JPL for the use on future missions.

The Contribution of Systems Analysis to Training Students in Cognitive Interdisciplinary Skills in Environmental Science Education

ERIC Educational Resources Information Center

Fortuin, K. P. J.; van Koppen, C. S. A.; Kroeze, C.

2013-01-01

Professionals in the environmental domain require cognitive interdisciplinary skills to be able to develop sustainable solutions to environmental problems. We demonstrate that education in environmental systems analysis allows for the development of these skills. We identify three components of cognitive interdisciplinary skills: (1) the ability…

Attitudes of French-Canadian university students toward use of condoms: a structural analysis.

PubMed

Bernard, J; Hébert, Y; de Man, A; Farrar, D

1989-12-01

164 French-Canadian university students took part in a study analyzing the factorial pattern of attitudes toward the use of condoms. A principal component analysis identified 7 factors, namely, positive general attitude toward use of condoms, contraceptive security, inhibition of sexual pleasure, inconvenience, embarrassment, responsibility, and lessening of physical pleasure.

Cognitive Task Analysis of Prioritization in Air Traffic Control.

ERIC Educational Resources Information Center

Redding, Richard E.; And Others

A cognitive task analysis was performed to analyze the key cognitive components of the en route air traffic controllers' jobs. The goals were to ascertain expert mental models and decision-making strategies and to identify important differences in controller knowledge, skills, and mental models as a function of expertise. Four groups of…

Analysis of alkylamides in Echinacea plant materials and dietary supplements by ultrafast liquid chromatography with diode array and mass spectrometric detection.

PubMed

Mudge, Elizabeth; Lopes-Lutz, Daise; Brown, Paula; Schieber, Andreas

2011-08-10

Alkylamides are a class of compounds present in plants of the genus Echinacea (Asteraceae), which have been shown to have high bioavailability and immunomodulatory effects. Fast analysis to identify these components in a variety of products is essential to profile products used in clinical trials and for quality control of these products. A method based on ultrafast liquid chromatography (UFLC) coupled with diode array detection and electrospray ionization mass spectrometry was developed for the analysis of alkylamides from the roots of Echinacea angustifolia (DC.) Hell., Echinacea purpurea (L.) Moench, and commercial dietary supplements. A total of 24 alkylamides were identified by LC-MS. The analysis time for these components is 15 min. Compared to the alkylamide profiles determined in the Echinacea root materials, the commercial products showed a more complex profile due to the blending of root and aerial parts of E. purpurea. This versatile method allows for the identification of alkylamides in a variety of Echinacea products and presents the most extensive characterization of alkylamides in E. angustifolia roots so far.

Public health laboratory quality management in a developing country.

PubMed

Wangkahat, Khwanjai; Nookhai, Somboon; Pobkeeree, Vallerut

2012-01-01

The article aims to give an overview of the system of public health laboratory quality management in Thailand and to produce a strengths, weaknesses, opportunities and threats (SWOT) analysis that is relevant to public health laboratories in the country. The systems for managing laboratory quality that are currently employed were described in the first component. The second component was a SWOT analysis, which used the opinions of laboratory professionals to identify any areas that could be improved to meet quality management systems. Various quality management systems were identified and the number of laboratories that met both international and national quality management requirements was different. The SWOT analysis found the opportunities and strengths factors offered the best chance to improve laboratory quality management in the country. The results are based on observations and brainstorming with medical laboratory professionals who can assist laboratories in accomplishing quality management. The factors derived from the analysis can help improve laboratory quality management in the country. This paper provides viewpoints and evidence-based approaches for the development of best possible practice of services in public health laboratories.

Evidence that molecular changes in cells occur before morphological alterations during the progression of breast ductal carcinoma

PubMed Central

Castro, Nadia P; Osório, Cynthia ABT; Torres, César; Bastos, Elen P; Mourão-Neto, Mário; Soares, Fernando A; Brentani, Helena P; Carraro, Dirce M

2008-01-01

Introduction Ductal carcinoma in situ (DCIS) of the breast includes a heterogeneous group of preinvasive tumors with uncertain evolution. Definition of the molecular factors necessary for progression to invasive disease is crucial to determining which lesions are likely to become invasive. To obtain insight into the molecular basis of DCIS, we compared the gene expression pattern of cells from the following samples: non-neoplastic, pure DCIS, in situ component of lesions with co-existing invasive ductal carcinoma, and invasive ductal carcinoma. Methods Forty-one samples were evaluated: four non-neoplastic, five pure DCIS, 22 in situ component of lesions with co-existing invasive ductal carcinoma, and 10 invasive ductal carcinoma. Pure cell populations were isolated using laser microdissection. Total RNA was purified, DNase treated, and amplified using the T7-based method. Microarray analysis was conducted using a customized cDNA platform. The concept of molecular divergence was applied to classify the sample groups using analysis of variance followed by Tukey's test. Results Among the tumor sample groups, cells from pure DCIS exhibited the most divergent molecular profile, consequently identifying cells from in situ component of lesions with co-existing invasive ductal carcinoma as very similar to cells from invasive lesions. Additionally, we identified 147 genes that were differentially expressed between pure DCIS and in situ component of lesions with co-existing invasive ductal carcinoma, which can discriminate samples representative of in situ component of lesions with co-existing invasive ductal carcinoma from 60% of pure DCIS samples. A gene subset was evaluated using quantitative RT-PCR, which confirmed differential expression for 62.5% and 60.0% of them using initial and partial independent sample groups, respectively. Among these genes, LOX and SULF-1 exhibited features that identify them as potential participants in the malignant process of DCIS. Conclusions We identified new genes that are potentially involved in the malignant transformation of DCIS, and our findings strongly suggest that cells from the in situ component of lesions with co-existing invasive ductal carcinoma exhibit molecular alterations that enable them to invade the surrounding tissue before morphological changes in the lesion become apparent. PMID:18928525

Evidence that molecular changes in cells occur before morphological alterations during the progression of breast ductal carcinoma.

PubMed

Castro, Nadia P; Osório, Cynthia A B T; Torres, César; Bastos, Elen P; Mourão-Neto, Mário; Soares, Fernando A; Brentani, Helena P; Carraro, Dirce M

2008-01-01

Ductal carcinoma in situ (DCIS) of the breast includes a heterogeneous group of preinvasive tumors with uncertain evolution. Definition of the molecular factors necessary for progression to invasive disease is crucial to determining which lesions are likely to become invasive. To obtain insight into the molecular basis of DCIS, we compared the gene expression pattern of cells from the following samples: non-neoplastic, pure DCIS, in situ component of lesions with co-existing invasive ductal carcinoma, and invasive ductal carcinoma. Forty-one samples were evaluated: four non-neoplastic, five pure DCIS, 22 in situ component of lesions with co-existing invasive ductal carcinoma, and 10 invasive ductal carcinoma. Pure cell populations were isolated using laser microdissection. Total RNA was purified, DNase treated, and amplified using the T7-based method. Microarray analysis was conducted using a customized cDNA platform. The concept of molecular divergence was applied to classify the sample groups using analysis of variance followed by Tukey's test. Among the tumor sample groups, cells from pure DCIS exhibited the most divergent molecular profile, consequently identifying cells from in situ component of lesions with co-existing invasive ductal carcinoma as very similar to cells from invasive lesions. Additionally, we identified 147 genes that were differentially expressed between pure DCIS and in situ component of lesions with co-existing invasive ductal carcinoma, which can discriminate samples representative of in situ component of lesions with co-existing invasive ductal carcinoma from 60% of pure DCIS samples. A gene subset was evaluated using quantitative RT-PCR, which confirmed differential expression for 62.5% and 60.0% of them using initial and partial independent sample groups, respectively. Among these genes, LOX and SULF-1 exhibited features that identify them as potential participants in the malignant process of DCIS. We identified new genes that are potentially involved in the malignant transformation of DCIS, and our findings strongly suggest that cells from the in situ component of lesions with co-existing invasive ductal carcinoma exhibit molecular alterations that enable them to invade the surrounding tissue before morphological changes in the lesion become apparent.

Identifying Nanoscale Structure-Function Relationships Using Multimodal Atomic Force Microscopy, Dimensionality Reduction, and Regression Techniques.

PubMed

Kong, Jessica; Giridharagopal, Rajiv; Harrison, Jeffrey S; Ginger, David S

2018-05-31

Correlating nanoscale chemical specificity with operational physics is a long-standing goal of functional scanning probe microscopy (SPM). We employ a data analytic approach combining multiple microscopy modes, using compositional information in infrared vibrational excitation maps acquired via photoinduced force microscopy (PiFM) with electrical information from conductive atomic force microscopy. We study a model polymer blend comprising insulating poly(methyl methacrylate) (PMMA) and semiconducting poly(3-hexylthiophene) (P3HT). We show that PiFM spectra are different from FTIR spectra, but can still be used to identify local composition. We use principal component analysis to extract statistically significant principal components and principal component regression to predict local current and identify local polymer composition. In doing so, we observe evidence of semiconducting P3HT within PMMA aggregates. These methods are generalizable to correlated SPM data and provide a meaningful technique for extracting complex compositional information that are impossible to measure from any one technique.

Architectural measures of the cancellous bone of the mandibular condyle identified by principal components analysis.

PubMed

Giesen, E B W; Ding, M; Dalstra, M; van Eijden, T M G J

2003-09-01

As several morphological parameters of cancellous bone express more or less the same architectural measure, we applied principal components analysis to group these measures and correlated these to the mechanical properties. Cylindrical specimens (n = 24) were obtained in different orientations from embalmed mandibular condyles; the angle of the first principal direction and the axis of the specimen, expressing the orientation of the trabeculae, ranged from 10 degrees to 87 degrees. Morphological parameters were determined by a method based on Archimedes' principle and by micro-CT scanning, and the mechanical properties were obtained by mechanical testing. The principal components analysis was used to obtain a set of independent components to describe the morphology. This set was entered into linear regression analyses for explaining the variance in mechanical properties. The principal components analysis revealed four components: amount of bone, number of trabeculae, trabecular orientation, and miscellaneous. They accounted for about 90% of the variance in the morphological variables. The component loadings indicated that a higher amount of bone was primarily associated with more plate-like trabeculae, and not with more or thicker trabeculae. The trabecular orientation was most determinative (about 50%) in explaining stiffness, strength, and failure energy. The amount of bone was second most determinative and increased the explained variance to about 72%. These results suggest that trabecular orientation and amount of bone are important in explaining the anisotropic mechanical properties of the cancellous bone of the mandibular condyle.

[Analysis of essential oil extracted from Lactuca sativa seeds growing in Xinjiang by GC-MS].

PubMed

Xu, Fang; Wang, Qiang; Haji, Akber Aisa

2011-12-01

To analyze the components of essential oil from Lactuca sativa seeds growing in Xinjiang. The components of essential oil from Lactuca sativa seeds were analyzed by gas chromatography-mass spectrometry (GC-MS). 62 components were identified from 71 separated peaks,amounting to total mass fraction 95.07%. The dominant compounds were n-Hexanol (36.31%), n-Hexanal (13.71%), trans-2-Octen-l-ol (8.09%) and 2-n-Pentylfuran (4.41%). The research provides a theoretical basis for the exploitation and use of Lactuca sativa seeds resource.

Automated analysis of blood pressure measurements (Korotkov sound)

NASA Technical Reports Server (NTRS)

Golden, D. P.; Hoffler, G. W.; Wolthuis, R. A.

1972-01-01

Automatic system for noninvasive measurements of arterial blood pressure is described. System uses Korotkov sound processor logic ratios to identify Korotkov sounds. Schematic diagram of system is provided to show components and method of operation.

An approach for evaluating the respiratory irritation of mixtures: application to metalworking fluids.

PubMed

Schaper, M M; Detwiler-Okabayashi, K A

1995-01-01

Recently, the sensory and pulmonary irritating properties of ten metalworking fluids (MWF) were assessed using a mouse bioassay. Relative potency of the MWFs was estimated, but it was not possible to identify the component(s) responsible for the the respiratory irritation induced by each MWF. One of the ten fluids, MWF "ET", produced sensory and pulmonary irritation in mice, and it was of moderate potency in comparison to the other nine MWFs. MWF "E" had three major components: tall oil fatty acids (TOFA), sodium sulfonate (SA), and paraffinic oil (PO). In the present study, the sensory and pulmonary irritating properties of these individual components of MWF "E" were evaluated. Mixtures of the three components were also prepared and similarly evaluated. This analysis revealed that the sensory irritation from MWF "E" was largely due to TOFA, whereas SA produced the pulmonary irritation observed with MWF "E". Both TOFA and SA were more potent irritants than was MWF "E", and the potency of TOFA and/or SA was diminished through combination with PO. There was no evidence of synergism of the components when combined to form MWF "E". This approach for identifying the biologically "active" component(s) in a mixture should be useful for other MWFs. Furthermore, the approach should be easily adapted for other applications involving concerns with mixtures.

Seromucinous component in endometrioid endometrial carcinoma as a histological predictor of prognosis.

PubMed

Miyamoto, Morikazu; Takano, Masashi; Aoyama, Tadashi; Soyama, Hiroaki; Yoshikawa, Tomoyuki; Tsuda, Hitoshi; Furuya, Kenichi

2018-03-01

In 2014 World Health Organization criteria, seromucinous carcinoma was defined as a new histological subtype in ovarian carcinomas, but "seromucinous carcinoma" was not defined in endometrial carcinomas. The aim of this study was to identify seromucinous carcinoma resembling ovarian seromucinous carcinoma in endometrial carcinomas, and to evaluate the clinical significance for prognoses of the patients. Central pathological review was conducted for patients with endometrioid carcinoma of the endometrium treated by primary surgery at our hospital between 1990 and 2013. Among 340 cases included in the study, no case had all tumor cells resembling ovarian seromucinous carcinoma in all specimens, and 31 cases (9.1%) had seromucinous component in combination with endometrioid carcinomas. Immunohistochemical analysis revealed seromucinous component had positive reactivity for cytokeratin (CK) 7, and negative reactivity for CK20 and caudal type homeobox 2 (CDX2) in all cases. Seromucinous component showed lower immunoreactivity of estrogen receptor and progesterone receptor, compared with endometrioid carcinoma component. Progression-free survival of the cases with seromucinous component was better than those without seromucinous component (p=0.049). Seromucinous component was identified in approximately 10% of endometrioid carcinoma, and could be a histological predictor for prognosis. Copyright © 2018. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology

Fetal source extraction from magnetocardiographic recordings by dependent component analysis

NASA Astrophysics Data System (ADS)

de Araujo, Draulio B.; Kardec Barros, Allan; Estombelo-Montesco, Carlos; Zhao, Hui; Roque da Silva Filho, A. C.; Baffa, Oswaldo; Wakai, Ronald; Ohnishi, Noboru

2005-10-01

Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.

Clustering of leptin and physical activity with components of metabolic syndrome in Iranian population: an exploratory factor analysis.

PubMed

Esteghamati, Alireza; Zandieh, Ali; Khalilzadeh, Omid; Morteza, Afsaneh; Meysamie, Alipasha; Nakhjavani, Manouchehr; Gouya, Mohammad Mehdi

2010-10-01

Metabolic syndrome (MetS), manifested by insulin resistance, dyslipidemia, central obesity, and hypertension, is conceived to be associated with hyperleptinemia and physical activity. The aim of this study was to elucidate the factors underlying components of MetS and also to test the suitability of leptin and physical activity as additional components of this syndrome. Data of the individuals without history of diabetes mellitus, aged 25-64 years, from third national surveillance of risk factors of non-communicable diseases (SuRFNCD-2007), were analyzed. Performing factor analysis on waist circumference, homeostasis model assessment of insulin resistance, systolic blood pressure, triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) led to extraction of two factors which explained around 59.0% of the total variance in both genders. When TG and HDL-C were replaced by TG to HDL-C ratio, a single factor was obtained. In contrast to physical activity, addition of leptin was consistent with one-factor structure of MetS and improved the ability of suggested models to identify obesity (BMI≥30 kg/m2, P<0.01), using receiver-operator characteristics curve analysis. In general, physical activity loaded on the first identified factor. Our study shows that one underlying factor structure of MetS is also plausible and the inclusion of leptin does not interfere with this structure. Further, this study suggests that physical activity influences MetS components via modulation of the main underlying pathophysiologic pathway of this syndrome.

Harmonic component detection: Optimized Spectral Kurtosis for operational modal analysis

NASA Astrophysics Data System (ADS)

Dion, J.-L.; Tawfiq, I.; Chevallier, G.

2012-01-01

This work is a contribution in the field of Operational Modal Analysis to identify the modal parameters of mechanical structures using only measured responses. The study deals with structural responses coupled with harmonic components amplitude and frequency modulated in a short range, a common combination for mechanical systems with engines and other rotating machines in operation. These harmonic components generate misleading data interpreted erroneously by the classical methods used in OMA. The present work attempts to differentiate maxima in spectra stemming from harmonic components and structural modes. The detection method proposed is based on the so-called Optimized Spectral Kurtosis and compared with others definitions of Spectral Kurtosis described in the literature. After a parametric study of the method, a critical study is performed on numerical simulations and then on an experimental structure in operation in order to assess the method's performance.

Heart sound segmentation of pediatric auscultations using wavelet analysis.

PubMed

Castro, Ana; Vinhoza, Tiago T V; Mattos, Sandra S; Coimbra, Miguel T

2013-01-01

Auscultation is widely applied in clinical activity, nonetheless sound interpretation is dependent on clinician training and experience. Heart sound features such as spatial loudness, relative amplitude, murmurs, and localization of each component may be indicative of pathology. In this study we propose a segmentation algorithm to extract heart sound components (S1 and S2) based on it's time and frequency characteristics. This algorithm takes advantage of the knowledge of the heart cycle times (systolic and diastolic periods) and of the spectral characteristics of each component, through wavelet analysis. Data collected in a clinical environment, and annotated by a clinician was used to assess algorithm's performance. Heart sound components were correctly identified in 99.5% of the annotated events. S1 and S2 detection rates were 90.9% and 93.3% respectively. The median difference between annotated and detected events was of 33.9 ms.

Major and trace element chemistry of Luna 24 samples from Mare Crisium

NASA Technical Reports Server (NTRS)

Blanchard, D. P.; Brannon, J. C.; Aaboe, E.; Budahn, J. R.

1978-01-01

Atomic absorption spectrometry and instrumental neutron activation analysis were employed to analyze six Luna 24 soils for major and trace elements. The analysis revealed well-mixed soils, though size fractions of each of the soils showed quite dissimilar compositions. Thus the regolith apparently has not been extensively reworked. Noritic breccia admixed preferentially to the finest size fractions and differential comminution of one or more other soil components accounted for the observed elemental distributions as a function of grain size. The ferrobasalt composition and one or more components with higher MgO contents have been identified in the samples.

Analysis of Indonesian Spice Essential Oil Compounds That Inhibit Locomotor Activity in Mice

PubMed Central

Muchtaridi; Diantini, Adjeng; Subarnas, Anas

2011-01-01

Some fragrance components of spices used for cooking are known to have an effect on human behavior. The aim of this investigation was to examine the effect of the essential oils of basil (Ocimum formacitratum L.) leaves, lemongrass (Cymbopogon citrates L.) herbs, ki lemo (Litsea cubeba L.) bark, and laja gowah (Alpinia malaccencis Roxb.) rhizomes on locomotor activity in mice and identify the active component(s) that might be responsible for the activity. The effect of the essential oils was studied by a wheel cage method and the active compounds of the essential oils were identified by GC/MS analysis. The essential oils were administered by inhalation at doses of 0.1, 0.3, and 0.5 mL/cage. The results showed that the four essential oils had inhibitory effects on locomotor activity in mice. Inhalation of the essential oils of basil leaves, lemongrass herbs, ki lemo bark, and laja gowah rhizomes showed the highest inhibitory activity at doses of 0.5 (57.64%), 0.1 (55.72%), 0.5 (60.75%), and 0.1 mL/cage (47.09%), respectively. The major volatile compounds 1,8-cineole, α-terpineol, 4-terpineol, citronelol, citronelal, and methyl cinnamate were identified in blood plasma of mice after inhalation of the four oils. These compounds had a significant inhibitory effect on locomotion after inhalation. The volatile compounds of essential oils identified in the blood plasma may correlate with the locomotor-inhibiting properties of the oil when administered by inhalation.

Nano-sized structures for the detection of food components and contaminants.

PubMed

Dudak, Fahriye Ceyda; Bas, Deniz; Basaran-Akgul, Nese; Tamer, Ugur; Boyaci, Ismail Hakki

2011-06-01

New methods to identify trace amount of food components and/or contaminants (infectious pathogens and chemicals) rapidly, accurately, and with high sensitivity are in constant demand to prevent foodborne illnesses. Multipurpose biofunctionalized engineered nanomaterials are very promising for the detection of food components and contaminants. The unique optical and magnetic properties of the nanoscale materials are very useful in the analysis of food. The objectives of this review paper are to discuss the development of applications of nanoscale structures related to food industries and to provide an overview of available methods of detecting food components and contaminants with particular emphasis on the use of nanoparticles.

Genome-scale analysis of the high-efficient protein secretion system of Aspergillus oryzae

PubMed Central

2014-01-01

Background The koji mold, Aspergillus oryzae is widely used for the production of industrial enzymes due to its particularly high protein secretion capacity and ability to perform post-translational modifications. However, systemic analysis of its secretion system is lacking, generally due to the poorly annotated proteome. Results Here we defined a functional protein secretory component list of A. oryzae using a previously reported secretory model of S. cerevisiae as scaffold. Additional secretory components were obtained by blast search with the functional components reported in other closely related fungal species such as Aspergillus nidulans and Aspergillus niger. To evaluate the defined component list, we performed transcriptome analysis on three α-amylase over-producing strains with varying levels of secretion capacities. Specifically, secretory components involved in the ER-associated processes (including components involved in the regulation of transport between ER and Golgi) were significantly up-regulated, with many of them never been identified for A. oryzae before. Furthermore, we defined a complete list of the putative A. oryzae secretome and monitored how it was affected by overproducing amylase. Conclusion In combination with the transcriptome data, the most complete secretory component list and the putative secretome, we improved the systemic understanding of the secretory machinery of A. oryzae in response to high levels of protein secretion. The roles of many newly predicted secretory components were experimentally validated and the enriched component list provides a better platform for driving more mechanistic studies of the protein secretory pathway in this industrially important fungus. PMID:24961398

Genome-scale analysis of the high-efficient protein secretion system of Aspergillus oryzae.

PubMed

Liu, Lifang; Feizi, Amir; Österlund, Tobias; Hjort, Carsten; Nielsen, Jens

2014-06-24

The koji mold, Aspergillus oryzae is widely used for the production of industrial enzymes due to its particularly high protein secretion capacity and ability to perform post-translational modifications. However, systemic analysis of its secretion system is lacking, generally due to the poorly annotated proteome. Here we defined a functional protein secretory component list of A. oryzae using a previously reported secretory model of S. cerevisiae as scaffold. Additional secretory components were obtained by blast search with the functional components reported in other closely related fungal species such as Aspergillus nidulans and Aspergillus niger. To evaluate the defined component list, we performed transcriptome analysis on three α-amylase over-producing strains with varying levels of secretion capacities. Specifically, secretory components involved in the ER-associated processes (including components involved in the regulation of transport between ER and Golgi) were significantly up-regulated, with many of them never been identified for A. oryzae before. Furthermore, we defined a complete list of the putative A. oryzae secretome and monitored how it was affected by overproducing amylase. In combination with the transcriptome data, the most complete secretory component list and the putative secretome, we improved the systemic understanding of the secretory machinery of A. oryzae in response to high levels of protein secretion. The roles of many newly predicted secretory components were experimentally validated and the enriched component list provides a better platform for driving more mechanistic studies of the protein secretory pathway in this industrially important fungus.

Chemical composition, insecticidal and insect repellent activity of Schinus molle L. leaf and fruit essential oils against Trogoderma granarium and Tribolium castaneum.

PubMed

Abdel-Sattar, Essam; Zaitoun, Ahmed A; Farag, Mohamed A; Gayed, Sabah H El; Harraz, Fathalla M H

2010-02-01

Fruit and leaf essential oils of Schinus molle showed insect repellent and insecticidal activity against Trogoderma granarium and Tribolium castaneum. In these oils, 65 components were identified by GC-MS analysis. Hydrocarbons dominated the oil composition with monoterpenes occurring in the largest amounts in fruits and leaves, 80.43 and 74.84%, respectively. p-Cymene was identified as a major component in both oils. The high yield and efficacy of S. molle essential oil against T. granarium and T. castaneum suggest that it may provide leads for active insecticidal agents.

Use of direct gradient analysis to uncover biological hypotheses in 16s survey data and beyond.

PubMed

Erb-Downward, John R; Sadighi Akha, Amir A; Wang, Juan; Shen, Ning; He, Bei; Martinez, Fernando J; Gyetko, Margaret R; Curtis, Jeffrey L; Huffnagle, Gary B

2012-01-01

This study investigated the use of direct gradient analysis of bacterial 16S pyrosequencing surveys to identify relevant bacterial community signals in the midst of a "noisy" background, and to facilitate hypothesis-testing both within and beyond the realm of ecological surveys. The results, utilizing 3 different real world data sets, demonstrate the utility of adding direct gradient analysis to any analysis that draws conclusions from indirect methods such as Principal Component Analysis (PCA) and Principal Coordinates Analysis (PCoA). Direct gradient analysis produces testable models, and can identify significant patterns in the midst of noisy data. Additionally, we demonstrate that direct gradient analysis can be used with other kinds of multivariate data sets, such as flow cytometric data, to identify differentially expressed populations. The results of this study demonstrate the utility of direct gradient analysis in microbial ecology and in other areas of research where large multivariate data sets are involved.

Maternal Food-Related Practices, Quality of Diet, and Well-Being: Profiles of Chilean Mother-Adolescent Dyads.

PubMed

Schnettler, Berta; Grunert, Klaus G; Lobos, Germán; Miranda-Zapata, Edgardo; Denegri, Marianela; Hueche, Clementina

2018-04-03

To identify mother-adolescent dyad profiles according to food-related parenting practices and to determine differences in diet quality, family meal frequency, life satisfaction, and sociodemographic characteristics. Cross-sectional study. Mothers and children were surveyed in their homes or at schools in Temuco, Chile. A total of 300 mothers (average age, 41.6 years) and their adolescent children (average age, 13.2 years; 48.7% female). Maternal feeding practices using the abbreviated Family Food Behavior Survey (AFFBS), life satisfaction, food-related and family life satisfaction, diet quality, and eating habits. Principal component factor analysis and confirmatory factor analysis were used to verify Family Food Behavior Survey components in mother and adolescent subsamples. Hierarchical cluster analysis was used to identify profiles. Three AFFBS components were detected: maternal control of child snacking behavior, maternal presence during eating, and child involvement in food consumption. Cluster analysis identified 3 mother-adolescent dyad profiles with different food-related parenting practices (P ≤ .001), mother (P ≤ .05) and child (P ≤ .001) diet quality, frequency of shared family meals (P ≤ .001), and mother (P ≤ .001) and child (P ≤ .05) life satisfaction levels. Results indicated that maternal well-being increased with an increased frequency of shared mealtime. Significantly, in contrast to the findings of previous studies, greater control over child eating habits was shown to affect adolescent well-being positively. These findings, among others, may contribute to the development of strategies for improving diet quality, overall well-being, and well-being in the food and family domains for all family members. Copyright © 2018 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Qualitative data analysis for an exploratory sensory study of Grechetto wine.

PubMed

Esti, Marco; González Airola, Ricardo L; Moneta, Elisabetta; Paperaio, Marina; Sinesio, Fiorella

2010-02-15

Grechetto is a traditional white-grape vine, widespread in Umbria and Lazio regions in central Italy. Despite the wine commercial diffusion, little literature on its sensory characteristics is available. The present study is an exploratory research conducted with the aim of identifying the sensory markers of Grechetto wine and of evaluating the effect of clone, geographical area, vintage and producer on sensory attributes. A qualitative sensory study was conducted on 16 wines, differing for vintage, Typical Geographic Indication, and clone, collected from 7 wineries, using a trained panel in isolation who referred to a glossary of 133 white wine descriptors. Sixty-five attributes identified by a minimum of 50% of the respondents were submitted to a correspondence analysis to link wine samples to the sensory attributes. Seventeen terms identified as common to all samples are considered as characteristics of Grechetto wine, 10 of which olfactory: fruity, apple, acacia flower, pineapple, banana, floral, herbaceous, honey, apricot and peach. In order to interpret the relationship between design variables and sensory attributes data on 2005 and 2006 wines, the 28 most discriminating descriptors were projected in a principal component analysis. The first principal component was best described by olfactory terms and the second by gustative attributes. Good reproducibility of results was obtained for the two vintages. For one winery, vintage effect (2002-2006) was described in a new principal component analysis model applied on 39 most discriminating descriptors, which globally explained about 84% of the variance. In the young wines the notes of sulphur, yeast, dried fruit, butter, combined with herbaceous fresh and tropical fruity notes (melon, grapefruit) were dominant. During wine aging, sweeter notes, like honey, caramel, jam, become more dominant as well as some mineral notes, such as tuff and flint. Copyright 2009 Elsevier B.V. All rights reserved.

Assessing agro-environmental performance of dairy farms in northwest Italy based on aggregated results from indicators.

PubMed

Gaudino, Stefano; Goia, Irene; Grignani, Carlo; Monaco, Stefano; Sacco, Dario

2014-07-01

Dairy farms control an important share of the agricultural area of Northern Italy. Zero grazing, large maize-cropped areas, high stocking densities, and high milk production make them intensive and prone to impact the environment. Currently, few published studies have proposed indicator sets able to describe the entire dairy farm system and their internal components. This work had four aims: i) to propose a list of agro-environmental indicators to assess dairy farms; ii) to understand which indicators classify farms best; iii) to evaluate the dairy farms based on the proposed indicator list; iv) to link farmer decisions to the consequent environmental pressures. Forty agro-environmental indicators selected for this study are described. Northern Italy dairy systems were analysed considering both farmer decision indicators (farm management) and the resulting pressure indicators that demonstrate environmental stress on the entire farming system, and its components: cropping system, livestock system, and milk production. The correlations among single indicators identified redundant indicators. Principal Components Analysis distinguished which indicators provided meaningful information about each pressure indicator group. Analysis of the communalities and the correlations among indicators identified those that best represented farm variability: Farm Gate N Balance, Greenhouse Gas Emission, and Net Energy of the farm system; Net Energy and Gross P Balance of the cropping system component; Energy Use Efficiency and Purchased Feed N Input of the livestock system component; N Eco-Efficiency of the milk production component. Farm evaluation, based on the complete list of selected indicators demonstrated organic farming resulted in uniformly high values, while farms with low milk-producing herds resulted in uniformly low values. Yet on other farms, the environmental quality varied greatly when different groups of pressure indicators were considered, which highlighted the importance of expanding environmental analysis to effects within the farm. Statistical analysis demonstrated positive correlations between all farmer decision and pressure group indicators. Consumption of mineral fertiliser and pesticide negatively influenced the cropping system. Furthermore, stocking rate was found to correlate positively with the milk production component and negatively with the farm system. This study provides baseline references for ex ante policy evaluation, and monitoring tools for analysis both in itinere and ex post environment policy implementation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Principal component analysis of three-dimensional face shape: Identifying shape features that change with age.

PubMed

Kurosumi, M; Mizukoshi, K

2018-05-01

The types of shape feature that constitutes a face have not been comprehensively established, and most previous studies of age-related changes in facial shape have focused on individual characteristics, such as wrinkle, sagging skin, etc. In this study, we quantitatively measured differences in face shape between individuals and investigated how shape features changed with age. We analyzed three-dimensionally the faces of 280 Japanese women aged 20-69 years and used principal component analysis to establish the shape features that characterized individual differences. We also evaluated the relationships between each feature and age, clarifying the shape features characteristic of different age groups. Changes in facial shape in middle age were a decreased volume of the upper face and increased volume of the whole cheeks and around the chin. Changes in older people were an increased volume of the lower cheeks and around the chin, sagging skin, and jaw distortion. Principal component analysis was effective for identifying facial shape features that represent individual and age-related differences. This method allowed straightforward measurements, such as the increase or decrease in cheeks caused by soft tissue changes or skeletal-based changes to the forehead or jaw, simply by acquiring three-dimensional facial images. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Analysis on unevenness of skin color using the melanin and hemoglobin components separated by independent component analysis of skin color image

NASA Astrophysics Data System (ADS)

Ojima, Nobutoshi; Fujiwara, Izumi; Inoue, Yayoi; Tsumura, Norimichi; Nakaguchi, Toshiya; Iwata, Kayoko

2011-03-01

Uneven distribution of skin color is one of the biggest concerns about facial skin appearance. Recently several techniques to analyze skin color have been introduced by separating skin color information into chromophore components, such as melanin and hemoglobin. However, there are not many reports on quantitative analysis of unevenness of skin color by considering type of chromophore, clusters of different sizes and concentration of the each chromophore. We propose a new image analysis and simulation method based on chromophore analysis and spatial frequency analysis. This method is mainly composed of three techniques: independent component analysis (ICA) to extract hemoglobin and melanin chromophores from a single skin color image, an image pyramid technique which decomposes each chromophore into multi-resolution images, which can be used for identifying different sizes of clusters or spatial frequencies, and analysis of the histogram obtained from each multi-resolution image to extract unevenness parameters. As the application of the method, we also introduce an image processing technique to change unevenness of melanin component. As the result, the method showed high capabilities to analyze unevenness of each skin chromophore: 1) Vague unevenness on skin could be discriminated from noticeable pigmentation such as freckles or acne. 2) By analyzing the unevenness parameters obtained from each multi-resolution image for Japanese ladies, agerelated changes were observed in the parameters of middle spatial frequency. 3) An image processing system modulating the parameters was proposed to change unevenness of skin images along the axis of the obtained age-related change in real time.

Nurses' fidelity to theory-based core components when implementing Family Health Conversations - a qualitative inquiry.

PubMed

Östlund, Ulrika; Bäckström, Britt; Lindh, Viveca; Sundin, Karin; Saveman, Britt-Inger

2015-09-01

A family systems nursing intervention, Family Health Conversation, has been developed in Sweden by adapting the Calgary Family Assessment and Intervention Models and the Illness Beliefs Model. The intervention has several theoretical assumptions, and one way translate the theory into practice is to identify core components. This may produce higher levels of fidelity to the intervention. Besides information about how to implement an intervention in accordance to how it was developed, evaluating whether it was actually implemented as intended is important. Accordingly, we describe the nurses' fidelity to the identified core components of Family Health Conversation. Six nurses, working in alternating pairs, conducted Family Health Conversations with seven families in which a family member younger than 65 had suffered a stroke. The intervention contained a series of three-1-hour conversations held at 2-3 week intervals. The nurses followed a conversation structure based on 12 core components identified from theoretical assumptions. The transcripts of the 21 conversations were analysed using manifest qualitative content analysis with a deductive approach. The 'core components' seemed to be useful even if nurses' fidelity varied among the core components. Some components were followed relatively well, but others were not. This indicates that the process for achieving fidelity to the intervention can be improved, and that it is necessary for nurses to continually learn theory and to practise family systems nursing. We suggest this can be accomplished through reflections, role play and training on the core components. Furthermore, as in this study, joint reflections on how the core components have been implemented can lead to deeper understanding and knowledge of how Family Health Conversation can be delivered as intended. © 2014 Nordic College of Caring Science.

An approach for quantitative image quality analysis for CT

NASA Astrophysics Data System (ADS)

Rahimi, Amir; Cochran, Joe; Mooney, Doug; Regensburger, Joe

2016-03-01

An objective and standardized approach to assess image quality of Compute Tomography (CT) systems is required in a wide variety of imaging processes to identify CT systems appropriate for a given application. We present an overview of the framework we have developed to help standardize and to objectively assess CT image quality for different models of CT scanners used for security applications. Within this framework, we have developed methods to quantitatively measure metrics that should correlate with feature identification, detection accuracy and precision, and image registration capabilities of CT machines and to identify strengths and weaknesses in different CT imaging technologies in transportation security. To that end we have designed, developed and constructed phantoms that allow for systematic and repeatable measurements of roughly 88 image quality metrics, representing modulation transfer function, noise equivalent quanta, noise power spectra, slice sensitivity profiles, streak artifacts, CT number uniformity, CT number consistency, object length accuracy, CT number path length consistency, and object registration. Furthermore, we have developed a sophisticated MATLAB based image analysis tool kit to analyze CT generated images of phantoms and report these metrics in a format that is standardized across the considered models of CT scanners, allowing for comparative image quality analysis within a CT model or between different CT models. In addition, we have developed a modified sparse principal component analysis (SPCA) method to generate a modified set of PCA components as compared to the standard principal component analysis (PCA) with sparse loadings in conjunction with Hotelling T2 statistical analysis method to compare, qualify, and detect faults in the tested systems.

The Relationship of Social Engagement and Social Support With Sense of Community.

PubMed

Tang, Fengyan; Chi, Iris; Dong, Xinqi

2017-07-01

We aimed to investigate the relationship of engagement in social and cognitive activities and social support with the sense of community (SOC) and its components among older Chinese Americans. The Sense of Community Index (SCI) was used to measure SOC and its four component factors: membership, influence, needs fulfillment, and emotional connection. Social engagement was assessed with 16 questions. Social support included positive support and negative strain. Principal component analysis was used to identify the SCI components. Linear regression analysis was used to detect the contribution of social engagement and social support to SOC and its components. After controlling for sociodemographics and self-rated health, social activity engagement and positive social support were positively related to SOC and its components. This study points to the importance of social activity engagement and positive support from family and friends in increasing the sense of community. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

FT-IR spectroscopy and multivariate analysis as an auxiliary tool for diagnosis of mental disorders: Bipolar and schizophrenia cases

NASA Astrophysics Data System (ADS)

Ogruc Ildiz, G.; Arslan, M.; Unsalan, O.; Araujo-Andrade, C.; Kurt, E.; Karatepe, H. T.; Yilmaz, A.; Yalcinkaya, O. B.; Herken, H.

2016-01-01

In this study, a methodology based on Fourier-transform infrared spectroscopy and principal component analysis and partial least square methods is proposed for the analysis of blood plasma samples in order to identify spectral changes correlated with some biomarkers associated with schizophrenia and bipolarity. Our main goal was to use the spectral information for the calibration of statistical models to discriminate and classify blood plasma samples belonging to bipolar and schizophrenic patients. IR spectra of 30 samples of blood plasma obtained from each, bipolar and schizophrenic patients and healthy control group were collected. The results obtained from principal component analysis (PCA) show a clear discrimination between the bipolar (BP), schizophrenic (SZ) and control group' (CG) blood samples that also give possibility to identify three main regions that show the major differences correlated with both mental disorders (biomarkers). Furthermore, a model for the classification of the blood samples was calibrated using partial least square discriminant analysis (PLS-DA), allowing the correct classification of BP, SZ and CG samples. The results obtained applying this methodology suggest that it can be used as a complimentary diagnostic tool for the detection and discrimination of these mental diseases.

Issues in Biomedical Research Data Management and Analysis: Needs and Barriers

PubMed Central

Anderson, Nicholas R.; Lee, E. Sally; Brockenbrough, J. Scott; Minie, Mark E.; Fuller, Sherrilynne; Brinkley, James; Tarczy-Hornoch, Peter

2007-01-01

Objectives A. Identify the current state of data management needs of academic biomedical researchers. B. Explore their anticipated data management and analysis needs. C. Identify barriers to addressing those needs. Design A multimodal needs analysis was conducted using a combination of an online survey and in-depth one-on-one semi-structured interviews. Subjects were recruited via an e-mail list representing a wide range of academic biomedical researchers in the Pacific Northwest. Measurements The results from 286 survey respondents were used to provide triangulation of the qualitative analysis of data gathered from 15 semi-structured in-depth interviews. Results Three major themes were identified: 1) there continues to be widespread use of basic general-purpose applications for core data management; 2) there is broad perceived need for additional support in managing and analyzing large datasets; and 3) the barriers to acquiring currently available tools are most commonly related to financial burdens on small labs and unmet expectations of institutional support. Conclusion Themes identified in this study suggest that at least some common data management needs will best be served by improving access to basic level tools such that researchers can solve their own problems. Additionally, institutions and informaticians should focus on three components: 1) facilitate and encourage the use of modern data exchange models and standards, enabling researchers to leverage a common layer of interoperability and analysis; 2) improve the ability of researchers to maintain provenance of data and models as they evolve over time though tools and the leveraging of standards; and 3) develop and support information management service cores that could assist in these previous components while providing researchers with unique data analysis and information design support within a spectrum of informatics capabilities. PMID:17460139

[Analysis and identification of emulsifying viscosity reducer by FTIR and 1H NMR].

PubMed

Zhu, Hong; Guan, Run-ling; Shen, Jing-mei

2007-01-01

Separation and purification of viscosity reducer for crude oil were performed with distillation and dissolution-precipitation. The functional group of its main component was identified by FTIR It was deduced that the main component of the crude oil viscosity reducer is the tricopolymer of poly ethyl acrylate/methyl methacrylate/acrylic acid. The structure of the component was also ascertained and quantitively analysed with 1 H NMR and MS. The mol ratio of the three monomers is 37. 1 : 25. 8 : 37. 1, and the mass ratio is 41. 1 : 28. 8 : 29. 8. The structure of the part soluble in methanol was identified by FTIR. The result showed that the nonionic sufactant is poly ethylene oxide with the moleculear mass range of 800-1600, and the anionic surfactant is alkylbenzene sulfonate. The residue is accessory ingredient and water.

Dynamics of resilience of wheat to drought in Australia from 1991-2010.

PubMed

Huai, Jianjun

2017-08-25

Although enhancing resilience is a well-recognized adaptation to climate change, little research has been undertaken on the dynamics of resilience. This occurs because complex relationships exist between adaptive capacity and resilience, and some issues also create challenges related to the construction, operation, and application of resilience. This study identified the dynamics of temporal, spatial changes of resilience found in a sample of wheat-drought resilience in Australia's wheat-sheep production zone during 1991-2010. I estimated resilience using principal component analysis, mapped resilience and its components, distinguished resilient and sensitive regions, and provided recommendations related to improving resilience. I frame that resilience is composed of social resilience including on- and off-site adaptive capacity as well as biophysical resilience including resistance and absorption. I found that resilience and its components have different temporal trends, spatial shifts and growth ratios in each region during different years, which results from complicated interactions, such as complementation and substitution among its components. In wheat-sheep zones, I recommend that identifying regional bottlenecks, science-policy engagement, and managing resilience components are the priorities for improving resilience.

Chemical composition and aroma evaluation of volatile oils from edible mushrooms (Pleurotus salmoneostramineus and Pleurotus sajor-caju).

PubMed

Usami, Atsushi; Nakaya, Satoshi; Nakahashi, Hiroshi; Miyazawa, Mitsuo

2014-01-01

This study is focused on the volatile oils from the fruiting bodies of Pleurotus salmoneostramineus (PS) and P. sajor-caju (PSC), which was extracted by hydrodistillation (HD) and solvent-assisted flavor evaporation (SAFE) methods. The oils are analyzed by gas chromatography-mass spectrometry (GC-MS), GC-olfactometry (GC-O), and aroma extract dilution analysis (AEDA). A total of 31, 31, 45, and 15 components were identified in PS (HD and SAFE) and PSC (HD and SAFE), representing about 80.3%, 92.2%, 88.9%, and 83.0% of the oils, respectively. Regarding the aroma-active components, 13, 12, 13, and 5 components were identified in PS (HD and SAFE) and PSC (HD and SAFE), respectively, by the GC-O analyses. The results of the sniffing test, odor activity value (OAV) and flavor dilution (FD) factor indicate that 1-octen-3-ol and 3-octanone are the main aroma-active components of PS oils. On the other hands, methional and 1-octen-3-ol were estimated as the main aroma-active components of PSC oils.

Segmenting the Stream of Consciousness: The Psychological Correlates of Temporal Structures in the Time Series Data of a Continuous Performance Task

ERIC Educational Resources Information Center

Smallwood, Jonathan; McSpadden, Merrill; Luus, Bryan; Schooler, Joanthan

2008-01-01

Using principal component analysis, we examined whether structural properties in the time series of response time would identify different mental states during a continuous performance task. We examined whether it was possible to identify regular patterns which were present in blocks classified as lacking controlled processing, either…

Cellulose as an extracellular matrix component present in Enterobacter sakazakii biofilms.

PubMed

Grimm, Maya; Stephan, Roger; Iversen, Carol; Manzardo, Giuseppe G G; Rattei, Thomas; Riedel, Kathrin; Ruepp, Andreas; Frishman, Dmitrij; Lehner, Angelika

2008-01-01

Cellulose was identified and characterized as an extracellular matrix component present in the biofilm of an Enterobacter sakazakii clinical isolate grown in nutrient-deficient (M9) medium. Using a bacterial artificial cloning approach in Escherichia coli and subsequent screening of transformants for fluorescence on calcofluor plates, nine genes organized in two operons were identified as putatively responsible for the biosynthesis of cellulose. In addition to the genes already described for cellulose production, two more genes were identified, putatively transcribed together with the genes from the first operon. Putative cellulose in E. sakazakii ES5 biofilm grown on glass coverslips was visualized by calcofluor staining and confocal fluorescence laser scanning microscopy. For the first time, the presence of cellulose in biofilms produced by E. sakazakii was confirmed by methylation analysis.

Application of Principal Component Analysis to NIR Spectra of Phyllosilicates: A Technique for Identifying Phyllosilicates on Mars

NASA Technical Reports Server (NTRS)

Rampe, E. B.; Lanza, N. L.

2012-01-01

Orbital near-infrared (NIR) reflectance spectra of the martian surface from the OMEGA and CRISM instruments have identified a variety of phyllosilicates in Noachian terrains. The types of phyllosilicates present on Mars have important implications for the aqueous environments in which they formed, and, thus, for recognizing locales that may have been habitable. Current identifications of phyllosilicates from martian NIR data are based on the positions of spectral absorptions relative to laboratory data of well-characterized samples and from spectral ratios; however, some phyllosilicates can be difficult to distinguish from one another with these methods (i.e. illite vs. muscovite). Here we employ a multivariate statistical technique, principal component analysis (PCA), to differentiate between spectrally similar phyllosilicate minerals. PCA is commonly used in a variety of industries (pharmaceutical, agricultural, viticultural) to discriminate between samples. Previous work using PCA to analyze raw NIR reflectance data from mineral mixtures has shown that this is a viable technique for identifying mineral types, abundances, and particle sizes. Here, we evaluate PCA of second-derivative NIR reflectance data as a method for classifying phyllosilicates and test whether this method can be used to identify phyllosilicates on Mars.

Screening of the key volatile organic compounds of Tuber melanosporum fermentation by aroma sensory evaluation combination with principle component analysis

PubMed Central

Liu, Rui-Sang; Jin, Guang-Huai; Xiao, Deng-Rong; Li, Hong-Mei; Bai, Feng-Wu; Tang, Ya-Jie

2015-01-01

Aroma results from the interplay of volatile organic compounds (VOCs) and the attributes of microbial-producing aromas are significantly affected by fermentation conditions. Among the VOCs, only a few of them contribute to aroma. Thus, screening and identification of the key VOCs is critical for microbial-producing aroma. The traditional method is based on gas chromatography-olfactometry (GC-O), which is time-consuming and laborious. Considering the Tuber melanosporum fermentation system as an example, a new method to screen and identify the key VOCs by combining the aroma evaluation method with principle component analysis (PCA) was developed in this work. First, an aroma sensory evaluation method was developed to screen 34 potential favorite aroma samples from 504 fermentation samples. Second, PCA was employed to screen nine common key VOCs from these 34 samples. Third, seven key VOCs were identified by the traditional method. Finally, all of the seven key VOCs identified by the traditional method were also identified, along with four others, by the new strategy. These results indicate the reliability of the new method and demonstrate it to be a viable alternative to the traditional method. PMID:26655663

Analysis of a Shock-Associated Noise Prediction Model Using Measured Jet Far-Field Noise Data

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Sharpe, Jacob A.

2014-01-01

A code for predicting supersonic jet broadband shock-associated noise was assessed using a database containing noise measurements of a jet issuing from a convergent nozzle. The jet was operated at 24 conditions covering six fully expanded Mach numbers with four total temperature ratios. To enable comparisons of the predicted shock-associated noise component spectra with data, the measured total jet noise spectra were separated into mixing noise and shock-associated noise component spectra. Comparisons between predicted and measured shock-associated noise component spectra were used to identify deficiencies in the prediction model. Proposed revisions to the model, based on a study of the overall sound pressure levels for the shock-associated noise component of the measured data, a sensitivity analysis of the model parameters with emphasis on the definition of the convection velocity parameter, and a least-squares fit of the predicted to the measured shock-associated noise component spectra, resulted in a new definition for the source strength spectrum in the model. An error analysis showed that the average error in the predicted spectra was reduced by as much as 3.5 dB for the revised model relative to the average error for the original model.

Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing

NASA Astrophysics Data System (ADS)

Rojo, Jesús; Rivero, Rosario; Romero-Morte, Jorge; Fernández-González, Federico; Pérez-Badia, Rosa

2017-02-01

Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture—for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments—as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series—daily Poaceae pollen concentrations over the period 2006-2014—was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.

Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing.

PubMed

Rojo, Jesús; Rivero, Rosario; Romero-Morte, Jorge; Fernández-González, Federico; Pérez-Badia, Rosa

2017-02-01

Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture-for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments-as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series-daily Poaceae pollen concentrations over the period 2006-2014-was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.

[Analysis of the chemical constituents of volatile oils of Metasequoia glyptostroboides leave].

PubMed

Shong, E; Lui, R

1997-10-01

The chemical constituents of volatile oils of Metasequoia glyptostroboides leave were analyzed by GC-MS-DS. 27 constituents were identified, alpha-pinene (70.65%) and caryophyllene (10.38%) of them are main components.

A Genome-Wide RNAi Screen for Modifiers of the Circadian Clock in Human Cells

PubMed Central

Zhang, Eric E.; Liu, Andrew C.; Hirota, Tsuyoshi; Miraglia, Loren J.; Welch, Genevieve; Pongsawakul, Pagkapol Y.; Liu, Xianzhong; Atwood, Ann; Huss, Jon W.; Janes, Jeff; Su, Andrew I.; Hogenesch, John B.; Kay, Steve A.

2009-01-01

Summary Two decades of research identified more than a dozen clock genes and defined a biochemical feedback mechanism of circadian oscillator function. To identify additional clock genes and modifiers, we conducted a genome-wide siRNA screen in a human cellular clock model. Knockdown of nearly a thousand genes reduced rhythm amplitude. Potent effects on period length or increased amplitude were less frequent; we found hundreds of these and confirmed them in secondary screens. Characterization of a subset of these genes demonstrated a dosage-dependent effect on oscillator function. Protein interaction network analysis showed that dozens of gene products directly or indirectly associate with known clock components. Pathway analysis revealed these genes are overrepresented for components of insulin and hedgehog signaling, the cell cycle, and the folate metabolism. Coupled with data showing many of these pathways are clock-regulated, we conclude the clock is interconnected with many aspects of cellular function. PMID:19765810

Gas Chromatography-Mass Spectrometry Analysis of Constituent Oil from Lingzhi or Reishi Medicinal Mushroom, Ganoderma lucidum (Agaricomycetes), from Nigeria.

PubMed

Ohiri, Reginald Chibueze; Bassey, Essien Eka

2016-01-01

Gas chromatography-mass spectrometry analysis of constituent oil from dried Ganoderma lucidum was carried out. Fresh G. lucidum obtained from its natural environment was thoroughly washed with distilled water and air-dried for 2 weeks and the component oils were extracted and analyzed. Four predominant components identified were pentadecanoic acid, 14-methyl-ester (retention time [RT] = 19.752 minutes; percentage total = 25.489), 9,12-octadecadienoic acid (Z,Z)- (RT = 21.629 minutes and 21.663 minutes; percentage total = 25.054), n-hexadecanoic acid (RT = 20.153 minutes; percentage total = 24.275), and 9-octadecenoic acid (Z)-, methyl ester (RT = 21.297 minutes; percentage total = 13.027). The two minor oils identified were 9,12-octadecadienoic acid, methyl ester, (E,E)- and octadecanoic acid, methyl ester (RT = 21.246 minutes and 21.503 minutes; percentage total = 7.057 and 5.097, respectively).

Drosophila as a model system to study autophagy.

PubMed

Zirin, Jonathan; Perrimon, Norbert

2010-12-01

Originally identified as a response to starvation in yeast, autophagy is now understood to fulfill a variety of roles in higher eukaryotes, from the maintenance of cellular homeostasis to the cellular response to stress, starvation, and infection. Although genetics and biochemical studies in yeast have identified many components involved in autophagy, the findings that some of the essential components of the yeast pathway are missing in higher organisms underscore the need to study autophagy in more complex systems. This review focuses on the use of the fruitfly, Drosophila melanogaster as a model system for analysis of autophagy. Drosophila is an organism well-suited for genetic analysis and represents an intermediate between yeast and mammals with respect to conservation of the autophagy machinery. Furthermore, the complex biology and physiology of Drosophila presents an opportunity to model human diseases in a tissue specific and analogous context.

Component spectra extraction from terahertz measurements of unknown mixtures.

PubMed

Li, Xian; Hou, D B; Huang, P J; Cai, J H; Zhang, G X

2015-10-20

The aim of this work is to extract component spectra from unknown mixtures in the terahertz region. To that end, a method, hard modeling factor analysis (HMFA), was applied to resolve terahertz spectral matrices collected from the unknown mixtures. This method does not require any expertise of the user and allows the consideration of nonlinear effects such as peak variations or peak shifts. It describes the spectra using a peak-based nonlinear mathematic model and builds the component spectra automatically by recombination of the resolved peaks through correlation analysis. Meanwhile, modifications on the method were made to take the features of terahertz spectra into account and to deal with the artificial baseline problem that troubles the extraction process of some terahertz spectra. In order to validate the proposed method, simulated wideband terahertz spectra of binary and ternary systems and experimental terahertz absorption spectra of amino acids mixtures were tested. In each test, not only the number of pure components could be correctly predicted but also the identified pure spectra had a good similarity with the true spectra. Moreover, the proposed method associated the molecular motions with the component extraction, making the identification process more physically meaningful and interpretable compared to other methods. The results indicate that the HMFA method with the modifications can be a practical tool for identifying component terahertz spectra in completely unknown mixtures. This work reports the solution to this kind of problem in the terahertz region for the first time, to the best of the authors' knowledge, and represents a significant advance toward exploring physical or chemical mechanisms of unknown complex systems by terahertz spectroscopy.

The centrosomal component CEP161 of Dictyostelium discoideum interacts with the Hippo signaling pathway

PubMed Central

Sukumaran, Salil K.; Blau-Wasser, Rosemarie; Rohlfs, Meino; Gallinger, Christoph; Schleicher, Michael; Noegel, Angelika A

2015-01-01

CEP161 is a novel component of the Dictyostelium discoideum centrosome which was identified as binding partner of the pericentriolar component CP250. Here we show that the amino acids 1-763 of the 1381 amino acids CEP161 are sufficient for CP250 binding, centrosomal targeting and centrosome association. Analysis of AX2 cells over-expressing truncated and full length CEP161 proteins revealed defects in growth and development. By immunoprecipitation experiments we identified the Hippo related kinase SvkA (Hrk-svk) as binding partner for CEP161. Both proteins colocalize at the centrosome. In in vitro kinase assays the N-terminal domain of CEP161 (residues 1-763) inhibited the kinase activity of Hrk-svk. A comparison of D. discoideum Hippo kinase mutants with mutants overexpressing CEP161 polypeptides revealed similar defects. We propose that the centrosomal component CEP161 is a novel player in the Hippo signaling pathway and affects various cellular properties through this interaction. PMID:25607232

The quasi-biennial variation in the geomagnetic field: a global characteristics analysis

NASA Astrophysics Data System (ADS)

Ou, Jiaming; Du, Aimin

2016-04-01

The periodicity of 1.5-3 years, namely the quasi-biennial oscillation (QBO), has been identified in the solar, geophysical, and atmospheric variability. Sugiura (1976) investigated the observatory annual means over 1900-1970 and confirmed the QBO in the geomagnetic field. At present, studying the quasi-biennial oscillation becomes substantial for separating the internal/external parts in the geomagnetic observations. For the internal field, two typical periodicities, namely the 6-year oscillation in the geomagnetic secular acceleration (SA) and the geomagnetic jerk (occurs in 1-2 years), have close period to the QBO. Recently, a global quasi-biennial fluctuation was identified in the geomagnetic core field model (Silva et al., 2012). Silva et al. speculated this 2.5 years signal to either external source remaining in the core field model or consequence of the methods used to construct the model. As more high-quality data from global observatories are available, it is a good opportunity to characterize the geomagnetic QBO in the global range. In this paper, we investigate the QBO in the observatory monthly geomagnetic field X, Y, and Z components spanning 1985-2010. We employ the observatory hourly means database from the World Data Center for Geomagnetism (WDC) for the investigation. Wavelet analysis is used to detect and identify the QBO, while Fast Fourier Transform (FFT) analysis to obtain the statistics of the QBO. We apply the spherical harmonic analysis on QBO's amplitude, in order to quantify and separate internal and external sources. Three salient periods respectively at 2.9, 2.2, and 1.7 years, are identified in the amplitude spectrum over 1988-2008. The oscillation with the period of ~2.2 years is most prominent in all field components and further studied. In the X component the QBO is attenuated towards the polar regions, while in the Z component the amplitude of QBO increases with increasing of the geomagnetic latitude. At the high latitudes, the QBO exhibits distinct anisotropic in the local time distribution. The QBO of the X and Z components are both stronger over LT 00:00-06:00. The results of spherical harmonic analysis indicate that the QBO is mainly contributed by the external sources. The QBO is highly correlated with various parameters of solar activity, solar wind at 1AU, and geomagnetic activity. Reference 1. Sugiura, M. (1976). Quasi-biennial geomagnetic variation caused by the Sun. Geophys. Res. Lett., 3(11), 643-646. 2. Silva, L., Jackson, L., and Mound, J., (2012), Assessing the importance and expression of the 6 year geomagnetic oscillation, J. Geophys. Res.: Solid Earth (1978-2012), 117.

Towards program theory validation: Crowdsourcing the qualitative analysis of participant experiences.

PubMed

Harman, Elena; Azzam, Tarek

2018-02-01

This exploratory study examines a novel tool for validating program theory through crowdsourced qualitative analysis. It combines a quantitative pattern matching framework traditionally used in theory-driven evaluation with crowdsourcing to analyze qualitative interview data. A sample of crowdsourced participants are asked to read an interview transcript and identify whether program theory components (Activities and Outcomes) are discussed and to highlight the most relevant passage about that component. The findings indicate that using crowdsourcing to analyze qualitative data can differentiate between program theory components that are supported by a participant's experience and those that are not. This approach expands the range of tools available to validate program theory using qualitative data, thus strengthening the theory-driven approach. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis of Floral Volatile Components and Antioxidant Activity of Different Varieties of Chrysanthemum morifolium.

PubMed

Yang, Lu; Cheng, Ping; Wang, Jin-Hui; Li, Hong

2017-10-23

This study investigated the volatile flavor compounds and antioxidant properties of the essential oil of chrysanthemums that was extracted from the fresh flowers of 10 taxa of Chrysanthemum morifolium from three species; namely Dendranthema morifolium (Ramat.) Yellow, Dendranthema morifolium (Ramat.) Red, Dendranthema morifolium (Ramat.) Pink, Dendranthema morifolium (Ramat.) White, Pericallis hybrid Blue, Pericallis hybrid Pink, Pericallis hybrid Purple, Bellis perennis Pink, Bellis perennis Yellow, and Bellis perennis White. The antioxidant capacity of the essential oil was assayed by spectrophotometric analysis. The volatile flavor compounds from the fresh flowers were collected using dynamic headspace collection, analyzed using auto thermal desorber-gas chromatography/mass spectrometry, and identified with quantification using the external standard method. The antioxidant activities of Chrysanthemum morifolium were evaluated by DPPH and FRAP assays, and the results showed that the antioxidant activity of each sample was not the same. The different varieties of fresh Chrysanthemum morifolium flowers were distinguished and classified by fingerprint similarity evaluation, principle component analysis (PCA), and cluster analysis. The results showed that the floral volatile component profiles were significantly different among the different Chrysanthemum morifolium varieties. A total of 36 volatile flavor compounds were identified with eight functional groups: hydrocarbons, terpenoids, aromatic compounds, alcohols, ketones, ethers, aldehydes, and esters. Moreover, the variability among Chrysanthemum morifolium in basis to the data, and the first three principal components (PC1, PC2, and PC3) accounted for 96.509% of the total variance (55.802%, 30.599%, and 10.108%, respectively). PCA indicated that there were marked differences among Chrysanthemum morifolium varieties. The cluster analysis confirmed the results of the PCA analysis. In conclusion, the results of this study provide a basis for breeding Chrysanthemum cultivars with desirable floral scents, and they further support the view that some plants are promising sources of natural antioxidants.

C-CAT: a computer software used to analyze and select Chinese characters and character components for psychological research.

PubMed

Lo, Ming; Hue, Chih-Wei

2008-11-01

The Character-Component Analysis Toolkit (C-CAT) software was designed to assist researchers in constructing experimental materials using traditional Chinese characters. The software package contains two sets of character stocks: one suitable for research using literate adults as subjects and one suitable for research using schoolchildren as subjects. The software can identify linguistic properties, such as the number of strokes contained, the character-component pronunciation regularity, and the arrangement of character components within a character. Moreover, it can compute a character's linguistic frequency, neighborhood size, and phonetic validity with respect to a user-selected character stock. It can also search the selected character stock for similar characters or for character components with user-specified linguistic properties.

Framing life and death on YouTube: the strategic communication of organ donation messages by organ procurement organizations.

PubMed

VanderKnyff, Jeremy; Friedman, Daniela B; Tanner, Andrea

2015-01-01

Using a sample of YouTube videos posted on the YouTube channels of organ procurement organizations, a content analysis was conducted to identify the frames used to strategically communicate prodonation messages. A total of 377 videos were coded for general characteristics, format, speaker characteristics, organs discussed, structure, problem definition, and treatment. Principal components analysis identified message frames, and k-means cluster analysis established distinct groupings of videos on the basis of the strength of their relationship to message frames. Analysis of these frames and clusters found that organ procurement organizations present multiple, and sometimes competing, video types and message frames on YouTube. This study serves as important formative research that will inform future studies to measure the effectiveness of the distinct message frames and clusters identified.

Examination of Trends and Evidence-Based Elements in State Physical Education Legislation: A Content Analysis

ERIC Educational Resources Information Center

Eyler, Amy A.; Brownson, Ross C.; Aytur, Semra A.; Cradock, Angie L.; Doescher, Mark; Evenson, Kelly R.; Kerr, Jacqueline; Maddock, Jay; Pluto, Delores L.; Steinman, Lesley; Tompkins, Nancy O'Hara; Troped, Philip; Schmid, Thomas L.

2010-01-01

Objectives: To develop a comprehensive inventory of state physical education (PE) legislation, examine trends in bill introduction, and compare bill factors. Methods: State PE legislation from January 2001 to July 2007 was identified using a legislative database. Analysis included components of evidence-based school PE from the Community Guide and…

An Analysis of Construction Contractor Performance Evaluation System

DTIC Science & Technology

2009-03-01

65 8. Summary of Determinant and KMO Values for Finalized...principle component analysis output is the KMO and Bartlett‘s Test. KMO or Kaiser-Meyer-Olkin measure of sampling adequacy is used to identify if a...set of variables, when factored together, yield distinct and reliable factors (Field, 2005). KMO statistics vary between values of 0 to 1. Kaiser

Temporal Sequence of Hemispheric Network Activation during Semantic Processing: A Functional Network Connectivity Analysis

ERIC Educational Resources Information Center

Assaf, Michal; Jagannathan, Kanchana; Calhoun, Vince; Kraut, Michael; Hart, John, Jr.; Pearlson, Godfrey

2009-01-01

To explore the temporal sequence of, and the relationship between, the left and right hemispheres (LH and RH) during semantic memory (SM) processing we identified the neural networks involved in the performance of functional MRI semantic object retrieval task (SORT) using group independent component analysis (ICA) in 47 healthy individuals. SORT…

Building bridges with clinicians.

PubMed

Brady, Timothy S; Hankins, Robert W

2003-06-01

Clinical and ancillary staff need and welcome the opportunity to be involved in healthcare financial management. To provide them with the financial tools they need, a group of clinical and nonclinical professionals identified the key components of a curriculum that addressed financial, managerial, and cost accounting, along with training objectives that include understanding cost allocation, trend analysis, and variance analysis.

An analysis of the adaptability of a professional development program in public health: results from the ALPS Study.

PubMed

Richard, Lucie; Torres, Sara; Tremblay, Marie-Claude; Chiocchio, François; Litvak, Éric; Fortin-Pellerin, Laurence; Beaudet, Nicole

2015-06-14

Professional development is a key component of effective public health infrastructures. To be successful, professional development programs in public health and health promotion must adapt to practitioners' complex real-world practice settings while preserving the core components of those programs' models and theoretical bases. An appropriate balance must be struck between implementation fidelity, defined as respecting the core nature of the program that underlies its effects, and adaptability to context to maximize benefit in specific situations. This article presents a professional development pilot program, the Health Promotion Laboratory (HPL), and analyzes how it was adapted to three different settings while preserving its core components. An exploratory analysis was also conducted to identify team and contextual factors that might have been at play in the emergence of implementation profiles in each site. This paper describes the program, its core components and adaptive features, along with three implementation experiences in local public health teams in Quebec, Canada. For each setting, documentary sources were analyzed to trace the implementation of activities, including temporal patterns throughout the project for each program component. Information about teams and their contexts/settings was obtained through documentary analysis and semi-structured interviews with HPL participants, colleagues and managers from each organization. While each team developed a unique pattern of implementing the activities, all the program's core components were implemented. Differences of implementation were observed in terms of numbers and percentages of activities related to different components of the program as well as in the patterns of activities across time. It is plausible that organizational characteristics influencing, for example, work schedule flexibility or learning culture might have played a role in the HPL implementation process. This paper shows how a professional development program model can be adapted to different contexts while preserving its core components. Capturing the heterogeneity of the intervention's exposure, as was done here, will make possible in-depth impact analyses involving, for example, the testing of program-context interactions to identify program outcomes predictors. Such work is essential to advance knowledge on the action mechanisms of professional development programs.

Genomic, proteomic, and biochemical analysis of the organohalide respiratory pathway in Desulfitobacterium dehalogenans.

PubMed

Kruse, Thomas; van de Pas, Bram A; Atteia, Ariane; Krab, Klaas; Hagen, Wilfred R; Goodwin, Lynne; Chain, Patrick; Boeren, Sjef; Maphosa, Farai; Schraa, Gosse; de Vos, Willem M; van der Oost, John; Smidt, Hauke; Stams, Alfons J M

2015-03-01

Desulfitobacterium dehalogenans is able to grow by organohalide respiration using 3-chloro-4-hydroxyphenyl acetate (Cl-OHPA) as an electron acceptor. We used a combination of genome sequencing, biochemical analysis of redox active components, and shotgun proteomics to study elements of the organohalide respiratory electron transport chain. The genome of Desulfitobacterium dehalogenans JW/IU-DC1(T) consists of a single circular chromosome of 4,321,753 bp with a GC content of 44.97%. The genome contains 4,252 genes, including six rRNA operons and six predicted reductive dehalogenases. One of the reductive dehalogenases, CprA, is encoded by a well-characterized cprTKZEBACD gene cluster. Redox active components were identified in concentrated suspensions of cells grown on formate and Cl-OHPA or formate and fumarate, using electron paramagnetic resonance (EPR), visible spectroscopy, and high-performance liquid chromatography (HPLC) analysis of membrane extracts. In cell suspensions, these components were reduced upon addition of formate and oxidized after addition of Cl-OHPA, indicating involvement in organohalide respiration. Genome analysis revealed genes that likely encode the identified components of the electron transport chain from formate to fumarate or Cl-OHPA. Data presented here suggest that the first part of the electron transport chain from formate to fumarate or Cl-OHPA is shared. Electrons are channeled from an outward-facing formate dehydrogenase via menaquinones to a fumarate reductase located at the cytoplasmic face of the membrane. When Cl-OHPA is the terminal electron acceptor, electrons are transferred from menaquinones to outward-facing CprA, via an as-yet-unidentified membrane complex, and potentially an extracellular flavoprotein acting as an electron shuttle between the quinol dehydrogenase membrane complex and CprA. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Genomic, Proteomic, and Biochemical Analysis of the Organohalide Respiratory Pathway in Desulfitobacterium dehalogenans

PubMed Central

van de Pas, Bram A.; Atteia, Ariane; Krab, Klaas; Hagen, Wilfred R.; Goodwin, Lynne; Chain, Patrick; Boeren, Sjef; Maphosa, Farai; Schraa, Gosse; de Vos, Willem M.; van der Oost, John; Smidt, Hauke

2014-01-01

Desulfitobacterium dehalogenans is able to grow by organohalide respiration using 3-chloro-4-hydroxyphenyl acetate (Cl-OHPA) as an electron acceptor. We used a combination of genome sequencing, biochemical analysis of redox active components, and shotgun proteomics to study elements of the organohalide respiratory electron transport chain. The genome of Desulfitobacterium dehalogenans JW/IU-DC1T consists of a single circular chromosome of 4,321,753 bp with a GC content of 44.97%. The genome contains 4,252 genes, including six rRNA operons and six predicted reductive dehalogenases. One of the reductive dehalogenases, CprA, is encoded by a well-characterized cprTKZEBACD gene cluster. Redox active components were identified in concentrated suspensions of cells grown on formate and Cl-OHPA or formate and fumarate, using electron paramagnetic resonance (EPR), visible spectroscopy, and high-performance liquid chromatography (HPLC) analysis of membrane extracts. In cell suspensions, these components were reduced upon addition of formate and oxidized after addition of Cl-OHPA, indicating involvement in organohalide respiration. Genome analysis revealed genes that likely encode the identified components of the electron transport chain from formate to fumarate or Cl-OHPA. Data presented here suggest that the first part of the electron transport chain from formate to fumarate or Cl-OHPA is shared. Electrons are channeled from an outward-facing formate dehydrogenase via menaquinones to a fumarate reductase located at the cytoplasmic face of the membrane. When Cl-OHPA is the terminal electron acceptor, electrons are transferred from menaquinones to outward-facing CprA, via an as-yet-unidentified membrane complex, and potentially an extracellular flavoprotein acting as an electron shuttle between the quinol dehydrogenase membrane complex and CprA. PMID:25512312

Fragility Analysis Methodology for Degraded Structures and Passive Components in Nuclear Power Plants - Illustrated using a Condensate Storage Tank

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

Nie, J.; Braverman, J.; Hofmayer, C.

2010-06-30

The Korea Atomic Energy Research Institute (KAERI) is conducting a five-year research project to develop a realistic seismic risk evaluation system which includes the consideration of aging of structures and components in nuclear power plants (NPPs). The KAERI research project includes three specific areas that are essential to seismic probabilistic risk assessment (PRA): (1) probabilistic seismic hazard analysis, (2) seismic fragility analysis including the effects of aging, and (3) a plant seismic risk analysis. Since 2007, Brookhaven National Laboratory (BNL) has entered into a collaboration agreement with KAERI to support its development of seismic capability evaluation technology for degraded structuresmore » and components. The collaborative research effort is intended to continue over a five year period. The goal of this collaboration endeavor is to assist KAERI to develop seismic fragility analysis methods that consider the potential effects of age-related degradation of structures, systems, and components (SSCs). The research results of this multi-year collaboration will be utilized as input to seismic PRAs. In the Year 1 scope of work, BNL collected and reviewed degradation occurrences in US NPPs and identified important aging characteristics needed for the seismic capability evaluations. This information is presented in the Annual Report for the Year 1 Task, identified as BNL Report-81741-2008 and also designated as KAERI/RR-2931/2008. The report presents results of the statistical and trending analysis of this data and compares the results to prior aging studies. In addition, the report provides a description of U.S. current regulatory requirements, regulatory guidance documents, generic communications, industry standards and guidance, and past research related to aging degradation of SSCs. In the Year 2 scope of work, BNL carried out a research effort to identify and assess degradation models for the long-term behavior of dominant materials that are determined to be risk significant to NPPs. Multiple models have been identified for concrete, carbon and low-alloy steel, and stainless steel. These models are documented in the Annual Report for the Year 2 Task, identified as BNL Report-82249-2009 and also designated as KAERI/TR-3757/2009. This report describes the research effort performed by BNL for the Year 3 scope of work. The objective is for BNL to develop the seismic fragility capacity for a condensate storage tank with various degradation scenarios. The conservative deterministic failure margin method has been utilized for the undegraded case and has been modified to accommodate the degraded cases. A total of five seismic fragility analysis cases have been described: (1) undegraded case, (2) degraded stainless tank shell, (3) degraded anchor bolts, (4) anchorage concrete cracking, and (5)a perfect combination of the three degradation scenarios. Insights from these fragility analyses are also presented.« less

Heavy metal contamination of agricultural soils affected by mining activities around the Ganxi River in Chenzhou, Southern China.

PubMed

Ma, Li; Sun, Jing; Yang, Zhaoguang; Wang, Lin

2015-12-01

Heavy metal contamination attracted a wide spread attention due to their strong toxicity and persistence. The Ganxi River, located in Chenzhou City, Southern China, has been severely polluted by lead/zinc ore mining activities. This work investigated the heavy metal pollution in agricultural soils around the Ganxi River. The total concentrations of heavy metals were determined by inductively coupled plasma-mass spectrometry. The potential risk associated with the heavy metals in soil was assessed by Nemerow comprehensive index and potential ecological risk index. In both methods, the study area was rated as very high risk. Multivariate statistical methods including Pearson's correlation analysis, hierarchical cluster analysis, and principal component analysis were employed to evaluate the relationships between heavy metals, as well as the correlation between heavy metals and pH, to identify the metal sources. Three distinct clusters have been observed by hierarchical cluster analysis. In principal component analysis, a total of two components were extracted to explain over 90% of the total variance, both of which were associated with anthropogenic sources.

Suppression of the µ Rhythm during Speech and Non-Speech Discrimination Revealed by Independent Component Analysis: Implications for Sensorimotor Integration in Speech Processing

PubMed Central

Bowers, Andrew; Saltuklaroglu, Tim; Harkrider, Ashley; Cuellar, Megan

2013-01-01

Background Constructivist theories propose that articulatory hypotheses about incoming phonetic targets may function to enhance perception by limiting the possibilities for sensory analysis. To provide evidence for this proposal, it is necessary to map ongoing, high-temporal resolution changes in sensorimotor activity (i.e., the sensorimotor μ rhythm) to accurate speech and non-speech discrimination performance (i.e., correct trials.) Methods Sixteen participants (15 female and 1 male) were asked to passively listen to or actively identify speech and tone-sweeps in a two-force choice discrimination task while the electroencephalograph (EEG) was recorded from 32 channels. The stimuli were presented at signal-to-noise ratios (SNRs) in which discrimination accuracy was high (i.e., 80–100%) and low SNRs producing discrimination performance at chance. EEG data were decomposed using independent component analysis and clustered across participants using principle component methods in EEGLAB. Results ICA revealed left and right sensorimotor µ components for 14/16 and 13/16 participants respectively that were identified on the basis of scalp topography, spectral peaks, and localization to the precentral and postcentral gyri. Time-frequency analysis of left and right lateralized µ component clusters revealed significant (pFDR<.05) suppression in the traditional beta frequency range (13–30 Hz) prior to, during, and following syllable discrimination trials. No significant differences from baseline were found for passive tasks. Tone conditions produced right µ beta suppression following stimulus onset only. For the left µ, significant differences in the magnitude of beta suppression were found for correct speech discrimination trials relative to chance trials following stimulus offset. Conclusions Findings are consistent with constructivist, internal model theories proposing that early forward motor models generate predictions about likely phonemic units that are then synthesized with incoming sensory cues during active as opposed to passive processing. Future directions and possible translational value for clinical populations in which sensorimotor integration may play a functional role are discussed. PMID:23991030

Suppression of the µ rhythm during speech and non-speech discrimination revealed by independent component analysis: implications for sensorimotor integration in speech processing.

PubMed

Bowers, Andrew; Saltuklaroglu, Tim; Harkrider, Ashley; Cuellar, Megan

2013-01-01

Constructivist theories propose that articulatory hypotheses about incoming phonetic targets may function to enhance perception by limiting the possibilities for sensory analysis. To provide evidence for this proposal, it is necessary to map ongoing, high-temporal resolution changes in sensorimotor activity (i.e., the sensorimotor μ rhythm) to accurate speech and non-speech discrimination performance (i.e., correct trials.). Sixteen participants (15 female and 1 male) were asked to passively listen to or actively identify speech and tone-sweeps in a two-force choice discrimination task while the electroencephalograph (EEG) was recorded from 32 channels. The stimuli were presented at signal-to-noise ratios (SNRs) in which discrimination accuracy was high (i.e., 80-100%) and low SNRs producing discrimination performance at chance. EEG data were decomposed using independent component analysis and clustered across participants using principle component methods in EEGLAB. ICA revealed left and right sensorimotor µ components for 14/16 and 13/16 participants respectively that were identified on the basis of scalp topography, spectral peaks, and localization to the precentral and postcentral gyri. Time-frequency analysis of left and right lateralized µ component clusters revealed significant (pFDR<.05) suppression in the traditional beta frequency range (13-30 Hz) prior to, during, and following syllable discrimination trials. No significant differences from baseline were found for passive tasks. Tone conditions produced right µ beta suppression following stimulus onset only. For the left µ, significant differences in the magnitude of beta suppression were found for correct speech discrimination trials relative to chance trials following stimulus offset. Findings are consistent with constructivist, internal model theories proposing that early forward motor models generate predictions about likely phonemic units that are then synthesized with incoming sensory cues during active as opposed to passive processing. Future directions and possible translational value for clinical populations in which sensorimotor integration may play a functional role are discussed.

Measuring farm sustainability using data envelope analysis with principal components: the case of Wisconsin cranberry.

PubMed

Dong, Fengxia; Mitchell, Paul D; Colquhoun, Jed

2015-01-01

Measuring farm sustainability performance is a crucial component for improving agricultural sustainability. While extensive assessments and indicators exist that reflect the different facets of agricultural sustainability, because of the relatively large number of measures and interactions among them, a composite indicator that integrates and aggregates over all variables is particularly useful. This paper describes and empirically evaluates a method for constructing a composite sustainability indicator that individually scores and ranks farm sustainability performance. The method first uses non-negative polychoric principal component analysis to reduce the number of variables, to remove correlation among variables and to transform categorical variables to continuous variables. Next the method applies common-weight data envelope analysis to these principal components to individually score each farm. The method solves weights endogenously and allows identifying important practices in sustainability evaluation. An empirical application to Wisconsin cranberry farms finds heterogeneity in sustainability practice adoption, implying that some farms could adopt relevant practices to improve the overall sustainability performance of the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

Geographical classification of Epimedium based on HPLC fingerprint analysis combined with multi-ingredients quantitative analysis.

PubMed

Xu, Ning; Zhou, Guofu; Li, Xiaojuan; Lu, Heng; Meng, Fanyun; Zhai, Huaqiang

2017-05-01

A reliable and comprehensive method for identifying the origin and assessing the quality of Epimedium has been developed. The method is based on analysis of HPLC fingerprints, combined with similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and multi-ingredient quantitative analysis. Nineteen batches of Epimedium, collected from different areas in the western regions of China, were used to establish the fingerprints and 18 peaks were selected for the analysis. Similarity analysis, HCA and PCA all classified the 19 areas into three groups. Simultaneous quantification of the five major bioactive ingredients in the Epimedium samples was also carried out to confirm the consistency of the quality tests. These methods were successfully used to identify the geographical origin of the Epimedium samples and to evaluate their quality. Copyright © 2016 John Wiley & Sons, Ltd.

Centrality Evolution of pt and yt Spectra from Au-Au Collisions at √ {sNN} = 200 GeV

NASA Astrophysics Data System (ADS)

Trainor, Thomas A.

A two-component analysis of spectra to pt = 12 GeV/c for identified pions and protons from 200 GeV Au-Au collisions is presented. The method is similar to an analysis of the nch dependence of pt spectra from p-p collisions at 200 GeV, but applied to Au-Au centrality dependence. The soft-component reference is a Lévy distribution on transverse mass mt. The hard-component reference is a Gaussian on transverse rapidity yt with exponential (pt power-law) tail. Deviations of data from the reference are described by hard-component ratio rAA, which generalizes nuclear modification factor RAA. The analysis suggests that centrality evolution of pion and proton spectra is dominated by changes in parton fragmentation. The structure of rAA suggests that parton energy loss produces a negative boost Δyt of a large fraction (but not all) of the minimum-bias fragment distribution, and that lower-energy partons suffer relatively less energy loss, possibly due to color screening. The analysis also suggests that the anomalous p/π ratio may be due to differences in the parton energy-loss process experienced by the two hadron species. This analysis provides no evidence for radial flow.

Joint variability of global runoff and global sea surface temperatures

USGS Publications Warehouse

McCabe, G.J.; Wolock, D.M.

2008-01-01

Global land surface runoff and sea surface temperatures (SST) are analyzed to identify the primary modes of variability of these hydroclimatic data for the period 1905-2002. A monthly water-balance model first is used with global monthly temperature and precipitation data to compute time series of annual gridded runoff for the analysis period. The annual runoff time series data are combined with gridded annual sea surface temperature data, and the combined dataset is subjected to a principal components analysis (PCA) to identify the primary modes of variability. The first three components from the PCA explain 29% of the total variability in the combined runoff/SST dataset. The first component explains 15% of the total variance and primarily represents long-term trends in the data. The long-term trends in SSTs are evident as warming in all of the oceans. The associated long-term trends in runoff suggest increasing flows for parts of North America, South America, Eurasia, and Australia; decreasing runoff is most notable in western Africa. The second principal component explains 9% of the total variance and reflects variability of the El Ni??o-Southern Oscillation (ENSO) and its associated influence on global annual runoff patterns. The third component explains 5% of the total variance and indicates a response of global annual runoff to variability in North Aflantic SSTs. The association between runoff and North Atlantic SSTs may explain an apparent steplike change in runoff that occurred around 1970 for a number of continental regions.

Multifaceted Genomic Risk for Brain Function in Schizophrenia

PubMed Central

Chen, Jiayu; Calhoun, Vince D.; Pearlson, Godfrey D.; Ehrlich, Stefan; Turner, Jessica A.; Ho, Beng-Choon; Wassink, Thomas H.; Michael, Andrew M; Liu, Jingyu

2012-01-01

Recently, deriving candidate endophenotypes from brain imaging data has become a valuable approach to study genetic influences on schizophrenia (SZ), whose pathophysiology remains unclear. In this work we utilized a multivariate approach, parallel independent component analysis, to identify genomic risk components associated with brain function abnormalities in SZ. 5157 candidate single nucleotide polymorphisms (SNPs) were derived from genome-wide array based on their possible connections with SZ and further investigated for their associations with brain activations captured with functional magnetic resonance imaging (fMRI) during a sensorimotor task. Using data from 92 SZ patients and 116 healthy controls, we detected a significant correlation (r= 0.29; p= 2.41×10−5) between one fMRI component and one SNP component, both of which significantly differentiated patients from controls. The fMRI component mainly consisted of precentral and postcentral gyri, the major activated regions in the motor task. On average, higher activation in these regions was observed in participants with higher loadings of the linked SNP component, predominantly contributed to by 253 SNPs. 138 identified SNPs were from known coding regions of 100 unique genes. 31 identified SNPs did not differ between groups, but moderately correlated with some other group-discriminating SNPs, indicating interactions among alleles contributing towards elevated SZ susceptibility. The genes associated with the identified SNPs participated in four neurotransmitter pathways: GABA receptor signaling, dopamine receptor signaling, neuregulin signaling and glutamate receptor signaling. In summary, our work provides further evidence for the complexity of genomic risk to the functional brain abnormality in SZ and suggests a pathological role of interactions between SNPs, genes and multiple neurotransmitter pathways. PMID:22440650

Chemical fingerprinting and quantitative constituent analysis of Siwu decoction categorized formulae by UPLC-QTOF/MS/MS and HPLC-DAD

PubMed Central

2013-01-01

Background Siwu decoction categorized formulae (SWDCF) are widely used for treating gynecological diseases. This study aims to elucidate the differences of bioactive constituents in SWDCF by ultra-high performance liquid chromatography coupled with time-of-flight mass spectrometry (UPLC - QTOF - MS /MS) and HPLC-DAD. Methods An efficient method based on UPLC - QTOF - MS /MS was developed for identifying the chemical profiles of SWDCF. HPLC-DAD method was used for quantifying seven chemical markers in SWDCF. Results Eighty four components were identified or characterized, including ten organic acids, thirty glycosides (monoterpene or iridoid or phenylpropanoids glycosides), fourteen lactones, eighteen flavonoids, and eleven alkaloids in the complex system. The datasets of tR-m/z pairs, ion intensities and sample codes were processed with supervised orthogonal partial least squared discriminant analysis to compare these decoction samples. After a clear classification was established, OPLS-DA was performed and 16 common components with relative quantity in SWDCF samples were determined. Gallic acid, protocatechuic acid, vanillic acid, caffeic acid, paeoniflorin, ferulic acid, and senkyunolide I were selected as the chemical markers to identify SWDCF by HPLC-DAD. Conclusion The chemical profiles with 84 components in SWDCF, including monoterpene glycosides, acetophenones, galloyl glucoses, even some isomers in the complex system were characterized by UPLC–QTOF–MS/MS. PMID:23453004

A risk-based approach to management of leachables utilizing statistical analysis of extractables.

PubMed

Stults, Cheryl L M; Mikl, Jaromir; Whelehan, Oliver; Morrical, Bradley; Duffield, William; Nagao, Lee M

2015-04-01

To incorporate quality by design concepts into the management of leachables, an emphasis is often put on understanding the extractable profile for the materials of construction for manufacturing disposables, container-closure, or delivery systems. Component manufacturing processes may also impact the extractable profile. An approach was developed to (1) identify critical components that may be sources of leachables, (2) enable an understanding of manufacturing process factors that affect extractable profiles, (3) determine if quantitative models can be developed that predict the effect of those key factors, and (4) evaluate the practical impact of the key factors on the product. A risk evaluation for an inhalation product identified injection molding as a key process. Designed experiments were performed to evaluate the impact of molding process parameters on the extractable profile from an ABS inhaler component. Statistical analysis of the resulting GC chromatographic profiles identified processing factors that were correlated with peak levels in the extractable profiles. The combination of statistically significant molding process parameters was different for different types of extractable compounds. ANOVA models were used to obtain optimal process settings and predict extractable levels for a selected number of compounds. The proposed paradigm may be applied to evaluate the impact of material composition and processing parameters on extractable profiles and utilized to manage product leachables early in the development process and throughout the product lifecycle.

Urban Development from the Perspective of Geodivesity in South Korea

NASA Astrophysics Data System (ADS)

Mo, Y.; Lee, D. K.; Han Kyul, H.

2015-12-01

In regard to ensuring a sustainable Earth, geodiversity (which is the variety of non-living nature) has been recognized as being equivalent to biodiversity in terms of importance. Geodiversity, as a platform, provides diverse habitats; however when a city is developed, threatened and vulnerable species are seen as important components of conservation but geodiversity is not. This study analyzes the components of geodiversity in cities that developed rapidly, and identifies which characteristics of geodiversity were related to this development. We estimated the components of geodiversity through the use of land cover maps and digital elevation maps (DEMs) in South Korean cities that developed rapidly between the 1980s and early-2010s, such as Yong-in and Namyangju. The relationships between land use changes and geodiversity components were analyzed and a factor analysis for land use changes was conducted using geodiversity components. Results show that even though adjacent rivers and streams, low elevation, and flat surfaces have high geodiversity, these areas were mainly converted into built-up areas and agricultural areas. As such, it can be stated that urban development in these cities has lowered geodiversity. Other geodiversity components related to urban development, such as slope, upslope contributing areas, streams, and lithology, were also identified. To conclude, this study showed how geodiversity components related to urban development has been affected by this growth and how they are intimately connected to each other. Identifying geodiversity in future development sites could help to estimate the most sensitive areas regarding the planned development; therefore, we need to view urban development from a geodiversity perspective.

Gender differences in metabolic syndrome components among the Korean 66-year-old population with metabolic syndrome.

PubMed

Lee, Sangjin; Ko, Young; Kwak, Chanyeong; Yim, Eun-Shil

2016-01-23

Gender is thought to be an important factor in metabolic syndrome and its outcomes. Despite a number of studies that have demonstrated differences in metabolism and its components that are dependent on gender, limited information about gender differences on the characteristics of metabolic syndrome and its components is available regarding the Korean old adult population. This study aimed to identify gender differences in characteristics of the metabolic syndrome and other risk factors for cardiovascular disease. Secondary analysis of data from a nationwide cross-sectional survey for health examination at the time of transitioning from midlife to old age was performed. Multiple logistic regression models were used to estimate adjusted odds ratios and 95% confidence intervals for gender differences among the Korean 66-year-old population with metabolic syndrome. Gender differences in metabolic syndrome components that contributed to the diagnosis of metabolic syndrome were identified. In males, the most common component was high blood sugar levels (87.5%), followed by elevated triglyceride levels (83.5%) and high blood pressure (83.1%). In females, the most commonly identified component was elevated triglyceride levels (79.0%), followed by high blood sugar levels (78.6%) and high blood pressure (78.5%). Gender differences for other risk factors for cardiovascular disease, including family history, health habits, and body mass index were observed. Gender-specific public health policies and management strategies to prevent cardiovascular disease among the older adult population should be developed for Koreans undergoing the physiological transition to old age.

Independent Component Analysis of Resting-State Functional Magnetic Resonance Imaging in Pedophiles.

PubMed

Cantor, J M; Lafaille, S J; Hannah, J; Kucyi, A; Soh, D W; Girard, T A; Mikulis, D J

2016-10-01

Neuroimaging and other studies have changed the common view that pedophilia is a result of childhood sexual abuse and instead is a neurologic phenomenon with prenatal origins. Previous research has identified differences in the structural connectivity of the brain in pedophilia. To identify analogous differences in functional connectivity. Functional magnetic resonance images were recorded from three groups of participants while they were at rest: pedophilic men with a history of sexual offenses against children (n = 37) and two control groups: non-pedophilic men who committed non-sexual offenses (n = 28) and non-pedophilic men with no criminal history (n = 39). Functional magnetic resonance imaging data were subjected to independent component analysis to identify known functional networks of the brain, and groups were compared to identify differences in connectivity with those networks (or "components"). The pedophilic group demonstrated wide-ranging increases in functional connectivity with the default mode network compared with controls and regional differences (increases and decreases) with the frontoparietal network. Of these brain regions (total = 23), 20 have been identified by meta-analytic studies to respond to sexually relevant stimuli. Conversely, of the brain areas known to be those that respond to sexual stimuli, nearly all emerged in the present data as significantly different in pedophiles. This study confirms the presence of significant differences in the functional connectivity of the brain in pedophilia consistent with previously reported differences in structural connectivity. The connectivity differences detected here and elsewhere are opposite in direction from those associated with anti-sociality, arguing against anti-sociality and for pedophilia as the source of the neuroanatomic differences detected. Copyright © 2016 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

Inequalities in neighbourhood socioeconomic characteristics: potential evidence-base for neighbourhood health planning

PubMed Central

Odoi, Agricola; Wray, Ron; Emo, Marion; Birch, Stephen; Hutchison, Brian; Eyles, John; Abernathy, Tom

2005-01-01

Background Population health planning aims to improve the health of the entire population and to reduce health inequities among population groups. Socioeconomic factors are increasingly being recognized as major determinants of many aspects of health and causes of health inequities. Knowledge of socioeconomic characteristics of neighbourhoods is necessary to identify their unique health needs and enhance identification of socioeconomically disadvantaged populations. Careful integration of this knowledge into health planning activities is necessary to ensure that health planning and service provision are tailored to unique neighbourhood population health needs. In this study, we identify unique neighbourhood socioeconomic characteristics and classify the neighbourhoods based on these characteristics. Principal components analysis (PCA) of 18 socioeconomic variables was used to identify the principal components explaining most of the variation in socioeconomic characteristics across the neighbourhoods. Cluster analysis was used to classify neighbourhoods based on their socioeconomic characteristics. Results Results of the PCA and cluster analysis were similar but the latter were more objective and easier to interpret. Five neighbourhood types with distinguishing socioeconomic and demographic characteristics were identified. The methodology provides a more complete picture of the neighbourhood socioeconomic characteristics than when a single variable (e.g. income) is used to classify neighbourhoods. Conclusion Cluster analysis is useful for generating neighbourhood population socioeconomic and demographic characteristics that can be useful in guiding neighbourhood health planning and service provision. This study is the first of a series of studies designed to investigate health inequalities at the neighbourhood level with a view to providing evidence-base for health planners, service providers and policy makers to help address health inequity issues at the neighbourhood level. Subsequent studies will investigate inequalities in health outcomes both within and across the neighbourhood types identified in the current study. PMID:16092969

Multivariate Genetic Correlates of the Auditory Paired Stimuli-Based P2 Event-Related Potential in the Psychosis Dimension From the BSNIP Study.

PubMed

Mokhtari, Mohammadreza; Narayanan, Balaji; Hamm, Jordan P; Soh, Pauline; Calhoun, Vince D; Ruaño, Gualberto; Kocherla, Mohan; Windemuth, Andreas; Clementz, Brett A; Tamminga, Carol A; Sweeney, John A; Keshavan, Matcheri S; Pearlson, Godfrey D

2016-05-01

The complex molecular etiology of psychosis in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is not well defined, presumably due to their multifactorial genetic architecture. Neurobiological correlates of psychosis can be identified through genetic associations of intermediate phenotypes such as event-related potential (ERP) from auditory paired stimulus processing (APSP). Various ERP components of APSP are heritable and aberrant in SZ, PBP and their relatives, but their multivariate genetic factors are less explored. We investigated the multivariate polygenic association of ERP from 64-sensor auditory paired stimulus data in 149 SZ, 209 PBP probands, and 99 healthy individuals from the multisite Bipolar-Schizophrenia Network on Intermediate Phenotypes study. Multivariate association of 64-channel APSP waveforms with a subset of 16 999 single nucleotide polymorphisms (SNPs) (reduced from 1 million SNP array) was examined using parallel independent component analysis (Para-ICA). Biological pathways associated with the genes were assessed using enrichment-based analysis tools. Para-ICA identified 2 ERP components, of which one was significantly correlated with a genetic network comprising multiple linearly coupled gene variants that explained ~4% of the ERP phenotype variance. Enrichment analysis revealed epidermal growth factor, endocannabinoid signaling, glutamatergic synapse and maltohexaose transport associated with P2 component of the N1-P2 ERP waveform. This ERP component also showed deficits in SZ and PBP. Aberrant P2 component in psychosis was associated with gene networks regulating several fundamental biologic functions, either general or specific to nervous system development. The pathways and processes underlying the gene clusters play a crucial role in brain function, plausibly implicated in psychosis. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

The transcriptome recipe for the venom cocktail of Tityus bahiensis scorpion.

PubMed

de Oliveira, Ursula Castro; Candido, Denise Maria; Dorce, Valquíria Abrão Coronado; Junqueira-de-Azevedo, Inácio de Loiola Meirelles

2015-03-01

Scorpion venom is a mixture of peptides, including antimicrobial, bradykinin-potentiating and anionic peptides and small to medium proteins, such as ion channel toxins, metalloproteinases and phospholipases that together cause severe clinical manifestation. Tityus bahiensis is the second most medically important scorpion species in Brazil and it is widely distributed in the country with the exception of the North Region. Here we sequenced and analyzed the transcripts from the venom glands of T. bahiensis, aiming at identifying and annotating venom gland expressed genes. A total of 116,027 long reads were generated by pyrosequencing and assembled in 2891 isotigs. An annotation process identified transcripts by similarity to known toxins, revealing that putative venom components represent 7.4% of gene expression. The major toxins identified are potassium and sodium channel toxins, whereas metalloproteinases showed an unexpected high abundance. Phylogenetic analysis of deduced metalloproteinases from T. bahiensis and other scorpions revealed a pattern of ancient and intraspecific gene expansions. Other venom molecules identified include antimicrobial, anionic and bradykinin-potentiating peptides, besides several putative new venom components. This report provides the first attempt to massively identify the venom components of this species and constitutes one of the few transcriptomic efforts on the genus Tityus. Copyright © 2015 Elsevier Ltd. All rights reserved.

Chemical discrimination of lubricant marketing types using direct analysis in real time time-of-flight mass spectrometry.

PubMed

Maric, Mark; Harvey, Lauren; Tomcsak, Maren; Solano, Angelique; Bridge, Candice

2017-06-30

In comparison to other violent crimes, sexual assaults suffer from very low prosecution and conviction rates especially in the absence of DNA evidence. As a result, the forensic community needs to utilize other forms of trace contact evidence, like lubricant evidence, in order to provide a link between the victim and the assailant. In this study, 90 personal bottled and condom lubricants from the three main marketing types, silicone-based, water-based and condoms, were characterized by direct analysis in real time time of flight mass spectrometry (DART-TOFMS). The instrumental data was analyzed by multivariate statistics including hierarchal cluster analysis, principal component analysis, and linear discriminant analysis. By interpreting the mass spectral data with multivariate statistics, 12 discrete groupings were identified, indicating inherent chemical diversity not only between but within the three main marketing groups. A number of unique chemical markers, both major and minor, were identified, other than the three main chemical components (i.e. PEG, PDMS and nonoxynol-9) currently used for lubricant classification. The data was validated by a stratified 20% withheld cross-validation which demonstrated that there was minimal overlap between the groupings. Based on the groupings identified and unique features of each group, a highly discriminating statistical model was then developed that aims to provide the foundation for the development of a forensic lubricant database that may eventually be applied to casework. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Probabilistic fault tree analysis of a radiation treatment system.

PubMed

Ekaette, Edidiong; Lee, Robert C; Cooke, David L; Iftody, Sandra; Craighead, Peter

2007-12-01

Inappropriate administration of radiation for cancer treatment can result in severe consequences such as premature death or appreciably impaired quality of life. There has been little study of vulnerable treatment process components and their contribution to the risk of radiation treatment (RT). In this article, we describe the application of probabilistic fault tree methods to assess the probability of radiation misadministration to patients at a large cancer treatment center. We conducted a systematic analysis of the RT process that identified four process domains: Assessment, Preparation, Treatment, and Follow-up. For the Preparation domain, we analyzed possible incident scenarios via fault trees. For each task, we also identified existing quality control measures. To populate the fault trees we used subjective probabilities from experts and compared results with incident report data. Both the fault tree and the incident report analysis revealed simulation tasks to be most prone to incidents, and the treatment prescription task to be least prone to incidents. The probability of a Preparation domain incident was estimated to be in the range of 0.1-0.7% based on incident reports, which is comparable to the mean value of 0.4% from the fault tree analysis using probabilities from the expert elicitation exercise. In conclusion, an analysis of part of the RT system using a fault tree populated with subjective probabilities from experts was useful in identifying vulnerable components of the system, and provided quantitative data for risk management.

A Unified Approach to Functional Principal Component Analysis and Functional Multiple-Set Canonical Correlation.

PubMed

Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S

2017-06-01

Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

Guidelines for developing NASA (National Aeronautics and Space Administration) ADP security risk management plans

NASA Technical Reports Server (NTRS)

Tompkins, F. G.

1983-01-01

This report presents guidance to NASA Computer security officials for developing ADP security risk management plans. The six components of the risk management process are identified and discussed. Guidance is presented on how to manage security risks that have been identified during a risk analysis performed at a data processing facility or during the security evaluation of an application system.

Analysis and Derivation of Allocations for Fiber Contaminants in Liquid Bipropellant Systems

NASA Technical Reports Server (NTRS)

Lowrey, N. M; ibrahim, K. Y.

2012-01-01

An analysis was performed to identify the engineering rationale for the existing particulate limits in MSFC-SPEC-164, Cleanliness of Components for Use in Oxygen, Fuel, and Pneumatic Systems, determine the applicability of this rationale to fibers, identify potential risks that may result from fiber contamination in liquid oxygen/fuel bipropellant systems, and bound each of these risks. The objective of this analysis was to determine whether fiber contamination exceeding the established quantitative limits for particulate can be tolerated in these systems and, if so, to derive and recommend quantitative allocations for fibers beyond the limits established for other particulate. Knowledge gaps were identified that limit a complete understanding of the risk of promoted ignition from an accumulation of fibers in a gaseous oxygen system.

Using host-pathogen protein interactions to identify and characterize Francisella tularensis virulence factors.

PubMed

Wallqvist, Anders; Memišević, Vesna; Zavaljevski, Nela; Pieper, Rembert; Rajagopala, Seesandra V; Kwon, Keehwan; Yu, Chenggang; Hoover, Timothy A; Reifman, Jaques

2015-12-29

Francisella tularensis is a select bio-threat agent and one of the most virulent intracellular pathogens known, requiring just a few organisms to establish an infection. Although several virulence factors are known, we lack an understanding of virulence factors that act through host-pathogen protein interactions to promote infection. To address these issues in the highly infectious F. tularensis subsp. tularensis Schu S4 strain, we deployed a combined in silico, in vitro, and in vivo analysis to identify virulence factors and their interactions with host proteins to characterize bacterial infection mechanisms. We initially used comparative genomics and literature to identify and select a set of 49 putative and known virulence factors for analysis. Each protein was then subjected to proteome-scale yeast two-hybrid (Y2H) screens with human and murine cDNA libraries to identify potential host-pathogen protein-protein interactions. Based on the bacterial protein interaction profile with both hosts, we selected seven novel putative virulence factors for mutant construction and animal validation experiments. We were able to create five transposon insertion mutants and used them in an intranasal BALB/c mouse challenge model to establish 50 % lethal dose estimates. Three of these, ΔFTT0482c, ΔFTT1538c, and ΔFTT1597, showed attenuation in lethality and can thus be considered novel F. tularensis virulence factors. The analysis of the accompanying Y2H data identified intracellular protein trafficking between the early endosome to the late endosome as an important component in virulence attenuation for these virulence factors. Furthermore, we also used the Y2H data to investigate host protein binding of two known virulence factors, showing that direct protein binding was a component in the modulation of the inflammatory response via activation of mitogen-activated protein kinases and in the oxidative stress response. Direct interactions with specific host proteins and the ability to influence interactions among host proteins are important components for F. tularensis to avoid host-cell defense mechanisms and successfully establish an infection. Although direct host-pathogen protein-protein binding is only one aspect of Francisella virulence, it is a critical component in directly manipulating and interfering with cellular processes in the host cell.

The Job Dimensions Underlying the Job Elements of the Position Analysis Questionnaire (PAQ) (Form B).

DTIC Science & Technology

The study was concerned with the identification of the job dimension underlying the job elements of the Position Analysis Questionnaire ( PAQ ), Form B...The PAQ is a structured job analysis instrument consisting of 187 worker-oriented job elements which are divided into six a priori major divisions...The statistical procedure of principal components analysis was used to identify the job dimensions of the PAQ . Forty-five job dimensions were

Identifying Contingency Requirements using Obstacle Analysis on an Unpiloted Aerial Vehicle

NASA Technical Reports Server (NTRS)

Lutz, Robyn R.; Nelson, Stacy; Patterson-Hine, Ann; Frost, Chad R.; Tal, Doron

2005-01-01

This paper describes experience using Obstacle Analysis to identify contingency requirements on an unpiloted aerial vehicle. A contingency is an operational anomaly, and may or may not involve component failure. The challenges to this effort were: ( I ) rapid evolution of the system while operational, (2) incremental autonomy as capabilities were transferred from ground control to software control and (3) the eventual safety-criticality of such systems as they begin to fly over populated areas. The results reported here are preliminary but show that Obstacle Analysis helped (1) identify new contingencies that appeared as autonomy increased; (2) identify new alternatives for handling both previously known and new contingencies; and (3) investigate the continued validity of existing software requirements for contingency handling. Since many mobile, intelligent systems are built using a development process that poses the same challenges, the results appear to have applicability to other similar systems.

Survey of whole air data from the second airborne Biomass Burning and Lightning Experiment using principal component analysis

NASA Astrophysics Data System (ADS)

Choi, Yunsoo; Elliott, Scott; Simpson, Isobel J.; Blake, Donald R.; Colman, Jonah J.; Dubey, Manvendra K.; Meinardi, Simone; Rowland, F. Sherwood; Shirai, Tomoko; Smith, Felisa A.

2003-03-01

Hydrocarbon and halocarbon measurements collected during the second airborne Biomass Burning and Lightning Experiment (BIBLE-B) were subjected to a principal component analysis (PCA), to test the capability for identifying intercorrelated compounds within a large whole air data set. The BIBLE expeditions have sought to quantify and understand the products of burning, electrical discharge, and general atmospheric chemical processes during flights arrayed along the western edge of the Pacific. Principal component analysis was found to offer a compact method for identifying the major modes of composition encountered in the regional whole air data set. Transecting the continental monsoon, urban and industrial tracers (e.g., combustion byproducts, chlorinated methanes and ethanes, xylenes, and longer chain alkanes) dominated the observed variability. Pentane enhancements reflected vehicular emissions. In general, ethyl and propyl nitrate groupings indicated oxidation under nitrogen oxide (NOx) rich conditions and hence city or lightning influences. Over the tropical ocean, methyl nitrate grouped with brominated compounds and sometimes with dimethyl sulfide and methyl iodide. Biomass burning signatures were observed during flights over the Australian continent. Strong indications of wetland anaerobics (methane) or liquefied petroleum gas leakage (propane) were conspicuous by their absence. When all flights were considered together, sources attributable to human activity emerged as the most important. We suggest that factor reductions in general and PCA in particular may soon play a vital role in the analysis of regional whole air data sets, as a complement to more familiar methods.

The Pregnant Women with HIV Attitude Scale: development and initial psychometric evaluation.

PubMed

Tyer-Viola, Lynda A; Duffy, Mary E

2010-08-01

This paper is a report of the development and initial psychometric evaluation of the Pregnant Women with HIV Attitude Scale. Previous research has identified that attitudes toward persons with HIV/AIDS have been judgmental and could affect clinical care and outcomes. Stigma towards persons with HIV has persisted as a barrier to nursing care globally. Women are more vulnerable during pregnancy. An instrument to specifically measure obstetric care provider's attitudes toward this population is needed to target identified gaps in providing respectful care. Existing literature and instruments were analysed and two existing measures, the Attitudes about People with HIV Scale and the Attitudes toward Women with HIV Scale, were combined to create an initial item pool to address attitudes toward HIV-positive pregnant women. The data were collected in 2003 with obstetric nurses attending a national conference in the United States of America (N = 210). Content validity was used for item pool development and principal component analysis and analysis of variance were used to determine construct validity. Reliability was analysed using Cronbach's Alpha. The new measure demonstrated high internal consistency (alpha estimates = 0.89). Principal component analysis yielded a two-component structure that accounted for 45% of the total variance: Mothering-Choice (alpha estimates = 0.89) and Sympathy-Rights (alpha estimates = 0.72). These data provided initial evidence of the psychometric properties of the Pregnant Women with HIV Attitude Scale. Further analysis is required of the validity of the constructs of this scale and its reliability with various obstetric care providers.

Preventing blood transfusion failures: FMEA, an effective assessment method.

PubMed

Najafpour, Zhila; Hasoumi, Mojtaba; Behzadi, Faranak; Mohamadi, Efat; Jafary, Mohamadreza; Saeedi, Morteza

2017-06-30

Failure Mode and Effect Analysis (FMEA) is a method used to assess the risk of failures and harms to patients during the medical process and to identify the associated clinical issues. The aim of this study was to conduct an assessment of blood transfusion process in a teaching general hospital, using FMEA as the method. A structured FMEA was recruited in our study performed in 2014, and corrective actions were implemented and re-evaluated after 6 months. Sixteen 2-h sessions were held to perform FMEA in the blood transfusion process, including five steps: establishing the context, selecting team members, analysis of the processes, hazard analysis, and developing a risk reduction protocol for blood transfusion. Failure modes with the highest risk priority numbers (RPNs) were identified. The overall RPN scores ranged from 5 to 100 among which, four failure modes were associated with RPNs over 75. The data analysis indicated that failures with the highest RPNs were: labelling (RPN: 100), transfusion of blood or the component (RPN: 100), patient identification (RPN: 80) and sampling (RPN: 75). The results demonstrated that mis-transfusion of blood or blood component is the most important error, which can lead to serious morbidity or mortality. Provision of training to the personnel on blood transfusion, knowledge raising on hazards and appropriate preventative measures, as well as developing standard safety guidelines are essential, and must be implemented during all steps of blood and blood component transfusion.

Chemical composition and in vitro antibacterial activity of the essential oil of Minthostachys mollis (Kunth) Griseb Vaught from the Venezuelan Andes.

PubMed

Mora, Flor D; Araque, María; Rojas, Luis B; Ramirez, Rosslyn; Silva, Bladimiro; Usubillaga, Alfredo

2009-07-01

Chemical constituents of the essential oil from the leaves of Minthostachys mollis (Kunth) Griseb Vaught var. mollis collected in January 2008 at Tuñame, Trujillo State, Venezuela, were separated and identified by GC-MS analysis. The essential oil was obtained by hydrodistillation and thirteen components (98.5% of the sample) were identified by comparison with the Wiley GC-MS library data base. The two major components were pulegone (55.2%) and trans-menthone (31.5%). The essential oil showed a significant inhibitory effect against Gram-positive and Gram-negative bacteria, especially Bacillus subtilis and Salmonella typhi (4 microg/mL).

A temporal and spatial analysis of ground-water levels for effective monitoring in Huron County, Michigan

USGS Publications Warehouse

Holtschlag, David J.; Sweat, M.J.

1999-01-01

Quarterly water-level measurements were analyzed to assess the effectiveness of a monitoring network of 26 wells in Huron County, Michigan. Trends were identified as constant levels and autoregressive components were computed at all wells on the basis of data collected from 1993 to 1997, using structural time series analysis. Fixed seasonal components were identified at 22 wells and outliers were identified at 23 wells. The 95- percent confidence intervals were forecast for water-levels during the first and second quarters of 1998. Intervals in the first quarter were consistent with 92.3 percent of the measured values. In the second quarter, measured values were within the forecast intervals only 65.4 percent of the time. Unusually low precipitation during the second quarter is thought to have contributed to the reduced reliability of the second-quarter forecasts. Spatial interrelations among wells were investigated on the basis of the autoregressive components, which were filtered to create a set of innovation sequences that were temporally uncorrelated. The empirical covariance among the innovation sequences indicated both positive and negative spatial interrelations. The negative covariance components are considered to be physically implausible and to have resulted from random sampling error. Graphical modeling, a form of multivariate analysis, was used to model the covariance structure. Results indicate that only 29 of the 325 possible partial correlations among the water-level innovations were statistically significant. The model covariance matrix, corresponding to the model partial correlation structure, contained only positive elements. This model covariance was sequentially partitioned to compute a set of partial covariance matrices that were used to rank the effectiveness of the 26 monitoring wells from greatest to least. Results, for example, indicate that about 50 percent of the uncertainty of the water-level innovations currently monitored by the 26- well network could be described by the 6 most effective wells.

Component Selection for Sterile Compounding.

PubMed

Dilzer, Richard H

2017-01-01

This article describes the factors to consider, as well as the process of proper component selection, for use in preparing compounded sterile preparations. Special emphasis is placed on individual chemical factors that may impact a preparation's accuracy and potency. Values reported in a typical certificate of analysis are discussed, including methods of identifying any required adjustments to a master formulation or compounding record during the compounding of sterile preparations. Proper screening of the certificate of analysis, the Safety Data Sheet, procedural documentation, and the filing of all certificates of conformance are crucial to the operation of a sterile compounding facility. Copyright© by International Journal of Pharmaceutical Compounding, Inc.

A study of facilities and fixtures for testing of a high speed civil transport wing component

NASA Technical Reports Server (NTRS)

Cerro, J. A.; Vause, R. F.; Bowman, L. M.; Jensen, J. K.; Martin, C. J., Jr.; Stockwell, A. E.; Waters, W. A., Jr.

1996-01-01

A study was performed to determine the feasibility of testing a large-scale High Speed Civil Transport wing component in the Structures and Materials Testing Laboratory in Building 1148 at NASA Langley Research Center. The report includes a survey of the electrical and hydraulic resources and identifies the backing structure and floor hard points which would be available for reacting the test loads. The backing structure analysis uses a new finite element model of the floor and backstop support system in the Structures Laboratory. Information on the data acquisition system and the thermal power requirements is also presented. The study identified the hardware that would be required to test a typical component, including the number and arrangement of hydraulic actuators required to simulate expected flight loads. Load introduction and reaction structure concepts were analyzed to investigate the effects of experimentally induced boundary conditions.

Quality of Life among Adults with Confirmed Dengue in Brazil

PubMed Central

Martelli, Celina Maria Turchi; Nascimento, Nazareth Elias; Suaya, Jose A.; Siqueira, Joao Bosco; Souza, Wayner Vieira; Turchi, Marilia Dalva; Guilarde, Adriana Oliveira; Peres, Joao Borges; Shepard, Donald S.

2011-01-01

The main objective of this study was to measure the quality of life (QoL) during a dengue episode. We conducted a facility-based survey in central Brazil in 2005 and recruited 372 laboratory-confirmed dengue patients greater than 12 years of age in hospital and ambulatory settings. We administered the World Health Organization QoL instrument approximately 15 days after the onset of symptoms. We used principal component analysis with varimax rotation to identify domains related to QoL. The median age of interviewees was 36 years. Most (85%) reported their general health status as very good or good before the dengue episode. Although ambulatory patients were mainly classified as having dengue fever, 44.8% of hospitalized patients had dengue hemorrhagic fever or intermediate dengue. Principal component analysis identified five principal components related to cognition, sleep and energy, mobility, self-care, pain, and discomfort, which explained 73% of the variability of the data matrix. Hospitalized patients had significantly lower mean scores for dimensions cognition, self-care, and pain than ambulatory patients. This investigation documented the generally poor QoL during a dengue episode caused by the large number of domains affected and significant differences between health care settings. PMID:21976580

Multi-analysis strategy for metabolism of Andrographis paniculata in rat using liquid chromatography/quadrupole time-of-flight mass spectrometry.

PubMed

Li, Wenlan; Sun, Xiangming; Xu, Ying; Wang, Xuezhi; Bai, Jing; Ji, Yubin

2015-07-01

Compared with chemical drugs, it is a huge challenge to identify active ingredients of multicomponent traditional Chinese medicine (TCM). For most TCMs, metabolism investigation of absorbed constituents is a feasible way to clarify the active material basis. Although Andrographis paniculata (AP) has been extensively researched by domestic and foreign scholars, its metabolism has seldom been fully addressed to date. In this paper, high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry was applied to analysis and characterization of AP metabolism in rat urine and feces samples after oral administration of ethanol extract. The differences in metabolites and metabolic pathways between the two biological samples were further compared. The chemical structures of 20 components were tentatively identified from drug-treated biological samples, including six prototype components and 14 metabolites, which underwent such main metabolic pathways as hydrolyzation, hydrogenation, dehydroxylation, deoxygenation, methylation, glucuronidation, sulfonation and sulfation. Two co-existing components were found in urine and feces samples, suggesting that some ingredients' metabolic processes were not unique. This study provides a comprehensive report on the metabolism of AP in rats, which will be helpful for understanding its mechanism. Copyright © 2014 John Wiley & Sons, Ltd.

Derivation of Boundary Manikins: A Principal Component Analysis

NASA Technical Reports Server (NTRS)

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

2008-01-01

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

Ultrasonic characterization of damage in a simulated CF-18 composite structure

NASA Astrophysics Data System (ADS)

McRae, K. I.; Finlayson, R. D.; Sturrock, W. R.; Liesch, D. S.

1993-02-01

A simulated CF-18 aircraft door component was constructed and subjected to treatment during manufacturing with the object of inducing damage in the composite material in a known and well-defined manner. The simulated component was then sent to participants in a nondestructive evaluation study. Results are reported for tests conducted with a scanning apparatus and data acquisition system which consisted of three components: ultrasonic transducer and scanner comprising a two-axis scanning frame to which a modified commercial transducer was attached; an acquisition system for ultrasonic data known as Signal Processing Ultrasonic Device (SPUD); and a data analysis and display system (DETECT/NDE) specifically designed to manipulate large three dimensional ultrasonic data sets. A series of five large-area scans was performed, each scan about 52 cm square. A total of eight regions of interest were identified for a more detailed analysis of the delamination damage, seven detailed scans covering a 13-cm square and one covering a 20.8-cm square. It was often possible to identify the probable source of the damage as that resulting from impact or caused by overloading of fasteners. Flaws of all significant dimensions were located and fully characterized using the ultrasonic procedure.

Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews.

PubMed

Shea, Beverley J; Grimshaw, Jeremy M; Wells, George A; Boers, Maarten; Andersson, Neil; Hamel, Candyce; Porter, Ashley C; Tugwell, Peter; Moher, David; Bouter, Lex M

2007-02-15

Our objective was to develop an instrument to assess the methodological quality of systematic reviews, building upon previous tools, empirical evidence and expert consensus. A 37-item assessment tool was formed by combining 1) the enhanced Overview Quality Assessment Questionnaire (OQAQ), 2) a checklist created by Sacks, and 3) three additional items recently judged to be of methodological importance. This tool was applied to 99 paper-based and 52 electronic systematic reviews. Exploratory factor analysis was used to identify underlying components. The results were considered by methodological experts using a nominal group technique aimed at item reduction and design of an assessment tool with face and content validity. The factor analysis identified 11 components. From each component, one item was selected by the nominal group. The resulting instrument was judged to have face and content validity. A measurement tool for the 'assessment of multiple systematic reviews' (AMSTAR) was developed. The tool consists of 11 items and has good face and content validity for measuring the methodological quality of systematic reviews. Additional studies are needed with a focus on the reproducibility and construct validity of AMSTAR, before strong recommendations can be made on its use.

Cell Wall Composition and Candidate Biosynthesis Gene Expression During Rice Development.

PubMed

Lin, Fan; Manisseri, Chithra; Fagerström, Alexandra; Peck, Matthew L; Vega-Sánchez, Miguel E; Williams, Brian; Chiniquy, Dawn M; Saha, Prasenjit; Pattathil, Sivakumar; Conlin, Brian; Zhu, Lan; Hahn, Michael G; Willats, William G T; Scheller, Henrik V; Ronald, Pamela C; Bartley, Laura E

2016-10-01

Cell walls of grasses, including cereal crops and biofuel grasses, comprise the majority of plant biomass and intimately influence plant growth, development and physiology. However, the functions of many cell wall synthesis genes, and the relationships among and the functions of cell wall components remain obscure. To better understand the patterns of cell wall accumulation and identify genes that act in grass cell wall biosynthesis, we characterized 30 samples from aerial organs of rice (Oryza sativa cv. Kitaake) at 10 developmental time points, 3-100 d post-germination. Within these samples, we measured 15 cell wall chemical components, enzymatic digestibility and 18 cell wall polysaccharide epitopes/ligands. We also used quantitative reverse transcription-PCR to measure expression of 50 glycosyltransferases, 15 acyltransferases and eight phenylpropanoid genes, many of which had previously been identified as being highly expressed in rice. Most cell wall components vary significantly during development, and correlations among them support current understanding of cell walls. We identified 92 significant correlations between cell wall components and gene expression and establish nine strong hypotheses for genes that synthesize xylans, mixed linkage glucan and pectin components. This work provides an extensive analysis of cell wall composition throughout rice development, identifies genes likely to synthesize grass cell walls, and provides a framework for development of genetically improved grasses for use in lignocellulosic biofuel production and agriculture. © The Author 2016. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Using passive sampling and zebrafish to identify developmental toxicants in complex mixtures.

PubMed

Bergmann, Alan J; Tanguay, Robert L; Anderson, Kim A

2017-09-01

Using effects-directed analysis, we investigated associations previously observed between polycyclic aromatic hydrocarbons (PAHs) and embryotoxicity in field-deployed low-density polyethylene (LDPE). We conducted effects-directed analysis using a zebrafish embryo assay and iterative fractionation of extracts of LDPE that were deployed in the Portland Harbor superfund megasite, Oregon (USA). Whole extracts induced toxicity including mortality, edema, and notochord distortion at 20% effect concentration (EC20) values of approximately 100, 100, and 10 mg LDPE/mL, respectively. Through fractionation, we determined that PAHs at concentrations similar to previous research did not contribute markedly to toxicity. We also eliminated pesticides, phthalates, musks, and other substances identified in toxic fractions by testing surrogate mixtures. We identified free fatty acids as lethal components of LDPE extracts and confirmed their toxicity with authentic standards. We found chromatographic evidence that dithiocarbamates are responsible for notochord and other sublethal effects, although exact matches were not obtained. Fatty acids and dithiocarbamates were previously unrecorded components of LDPE extracts and likely contribute to the toxicity of the whole mixture. The present study demonstrates the success of effects-directed analysis in nontargeted hazard identification using the zebrafish embryo test as a self-contained battery of bioassays that allows identification of multiple chemicals with different modes of action. This is the first effects-directed analysis to combine LDPE and zebrafish, approaches that are widely applicable to identifying developmental hazards in the bioavailable fraction of hydrophobic organic compounds. Environ Toxicol Chem 2017;36:2290-2298. © 2017 SETAC. © 2017 SETAC.

Electromagnetic pulse (EMP) coupling codes for use with the vulnerability/lethality (VIL) taxonomy. Final report, June-October 1984

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

Mar, M.H.

1995-07-01

Based on the vulnerability Lethality (V/L) taxonomy developed by the Ballistic Vulnerability Lethality Division (BVLD) of the Survivability Lethality Analysis Directorate (SLAD), a nuclear electromagnetic pulse (EMP) coupling V/L analysis taxonomy has been developed. A nuclear EMP threat to a military system can be divided into two levels: (1) coupling to a system level through a cable, antenna, or aperture; and (2) the component level. This report will focus on the initial condition, which includes threat definition and target description, as well as the mapping process from the initial condition to damaged components state. EMP coupling analysis at a systemmore » level is used to accomplish this. This report introduces the nature of EMP threat, interaction between the threat and target, and how the output of EMP coupling analysis at a system level becomes the input to the component level analysis. Many different tools (EMP coupling codes) will be discussed for the mapping process, which correponds to the physics of phenomenology. This EMP coupling V/L taxonomy and the models identified in this report will provide the tools necessary to conduct basic V/L analysis of EMP coupling.« less

Analysis of the Supporting Websites for the Use of Instructional Games in K-12 Settings

ERIC Educational Resources Information Center

Kebritchi, Mansureh; Hirumi, Atsusi; Kappers, Wendi; Henry, Renee

2009-01-01

This paper identifies resources to be included in a website designed to facilitate the integration of instructional games in K-12 settings. Guidelines and supporting components are based on a survey of K-12 educators who are integrating games, an analysis of existing instructional game websites, and summaries of literature on the use of…

Characterization of volatile profile from ten different varieties of Chinese jujubes by HS-SPME/GC-MS coupled with E-nose.

PubMed

Chen, Qinqin; Song, Jianxin; Bi, Jinfeng; Meng, Xianjun; Wu, Xinye

2018-03-01

Volatile profile of ten different varieties of fresh jujubes was characterized by HS-SPME/GC-MS (headspace solid phase micro-extraction combined with gas chromatography-mass spectrometry) and E-nose (electronic nose). GC-MS results showed that a total of 51 aroma compounds were identified in jujubes, hexanoic acid, hexanal, (E)-2-hexenal, (Z)-2-heptenal, benzaldehyde and (E)-2-nonenal were the main aroma components with contributions that over 70%. Differentiation of jujube varieties was conducted by cluster analysis of GC-MS data and principal component analysis & linear discriminant analysis of E-nose data. Both results showed that jujubes could be mainly divided into two groups: group A (JZ, PDDZ, JSXZ and LWZZ) and group B (BZ, YZ, MZ, XZ and DZ). There were significant differences in contents of alcohols, acids and aromatic compounds between group A and B. GC-MS coupled with E-nose could be a fast and accurate method to identify the general flavor difference in different varieties of jujubes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rhetorical skills as a component of midwifery care.

PubMed

Domajnko, Barbara; Drglin, Zalka; Pahor, Majda

2011-04-01

this article argues that rhetorical skills are an important quality factor of midwifery care. In particular, it aims to identify and discuss the relevance of three classical means of persuasion: ethos, pathos and logos. secondary analysis, rhetorical analysis of semi-structured interviews. Slovenia. Interviews were carried out predominantly in 2006. Data refer to childbirths in 2005 and 2006. four women with recent experience of childbirth. analysis identified the presence of all three means of persuasion in the interaction between midwives and women. Focusing on midwives, the quality of their awareness and command of rhetorical skills remains questionable. In particular, women experienced lack of a rational account of the situation and decisions made by health-care professionals involved in maternity care. acknowledging professional ethics, awareness and good command of all three means of persuasion [but above all, argumentative persuasion (logos)] is an integral component of midwifery care. It can contribute to collaborative relations between midwives and women, and thus promote women-centred midwifery care. knowledge of the three classical rhetorical means of persuasion should be integrated into professional midwifery curricula. Copyright © 2009 Elsevier Ltd. All rights reserved.

Comparative Proteomic Analysis of Two Varieties of Genetically Modified (GM) Embrapa 5.1 Common Bean (Phaseolus vulgaris L.) and Their Non-GM Counterparts.

PubMed

Balsamo, Geisi M; Valentim-Neto, Pedro A; Mello, Carla S; Arisi, Ana C M

2015-12-09

The genetically modified (GM) common bean event Embrapa 5.1 was commercially approved in Brazil in 2011; it is resistant to golden mosaic virus infection. In the present work grain proteome profiles of two Embrapa 5.1 common bean varieties, Pérola and Pontal, and their non-GM counterparts were compared by two-dimensional gel electrophoresis (2-DE) followed by mass spectrometry (MS). Analyses detected 23 spots differentially accumulated between GM Pérola and non-GM Pérola and 21 spots between GM Pontal and non-GM Pontal, although they were not the same proteins in Pérola and Pontal varieties, indicating that the variability observed may not be due to the genetic transformation. Among them, eight proteins were identified in Pérola varieties, and four proteins were identified in Pontal. Moreover, we applied principal component analysis (PCA) on 2-DE data, and variation between varieties was explained in the first two principal components. This work provides a first 2-DE-MS/MS-based analysis of Embrapa 5.1 common bean grains.

Screening and analysis of aconitum alkaloids and their metabolites in rat urine after oral administration of aconite roots extract using LC-TOFMS-based metabolomics.

PubMed

Tan, Guangguo; Lou, Ziyang; Jing, Jing; Li, Wuhong; Zhu, Zhenyu; Zhao, Liang; Zhang, Guoqing; Chai, Yifeng

2011-12-01

Aconite roots are popularly used in herbal medicines in China. Many cases of accidental and intentional intoxication with this plant have been reported; some of these are fatal because the toxicity of aconitum is very high. It is thus important to detect and identify aconitum alkaloids in biofluids. In this work, an improved method employing LC-TOFMS with multivariate data analysis was developed for screening and analysis of major aconitum alkaloids and their metabolites in rat urine following oral administration of aconite roots extract. Thirty-four signals highlighted by multivariate statistical analyses including 24 parent components and 10 metabolites were screened out and further identified by adjustment of the fragmentor voltage to produce structure-relevant fragment ions. It is helpful for studying aconite roots in toxicology, pharmacology and forensic medicine. This work also confirmed that the metabolomic approach provides effective tools for screening multiple absorbed and metabolic components of Chinese herbal medicines in vivo. Copyright © 2011 John Wiley & Sons, Ltd.

Multivariate analysis of variance of designed chromatographic data. A case study involving fermentation of rooibos tea.

PubMed

Marini, Federico; de Beer, Dalene; Walters, Nico A; de Villiers, André; Joubert, Elizabeth; Walczak, Beata

2017-03-17

An ultimate goal of investigations of rooibos plant material subjected to different stages of fermentation is to identify the chemical changes taking place in the phenolic composition, using an untargeted approach and chromatographic fingerprints. Realization of this goal requires, among others, identification of the main components of the plant material involved in chemical reactions during the fermentation process. Quantitative chromatographic data for the compounds for extracts of green, semi-fermented and fermented rooibos form the basis of preliminary study following a targeted approach. The aim is to estimate whether treatment has a significant effect based on all quantified compounds and to identify the compounds, which contribute significantly to it. Analysis of variance is performed using modern multivariate methods such as ANOVA-Simultaneous Component Analysis, ANOVA - Target Projection and regularized MANOVA. This study is the first one in which all three approaches are compared and evaluated. For the data studied, all tree methods reveal the same significance of the fermentation effect on the extract compositions, but they lead to its different interpretation. Copyright © 2017 Elsevier B.V. All rights reserved.

Extended Testability Analysis Tool

NASA Technical Reports Server (NTRS)

Melcher, Kevin; Maul, William A.; Fulton, Christopher

2012-01-01

The Extended Testability Analysis (ETA) Tool is a software application that supports fault management (FM) by performing testability analyses on the fault propagation model of a given system. Fault management includes the prevention of faults through robust design margins and quality assurance methods, or the mitigation of system failures. Fault management requires an understanding of the system design and operation, potential failure mechanisms within the system, and the propagation of those potential failures through the system. The purpose of the ETA Tool software is to process the testability analysis results from a commercial software program called TEAMS Designer in order to provide a detailed set of diagnostic assessment reports. The ETA Tool is a command-line process with several user-selectable report output options. The ETA Tool also extends the COTS testability analysis and enables variation studies with sensor sensitivity impacts on system diagnostics and component isolation using a single testability output. The ETA Tool can also provide extended analyses from a single set of testability output files. The following analysis reports are available to the user: (1) the Detectability Report provides a breakdown of how each tested failure mode was detected, (2) the Test Utilization Report identifies all the failure modes that each test detects, (3) the Failure Mode Isolation Report demonstrates the system s ability to discriminate between failure modes, (4) the Component Isolation Report demonstrates the system s ability to discriminate between failure modes relative to the components containing the failure modes, (5) the Sensor Sensor Sensitivity Analysis Report shows the diagnostic impact due to loss of sensor information, and (6) the Effect Mapping Report identifies failure modes that result in specified system-level effects.

Relationship between polycystic ovary syndrome and ancestry in European Americans.

PubMed

Bjonnes, Andrew C; Saxena, Richa; Welt, Corrine K

2016-12-01

To determine whether European Americans with polycystic ovary syndrome (PCOS) exhibit genetic differences associated with PCOS status and phenotypic features. Case-control association study in European Americans. Academic center. Women with PCOS diagnosed with the use of the National Institutes of Health criteria (n = 532) and control women with regular menstrual cycles and no evidence of hyperandrogenism (n = 432). Blood was drawn for measurement of sex steroids, metabolic parameters, and genotyping. Associations among PCOS status, phenotype, and genetic background identified with the use of principal component analysis. Principal component analysis identified five principal components (PCs). PC1 captured northwest-to-southeast European genetic variation and was associated with PCOS status. Acanthosis was associated with southern European ancestry, and larger waist:hip ratio was associated with northern European ancestry. PC2 was associated with east-to-west European genetic variation and cholesterol levels. These data provide evidence for genetic influence based on European ethnicity in women with PCOS. There is also evidence for a genetic component in the phenotypic features of PCOS within a mixed European population. The data point to the need to control for population stratification in genetic studies in women of mixed European ethnicity. They also emphasize the need for better studies of PCOS prevalence and phenotype as a function of genetic background. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Chromophoric dissolved organic matter (CDOM) variability in Barataria Basin using excitation-emission matrix (EEM) fluorescence and parallel factor analysis (PARAFAC).

PubMed

Singh, Shatrughan; D'Sa, Eurico J; Swenson, Erick M

2010-07-15

Chromophoric dissolved organic matter (CDOM) variability in Barataria Basin, Louisiana, USA,was examined by excitation emission matrix (EEM) fluorescence combined with parallel factor analysis (PARAFAC). CDOM optical properties of absorption and fluorescence at 355nm along an axial transect (36 stations) during March, April, and May 2008 showed an increasing trend from the marine end member to the upper basin with mean CDOM absorption of 11.06 + or - 5.01, 10.05 + or - 4.23, 11.67 + or - 6.03 (m(-)(1)) and fluorescence 0.80 + or - 0.37, 0.78 + or - 0.39, 0.75 + or - 0.51 (RU), respectively. PARAFAC analysis identified two terrestrial humic-like (component 1 and 2), one non-humic like (component 3), and one soil derived humic acid like (component 4) components. The spatial variation of the components showed an increasing trend from station 1 (near the mouth of basin) to station 36 (end member of bay; upper basin). Deviations from this increasing trend were observed at a bayou channel with very high chlorophyll-a concentrations especially for component 3 in May 2008 that suggested autochthonous production of CDOM. The variability of components with salinity indicated conservative mixing along the middle part of the transect. Component 1 and 4 were found to be relatively constant, while components 2 and 3 revealed an inverse relationship for the sampling period. Total organic carbon showed increasing trend for each of the components. An increase in humification and a decrease in fluorescence indices along the transect indicated an increase in terrestrial derived organic matter and reduced microbial activity from lower to upper basin. The use of these indices along with PARAFAC results improved dissolved organic matter characterization in the Barataria Basin. Copyright 2010 Elsevier B.V. All rights reserved.

[Extraction and analysis of chemical components of essential oil in Thymus vulgaris of tissue culture].

PubMed

Li, Xiao-Dong; Yang, Li; Xu, Shi-Qian; Li, Jian-Guo; Cheng, Zhi-Hui; Dang, Jian-Zhang

2011-10-01

To extract the essential oils from the Seedlings, the Aseptic Seedlings and the Tissue Culture Seedlings of Thymus vulgaris and analyze their chemical components and the relative contents. The essential oils were extracted by steam distillation, the chemical components and the relative contents were identified and analyzed by gas chromatography-mass spectrometry (GC/MS) and peak area normalization method. The main chemical components of essential oil in these three samples had no significant difference, they all contained the main components of essential oil in Thymus vulgaris: Thymol, Carvacrol, o-Cymene, gamma-Terpinene, Caryophyllene et al. and only had a slight difference in the relative content. This study provides important theoretical foundation and data reference for further study on production of essential oil in thyme by tissue culture technology.

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.

Task analysis exemplified: the process of resolving unfinished business.

PubMed

Greenberg, L S; Foerster, F S

1996-06-01

The steps of a task-analytic research program designed to identify the in-session performances involved in resolving lingering bad feelings toward a significant other are described. A rational-empirical methodology of repeatedly cycling between rational conjecture and empirical observations is demonstrated as a method of developing an intervention manual and the components of client processes of resolution. A refined model of the change process developed by these procedures is validated by comparing 11 successful and 11 unsuccessful performances. Four performance components-intense expression of feeling, expression of need, shift in representation of other, and self-validation or understanding of the other-were found to discriminate between resolution and nonresolution performances. These components were measured on 4 process measures: the Structural Analysis of Social Behavior, the Experiencing Scale, the Client's Emotional Arousal Scale, and a need scale.

Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers

PubMed Central

Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F; Glasser, Matthew F; Griffanti, Ludovica; Smith, Stephen M

2014-01-01

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB’s ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different Classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL. PMID:24389422

Token Economy: A Systematic Review of Procedural Descriptions.

PubMed

Ivy, Jonathan W; Meindl, James N; Overley, Eric; Robson, Kristen M

2017-09-01

The token economy is a well-established and widely used behavioral intervention. A token economy is comprised of six procedural components: the target response(s), a token that functions as a conditioned reinforcer, backup reinforcers, and three interconnected schedules of reinforcement. Despite decades of applied research, the extent to which the procedures of a token economy are described in complete and replicable detail has not been evaluated. Given the inherent complexity of a token economy, an analysis of the procedural descriptions may benefit future token economy research and practice. Articles published between 2000 and 2015 that included implementation of a token economy within an applied setting were identified and reviewed with a focus on evaluating the thoroughness of procedural descriptions. The results show that token economy components are regularly omitted or described in vague terms. Of the articles included in this analysis, only 19% (18 of 96 articles reviewed) included replicable and complete descriptions of all primary components. Missing or vague component descriptions could negatively affect future research or applied practice. Recommendations are provided to improve component descriptions.

A component analysis of positive behaviour support plans.

PubMed

McClean, Brian; Grey, Ian

2012-09-01

Positive behaviour support (PBS) emphasises multi-component interventions by natural intervention agents to help people overcome challenging behaviours. This paper investigates which components are most effective and which factors might mediate effectiveness. Sixty-one staff working with individuals with intellectual disability and challenging behaviours completed longitudinal competency-based training in PBS. Each staff participant conducted a functional assessment and developed and implemented a PBS plan for one prioritised individual. A total of 1,272 interventions were available for analysis. Measures of challenging behaviour were taken at baseline, after 6 months, and at an average of 26 months follow-up. There was a significant reduction in the frequency, management difficulty, and episodic severity of challenging behaviour over the duration of the study. Escape was identified by staff as the most common function, accounting for 77% of challenging behaviours. The most commonly implemented components of intervention were setting event changes and quality-of-life-based interventions. Only treatment acceptability was found to be related to decreases in behavioural frequency. No single intervention component was found to have a greater association with reductions in challenging behaviour.

Prognostic Significance of Solid and Micropapillary Components in Invasive Lung Adenocarcinomas Measuring ≤3 cm.

PubMed

Matsuoka, Yuki; Yurugi, Yohei; Takagi, Yuzo; Wakahara, Makoto; Kubouchi, Yasuaki; Sakabe, Tomohiko; Haruki, Tomohiro; Araki, Kunio; Taniguchi, Yuji; Nakamura, Hiroshige; Umekita, Yoshihisa

2016-09-01

We aimed to analyze the clinical impact of solid and micropapillary components in a series of Japanese patients resected for ≤3 cm lung adenocarcinoma. A total of 115 patients with ≤3 cm lung adenocarcinomas were reviewed and classified according to the American Thoracic Society and the European Respiratory Society classification. The presence of solid (S+) or micropapillary component (MP+) was defined when the component constituted ≥1% of the entire tumor. The impact of these components on disease-free (DFS) and disease-specific (DSS) survival was analyzed. Thirty (26.1%) cases with S+ and 27 (23.5%) with MP+ were identified, and multivariate analysis indicated that S+ status significantly reduced the duration of DFS and DSS. In 86 patients of acinar- and papillary-predominant subgroups, S+ and/or MP+ had the most significant effect on DFS and DSS by multivariate analysis. S+ and/or MP+ status predict worse prognosis in patients with acinar- and papillary-predominant lung adenocarcinoma. Copyright© 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

HPLC-DAD-ESI-MS Analysis of Flavonoids from Leaves of Different Cultivars of Sweet Osmanthus.

PubMed

Wang, Yiguang; Fu, Jianxin; Zhang, Chao; Zhao, Hongbo

2016-09-14

Osmanthus fragrans Lour. has traditionally been a popular ornamental plant in China. In this study, ethanol extracts of the leaves of four cultivar groups of O. fragrans were analyzed by high-performance liquid chromatography coupled with diode array detection (HPLC-DAD) and high-performance liquid chromatography with electrospray ionization and mass spectrometry (HPLC-ESI-MS). The results suggest that variation in flavonoids among O. fragrans cultivars is quantitative, rather than qualitative. Fifteen components were detected and separated, among which, the structures of 11 flavonoids and two coumarins were identified or tentatively identified. According to principal component analysis (PCA) and hierarchical cluster analysis (HCA) based on the abundance of these components (expressed as rutin equivalents), 22 selected cultivars were classified into four clusters. The seven cultivars from Cluster III ('Xiaoye Sugui', 'Boye Jingui', 'Wuyi Dangui', 'Yingye Dangui', 'Danzhuang', 'Foding Zhu', and 'Tianxiang Taige'), which are enriched in rutin and total flavonoids, and 'Sijigui' from Cluster II which contained the highest amounts of kaempferol glycosides and apigenin 7-O-glucoside, could be selected as potential pharmaceutical resources. However, the chemotaxonomy in this paper does not correlate with the distribution of the existing cultivar groups, demonstrating that the distribution of flavonoids in O. fragrans leaves does not provide an effective means of classification for O. fragrans cultivars based on flower color.

Identification of novel candidate maternal serum protein markers for Down syndrome by integrated proteomic and bioinformatic analysis.

PubMed

Kang, Yuan; Dong, Xinran; Zhou, Qiongjie; Zhang, Ying; Cheng, Yan; Hu, Rong; Su, Cuihong; Jin, Hong; Liu, Xiaohui; Ma, Duan; Tian, Weidong; Li, Xiaotian

2012-03-01

This study aimed to identify candidate protein biomarkers from maternal serum for Down syndrome (DS) by integrated proteomic and bioinformatics analysis. A pregnancy DS group of 18 women and a control group with the same number were prepared, and the maternal serum proteins were analyzed by isobaric tags for relative and absolute quantitation and mass spectrometry, to identify DS differentially expressed maternal serum proteins (DS-DEMSPs). Comprehensive bioinformatics analysis was then employed to analyze DS-DEMSPs both in this paper and seven related publications. Down syndrome differentially expressed maternal serum proteins from different studies are significantly enriched with common Gene Ontology functions, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, transcription factor binding sites, and Pfam protein domains, However, the DS-DEMSPs are less functionally related to known DS-related genes. These evidences suggest that common molecular mechanisms induced by secondary effects may be present upon DS carrying. A simple scoring scheme revealed Alpha-2-macroglobulin, Apolipoprotein A1, Apolipoprotein E, Complement C1s subcomponent, Complement component 5, Complement component 8, alpha polypeptide, Complement component 8, beta polypeptide and Fibronectin as potential DS biomarkers. The integration of proteomics and bioinformatics studies provides a novel approach to develop new prenatal screening methods for noninvasive yet accurate diagnosis of DS. Copyright © 2012 John Wiley & Sons, Ltd.

Speciation and Sources of Brown Carbon in Precipitation at Seoul, Korea: Insights from Excitation-Emission Matrix Spectroscopy and Carbon Isotopic Analysis.

PubMed

Yan, Ge; Kim, Guebuem

2017-10-17

Brown carbon (BrC) plays a significant role in the Earth's radiative balance, yet its sources and chemical composition remain poorly understood. In this work, we investigated BrC in the atmospheric environment of Seoul by characterizing dissolved organic matter in precipitation using excitation-emission matrix (EEM) fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC). The two independent fluorescent components identified by PARAFAC were attributed to humic-like substance (HULIS) and biologically derived material based on their significant correlations with measured HULIS isolated using solid-phase extraction and total hydrolyzable tyrosine. The year-long observation shows that HULIS contributes to 66 ± 13% of total fluorescence intensity of our samples on average. By using dual carbon ( 13 C and 14 C) isotopic analysis conducted on isolated HULIS, the HULIS fraction of BrC was found to be primarily derived from biomass burning and emission of terrestrial biogenic gases and particles (>70%), with minor contributions from fossil-fuel combustion. The knowledge derived from this study could contribute to the establishment of a characterizing system of BrC components identified by EEM spectroscopy. Our work demonstrates that, EEM fluorescence spectroscopy is a powerful tool in BrC study, on the basis of its chromophore resolving power, allowing investigation into individual components of BrC by other organic matter characterization techniques.

Development and Validation of the Work-Related Well-Being Index: Analysis of the Federal Employee Viewpoint Survey.

PubMed

Eaton, Jennifer L; Mohr, David C; Hodgson, Michael J; McPhaul, Kathleen M

2018-02-01

To describe development and validation of the work-related well-being (WRWB) index. Principal components analysis was performed using Federal Employee Viewpoint Survey (FEVS) data (N = 392,752) to extract variables representing worker well-being constructs. Confirmatory factor analysis was performed to verify factor structure. To validate the WRWB index, we used multiple regression analysis to examine relationships with burnout associated outcomes. Principal Components Analysis identified three positive psychology constructs: "Work Positivity", "Co-worker Relationships", and "Work Mastery". An 11 item index explaining 63.5% of variance was achieved. The structural equation model provided a very good fit to the data. Higher WRWB scores were positively associated with all three employee experience measures examined in regression models. The new WRWB index shows promise as a valid and widely accessible instrument to assess worker well-being.

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of 18F-FDG PET Data.

PubMed

Pagani, Marco; Giuliani, Alessandro; Öberg, Johanna; De Carli, Fabrizio; Morbelli, Silvia; Girtler, Nicola; Arnaldi, Dario; Accardo, Jennifer; Bauckneht, Matteo; Bongioanni, Francesca; Chincarini, Andrea; Sambuceti, Gianmario; Jonsson, Cathrine; Nobili, Flavio

2017-07-01

Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia (AD) has also been observed. Methods: We implemented independent-component analysis of 18 F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment (MCI) not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups. Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. AD-specific independent components first appeared in the late-MCI stage and could discriminate converting MCI and AD from nonconverting MCI with an accuracy of 83.5%. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. Conclusion: Independent-component analysis of 18 F-FDG PET data showed a gradual disruption of functional brain connectivity with progression of cognitive decline in AD. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Insights into the interaction between carbamazepine and natural dissolved organic matter in the Yangtze Estuary using fluorescence excitation-emission matrix spectra coupled with parallel factor analysis.

PubMed

Wang, Ying; Zhang, Manman; Fu, Jun; Li, Tingting; Wang, Jinggang; Fu, Yingyu

2016-10-01

The interaction between carbamazepine (CBZ) and dissolved organic matter (DOM) from three zones (the nearshore, the river channel, and the coastal areas) in the Yangtze Estuary was investigated using fluorescence quenching titration combined with excitation emission matrix spectra and parallel factor analysis (PARAFAC). The complexation between CBZ and DOM was demonstrated by the increase in hydrogen bonding and the disappearance of the C=O stretch obtained from the Fourier transform infrared spectroscopy analysis. The results indicated that two protein-like substances (component 2 and component3) and two humic-like substances (component 1 and 4) were identified in the DOM from the Yangtze Estuary. The fluorescence quenching curves of each component with the addition of CBZ and the Ryan and Weber model calculation results both demonstrated that the different components exhibited different complexation activities with CBZ. The protein-like components had a stronger affinity with CBZ than did the humic-like substances. On the other hand, the autochthonous tyrosine-like C2 played an important role in the complexation with DOM from the river channel and coastal areas, while C3 influenced by anthropogenic activities showed an obvious effect in the nearshore area. DOMs from the river channel have the highest binding capacity for CBZ, which may ascribe to the relatively high phenol content group in the DOM.

Markov and semi-Markov switching linear mixed models used to identify forest tree growth components.

PubMed

Chaubert-Pereira, Florence; Guédon, Yann; Lavergne, Christian; Trottier, Catherine

2010-09-01

Tree growth is assumed to be mainly the result of three components: (i) an endogenous component assumed to be structured as a succession of roughly stationary phases separated by marked change points that are asynchronous among individuals, (ii) a time-varying environmental component assumed to take the form of synchronous fluctuations among individuals, and (iii) an individual component corresponding mainly to the local environment of each tree. To identify and characterize these three components, we propose to use semi-Markov switching linear mixed models, i.e., models that combine linear mixed models in a semi-Markovian manner. The underlying semi-Markov chain represents the succession of growth phases and their lengths (endogenous component) whereas the linear mixed models attached to each state of the underlying semi-Markov chain represent-in the corresponding growth phase-both the influence of time-varying climatic covariates (environmental component) as fixed effects, and interindividual heterogeneity (individual component) as random effects. In this article, we address the estimation of Markov and semi-Markov switching linear mixed models in a general framework. We propose a Monte Carlo expectation-maximization like algorithm whose iterations decompose into three steps: (i) sampling of state sequences given random effects, (ii) prediction of random effects given state sequences, and (iii) maximization. The proposed statistical modeling approach is illustrated by the analysis of successive annual shoots along Corsican pine trunks influenced by climatic covariates. © 2009, The International Biometric Society.

New insights into comparison between synthetic and practical municipal wastewater in cake layer characteristic analysis of membrane bioreactor.

PubMed

Zhou, Lijie; Zhuang, Wei-Qin; Wang, Xin; Yu, Ke; Yang, Shufang; Xia, Siqing

2017-11-01

In previous studies, cake layer analysis in membrane bioreactor (MBR) was both carried out with synthetic and practical municipal wastewater (SMW and PMW), leading to different results. This study aimed to identify the comparison between SMW and PMW in cake layer characteristic analysis of MBR. Two laboratory-scale anoxic/oxic MBRs were operated for over 90days with SMW and PMW, respectively. Results showed that PMW led to rough cake layer surface with particles, and the aggravation of cake layer formation with thinner and denser cake layer. Additionally, inorganic components, especially Si and Al, in PMW accumulated into cake layer and strengthened the cake layer structure, inducing severer biofouling. However, SMW promoted bacterial metabolism during cake layer formation, thus aggravated the accumulation of organic components into cake layer. Therefore, SMW highlighted the organic components in cake layer, but weakened the inorganic functions in practical MBR operation. Copyright © 2017 Elsevier Ltd. All rights reserved.

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques.

PubMed

Long, Zhiying; Chen, Kewei; Wu, Xia; Reiman, Eric; Peng, Danling; Yao, Li

2009-02-01

Spatial Independent component analysis (sICA) has been widely used to analyze functional magnetic resonance imaging (fMRI) data. The well accepted implicit assumption is the spatially statistical independency of intrinsic sources identified by sICA, making the sICA applications difficult for data in which there exist interdependent sources and confounding factors. This interdependency can arise, for instance, from fMRI studies investigating two tasks in a single session. In this study, we introduced a linear projection approach and considered its utilization as a tool to separate task-related components from two-task fMRI data. The robustness and feasibility of the method are substantiated through simulation on computer data and fMRI real rest data. Both simulated and real two-task fMRI experiments demonstrated that sICA in combination with the projection method succeeded in separating spatially dependent components and had better detection power than pure model-based method when estimating activation induced by each task as well as both tasks.

Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

NASA Astrophysics Data System (ADS)

Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi

2012-07-01

The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.

A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification.

PubMed

LeVan, P; Urrestarazu, E; Gotman, J

2006-04-01

To devise an automated system to remove artifacts from ictal scalp EEG, using independent component analysis (ICA). A Bayesian classifier was used to determine the probability that 2s epochs of seizure segments decomposed by ICA represented EEG activity, as opposed to artifact. The classifier was trained using numerous statistical, spectral, and spatial features. The system's performance was then assessed using separate validation data. The classifier identified epochs representing EEG activity in the validation dataset with a sensitivity of 82.4% and a specificity of 83.3%. An ICA component was considered to represent EEG activity if the sum of the probabilities that its epochs represented EEG exceeded a threshold predetermined using the training data. Otherwise, the component represented artifact. Using this threshold on the validation set, the identification of EEG components was performed with a sensitivity of 87.6% and a specificity of 70.2%. Most misclassified components were a mixture of EEG and artifactual activity. The automated system successfully rejected a good proportion of artifactual components extracted by ICA, while preserving almost all EEG components. The misclassification rate was comparable to the variability observed in human classification. Current ICA methods of artifact removal require a tedious visual classification of the components. The proposed system automates this process and removes simultaneously multiple types of artifacts.

Artifact suppression and analysis of brain activities with electroencephalography signals.

PubMed

Rashed-Al-Mahfuz, Md; Islam, Md Rabiul; Hirose, Keikichi; Molla, Md Khademul Islam

2013-06-05

Brain-computer interface is a communication system that connects the brain with computer (or other devices) but is not dependent on the normal output of the brain (i.e., peripheral nerve and muscle). Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This paper presented a data adaptive technique for artifact suppression and brain wave extraction from electroencephalography signals to detect regional brain activities. Empirical mode decomposition based adaptive thresholding approach was employed here to suppress the electro-oculogram artifact. Fractional Gaussian noise was used to determine the threshold level derived from the analysis data without any training. The purified electroencephalography signal was composed of the brain waves also called rhythmic components which represent the brain activities. The rhythmic components were extracted from each electroencephalography channel using adaptive wiener filter with the original scale. The regional brain activities were mapped on the basis of the spatial distribution of rhythmic components, and the results showed that different regions of the brain are activated in response to different stimuli. This research analyzed the activities of a single rhythmic component, alpha with respect to different motor imaginations. The experimental results showed that the proposed method is very efficient in artifact suppression and identifying individual motor imagery based on the activities of alpha component.

Analysis of a Shock-Associated Noise Prediction Model Using Measured Jet Far-Field Noise Data

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Sharpe, Jacob A.

2014-01-01

A code for predicting supersonic jet broadband shock-associated noise was assessed us- ing a database containing noise measurements of a jet issuing from a convergent nozzle. The jet was operated at 24 conditions covering six fully expanded Mach numbers with four total temperature ratios. To enable comparisons of the predicted shock-associated noise component spectra with data, the measured total jet noise spectra were separated into mixing noise and shock-associated noise component spectra. Comparisons between predicted and measured shock-associated noise component spectra were used to identify de ciencies in the prediction model. Proposed revisions to the model, based on a study of the overall sound pressure levels for the shock-associated noise component of the mea- sured data, a sensitivity analysis of the model parameters with emphasis on the de nition of the convection velocity parameter, and a least-squares t of the predicted to the mea- sured shock-associated noise component spectra, resulted in a new de nition for the source strength spectrum in the model. An error analysis showed that the average error in the predicted spectra was reduced by as much as 3.5 dB for the revised model relative to the average error for the original model.

[Quantitative analysis of nucleotide mixtures with terahertz time domain spectroscopy].

PubMed

Zhang, Zeng-yan; Xiao, Ti-qiao; Zhao, Hong-wei; Yu, Xiao-han; Xi, Zai-jun; Xu, Hong-jie

2008-09-01

Adenosine, thymidine, guanosine, cytidine and uridine form the building blocks of ribose nucleic acid (RNA) and deoxyribose nucleic acid (DNA). Nucleosides and their derivants are all have biological activities. Some of them can be used as medicine directly or as materials to synthesize other medicines. It is meaningful to detect the component and content in nucleosides mixtures. In the present paper, components and contents of the mixtures of adenosine, thymidine, guanosine, cytidine and uridine were analyzed. THz absorption spectra of pure nucleosides were set as standard spectra. The mixture's absorption spectra were analyzed by linear regression with non-negative constraint to identify the components and their relative content in the mixtures. The experimental and analyzing results show that it is simple and effective to get the components and their relative percentage in the mixtures by terahertz time domain spectroscopy with a relative error less than 10%. Component which is absent could be excluded exactly by this method, and the error sources were also analyzed. All the experiments and analysis confirms that this method is of no damage or contamination to the sample. This means that it will be a simple, effective and new method in biochemical materials analysis, which extends the application field of THz-TDS.

Modelling Creativity: Identifying Key Components through a Corpus-Based Approach.

PubMed

Jordanous, Anna; Keller, Bill

2016-01-01

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.

Quality assessment of raw and processed Arctium lappa L. through multicomponent quantification, chromatographic fingerprint, and related chemometric analysis.

PubMed

Qin, Kunming; Wang, Bin; Li, Weidong; Cai, Hao; Chen, Danni; Liu, Xiao; Yin, Fangzhou; Cai, Baochang

2015-05-01

In traditional Chinese medicine, raw and processed herbs are used to treat different diseases. Suitable quality assessment methods are crucial for the discrimination between raw and processed herbs. The dried fruit of Arctium lappa L. and their processed products are widely used in traditional Chinese medicine, yet their therapeutic effects are different. In this study, a novel strategy using high-performance liquid chromatography and diode array detection coupled with multivariate statistical analysis to rapidly explore raw and processed Arctium lappa L. was proposed and validated. Four main components in a total of 30 batches of raw and processed Fructus Arctii samples were analyzed, and ten characteristic peaks were identified in the fingerprint common pattern. Furthermore, similarity evaluation, principal component analysis, and hierachical cluster analysis were applied to demonstrate the distinction. The results suggested that the relative amounts of the chemical components of raw and processed Fructus Arctii samples are different. This new method has been successfully applied to detect the raw and processed Fructus Arctii in marketed herbal medicinal products. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Principal component analysis of normalized full spectrum mass spectrometry data in multiMS-toolbox: An effective tool to identify important factors for classification of different metabolic patterns and bacterial strains.

PubMed

Cejnar, Pavel; Kuckova, Stepanka; Prochazka, Ales; Karamonova, Ludmila; Svobodova, Barbora

2018-06-15

Explorative statistical analysis of mass spectrometry data is still a time-consuming step. We analyzed critical factors for application of principal component analysis (PCA) in mass spectrometry and focused on two whole spectrum based normalization techniques and their application in the analysis of registered peak data and, in comparison, in full spectrum data analysis. We used this technique to identify different metabolic patterns in the bacterial culture of Cronobacter sakazakii, an important foodborne pathogen. Two software utilities, the ms-alone, a python-based utility for mass spectrometry data preprocessing and peak extraction, and the multiMS-toolbox, an R software tool for advanced peak registration and detailed explorative statistical analysis, were implemented. The bacterial culture of Cronobacter sakazakii was cultivated on Enterobacter sakazakii Isolation Agar, Blood Agar Base and Tryptone Soya Agar for 24 h and 48 h and applied by the smear method on an Autoflex speed MALDI-TOF mass spectrometer. For three tested cultivation media only two different metabolic patterns of Cronobacter sakazakii were identified using PCA applied on data normalized by two different normalization techniques. Results from matched peak data and subsequent detailed full spectrum analysis identified only two different metabolic patterns - a cultivation on Enterobacter sakazakii Isolation Agar showed significant differences to the cultivation on the other two tested media. The metabolic patterns for all tested cultivation media also proved the dependence on cultivation time. Both whole spectrum based normalization techniques together with the full spectrum PCA allow identification of important discriminative factors in experiments with several variable condition factors avoiding any problems with improper identification of peaks or emphasis on bellow threshold peak data. The amounts of processed data remain still manageable. Both implemented software utilities are available free of charge from http://uprt.vscht.cz/ms. Copyright © 2018 John Wiley & Sons, Ltd.

Predicting Essential Components of Signal Transduction Networks: A Dynamic Model of Guard Cell Abscisic Acid Signaling

PubMed Central

Li, Song; Assmann, Sarah M; Albert, Réka

2006-01-01

Plants both lose water and take in carbon dioxide through microscopic stomatal pores, each of which is regulated by a surrounding pair of guard cells. During drought, the plant hormone abscisic acid (ABA) inhibits stomatal opening and promotes stomatal closure, thereby promoting water conservation. Dozens of cellular components have been identified to function in ABA regulation of guard cell volume and thus of stomatal aperture, but a dynamic description is still not available for this complex process. Here we synthesize experimental results into a consistent guard cell signal transduction network for ABA-induced stomatal closure, and develop a dynamic model of this process. Our model captures the regulation of more than 40 identified network components, and accords well with previous experimental results at both the pathway and whole-cell physiological level. By simulating gene disruptions and pharmacological interventions we find that the network is robust against a significant fraction of possible perturbations. Our analysis reveals the novel predictions that the disruption of membrane depolarizability, anion efflux, actin cytoskeleton reorganization, cytosolic pH increase, the phosphatidic acid pathway, or K+ efflux through slowly activating K+ channels at the plasma membrane lead to the strongest reduction in ABA responsiveness. Initial experimental analysis assessing ABA-induced stomatal closure in the presence of cytosolic pH clamp imposed by the weak acid butyrate is consistent with model prediction. Simulations of stomatal response as derived from our model provide an efficient tool for the identification of candidate manipulations that have the best chance of conferring increased drought stress tolerance and for the prioritization of future wet bench analyses. Our method can be readily applied to other biological signaling networks to identify key regulatory components in systems where quantitative information is limited. PMID:16968132

Advanced Treatment Monitoring for Olympic-Level Athletes Using Unsupervised Modeling Techniques

PubMed Central

Siedlik, Jacob A.; Bergeron, Charles; Cooper, Michael; Emmons, Russell; Moreau, William; Nabhan, Dustin; Gallagher, Philip; Vardiman, John P.

2016-01-01

Context Analysis of injury and illness data collected at large international competitions provides the US Olympic Committee and the national governing bodies for each sport with information to best prepare for future competitions. Research in which authors have evaluated medical contacts to provide the expected level of medical care and sports medicine services at international competitions is limited. Objective To analyze the medical-contact data for athletes, staff, and coaches who participated in the 2011 Pan American Games in Guadalajara, Mexico, using unsupervised modeling techniques to identify underlying treatment patterns. Design Descriptive epidemiology study. Setting Pan American Games. Patients or Other Participants A total of 618 US athletes (337 males, 281 females) participated in the 2011 Pan American Games. Main Outcome Measure(s) Medical data were recorded from the injury-evaluation and injury-treatment forms used by clinicians assigned to the central US Olympic Committee Sport Medicine Clinic and satellite locations during the operational 17-day period of the 2011 Pan American Games. We used principal components analysis and agglomerative clustering algorithms to identify and define grouped modalities. Lift statistics were calculated for within-cluster subgroups. Results Principal component analyses identified 3 components, accounting for 72.3% of the variability in datasets. Plots of the principal components showed that individual contacts focused on 4 treatment clusters: massage, paired manipulation and mobilization, soft tissue therapy, and general medical. Conclusions Unsupervised modeling techniques were useful for visualizing complex treatment data and provided insights for improved treatment modeling in athletes. Given its ability to detect clinically relevant treatment pairings in large datasets, unsupervised modeling should be considered a feasible option for future analyses of medical-contact data from international competitions. PMID:26794628

PERCHLORATE IN FERTILIZERS?: ANALYSIS BY RAMAN SPECTROSCOPY

EPA Science Inventory

Recently, we and others found perchlorate at high levels (approximately 500 - 8000 mg/kg) in 90+% of 25+ fertilizers (primarily lawn-and-garden products) that are not identified as containing components derived from mined Chile saltpeter, which is known to contain perchlorate as ...

Group Process: A Systematic Analysis.

ERIC Educational Resources Information Center

Roark, Albert E.; Radl, Myrna C.

1984-01-01

Identifies components of group process and describes leader functions. Discusses personal elements, focus of interaction/psychological distance, group development, content, quality of interaction, and self-reflective/meaning attribution, illustrated by a case study of a group of persons (N=5) arrested for drunk driving. (JAC)

"Dateline NBC"'s Persuasive Attack on Wal-Mart.

ERIC Educational Resources Information Center

Benoit, William L.; Dorries, Bruce

1996-01-01

Develops a typology of persuasive attack strategies. Identifies two key components of persuasive attack: responsibility and offensiveness. Describes several strategies for intensifying each of these elements. Applies this analysis to "Dateline NBC"'s allegations that Wal-Mart's "Buy American" campaign was deceptive. Concludes…

NHEXAS PHASE I ARIZONA STUDY--LIST OF STANDARD OPERATING PROCEDURES

EPA Science Inventory

This document lists available protocols and SOPs for the NHEXAS Phase I Arizona study. It identifies protocols and SOPs for the following study components: (1) Sample collection and field operations, (2) Sample analysis, (3) General laboratory procedures, (4) Quality Assurance, (...

Analysis of Peanut Seed Oil by NIR

USDA-ARS?s Scientific Manuscript database

Near infrared reflectance spectra (NIRS) were collected from Arachis hypogaea seed samples and used in predictive models to rapidly identify varieties with high oleic acid. The method was developed for shelled peanut seeds with intact testa. Spectra were evaluated initially by principal component an...

The Application of Acoustic Measurements and Audio Recordings for Diagnosis of In-Flight Hardware Anomalies

NASA Technical Reports Server (NTRS)

Welsh, David; Denham, Samuel; Allen, Christopher

2011-01-01

In many cases, an initial symptom of hardware malfunction is unusual or unexpected acoustic noise. Many industries such as automotive, heating and air conditioning, and petro-chemical processing use noise and vibration data along with rotating machinery analysis techniques to identify noise sources and correct hardware defects. The NASA/Johnson Space Center Acoustics Office monitors the acoustic environment of the International Space Station (ISS) through periodic sound level measurement surveys. Trending of the sound level measurement survey results can identify in-flight hardware anomalies. The crew of the ISS also serves as a "detection tool" in identifying unusual hardware noises; in these cases the spectral analysis of audio recordings made on orbit can be used to identify hardware defects that are related to rotating components such as fans, pumps, and compressors. In this paper, three examples of the use of sound level measurements and audio recordings for the diagnosis of in-flight hardware anomalies are discussed: identification of blocked inter-module ventilation (IMV) ducts, diagnosis of abnormal ISS Crew Quarters rack exhaust fan noise, and the identification and replacement of a defective flywheel assembly in the Treadmill with Vibration Isolation (TVIS) hardware. In each of these examples, crew time was saved by identifying the off nominal component or condition that existed and in directing in-flight maintenance activities to address and correct each of these problems.

How to select equivalent and complimentary reversed phase liquid chromatography columns from column characterization databases.

PubMed

Borges, Endler M

2014-01-07

Three RP-LC column characterization protocols [Tanaka et al. (1989), Snyder et al. (PQRI, 2002), and NIST SRM 870 (2000)] were evaluated using both Euclidian distance and Principal Components Analysis to evaluate effectiveness at identifying equivalent columns. These databases utilize specific chromatographic properties such as hydrophobicity, hydrogen bonding, shape/steric selectivity, and ion exchange capacity of stationary phases. The chromatographic parameters of each test were shown to be uncorrelated. Despite this, the three protocols were equally successful in identifying similar and/or dissimilar stationary phases. The veracity of the results has been supported by some real life pharmaceutical separations. The use of Principal Component Analysis to identify similar/dissimilar phases appears to have some limitations in terms of loss of information. In contrast, the use of Euclidian distances is a much more convenient and reliable approach. The use of auto scaled data is favoured over the use of weighted factors as the former data transformation is less affected by the addition or removal of columns from the database. The use of these free databases and their corresponding software tools shown to be valid for identifying similar columns with equivalent chromatographic selectivity and retention as a "backup column". In addition, dissimilar columns with complimentary chromatographic selectivity can be identified for method development screening strategies. Copyright © 2013 Elsevier B.V. All rights reserved.

[An ultra-high-pressure liquid chromatography/linear ion trap-Orbitrap mass spectrometry method coupled with a diagnostic fragment ions-searching-based strategy for rapid identification and characterization of chemical components in Polygonum cuspidatum].

PubMed

Pan, Zhiran; Liang, Hailong; Liang, Chabhufi; Xu, Wen

2015-01-01

A method for qualitative analysis of constituents in Polygonum cuspidatum by ultra-high-pressure liquid chromatography coupled with linear ion trap-Orbitrap mass spectrometry (UHPLC-LTQ-Orbitrap MS) has been established. The methanol extract of Polygonum cuspidatumrn was separated on a Waters UPLC C18 column using acetonitrile-water (containing formic acid) eluting system and detected by LTQ-Orbitrap hybrid mass spectrometer in negative mode. The targeted components were further fragmented in LTQ and high accuracy data were acquired by Orbitrap MS. The summarized fragmentation pathways of typical reference components and a diagnostic fragment ions-searching-based strategy were used for detection and identification of the main phenolic components in Polygonum cuspidatum. Other clues such as nitrogen rule, even electron rule, degree of unsaturation rule and isotopic peak data were included for the structural elucidation as well. The whole analytical procedure was within 10 min and more than 30 components were identified or tentatively identified. This method is helpful for further phytochemical research and quality control on Polygonum cuspidatum and related preparations.

Toward understanding the role of individual fluorescent components in DOM-metal binding.

PubMed

Wu, Jun; Zhang, Hua; Yao, Qi-Sheng; Shao, Li-Ming; He, Pin-Jing

2012-05-15

Knowledge on the function of individual fractions in dissolved organic matter (DOM) is essential for understanding the impact of DOM on metal speciation and migration. Herein, fluorescence excitation-emission matrix quenching and parallel factor (PARAFAC) analysis were adopted for bulk DOM and chemically isolated fractions from landfill leachate, i.e., humic acids (HA), fulvic acids and hydrophilic (HyI) fraction, to elucidate the role of individual fluorescent components in metal binding (Cu(II) and Cd(II)). Three components were identified by PARAFAC model, including one humic substance (HS)-like, one protein-like and one component highly correlated with the HyI fraction. Among them, the HS-like and protein-like components were responsible for Cu(II) binding, while the protein-like component was the only fraction involved in Cd(II) complexation. It was further identified that the slight quenching effect of HA fraction by Cd(II) was induced by the presence of proteinaceous materials in HA. Fluorescent substances in the HyI fraction of landfill leachate did not play as important a role as HS did. Therefore, it was suggested that the potential risk of aged leachate (more humified) as a carrier of heavy metal should not be overlooked. Copyright © 2012 Elsevier B.V. All rights reserved.

Simultaneous characterization of pancreatic stellate cells and other pancreatic components within three-dimensional tissue environment during chronic pancreatitis

NASA Astrophysics Data System (ADS)

Hu, Wenyan; Fu, Ling

2013-05-01

Pancreatic stellate cells (PSCs) and other pancreatic components that play a critical role in exocrine pancreatic diseases are generally identified separately by conventional studies, which provide indirect links between these components. Here, nonlinear optical microscopy was evaluated for simultaneous characterization of these components within a three-dimensional (3-D) tissue environment, primarily based on multichannel detection of intrinsic optical emissions and cell morphology. Fresh rat pancreatic tissues harvested at 1 day, 7 days, and 28 days after induction of chronic pancreatitis were imaged, respectively. PSCs, inflammatory cells, blood vessels, and collagen fibers were identified simultaneously. The PSCs at day 1 of chronic pancreatitis showed significant enlargement compared with those in normal pancreas (p<0.001, analysis of variance linear contrast; n=8 for each group). Pathological events relating to these components were observed, including presence of inflammatory cells, deposited collagen, and phenotype conversion of PSCs. We demonstrate that label-free nonlinear optical microscopy is an efficient tool for dissecting PSCs and other pancreatic components coincidently within 3-D pancreatic tissues. It is a prospect for intravital observation of dynamic events under natural physiological conditions, and might help uncover the key mechanisms of exocrine pancreatic diseases, leading to more effective treatments.

Multifamily Building Operator Job/Task Analysis and Report: September 2013

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

Owens, C. M.

The development of job/task analyses (JTAs) is one of three components of the Guidelines for Home Energy Professionals project and will allow industry to develop training resources, quality assurance protocols, accredited training programs, and professional certifications. The Multifamily Building Operator JTA identifies and catalogs all of the tasks performed by multifamily building operators, as well as the knowledge, skills, and abilities (KSAs) needed to perform the identified tasks.

Multifamily Energy Auditor Job/Task Analysis and Report: September 2013

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

Owens, C. M.

The development of job/task analyses (JTAs) is one of three components of the Guidelines for Home Energy Professionals project and will allow industry to develop training resources, quality assurance protocols, accredited training programs, and professional certifications. The Multifamily Energy Auditor JTA identifies and catalogs all of the tasks performed by multifamily energy auditors, as well as the knowledge, skills, and abilities (KSAs) needed to perform the identified tasks.

A Novel Framework for Identifying the Interactions between Biophysical and Social Components of an Agricultural System: A Guide for Improving Wheat Production in Haryana, NW India

ERIC Educational Resources Information Center

Coventry, D. R.; Poswal, R. S.; Yadav, Ashok; Zhou, Yi; Riar, Amritbir; Kumar, Anuj; Sharma, R. K.; Chhokar, R. S.; Gupta, R. K.; Mehta, A. K.; Chand, Ramesh; Denton, M. D.; Cummins, J. A.

2018-01-01

Purpose: The purpose of this study is to develop a conceptual framework with related analysis methodologies that identifies the influence of social environment on an established cropping system. Design/Methodology/Approach: A stratified survey including 103 villages and 823 farmers was conducted in all districts of Haryana (India). Firstly,…

A Leadership and Managerial Competency Framework for Public Hospital Managers in Vietnam

PubMed Central

Van Tuong, Phan; Duc Thanh, Nguyen

2017-01-01

Objective The aim of this paper was to develop a leadership and managerial competency framework for public hospital managers in Vietnam. Methods This mixed-method study used a four-step approach. The first step was a position description content analysis to identify the tasks hospital managers are required to carry out. The resulting data were used to identify the leadership and managerial competency factors and items in the second step. In the third step, a workshop was organized to reach consensus about the validity of these competency factors and items. Finally, a quantitative survey was conducted across a sample of 891 hospital managers who are working in the selected hospitals in seven geographical regions in Vietnam to validate the competency scales using exploratory factor analysis (EFA) and Cronbach's alpha. Results The study identified a number of tasks required for public hospital managers and confirmed the competencies for implementing these tasks effectively. Four dimensions with 14 components and 81 items of leadership and managerial competencies were identified. These components exhibited 83.8% of variance and Cronbach's alpha were at good level of 0.9. Conclusions These competencies are required for public hospital managers which provide guidance to the further development of the competency-based training for the current management taskforce and preparing future hospital managers. PMID:29546227

Identifying the arterial input function from dynamic contrast-enhanced magnetic resonance images using an apex-seeking technique

NASA Astrophysics Data System (ADS)

Martel, Anne L.

2004-04-01

In order to extract quantitative information from dynamic contrast-enhanced MR images (DCE-MRI) it is usually necessary to identify an arterial input function. This is not a trivial problem if there are no major vessels present in the field of view. Most existing techniques rely on operator intervention or use various curve parameters to identify suitable pixels but these are often specific to the anatomical region or the acquisition method used. They also require the signal from several pixels to be averaged in order to improve the signal to noise ratio, however this introduces errors due to partial volume effects. We have described previously how factor analysis can be used to automatically separate arterial and venous components from DCE-MRI studies of the brain but although that method works well for single slice images through the brain when the blood brain barrier technique is intact, it runs into problems for multi-slice images with more complex dynamics. This paper will describe a factor analysis method that is more robust in such situations and is relatively insensitive to the number of physiological components present in the data set. The technique is very similar to that used to identify spectral end-members from multispectral remote sensing images.

A Leadership and Managerial Competency Framework for Public Hospital Managers in Vietnam.

PubMed

Van Tuong, Phan; Duc Thanh, Nguyen

2017-01-01

The aim of this paper was to develop a leadership and managerial competency framework for public hospital managers in Vietnam. This mixed-method study used a four-step approach. The first step was a position description content analysis to identify the tasks hospital managers are required to carry out. The resulting data were used to identify the leadership and managerial competency factors and items in the second step. In the third step, a workshop was organized to reach consensus about the validity of these competency factors and items. Finally, a quantitative survey was conducted across a sample of 891 hospital managers who are working in the selected hospitals in seven geographical regions in Vietnam to validate the competency scales using exploratory factor analysis (EFA) and Cronbach's alpha. The study identified a number of tasks required for public hospital managers and confirmed the competencies for implementing these tasks effectively. Four dimensions with 14 components and 81 items of leadership and managerial competencies were identified. These components exhibited 83.8% of variance and Cronbach's alpha were at good level of 0.9. These competencies are required for public hospital managers which provide guidance to the further development of the competency-based training for the current management taskforce and preparing future hospital managers.

Systems Engineering Provides Successful High Temperature Steam Electrolysis Project

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

Charles V. Park; Emmanuel Ohene Opare, Jr.

2011-06-01

This paper describes two Systems Engineering Studies completed at the Idaho National Laboratory (INL) to support development of the High Temperature Stream Electrolysis (HTSE) process. HTSE produces hydrogen from water using nuclear power and was selected by the Department of Energy (DOE) for integration with the Next Generation Nuclear Plant (NGNP). The first study was a reliability, availability and maintainability (RAM) analysis to identify critical areas for technology development based on available information regarding expected component performance. An HTSE process baseline flowsheet at commercial scale was used as a basis. The NGNP project also established a process and capability tomore » perform future RAM analyses. The analysis identified which components had the greatest impact on HTSE process availability and indicated that the HTSE process could achieve over 90% availability. The second study developed a series of life-cycle cost estimates for the various scale-ups required to demonstrate the HTSE process. Both studies were useful in identifying near- and long-term efforts necessary for successful HTSE process deployment. The size of demonstrations to support scale-up was refined, which is essential to estimate near- and long-term cost and schedule. The life-cycle funding profile, with high-level allocations, was identified as the program transitions from experiment scale R&D to engineering scale demonstration.« less

A precipitation regionalization and regime for Iran based on multivariate analysis

NASA Astrophysics Data System (ADS)

Raziei, Tayeb

2018-02-01

Monthly precipitation time series of 155 synoptic stations distributed over Iran, covering 1990-2014 time period, were used to identify areas with different precipitation time variability and regimes utilizing S-mode principal component analysis (PCA) and cluster analysis (CA) preceded by T-mode PCA, respectively. Taking into account the maximum loading values of the rotated components, the first approach revealed five sub-regions characterized by different precipitation time variability, while the second method delineated eight sub-regions featured with different precipitation regimes. The sub-regions identified by the two used methods, although partly overlapping, are different considering their areal extent and complement each other as they are useful for different purposes and applications. Northwestern Iran and the Caspian Sea area were found as the two most distinctive Iranian precipitation sub-regions considering both time variability and precipitation regime since they were well captured with relatively identical areas by the two used approaches. However, the areal extents of the other three sub-regions identified by the first approach were not coincident with the coverage of their counterpart sub-regions defined by the second approach. Results suggest that the precipitation sub-region identified by the two methods would not be necessarily the same, as the first method which accounts for the variance of the data grouped stations with similar temporal variability while the second one which considers a fixed climatology defined by the average over the period 1990-2014 clusters stations having a similar march of monthly precipitation.

Proteomic and computational analysis of bronchoalveolar proteins during the course of the acute respiratory distress syndrome.

PubMed

Chang, Dong W; Hayashi, Shinichi; Gharib, Sina A; Vaisar, Tomas; King, S Trevor; Tsuchiya, Mitsuhiro; Ruzinski, John T; Park, David R; Matute-Bello, Gustavo; Wurfel, Mark M; Bumgarner, Roger; Heinecke, Jay W; Martin, Thomas R

2008-10-01

Acute lung injury causes complex changes in protein expression in the lungs. Whereas most prior studies focused on single proteins, newer methods allowing the simultaneous study of many proteins could lead to a better understanding of pathogenesis and new targets for treatment. The purpose of this study was to examine the changes in protein expression in the bronchoalveolar lavage fluid (BALF) of patients during the course of the acute respiratory distress syndrome (ARDS). Using two-dimensional difference gel electrophoresis (DIGE), the expression of proteins in the BALF from patients on Days 1 (n = 7), 3 (n = 8), and 7 (n = 5) of ARDS were compared with findings in normal volunteers (n = 9). The patterns of protein expression were analyzed using principal component analysis (PCA). Biological processes that were enriched in the BALF proteins of patients with ARDS were identified using Gene Ontology (GO) analysis. Protein networks that model the protein interactions in the BALF were generated using Ingenuity Pathway Analysis. An average of 991 protein spots were detected using DIGE. Of these, 80 protein spots, representing 37 unique proteins in all of the fluids, were identified using mass spectrometry. PCA confirmed important differences between the proteins in the ARDS and normal samples. GO analysis showed that these differences are due to the enrichment of proteins involved in inflammation, infection, and injury. The protein network analysis showed that the protein interactions in ARDS are complex and redundant, and revealed unexpected central components in the protein networks. Proteomics and protein network analysis reveals the complex nature of lung protein interactions in ARDS. The results provide new insights about protein networks in injured lungs, and identify novel mediators that are likely to be involved in the pathogenesis and progression of acute lung injury.

The Coplane Analysis Technique for Three-Dimensional Wind Retrieval Using the HIWRAP Airborne Doppler Radar

NASA Technical Reports Server (NTRS)

Didlake, Anthony C., Jr.; Heymsfield, Gerald M.; Tian, Lin; Guimond, Stephen R.

2015-01-01

The coplane analysis technique for mapping the three-dimensional wind field of precipitating systems is applied to the NASA High Altitude Wind and Rain Airborne Profiler (HIWRAP). HIWRAP is a dual-frequency Doppler radar system with two downward pointing and conically scanning beams. The coplane technique interpolates radar measurements to a natural coordinate frame, directly solves for two wind components, and integrates the mass continuity equation to retrieve the unobserved third wind component. This technique is tested using a model simulation of a hurricane and compared to a global optimization retrieval. The coplane method produced lower errors for the cross-track and vertical wind components, while the global optimization method produced lower errors for the along-track wind component. Cross-track and vertical wind errors were dependent upon the accuracy of the estimated boundary condition winds near the surface and at nadir, which were derived by making certain assumptions about the vertical velocity field. The coplane technique was then applied successfully to HIWRAP observations of Hurricane Ingrid (2013). Unlike the global optimization method, the coplane analysis allows for a transparent connection between the radar observations and specific analysis results. With this ability, small-scale features can be analyzed more adequately and erroneous radar measurements can be identified more easily.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

PubMed Central

Wagatsuma, Hiroaki

2017-01-01

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

Investigation of reliability indicators of information analysis systems based on Markov’s absorbing chain model

NASA Astrophysics Data System (ADS)

Gilmanshin, I. R.; Kirpichnikov, A. P.

2017-09-01

In the result of study of the algorithm of the functioning of the early detection module of excessive losses, it is proven the ability to model it by using absorbing Markov chains. The particular interest is in the study of probability characteristics of early detection module functioning algorithm of losses in order to identify the relationship of indicators of reliability of individual elements, or the probability of occurrence of certain events and the likelihood of transmission of reliable information. The identified relations during the analysis allow to set thresholds reliability characteristics of the system components.

Identifying potential selective fluorescent probes for cancer-associated protein carbonic anhydrase IX using a computational approach.

PubMed

Kamstra, Rhiannon L; Floriano, Wely B

2014-11-01

Carbonic anhydrase IX (CAIX) is a biomarker for tumor hypoxia. Fluorescent inhibitors of CAIX have been used to study hypoxic tumor cell lines. However, these inhibitor-based fluorescent probes may have a therapeutic effect that is not appropriate for monitoring treatment efficacy. In the search for novel fluorescent probes that are not based on known inhibitors, a database of 20,860 fluorescent compounds was virtually screened against CAIX using hierarchical virtual ligand screening (HierVLS). The screening database contained 14,862 compounds tagged with the ATTO680 fluorophore plus an additional 5998 intrinsically fluorescent compounds. Overall ranking of compounds to identify hit molecular probe candidates utilized a principal component analysis (PCA) approach. Four potential binding sites, including the catalytic site, were identified within the structure of the protein and targeted for virtual screening. Available sequence information for 23 carbonic anhydrase isoforms was used to prioritize the four sites based on the estimated "uniqueness" of each site in CAIX relative to the other isoforms. A database of 32 known inhibitors and 478 decoy compounds was used to validate the methodology. A receiver-operating characteristic (ROC) analysis using the first principal component (PC1) as predictive score for the validation database yielded an area under the curve (AUC) of 0.92. AUC is interpreted as the probability that a binder will have a better score than a non-binder. The use of first component analysis of binding energies for multiple sites is a novel approach for hit selection. The very high prediction power for this approach increases confidence in the outcome from the fluorescent library screening. Ten of the top scoring candidates for isoform-selective putative binding sites are suggested for future testing as fluorescent molecular probe candidates. Copyright © 2014 Elsevier Inc. All rights reserved.

BA3b and BA1 activate in a serial fashion after median nerve stimulation: direct evidence from combining source analysis of evoked fields and cytoarchitectonic probabilistic maps.

PubMed

Papadelis, Christos; Eickhoff, Simon B; Zilles, Karl; Ioannides, Andreas A

2011-01-01

This study combines source analysis imaging data for early somatosensory processing and the probabilistic cytoarchitectonic maps (PCMs). Human somatosensory evoked fields (SEFs) were recorded by stimulating left and right median nerves. Filtering the recorded responses in different frequency ranges identified the most responsive frequency band. The short-latency averaged SEFs were analyzed using a single equivalent current dipole (ECD) model and magnetic field tomography (MFT). The identified foci of activity were superimposed with PCMs. Two major components of opposite polarity were prominent around 21 and 31 ms. A weak component around 25 ms was also identified. For the most responsive frequency band (50-150 Hz) ECD and MFT revealed one focal source at the contralateral Brodmann area 3b (BA3b) at the peak of N20. The component ~25 ms was localised in Brodmann area 1 (BA1) in 50-150 Hz. By using ECD, focal generators around 28-30 ms located initially in BA3b and 2 ms later to BA1. MFT also revealed two focal sources - one in BA3b and one in BA1 for these latencies. Our results provide direct evidence that the earliest cortical response after median nerve stimulation is generated within the contralateral BA3b. BA1 activation few milliseconds later indicates a serial mode of somatosensory processing within cytoarchitectonic SI subdivisions. Analysis of non-invasive magnetoencephalography (MEG) data and the use of PCMs allow unambiguous and quantitative (probabilistic) interpretation of cytoarchitectonic identity of activated areas following median nerve stimulation, even with the simple ECD model, but only when the model fits the data extremely well. Copyright © 2010 Elsevier Inc. All rights reserved.

Intrinsic functional component analysis via sparse representation on Alzheimer's disease neuroimaging initiative database.

PubMed

Jiang, Xi; Zhang, Xin; Zhu, Dajiang

2014-10-01

Alzheimer's disease (AD) is the most common type of dementia (accounting for 60% to 80%) and is the fifth leading cause of death for those people who are 65 or older. By 2050, one new case of AD in United States is expected to develop every 33 sec. Unfortunately, there is no available effective treatment that can stop or slow the death of neurons that causes AD symptoms. On the other hand, it is widely believed that AD starts before development of the associated symptoms, so its prestages, including mild cognitive impairment (MCI) or even significant memory concern (SMC), have received increasing attention, not only because of their potential as a precursor of AD, but also as a possible predictor of conversion to other neurodegenerative diseases. Although these prestages have been defined clinically, accurate/efficient diagnosis is still challenging. Moreover, brain functional abnormalities behind those alterations and conversions are still unclear. In this article, by developing novel sparse representations of whole-brain resting-state functional magnetic resonance imaging signals and by using the most updated Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we successfully identified multiple functional components simultaneously, and which potentially represent those intrinsic functional networks involved in the resting-state activities. Interestingly, these identified functional components contain all the resting-state networks obtained from traditional independent-component analysis. Moreover, by using the features derived from those functional components, it yields high classification accuracy for both AD (94%) and MCI (92%) versus normal controls. Even for SMC we can still have 92% accuracy.

An evaluation of independent component analyses with an application to resting-state fMRI

PubMed Central

Matteson, David S.; Ruppert, David; Eloyan, Ani; Caffo, Brian S.

2013-01-01

Summary We examine differences between independent component analyses (ICAs) arising from different as-sumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA can be viewed as a generalization of principal component analysis (PCA) that takes into account higher-order cross-correlations. Whereas the PCA solution is unique, there are many ICA methods–whose solutions may differ. Infomax, FastICA, and JADE are commonly applied to fMRI studies, with FastICA being arguably the most popular. Hastie and Tibshirani (2003) demonstrated that ProDenICA outperformed FastICA in simulations with two components. We introduce the application of ProDenICA to simulations with more components and to fMRI data. ProDenICA was more accurate in simulations, and we identified differences between biologically meaningful ICs from ProDenICA versus other methods in the fMRI analysis. ICA methods require nonconvex optimization, yet current practices do not recognize the importance of, nor adequately address sensitivity to, initial values. We found that local optima led to dramatically different estimates in both simulations and group ICA of fMRI, and we provide evidence that the global optimum from ProDenICA is the best estimate. We applied a modification of the Hungarian (Kuhn-Munkres) algorithm to match ICs from multiple estimates, thereby gaining novel insights into how brain networks vary in their sensitivity to initial values and ICA method. PMID:24350655

Mass and Reliability Source (MaRS) Database

NASA Technical Reports Server (NTRS)

Valdenegro, Wladimir

2017-01-01

The Mass and Reliability Source (MaRS) Database consolidates components mass and reliability data for all Oribital Replacement Units (ORU) on the International Space Station (ISS) into a single database. It was created to help engineers develop a parametric model that relates hardware mass and reliability. MaRS supplies relevant failure data at the lowest possible component level while providing support for risk, reliability, and logistics analysis. Random-failure data is usually linked to the ORU assembly. MaRS uses this data to identify and display the lowest possible component failure level. As seen in Figure 1, the failure point is identified to the lowest level: Component 2.1. This is useful for efficient planning of spare supplies, supporting long duration crewed missions, allowing quicker trade studies, and streamlining diagnostic processes. MaRS is composed of information from various databases: MADS (operating hours), VMDB (indentured part lists), and ISS PART (failure data). This information is organized in Microsoft Excel and accessed through a program made in Microsoft Access (Figure 2). The focus of the Fall 2017 internship tour was to identify the components that were the root cause of failure from the given random-failure data, develop a taxonomy for the database, and attach material headings to the component list. Secondary objectives included verifying the integrity of the data in MaRS, eliminating any part discrepancies, and generating documentation for future reference. Due to the nature of the random-failure data, data mining had to be done manually without the assistance of an automated program to ensure positive identification.

Analysis of Moisture Content in Beetroot using Fourier Transform Infrared Spectroscopy and by Principal Component Analysis.

PubMed

Nesakumar, Noel; Baskar, Chanthini; Kesavan, Srinivasan; Rayappan, John Bosco Balaguru; Alwarappan, Subbiah

2018-05-22

The moisture content of beetroot varies during long-term cold storage. In this work, we propose a strategy to identify the moisture content and age of beetroot using principal component analysis coupled Fourier transform infrared spectroscopy (FTIR). Frequent FTIR measurements were recorded directly from the beetroot sample surface over a period of 34 days for analysing its moisture content employing attenuated total reflectance in the spectral ranges of 2614-4000 and 1465-1853 cm -1 with a spectral resolution of 8 cm -1 . In order to estimate the transmittance peak height (T p ) and area under the transmittance curve [Formula: see text] over the spectral ranges of 2614-4000 and 1465-1853 cm -1 , Gaussian curve fitting algorithm was performed on FTIR data. Principal component and nonlinear regression analyses were utilized for FTIR data analysis. Score plot over the ranges of 2614-4000 and 1465-1853 cm -1 allowed beetroot quality discrimination. Beetroot quality predictive models were developed by employing biphasic dose response function. Validation experiment results confirmed that the accuracy of the beetroot quality predictive model reached 97.5%. This research work proves that FTIR spectroscopy in combination with principal component analysis and beetroot quality predictive models could serve as an effective tool for discriminating moisture content in fresh, half and completely spoiled stages of beetroot samples and for providing status alerts.

Matrix and fine-grained rims in the unequilibrated CO3 chondrite, ALHA77307 - Origins and evidence for diverse, primitive nebular dust components

NASA Technical Reports Server (NTRS)

Brearley, Adrian J.

1993-01-01

SEM, TEM, and electron microprobe analysis were used to investigate in detail the mineralogical and chemical characteristics of dark matrix and fine-grained rims in the unequilibrated CO3 chondrite ALHA77307. Data obtained revealed that there was a remarkable diversity of distinct mineralogical components, which can be identified using their chemical and textural characteristics. The matrix and rim components in ALHA77307 formed by disequilibrium condensation process as fine-grained amorphous dust that is represented by the abundant amorphous component in the matrix. Subsequent thermal processing of this condensate material, in a variety of environments in the nebula, caused partial or complete recrystallization of the fine-grained dust.

Rapid analysis of the main components of the total glycosides of Ranunculus japonicus by UPLC/Q-TOF-MS.

PubMed

Rui, Wen; Chen, Hongyuan; Tan, Yuzhi; Zhong, Yanmei; Feng, Yifan

2010-05-01

A rapid method for the analysis of the main components of the total glycosides of Ranunculus japonicus (TGOR) was developed using ultra-performance liquid chromatography with quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS). The separation analysis was performed on a Waters Acquity UPLC system and the accurate mass of molecules and their fragment ions were determined by Q-TOF MS. Twenty compounds, including lactone glycosides, flavonoid glycosides and flavonoid aglycones, were identified and tentatively deduced on the basis of their elemental compositions, MS/MS data and relevant literature. The results demonstrated that lactone glycosides and flavonoids were the main constituents of TGOR. Furthermore, an effective and rapid pattern was established allowing for the comprehensive and systematic characterization of the complex samples.

Chemical imaging of molecular changes in a hydrated single cell by dynamic secondary ion mass spectrometry and super-resolution microscopy

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

Hua, Xin; Szymanski, Craig; Wang, Zhaoying

2016-01-01

Chemical imaging of single cells is important in capturing biological dynamics. Single cell correlative imaging is realized between structured illumination microscopy (SIM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) using System for Analysis at the Liquid Vacuum Interface (SALVI), a multimodal microreactor. SIM characterized cells and guided subsequent ToF-SIMS analysis. Dynamic ToF-SIMS provided time- and space-resolved cell molecular mapping. Lipid fragments were identified in the hydrated cell membrane. Principal component analysis was used to elucidate chemical component differences among mouse lung cells that uptake zinc oxide nanoparticles. Our results provided submicron chemical spatial mapping for investigations of cell dynamics atmore » the molecular level.« less

Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation.

PubMed

Moosmann, Matthias; Eichele, Tom; Nordby, Helge; Hugdahl, Kenneth; Calhoun, Vince D

2008-03-01

An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

SPATIALLY RESOLVED SPECTROSCOPY OF EUROPA: THE DISTINCT SPECTRUM OF LARGE-SCALE CHAOS

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

Fischer, P. D.; Brown, M. E.; Hand, K. P., E-mail: pfischer@caltech.edu

2015-11-15

We present a comprehensive analysis of spatially resolved moderate spectral resolution near-infrared spectra obtained with the adaptive optics system at the Keck Observatory. We identify three compositionally distinct end member regions: the trailing hemisphere bullseye, the leading hemisphere upper latitudes, and a third component associated with leading hemisphere chaos units. We interpret the composition of the three end member regions to be dominated by irradiation products, water ice, and evaporite deposits or salt brines, respectively. The third component is associated with geological features and distinct from the geography of irradiation, suggesting an endogenous identity. Identifying the endogenous composition is ofmore » particular interest for revealing the subsurface composition. However, its spectrum is not consistent with linear mixtures of the salt minerals previously considered relevant to Europa. The spectrum of this component is distinguished by distorted hydration features rather than distinct spectral features, indicating hydrated minerals but making unique identification difficult. In particular, it lacks features common to hydrated sulfate minerals, challenging the traditional view of an endogenous salty component dominated by Mg-sulfates. Chloride evaporite deposits are one possible alternative.« less

Measurement of the Exchange Rate of Waters of Hydration in Elastin by 2D T(2)-T(2) Correlation Nuclear Magnetic Resonance Spectroscopy.

PubMed

Sun, Cheng; Boutis, Gregory S

2011-02-28

We report on the direct measurement of the exchange rate of waters of hydration in elastin by T(2)-T(2) exchange spectroscopy. The exchange rates in bovine nuchal ligament elastin and aortic elastin at temperatures near, below and at the physiological temperature are reported. Using an Inverse Laplace Transform (ILT) algorithm, we are able to identify four components in the relaxation times. While three of the components are in good agreement with previous measurements that used multi-exponential fitting, the ILT algorithm distinguishes a fourth component having relaxation times close to that of free water and is identified as water between fibers. With the aid of scanning electron microscopy, a model is proposed allowing for the application of a two-site exchange analysis between any two components for the determination of exchange rates between reservoirs. The results of the measurements support a model (described elsewhere [1]) wherein the net entropy of bulk waters of hydration should increase upon increasing temperature in the inverse temperature transition.

Chemical Composition and Character Impact Odorants in Volatile Oils from Edible Mushrooms.

PubMed

Usami, Atsushi; Motooka, Ryota; Nakahashi, Hiroshi; Marumoto, Shinsuke; Miyazawa, Mitsuo

2015-11-01

The aim of this study was to investigate the chemical composition and the odor-active components of volatile oils from three edible mushrooms, Pleurotus ostreatus, Pleurotus eryngii, and Pleurotus abalonus, which are well-known edible mushrooms. The volatile components in these oils were extracted by hydrodistillation and identified by GC/MS, GC-olfactometry (GC-O), and aroma extract dilution analysis (AEDA). The oils contained 40, 20, and 53 components, representing 83.4, 86.0, and 90.8% of the total oils in P. ostreatus, P. eryngii, and P. abalonus, respectively. Odor evaluation of the volatile oils from the three edible mushrooms was also carried out using GC-O, AEDA, and odor activity values, by which 13, eight, and ten aroma-active components were identified in P. ostreatus, P. eryngii, and P. abalonus, respectively. The most aroma-active compounds were C8 -aliphatic compounds (oct-1-en-3-ol, octan-3-one, and octanal) and/or C9 -aliphatic aldehydes (nonanal and (2E)-non-2-enal). Copyright © 2015 Verlag Helvetica Chimica Acta AG, Zürich.

Reliability of the Ego-Grasping Scale.

PubMed

Lester, David

2012-04-01

Research using Knoblauch and Falconer's Ego-Grasping Scale is reviewed. Using a sample of 695 undergraduate students, the scale had moderate reliability (Cronbach alpha, odd-even numbered items, and test-retest), but a principal-components analysis with a varimax rotation identified five components, indicating heterogeneity in the content of the items. Lower Ego-Grasping scores appear to be associated with better psychological health. The scale has been translated and used with Korean, Kuwaiti, and Turkish students, indicating that the scale can be useful in cross-cultural studies.

X-ray and optical observations of 2 new cataclysmic variables

NASA Technical Reports Server (NTRS)

Singh, K. P.; Szkody, P.; Barrett, P.; Schlegel, E.; White, N. E.; Silber, A.; Fierce, E.; Hoard, D.; Hakala, P. J.; Piirola, V.;

Analysis of volatile components from Melipona beecheii geopropolis from Southeast Mexico by headspace solid-phase microextraction.

PubMed

Torres-González, Ahira; López-Rivera, Paulina; Duarte-Lisci, Georgina; López-Ramírez, Ángel; Correa-Benítez, Adriana; Rivero-Cruz, J Fausto

2016-01-01

A head space solid-phase microextraction method combined with gas chromatography-mass spectrometry was developed and optimised to extract and analyse volatile compounds of Melipona beecheii geopropolis. Seventy-three constituents were identified using this technique in the sample of geopropolis collected. The main compounds detected include β-fenchene (14.53-15.45%), styrene (8.72-9.98%), benzaldehyde (7.44-7.82%) and the most relevant volatile components presents at high level in the geopropolis were terpenoids (58.17%).

Nontargeted diagnostic ion network analysis (NINA): A software to streamline the analytical workflow for untargeted characterization of natural medicines.

PubMed

Ye, Hui; Zhu, Lin; Sun, Di; Luo, Xiaozhuo; Lu, Gaoyuan; Wang, Hong; Wang, Jing; Cao, Guoxiu; Xiao, Wei; Wang, Zhenzhong; Wang, Guangji; Hao, Haiping

2016-11-30

The characterization of herbal prescriptions serves as a foundation for quality control and regulation of herbal medicines. Previously, the characterization of herbal chemicals from natural medicines often relied on the analysis of signature fragment ions from the acquired tandem mass spectrometry (MS/MS) spectra with prior knowledge of the herbal species present in the herbal prescriptions of interest. Nevertheless, such an approach is often limited to target components, and it risks missing the critical components that we have no prior knowledge of. We previously reported a "diagnostic ion-guided network bridging" strategy. It is a generally applicable and robust approach to analyze unknown substances from complex mixtures in an untargeted manner. In this study, we have developed a standalone software named "Nontargeted Diagnostic Ion Network Analysis (NINA)" with a graphical user interface based on a strategy for post-acquisition data analysis. NINA allows one to rapidly determine the nontargeted diagnostic ions (NIs) by summarizing all of the fragment ions shared by the precursors from the acquired MS/MS spectra. A NI-guided network using bridging components that possess two or more NIs can then be established via NINA. With such a network, we could sequentially identify the structures of all the NIs once a single compound has been identified de novo. The structures of NIs can then be used as "priori" knowledge to narrow the candidates containing the sub-structure of the corresponding NI from the database hits. Subsequently, we applied the NINA software to the characterization of a model herbal prescription, Re-Du-Ning injection, and rapidly identified 56 herbal chemicals from the prescription using an ultra-performance liquid chromatography quadrupole time-of-flight system in the negative mode with no knowledge of the herbal species or herbal chemicals in the mixture. Therefore, we believe the applications of NINA will greatly facilitate the characterization of complex mixtures, such as natural medicines, especially when no advance information is available. In addition to herbal medicines, the NINA-based workflow will also benefit many other fields, such as environmental analysis, nutritional science, and forensic analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

Atmospheric polycyclic aromatic hydrocarbons in the urban environment: Occurrence, toxicity and source apportionment.

PubMed

Mishra, Nitika; Ayoko, Godwin A; Morawska, Lidia

2016-01-01

Polycyclic Aromatic Hydrocarbons (PAHs) represent a major class of toxic pollutants because of their carcinogenic and mutagenic characteristics. People living in urban areas are regularly exposed to PAHs because of abundance of their emission sources. Within this context, this study aimed to: (i) identify and quantify the levels of ambient PAHs in an urban environment; (ii) evaluate their toxicity; and (iii) identify their sources as well as the contribution of specific sources to measured concentrations. Sixteen PAHs were identified and quantified in air samples collected from Brisbane. Principal Component Analysis - Absolute Principal Component Scores (PCA-APCS) was used in order to conduct source apportionment of the measured PAHs. Vehicular emissions, natural gas combustion, petrol emissions and evaporative/unburned fuel were the sources identified; contributing 56%, 21%, 15% and 8% of the total PAHs emissions, respectively, all of which need to be considered for any pollution control measures implemented in urban areas. Copyright © 2015 Elsevier Ltd. All rights reserved.

Component-Level Electronic-Assembly Repair (CLEAR) Spacecraft Circuit Diagnostics by Analog and Complex Signature Analysis

NASA Technical Reports Server (NTRS)

Oeftering, Richard C.; Wade, Raymond P.; Izadnegahdar, Alain

2011-01-01

The Component-Level Electronic-Assembly Repair (CLEAR) project at the NASA Glenn Research Center is aimed at developing technologies that will enable space-flight crews to perform in situ component-level repair of electronics on Moon and Mars outposts, where there is no existing infrastructure for logistics spares. These technologies must provide effective repair capabilities yet meet the payload and operational constraints of space facilities. Effective repair depends on a diagnostic capability that is versatile but easy to use by crew members that have limited training in electronics. CLEAR studied two techniques that involve extensive precharacterization of "known good" circuits to produce graphical signatures that provide an easy-to-use comparison method to quickly identify faulty components. Analog Signature Analysis (ASA) allows relatively rapid diagnostics of complex electronics by technicians with limited experience. Because of frequency limits and the growing dependence on broadband technologies, ASA must be augmented with other capabilities. To meet this challenge while preserving ease of use, CLEAR proposed an alternative called Complex Signature Analysis (CSA). Tests of ASA and CSA were used to compare capabilities and to determine if the techniques provided an overlapping or complementary capability. The results showed that the methods are complementary.

Kinematic constraints associated with the acquisition of overarm throwing part II: upper extremity actions.

PubMed

Stodden, David F; Langendorfer, Stephen J; Fleisig, Glenn S; Andrews, James R

2006-12-01

The purposes of this study were to: (a) examine the differences within 11 specific kinematic variables and an outcome measure (ball velocity) associated with component developmental levels of humerus and forearm action (Roberton & Halverson, 1984), and (b) if the differences in kinematic variables were significantly associated with the differences in component levels, determine potential kinematic constraints associated with skilled throwing acquisition. Significant differences among component levels in five of six humerus kinematic variables (p <.01) and all five forearm kinematic variables (p < .01) were identified using multivariate analysis of variance. These kinematic variables represent potential control parameters and, therefore, constraints on overarm throwing acquisition.

Preparation and analysis of a two-components breath figure at the nanoscale

NASA Astrophysics Data System (ADS)

Kofman, R.; Allione, M.; Celestini, F.; Barkay, Z.; Lereah, Y.

2008-12-01

Solid/liquid two-components Ga-Pb structures in isolated nanometer sized particles have been produced and studied by electron microscopy. Production is based on the breath figure technique and we investigate the way the two components are distributed. We clearly identify two growth regimes associated with the two different ways a Pb atom incorporates into a Ga nanodrop. Using TEM and SEM, the shape and microstructure of the nanoparticles are studied and the results obtained are in good agreement with the proposed model. The experimental technique used appears to be appropriate to produce Pb nanocrystals in liquid Ga nano-containers.

Utilizing Hierarchical Clustering to improve Efficiency of Self-Organizing Feature Map to Identify Hydrological Homogeneous Regions

NASA Astrophysics Data System (ADS)

Farsadnia, Farhad; Ghahreman, Bijan

2016-04-01

Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self-Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces. At first by principal component analysis, we reduced SOM input matrix dimension, then the SOM was used to form a two-dimensional features map. To determine homogeneous regions for flood frequency analysis, SOM output nodes were used as input into the Ward method. Generally, the regions identified by the clustering algorithms are not statistically homogeneous. Consequently, they have to be adjusted to improve their homogeneity. After adjustment of the homogeneity regions by L-moment tests, five hydrologic homogeneous regions were identified. Finally, adjusted regions were created by a two-level SOM and then the best regional distribution function and associated parameters were selected by the L-moment approach. The results showed that the combination of self-organizing maps and Ward hierarchical clustering by principal components as input is more effective than the hierarchical method, by principal components or standardized inputs to achieve hydrologic homogeneous regions.

Analysis of Propagation Plans in NSF-Funded Education Development Projects

ERIC Educational Resources Information Center

Stanford, Courtney; Cole, Renee; Froyd, Jeff; Henderson, Charles; Friedrichsen, Debra; Khatri, Raina

2017-01-01

Increasing adoption and adaptation of promising instructional strategies and materials has been identified as a critical component needed to improve science, technology, engineering, and mathematics (STEM) education. This paper examines typical propagation practices and resulting outcomes of proposals written by developers of educational…

Data Analysis and Instrumentation Requirements for Evaluating Rail Joints and Rail Fasteners in Urban Track

DOT National Transportation Integrated Search

1975-02-01

Rail fasteners for concrete ties and direct fixation and bolted rail joints have been identified as key components for improving track performance. However, the lack of statistical load data limits the development of improved design criteria and eval...

A Discrepancy-Based Methodology for Nuclear Training Program Evaluation.

ERIC Educational Resources Information Center

Cantor, Jeffrey A.

1991-01-01

A three-phase comprehensive process for commercial nuclear power training program evaluation is presented. The discrepancy-based methodology was developed after the Three Mile Island nuclear reactor accident. It facilitates analysis of program components to identify discrepancies among program specifications, actual outcomes, and industry…

[Optimization for supercritical CO2 extraction with response surface methodology and component analysis of Sapindus mukorossi oil].

PubMed

Wu, Yan; Xiao, Xin-yu; Ge, Fa-huan

2012-02-01

To study the extraction conditions of Sapindus mukorossi oil by Supercritical CO2 Extraction and identify its components. Optimized SFE-CO2 Extraction by response surface methodology and used GC-MS to analysie Sapindus mukorossi oil compounds. Established the model of an equation for the extraction rate of Sapindus mukorossi oil by Supercritical CO2 Extraction, and the optimal parameters for the Supercritical CO2 Extraction determined by the equation were: the extraction pressure was 30 MPa, temperature was 40 degrees C; The separation I pressure was 14 MPa, temperature was 45 degrees C; The separation II pressure was 6 MPa, temperature was 40 degrees C; The extraction time was 60 min and the extraction rate of Sapindus mukorossi oil of 17.58%. 22 main compounds of Sapindus mukorossi oil extracted by supercritical CO2 were identified by GC-MS, unsaturated fatty acids were 86.59%. This process is reliable, safe and with simple operation, and can be used for the extraction of Sapindus mukorossi oil.

Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data

NASA Astrophysics Data System (ADS)

Miller, Shelly L.; Anderson, Melissa J.; Daly, Eileen P.; Milford, Jana B.

Four receptor-oriented source apportionment models were evaluated by applying them to simulated personal exposure data for select volatile organic compounds (VOCs) that were generated by Monte Carlo sampling from known source contributions and profiles. The exposure sources modeled are environmental tobacco smoke, paint emissions, cleaning and/or pesticide products, gasoline vapors, automobile exhaust, and wastewater treatment plant emissions. The receptor models analyzed are chemical mass balance, principal component analysis/absolute principal component scores, positive matrix factorization (PMF), and graphical ratio analysis for composition estimates/source apportionment by factors with explicit restriction, incorporated in the UNMIX model. All models identified only the major contributors to total exposure concentrations. PMF extracted factor profiles that most closely represented the major sources used to generate the simulated data. None of the models were able to distinguish between sources with similar chemical profiles. Sources that contributed <5% to the average total VOC exposure were not identified.

Influence of Environment on Glandular Trichomes and Composition of Essential Oil of Perovskia abrotanoides Karel.

PubMed

Oreizi, Elaheh; Rahiminejad, Mohammad Reza; Asghari, Gholamreza

2014-11-01

Perovskia abrotanoides Karel. is a medicinal plant used in Iranian folk medicine as a pain killer. Forty-one components have been identified in P. abrotanoides samples collected from Baluchistan Province, and 29 components have been recognized in samples collected from Khorasan Province. The leaves of P. abrotanoides have glandular trichomes (capitates and peltate) on both sides of the lamina. This study aimed to evaluate the variation of oil constituents of the plant and illustrate the glandular trichomes types and then show the influence of environment on oil constituents and glandular trichomes. The essential oil of the plant was obtained using hydrodistillation and the analysis of oils carried out using GC-MS. The anatomical analysis of leaves was done by fixing, coloring, and photoing the sections. Glandular trichomes composed of capitates and peltate trichomes. The essential oil composition differs. Viridiflora and neryl acetate were not identified in yellow glandular trichomes. It seems that there is no relation between anatomical characteristics of the plant leaves and its essential oil composition.

Detection of plant oil DNA using high resolution melting (HRM) post PCR analysis: a tool for disclosure of olive oil adulteration.

PubMed

Vietina, Michelangelo; Agrimonti, Caterina; Marmiroli, Nelson

2013-12-15

Extra virgin olive oil is frequently subjected to adulterations with addition of oils obtained from plants other than olive. DNA analysis is a fast and economic tool to identify plant components in oils. Extraction and amplification of DNA by PCR was tested in olives, in milled seeds and in oils, to investigate its use in olive oil traceability. DNA was extracted from different oils made of hazelnut, maize, sunflower, peanut, sesame, soybean, rice and pumpkin. Comparing the DNA melting profiles in reference plant materials and in the oils, it was possible to identify any plant components in oils and mixtures of oils. Real-Time PCR (RT-PCR) platform has been added of the new methodology of high resolution melting (HRM), both were used to analyse olive oils mixed with different percentage of other oils. Results showed HRM a cost effective method for efficient detection of adulterations in olive oils. Copyright © 2013 Elsevier Ltd. All rights reserved.

Identifying the essential components of cultural competence in a Chinese nursing context: A qualitative study.

PubMed

Cai, Duanying; Kunaviktikul, Wipada; Klunklin, Areewan; Sripusanapan, Acharaporn; Avant, Patricia Kay

2017-06-01

This qualitative study using semi-structured interviews was conducted to identify the essential components of cultural competence from the perspective of Chinese nurses. A purposive sample of 20 nurse experts, including senior clinical nurses, nurse administrators, and educators in transcultural nursing, was recruited. Using thematic analysis, four themes: awareness, attitudes, knowledge, and skills, with two subthemes for each, were identified. Notably, culture in China was understood in a broad way. The participants' responses focused upon demographic attributes, individuality, and efforts to facilitate quality care rather than on the cultural differences of ethnicity and race and developing the capacity to change discrimination or health disparities. A greater understanding of cultural competence in the Chinese nursing context, in which a dominant cultural group exists, is essential to facilitate the provision of culturally competent care to diverse populations. © 2016 John Wiley & Sons Australia, Ltd.

Daughter of sevenless is a substrate of the phosphotyrosine phosphatase Corkscrew and functions during sevenless signaling.

PubMed

Herbst, R; Carroll, P M; Allard, J D; Schilling, J; Raabe, T; Simon, M A

1996-06-14

The SH2 domain-containing phosphotyrosine phosphatase Corkscrew (CSW) is an essential component of the signaling pathway initiated by the activation of the sevenless receptor tyrosine kinase (SEV) during Drosophila eye development. We have used genetic and biochemical approaches to identify a substrate for CSW. Expression of a catalytically inactive CSW was used to trap CSW in a complex with a 115 kDa tyrosine-phosphorylated substrate. This substrate was purified and identified as the product of the daughter of sevenless (dos) gene. Mutations of dos were identified in a screen for dominant mutations which enhance the phenotype caused by overexpression of inactive CSW during photoreceptor development. Analysis of dos mutations indicates that DOS is a positive component of the SEV signaling pathway and suggests that DOS dephosphorylation by CSW may be a key event during signaling by SEV.

Emotional intelligence education in pre-registration nursing programmes: an integrative review.

PubMed

Foster, Kim; McCloughen, Andrea; Delgado, Cynthia; Kefalas, Claudia; Harkness, Emily

2015-03-01

To investigate the state of knowledge on emotional intelligence (EI) education in pre-registration nursing programmes. Integrative literature review. CINAHL, Medline, Scopus, ERIC, and Web of Knowledge electronic databases were searched for abstracts published in English between 1992-2014. Data extraction and constant comparative analysis of 17 articles. Three categories were identified: Constructs of emotional intelligence; emotional intelligence curricula components; and strategies for emotional intelligence education. A wide range of emotional intelligence constructs were found, with a predominance of trait-based constructs. A variety of strategies to enhance students' emotional intelligence skills were identified, but limited curricula components and frameworks reported in the literature. An ability-based model for curricula and learning and teaching approaches is recommended. Copyright © 2014. Published by Elsevier Ltd.

Tensor decomposition-based and principal-component-analysis-based unsupervised feature extraction applied to the gene expression and methylation profiles in the brains of social insects with multiple castes.

PubMed

Taguchi, Y-H

2018-05-08

Even though coexistence of multiple phenotypes sharing the same genomic background is interesting, it remains incompletely understood. Epigenomic profiles may represent key factors, with unknown contributions to the development of multiple phenotypes, and social-insect castes are a good model for elucidation of the underlying mechanisms. Nonetheless, previous studies have failed to identify genes associated with aberrant gene expression and methylation profiles because of the lack of suitable methodology that can address this problem properly. A recently proposed principal component analysis (PCA)-based and tensor decomposition (TD)-based unsupervised feature extraction (FE) can solve this problem because these two approaches can deal with gene expression and methylation profiles even when a small number of samples is available. PCA-based and TD-based unsupervised FE methods were applied to the analysis of gene expression and methylation profiles in the brains of two social insects, Polistes canadensis and Dinoponera quadriceps. Genes associated with differential expression and methylation between castes were identified, and analysis of enrichment of Gene Ontology terms confirmed reliability of the obtained sets of genes from the biological standpoint. Biologically relevant genes, shown to be associated with significant differential gene expression and methylation between castes, were identified here for the first time. The identification of these genes may help understand the mechanisms underlying epigenetic control of development of multiple phenotypes under the same genomic conditions.

Finite Element Creep-Fatigue Analysis of a Welded Furnace Roll for Identifying Failure Root Cause

NASA Astrophysics Data System (ADS)

Yang, Y. P.; Mohr, W. C.

2015-11-01

Creep-fatigue induced failures are often observed in engineering components operating under high temperature and cyclic loading. Understanding the creep-fatigue damage process and identifying failure root cause are very important for preventing such failures and improving the lifetime of engineering components. Finite element analyses including a heat transfer analysis and a creep-fatigue analysis were conducted to model the cyclic thermal and mechanical process of a furnace roll in a continuous hot-dip coating line. Typically, the roll has a short life, <1 year, which has been a problem for a long time. The failure occurred in the weld joining an end bell to a roll shell and resulted in the complete 360° separation of the end bell from the roll shell. The heat transfer analysis was conducted to predict the temperature history of the roll by modeling heat convection from hot air inside the furnace. The creep-fatigue analysis was performed by inputting the predicted temperature history and applying mechanical loads. The analysis results showed that the failure was resulted from a creep-fatigue mechanism rather than a creep mechanism. The difference of material properties between the filler metal and the base metal is the root cause for the roll failure, which induces higher creep strain and stress in the interface between the weld and the HAZ.

A meta-analysis of the effect of neuromuscular training on the prevention of the anterior cruciate ligament injury in female athletes.

PubMed

Yoo, Jae Ho; Lim, Bee Oh; Ha, Mina; Lee, Soo Won; Oh, Soo Jin; Lee, Yong Seuk; Kim, Jin Goo

2010-06-01

Female athletes are more prone to anterior cruciate ligament (ACL) injury than their male counterparts, presumably because of anatomical, hormonal, and neuromuscular differences. Of these three, only the neuromuscular component can be modified by preventive exercise. We aimed to evaluate the effect of a neuromuscular protocol on the prevention of ACL injury by performing meta-analysis, and to identify essential factors by subgroup analysis. An extensive literature review was conducted to identify relevant studies, and eventually, only seven randomized controlled trials or prospective cohort studies were included in the analysis. The odds ratios (OR) and the confidence interval (CI) for the overall effects of training and of potentially contributory factors were estimated. The OR and the 95% CI for the overall effect of the preventive training were 0.40 and [0.27, 0.60], respectively. Subgroup analysis revealed that an age under 18, soccer rather than handball, pre- and in-season training rather than either pre- or in-season training, and the plyometrics and strengthening components rather than balancing were significant. Meta-analysis showed that pre- and in-season neuromuscular training with an emphasis on plyometrics and strengthening exercises was effective at preventing ACL injury in female athletes, especially in those under 18 years of age. Further study is required to develop a relevant training program protocol of appropriate intensity.

Identification of chemical components in Baidianling Capsule based on gas chromatography-mass spectrometry and high-performance liquid chromatography combined with Fourier transform ion cyclotron resonance mass spectrometry.

PubMed

Wu, Wenying; Chen, Yu; Wang, Binjie; Sun, Xiaoyang; Guo, Ping; Chen, Xiaohui

2017-08-01

Baidianling Capsule, which is made from 16 Chinese herbs, has been widely used for treating vitiligo clinically. In this study, the sensitive and rapid method has been developed for the analysis of chemical components in Baidianling Capsule by gas chromatography-mass spectrometry in combination with retention indices and high-performance liquid chromatography combined with Fourier transform ion cyclotron resonance mass spectrometry. Firstly, a total of 110 potential volatile compounds obtained from different extraction procedures including alkanes, alkenes, alkynes, ketones, ethers, aldehydes, alcohols, phenols, organic acids, esters, furans, pyrrole, acid amides, heterocycles, and oxides were detected from Baidianling Capsule by gas chromatography-mass spectrometry, of which 75 were identified by mass spectrometry in combination with the retention index. Then, a total of 124 components were tentatively identified by high-performance liquid chromatography combined with Fourier transform ion cyclotron resonance mass spectrometry. Fifteen constituents from Baidianling Capsule were accurately identified by comparing the retention times with those of reference compounds, others were identified by comparing the retention times and mass spectrometry data, as well as retrieving the reference literature. This study provides a practical strategy for rapidly screening and identifying the multiple constituents of a complex traditional Chinese medicine. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparison of the gene expression profiles between gallstones and gallbladder polyps.

PubMed

Li, Quanfu; Ge, Xin; Xu, Xu; Zhong, Yonggang; Qie, Zengwang

2014-01-01

Gallstones and gallbladder polyps (GPs) are two major types of gallbladder diseases that share multiple common symptoms. However, their pathological mechanism remains largely unknown. The aim of our study is to identify gallstones and GPs related-genes and gain an insight into the underlying genetic basis of these diseases. We enrolled 7 patients with gallstones and 2 patients with GP for RNA-Seq and we conducted functional enrichment analysis and protein-protein interaction (PPI) networks analysis for identified differentially expressed genes (DEGs). RNA-Seq produced 41.7 million in gallstones and 32.1 million pairs in GPs. A total of 147 DEGs was identified between gallstones and GPs. We found GO terms for molecular functions significantly enriched in antigen binding (GO:0003823, P=5.9E-11), while for biological processes, the enriched GO terms were immune response (GO:0006955, P=2.6E-15), and for cellular component, the enriched GO terms were extracellular region (GO:0005576, P=2.7E-15). To further evaluate the biological significance for the DEGs, we also performed the KEGG pathway enrichment analysis. The most significant pathway in our KEGG analysis was Cytokine-cytokine receptor interaction (P=7.5E-06). PPI network analysis indicated that the significant hub proteins containing S100A9 (S100 calcium binding protein A9, Degree=94) and CR2 (complement component receptor 2, Degree=8). This present study suggests some promising genes and may provide a clue to the role of these genes playing in the development of gallstones and GPs.