Regularized Generalized Canonical Correlation Analysis

ERIC Educational Resources Information Center

Tenenhaus, Arthur; Tenenhaus, Michel

2011-01-01

Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to three or more sets of variables. It constitutes a general framework for many multi-block data analysis methods. It combines the power of multi-block data analysis methods (maximization of well identified criteria) and…

Attenuation of the Squared Canonical Correlation Coefficient under Varying Estimates of Score Reliability

ERIC Educational Resources Information Center

Wilson, Celia M.

2010-01-01

Research pertaining to the distortion of the squared canonical correlation coefficient has traditionally been limited to the effects of sampling error and associated correction formulas. The purpose of this study was to compare the degree of attenuation of the squared canonical correlation coefficient under varying conditions of score reliability.…

Thresholding functional connectomes by means of mixture modeling.

PubMed

Bielczyk, Natalia Z; Walocha, Fabian; Ebel, Patrick W; Haak, Koen V; Llera, Alberto; Buitelaar, Jan K; Glennon, Jeffrey C; Beckmann, Christian F

2018-05-01

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Creativity and Brain-Functioning in Product Development Engineers: A Canonical Correlation Analysis

ERIC Educational Resources Information Center

Travis, Frederick; Lagrosen, Yvonne

2014-01-01

This study used canonical correlation analysis to explore the relation among scores on the Torrance test of figural and verbal creativity and demographic, psychological and physiological measures in Swedish product-development engineers. The first canonical variate included figural and verbal flexibility and originality as dependent measures and…

Canonical ensemble ground state and correlation entropy of Bose-Einstein condensate

NASA Astrophysics Data System (ADS)

Svidzinsky, Anatoly; Kim, Moochan; Agarwal, Girish; Scully, Marlan O.

2018-01-01

Constraint of a fixed total number of particles yields a correlation between the fluctuation of particles in different states in the canonical ensemble. Here we show that, below the temperature of Bose-Einstein condensation (BEC), the correlation part of the entropy of an ideal Bose gas is cancelled by the ground-state contribution. Thus, in the BEC region, the thermodynamic properties of the gas in the canonical ensemble can be described accurately in a simplified model which excludes the ground state and assumes no correlation between excited levels.

Relationship between climatic variables and the variation in bulk tank milk composition using canonical correlation analysis.

PubMed

Stürmer, Morgana; Busanello, Marcos; Velho, João Pedro; Heck, Vanessa Isabel; Haygert-Velho, Ione Maria Pereira

2018-06-04

A number of studies have addressed the relations between climatic variables and milk composition, but these works used univariate statistical approaches. In our study, we used a multivariate approach (canonical correlation) to study the impact of climatic variables on milk composition, price, and monthly milk production at a dairy farm using bulk tank milk data. Data on milk composition, price, and monthly milk production were obtained from a dairy company that purchased the milk from the farm, while climatic variable data were obtained from the National Institute of Meteorology (INMET). The data are from January 2014 to December 2016. Univariate correlation analysis and canonical correlation analysis were performed. Few correlations between the climatic variables and milk composition were found using a univariate approach. However, using canonical correlation analysis, we found a strong and significant correlation (r c  = 0.95, p value = 0.0029). Lactose, ambient temperature measures (mean, minimum, and maximum), and temperature-humidity index (THI) were found to be the most important variables for the canonical correlation. Our study indicated that 10.2% of the variation in milk composition, pricing, and monthly milk production can be explained by climatic variables. Ambient temperature variables, together with THI, seem to have the most influence on variation in milk composition.

Relationship between climatic variables and the variation in bulk tank milk composition using canonical correlation analysis

NASA Astrophysics Data System (ADS)

Stürmer, Morgana; Busanello, Marcos; Velho, João Pedro; Heck, Vanessa Isabel; Haygert-Velho, Ione Maria Pereira

2018-06-01

A number of studies have addressed the relations between climatic variables and milk composition, but these works used univariate statistical approaches. In our study, we used a multivariate approach (canonical correlation) to study the impact of climatic variables on milk composition, price, and monthly milk production at a dairy farm using bulk tank milk data. Data on milk composition, price, and monthly milk production were obtained from a dairy company that purchased the milk from the farm, while climatic variable data were obtained from the National Institute of Meteorology (INMET). The data are from January 2014 to December 2016. Univariate correlation analysis and canonical correlation analysis were performed. Few correlations between the climatic variables and milk composition were found using a univariate approach. However, using canonical correlation analysis, we found a strong and significant correlation (r c = 0.95, p value = 0.0029). Lactose, ambient temperature measures (mean, minimum, and maximum), and temperature-humidity index (THI) were found to be the most important variables for the canonical correlation. Our study indicated that 10.2% of the variation in milk composition, pricing, and monthly milk production can be explained by climatic variables. Ambient temperature variables, together with THI, seem to have the most influence on variation in milk composition.

Multicollinearity in canonical correlation analysis in maize.

PubMed

Alves, B M; Cargnelutti Filho, A; Burin, C

2017-03-30

The objective of this study was to evaluate the effects of multicollinearity under two methods of canonical correlation analysis (with and without elimination of variables) in maize (Zea mays L.) crop. Seventy-six maize genotypes were evaluated in three experiments, conducted in a randomized block design with three replications, during the 2009/2010 crop season. Eleven agronomic variables (number of days from sowing until female flowering, number of days from sowing until male flowering, plant height, ear insertion height, ear placement, number of plants, number of ears, ear index, ear weight, grain yield, and one thousand grain weight), 12 protein-nutritional variables (crude protein, lysine, methionine, cysteine, threonine, tryptophan, valine, isoleucine, leucine, phenylalanine, histidine, and arginine), and 6 energetic-nutritional variables (apparent metabolizable energy, apparent metabolizable energy corrected for nitrogen, ether extract, crude fiber, starch, and amylose) were measured. A phenotypic correlation matrix was first generated among the 29 variables for each of the experiments. A multicollinearity diagnosis was later performed within each group of variables using methodologies such as variance inflation factor and condition number. Canonical correlation analysis was then performed, with and without the elimination of variables, among groups of agronomic and protein-nutritional, and agronomic and energetic-nutritional variables. The canonical correlation analysis in the presence of multicollinearity (without elimination of variables) overestimates the variability of canonical coefficients. The elimination of variables is an efficient method to circumvent multicollinearity in canonical correlation analysis.

Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

PubMed Central

Gutmann, Michael U.; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús

2014-01-01

Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation. PMID:24533049

Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images.

PubMed

Gutmann, Michael U; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús

2014-01-01

Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation.

Low photon count based digital holography for quadratic phase cryptography.

PubMed

Muniraj, Inbarasan; Guo, Changliang; Malallah, Ra'ed; Ryle, James P; Healy, John J; Lee, Byung-Geun; Sheridan, John T

2017-07-15

Recently, the vulnerability of the linear canonical transform-based double random phase encryption system to attack has been demonstrated. To alleviate this, we present for the first time, to the best of our knowledge, a method for securing a two-dimensional scene using a quadratic phase encoding system operating in the photon-counted imaging (PCI) regime. Position-phase-shifting digital holography is applied to record the photon-limited encrypted complex samples. The reconstruction of the complex wavefront involves four sparse (undersampled) dataset intensity measurements (interferograms) at two different positions. Computer simulations validate that the photon-limited sparse-encrypted data has adequate information to authenticate the original data set. Finally, security analysis, employing iterative phase retrieval attacks, has been performed.

Complex sparse spatial filter for decoding mixed frequency and phase coded steady-state visually evoked potentials.

PubMed

Morikawa, Naoki; Tanaka, Toshihisa; Islam, Md Rabiul

2018-07-01

Mixed frequency and phase coding (FPC) can achieve the significant increase of the number of commands in steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI). However, the inconsistent phases of the SSVEP over channels in a trial and the existence of non-contributing channels due to noise effects can decrease accurate detection of stimulus frequency. We propose a novel command detection method based on a complex sparse spatial filter (CSSF) by solving ℓ 1 - and ℓ 2,1 -regularization problems for a mixed-coded SSVEP-BCI. In particular, ℓ 2,1 -regularization (aka group sparsification) can lead to the rejection of electrodes that are not contributing to the SSVEP detection. A calibration data based canonical correlation analysis (CCA) and CSSF with ℓ 1 - and ℓ 2,1 -regularization cases were demonstrated for a 16-target stimuli with eleven subjects. The results of statistical test suggest that the proposed method with ℓ 1 - and ℓ 2,1 -regularization significantly achieved the highest ITR. The proposed approaches do not need any reference signals, automatically select prominent channels, and reduce the computational cost compared to the other mixed frequency-phase coding (FPC)-based BCIs. The experimental results suggested that the proposed method can be usable implementing BCI effectively with reduce visual fatigue. Copyright © 2018 Elsevier B.V. All rights reserved.

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

2011-04-01

Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

Analyzing Baryon Acoustic Oscillations in Sparse Spectroscopic Samples via Cross-Correlation with Dense Photometry

NASA Astrophysics Data System (ADS)

Patej, Anna; Eisenstein, Daniel J.

2018-04-01

We develop a formalism for measuring the cosmological distance scale from baryon acoustic oscillations (BAO) using the cross-correlation of a sparse redshift survey with a denser photometric sample. This reduces the shot noise that would otherwise affect the auto-correlation of the sparse spectroscopic map. As a proof of principle, we make the first on-sky application of this method to a sparse sample defined as the z > 0.6 tail of the Sloan Digital Sky Survey's (SDSS) BOSS/CMASS sample of galaxies and a dense photometric sample from SDSS DR9. We find a 2.8σ preference for the BAO peak in the cross-correlation at an effective z = 0.64, from which we measure the angular diameter distance DM(z = 0.64) = (2418 ± 73 Mpc)(rs/rs, fid). Accordingly, we expect that using this method to combine sparse spectroscopy with the deep, high quality imaging that is just now becoming available will enable higher precision BAO measurements than possible with the spectroscopy alone.

Predicting phonetic transcription agreement: Insights from research in infant vocalizations

PubMed Central

RAMSDELL, HEATHER L.; OLLER, D. KIMBROUGH; ETHINGTON, CORINNA A.

2010-01-01

The purpose of this study is to provide new perspectives on correlates of phonetic transcription agreement. Our research focuses on phonetic transcription and coding of infant vocalizations. The findings are presumed to be broadly applicable to other difficult cases of transcription, such as found in severe disorders of speech, which similarly result in low reliability for a variety of reasons. We evaluated the predictiveness of two factors not previously documented in the literature as influencing transcription agreement: canonicity and coder confidence. Transcribers coded samples of infant vocalizations, judging both canonicity and confidence. Correlation results showed that canonicity and confidence were strongly related to agreement levels, and regression results showed that canonicity and confidence both contributed significantly to explanation of variance. Specifically, the results suggest that canonicity plays a major role in transcription agreement when utterances involve supraglottal articulation, with coder confidence offering additional power in predicting transcription agreement. PMID:17882695

A Canonical Correlation Analysis of AIDS Restriction Genes and Metabolic Pathways Identifies Purine Metabolism as a Key Cooperator.

PubMed

Ye, Hanhui; Yuan, Jinjin; Wang, Zhengwu; Huang, Aiqiong; Liu, Xiaolong; Han, Xiao; Chen, Yahong

2016-01-01

Human immunodeficiency virus causes a severe disease in humans, referred to as immune deficiency syndrome. Studies on the interaction between host genetic factors and the virus have revealed dozens of genes that impact diverse processes in the AIDS disease. To resolve more genetic factors related to AIDS, a canonical correlation analysis was used to determine the correlation between AIDS restriction and metabolic pathway gene expression. The results show that HIV-1 postentry cellular viral cofactors from AIDS restriction genes are coexpressed in human transcriptome microarray datasets. Further, the purine metabolism pathway comprises novel host factors that are coexpressed with AIDS restriction genes. Using a canonical correlation analysis for expression is a reliable approach to exploring the mechanism underlying AIDS.

Visualising associations between paired ‘omics’ data sets

PubMed Central

2012-01-01

Background Each omics platform is now able to generate a large amount of data. Genomics, proteomics, metabolomics, interactomics are compiled at an ever increasing pace and now form a core part of the fundamental systems biology framework. Recently, several integrative approaches have been proposed to extract meaningful information. However, these approaches lack of visualisation outputs to fully unravel the complex associations between different biological entities. Results The multivariate statistical approaches ‘regularized Canonical Correlation Analysis’ and ‘sparse Partial Least Squares regression’ were recently developed to integrate two types of highly dimensional ‘omics’ data and to select relevant information. Using the results of these methods, we propose to revisit few graphical outputs to better understand the relationships between two ‘omics’ data and to better visualise the correlation structure between the different biological entities. These graphical outputs include Correlation Circle plots, Relevance Networks and Clustered Image Maps. We demonstrate the usefulness of such graphical outputs on several biological data sets and further assess their biological relevance using gene ontology analysis. Conclusions Such graphical outputs are undoubtedly useful to aid the interpretation of these promising integrative analysis tools and will certainly help in addressing fundamental biological questions and understanding systems as a whole. Availability The graphical tools described in this paper are implemented in the freely available R package mixOmics and in its associated web application. PMID:23148523

Canonical Correlational Models of Students' Perceptions of Assessment Tasks, Motivational Orientations, and Learning Strategies

ERIC Educational Resources Information Center

Alkharusi, Hussain

2013-01-01

The present study aims at deriving correlational models of students' perceptions of assessment tasks, motivational orientations, and learning strategies using canonical analyses. Data were collected from 198 Omani tenth grade students. Results showed that high degrees of authenticity and transparency in assessment were associated with positive…

Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago.

PubMed

Binaku, Katrina; O'Brien, Timothy; Schmeling, Martina; Fosco, Tinamarie

2013-09-01

Both canonical correlation analysis (CCA) and principal component analysis (PCA) were applied to atmospheric aerosol and trace gas concentrations and meteorological data collected in Chicago during the summer months of 2002, 2003, and 2004. Concentrations of ammonium, calcium, nitrate, sulfate, and oxalate particulate matter, as well as, meteorological parameters temperature, wind speed, wind direction, and humidity were subjected to CCA and PCA. Ozone and nitrogen oxide mixing ratios were also included in the data set. The purpose of statistical analysis was to determine the extent of existing linear relationship(s), or lack thereof, between meteorological parameters and pollutant concentrations in addition to reducing dimensionality of the original data to determine sources of pollutants. In CCA, the first three canonical variate pairs derived were statistically significant at the 0.05 level. Canonical correlation between the first canonical variate pair was 0.821, while correlations of the second and third canonical variate pairs were 0.562 and 0.461, respectively. The first canonical variate pair indicated that increasing temperatures resulted in high ozone mixing ratios, while the second canonical variate pair showed wind speed and humidity's influence on local ammonium concentrations. No new information was uncovered in the third variate pair. Canonical loadings were also interpreted for information regarding relationships between data sets. Four principal components (PCs), expressing 77.0 % of original data variance, were derived in PCA. Interpretation of PCs suggested significant production and/or transport of secondary aerosols in the region (PC1). Furthermore, photochemical production of ozone and wind speed's influence on pollutants were expressed (PC2) along with overall measure of local meteorology (PC3). In summary, CCA and PCA results combined were successful in uncovering linear relationships between meteorology and air pollutants in Chicago and aided in determining possible pollutant sources.

Integral formulae of the canonical correlation functions for the one dimensional transverse Ising model

NASA Astrophysics Data System (ADS)

Inoue, Makoto

2017-12-01

Some new formulae of the canonical correlation functions for the one dimensional quantum transverse Ising model are found by the ST-transformation method using a Morita's sum rule and its extensions for the two dimensional classical Ising model. As a consequence we obtain a time-independent term of the dynamical correlation functions. Differences of quantum version and classical version of these formulae are also discussed.

Association Study between Lead and Zinc Accumulation at Different Physiological Systems of Cattle by Canonical Correlation and Canonical Correspondence Analyses

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

Karmakar, Partha; Das, Pradip Kumar; Mondal, Seema Sarkar

2010-10-26

Pb pollution from automobile exhausts around highways is a persistent problem in India. Pb intoxication in mammalian body is a complex phenomenon which is influence by agonistic and antagonistic interactions of several other heavy metals and micronutrients. An attempt has been made to study the association between Pb and Zn accumulation in different physiological systems of cattles (n = 200) by application of both canonical correlation and canonical correspondence analyses. Pb was estimated from plasma, liver, bone, muscle, kidney, blood and milk where as Zn was measured from all these systems except bone, blood and milk. Both statistical techniques demonstratedmore » that there was a strong association among blood-Pb, liver-Zn, kidney-Zn and muscle-Zn. From observations, it can be assumed that Zn accumulation in cattles' muscle, liver and kidney directs Pb mobilization from those organs which in turn increases Pb pool in blood. It indicates antagonistic activity of Zn to the accumulation of Pb. Although there were some contradictions between the observations obtained from the two different statistical methods, the overall pattern of Pb accumulation in various organs as influenced by Zn were same. It is mainly due to the fact that canonical correlation is actually a special type of canonical correspondence analyses where linear relationship is followed between two groups of variables instead of Gaussian relationship.« less

Association Study between Lead and Zinc Accumulation at Different Physiological Systems of Cattle by Canonical Correlation and Canonical Correspondence Analyses

NASA Astrophysics Data System (ADS)

Karmakar, Partha; Das, Pradip Kumar; Mondal, Seema Sarkar; Karmakar, Sougata; Mazumdar, Debasis

2010-10-01

Pb pollution from automobile exhausts around highways is a persistent problem in India. Pb intoxication in mammalian body is a complex phenomenon which is influence by agonistic and antagonistic interactions of several other heavy metals and micronutrients. An attempt has been made to study the association between Pb and Zn accumulation in different physiological systems of cattles (n = 200) by application of both canonical correlation and canonical correspondence analyses. Pb was estimated from plasma, liver, bone, muscle, kidney, blood and milk where as Zn was measured from all these systems except bone, blood and milk. Both statistical techniques demonstrated that there was a strong association among blood-Pb, liver-Zn, kidney-Zn and muscle-Zn. From observations, it can be assumed that Zn accumulation in cattles' muscle, liver and kidney directs Pb mobilization from those organs which in turn increases Pb pool in blood. It indicates antagonistic activity of Zn to the accumulation of Pb. Although there were some contradictions between the observations obtained from the two different statistical methods, the overall pattern of Pb accumulation in various organs as influenced by Zn were same. It is mainly due to the fact that canonical correlation is actually a special type of canonical correspondence analyses where linear relationship is followed between two groups of variables instead of Gaussian relationship.

Relating drug–protein interaction network with drug side effects

PubMed Central

Mizutani, Sayaka; Pauwels, Edouard; Stoven, Véronique; Goto, Susumu; Yamanishi, Yoshihiro

2012-01-01

Motivation: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system–wide approaches for linking different scales of drug actions; namely drug-protein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. Results: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the co-occurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug–targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles. Supplementary information: Datasets and all results are available at http://web.kuicr.kyoto-u.ac.jp/supp/smizutan/target-effect/. Availability: Software is available at the above supplementary website. Contact: yamanishi@bioreg.kyushu-u.ac.jp, or goto@kuicr.kyoto-u.ac.jp PMID:22962476

Analysing baryon acoustic oscillations in sparse spectroscopic samples via cross-correlation with dense photometry

NASA Astrophysics Data System (ADS)

Patej, A.; Eisenstein, D. J.

2018-07-01

We develop a formalism for measuring the cosmological distance scale from baryon acoustic oscillations (BAO) using the cross-correlation of a sparse redshift survey with a denser photometric sample. This reduces the shot noise that would otherwise affect the autocorrelation of the sparse spectroscopic map. As a proof of principle, we make the first on-sky application of this method to a sparse sample defined as the z > 0.6 tail of the Sloan Digital Sky Survey's (SDSS) BOSS/CMASS sample of galaxies and a dense photometric sample from SDSS DR9. We find a 2.8σ preference for the BAO peak in the cross-correlation at an effective z = 0.64, from which we measure the angular diameter distance DM(z = 0.64) = (2418 ± 73 Mpc)(rs/rs, fid). Accordingly, we expect that using this method to combine sparse spectroscopy with the deep, high-quality imaging that is just now becoming available will enable higher precision BAO measurements than possible with the spectroscopy alone.

Long-term forecasting of meteorological time series using Nonlinear Canonical Correlation Analysis (NLCCA)

NASA Astrophysics Data System (ADS)

Woldesellasse, H. T.; Marpu, P. R.; Ouarda, T.

2016-12-01

Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. A number of linear and nonlinear multivariate techniques has been used to predict the stochastic character of wind speed. A wind forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study Nonlinear Canonical Correlation Analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed and wind direction, of Abu Dhabi, UAE. The wind dataset was obtained from six ground stations. The first mode of NLCCA is capable of capturing the nonlinear mode of the climate indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE) and Mean absolute error (MAE). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behaviour of the dataset of variables than linear CCA model in terms of the correlation and root mean square error. Key words: Nonlinear Canonical Correlation Analysis (NLCCA), Canonical Correlation Analysis, Neural Network, Climate Indices, wind speed, wind direction

Data analytics using canonical correlation analysis and Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Rickman, Jeffrey M.; Wang, Yan; Rollett, Anthony D.; Harmer, Martin P.; Compson, Charles

2017-07-01

A canonical correlation analysis is a generic parametric model used in the statistical analysis of data involving interrelated or interdependent input and output variables. It is especially useful in data analytics as a dimensional reduction strategy that simplifies a complex, multidimensional parameter space by identifying a relatively few combinations of variables that are maximally correlated. One shortcoming of the canonical correlation analysis, however, is that it provides only a linear combination of variables that maximizes these correlations. With this in mind, we describe here a versatile, Monte-Carlo based methodology that is useful in identifying non-linear functions of the variables that lead to strong input/output correlations. We demonstrate that our approach leads to a substantial enhancement of correlations, as illustrated by two experimental applications of substantial interest to the materials science community, namely: (1) determining the interdependence of processing and microstructural variables associated with doped polycrystalline aluminas, and (2) relating microstructural decriptors to the electrical and optoelectronic properties of thin-film solar cells based on CuInSe2 absorbers. Finally, we describe how this approach facilitates experimental planning and process control.

SU-F-P-64: The Impact of Plan Complexity Parameters On the Plan Quality and Deliverability of Volumetric Modulated Arc Therapy with Canonical Correlation Analysis

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

Jin, X; Yi, J; Xie, C

Purpose: To evaluate the impact of complexity indices on the plan quality and deliverability of volumetric modulated arc therapy (VMAT), and to determine the most significant parameters in the generation of an ideal VMAT plan. Methods: A multi-dimensional exploratory statistical method, canonical correlation analysis (CCA) was adopted to study the correlations between VMAT parameters of complexity, quality and deliverability, as well as their contribution weights with 32 two-arc VMAT nasopharyngeal cancer (NPC) patients and 31 one-arc VMAT prostate cancer patients. Results: The MU per arc (MU/Arc) and MU per control point (MU/CP) of NPC were 337.8±25.2 and 3.7±0.3, respectively, whichmore » were significantly lower than those of prostate cancer patients (MU/Arc : 506.9±95.4, MU/CP : 5.6±1.1). The plan complexity indices indicated that two-arc VMAT plans were more complex than one-arc VMAT plans. Plan quality comparison confirmed that one-arc VMAT plans had a high quality than two-arc VMAT plans. CCA results implied that plan complexity parameters were highly correlated with plan quality with the first two canonical correlations of 0.96, 0.88 (both p<0.001) and significantly correlated with deliverability with the first canonical correlation of 0.79 (p<0.001), plan quality and deliverability was also correlated with the first canonical correlation of 0.71 (p=0.02). Complexity parameters of MU/CP, segment area (SA) per CP, percent of MU/CP less 3 and planning target volume (PTV) were weighted heavily in correlation with plan quality and deliveability . Similar results obtained from individual NPC and prostate CCA analysis. Conclusion: Relationship between complexity, quality, and deliverability parameters were investigated with CCA. MU, SA related parameters and PTV volume were found to have strong effect on the plan quality and deliverability. The presented correlation among different quantified parameters could be used to improve the plan quality and the efficiency of the radiotherapy process when creating a complex VMAT plan.« less

A Study on the Oral Disfluencies Developmental Traits of EFL Students--A Report Based on Canonical Correlation Analysis

ERIC Educational Resources Information Center

Gao, Ying; Du, Wanyi

2013-01-01

This paper traces 9 non-English major EFL students and collects their oral productions in 4 successive oral exams in 2 years. The canonical correlation analysis approach of SPSS is adopted to study the disfluencies developmental traits under the influence of language acquisition development. We find that as language acquisition develops, the total…

Uncertainty relations, zero point energy and the linear canonical group

NASA Technical Reports Server (NTRS)

Sudarshan, E. C. G.

1993-01-01

The close relationship between the zero point energy, the uncertainty relations, coherent states, squeezed states, and correlated states for one mode is investigated. This group-theoretic perspective enables the parametrization and identification of their multimode generalization. In particular the generalized Schroedinger-Robertson uncertainty relations are analyzed. An elementary method of determining the canonical structure of the generalized correlated states is presented.

Integrating Multiple Criteria in Selection Procedures for Improving Student Quality and Reducing Cost Per Graduate. AIR Forum 1979 Paper.

ERIC Educational Resources Information Center

Jones, Gerald L.; Westen, Risdon J.

The multivariate approach of canonical correlation was used to assess selection procedures of the Air Force Academy. It was felt that improved student selection methods might reduce the number of dropouts while maintaining or improving the quality of graduates. The method of canonical correlation was designed to maximize prediction of academic…

Multireference quantum chemistry through a joint density matrix renormalization group and canonical transformation theory.

PubMed

Yanai, Takeshi; Kurashige, Yuki; Neuscamman, Eric; Chan, Garnet Kin-Lic

2010-01-14

We describe the joint application of the density matrix renormalization group and canonical transformation theory to multireference quantum chemistry. The density matrix renormalization group provides the ability to describe static correlation in large active spaces, while the canonical transformation theory provides a high-order description of the dynamic correlation effects. We demonstrate the joint theory in two benchmark systems designed to test the dynamic and static correlation capabilities of the methods, namely, (i) total correlation energies in long polyenes and (ii) the isomerization curve of the [Cu(2)O(2)](2+) core. The largest complete active spaces and atomic orbital basis sets treated by the joint DMRG-CT theory in these systems correspond to a (24e,24o) active space and 268 atomic orbitals in the polyenes and a (28e,32o) active space and 278 atomic orbitals in [Cu(2)O(2)](2+).

Thermodynamics of pairing in mesoscopic systems

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

Sumaryada, Tony; Volya, Alexander

Using numerical and analytical methods implemented for different models, we conduct a systematic study of the thermodynamic properties of pairing correlations in mesoscopic nuclear systems. Various quantities are calculated and analyzed using the exact solution of pairing. An in-depth comparison of canonical, grand canonical, and microcanonical ensembles is conducted. The nature of the pairing phase transition in a small system is of a particular interest. We discuss the onset of discontinuity in the thermodynamic variables, fluctuations, and evolution of zeros of the canonical and grand canonical partition functions in the complex plane. The behavior of the invariant correlational entropy ismore » also studied in the transitional region of interest. The change in the character of the phase transition due to the presence of a magnetic field is discussed along with studies of superconducting thermodynamics.« less

Correlated activity supports efficient cortical processing

PubMed Central

Hung, Chou P.; Cui, Ding; Chen, Yueh-peng; Lin, Chia-pei; Levine, Matthew R.

2015-01-01

Visual recognition is a computational challenge that is thought to occur via efficient coding. An important concept is sparseness, a measure of coding efficiency. The prevailing view is that sparseness supports efficiency by minimizing redundancy and correlations in spiking populations. Yet, we recently reported that “choristers”, neurons that behave more similarly (have correlated stimulus preferences and spontaneous coincident spiking), carry more generalizable object information than uncorrelated neurons (“soloists”) in macaque inferior temporal (IT) cortex. The rarity of choristers (as low as 6% of IT neurons) indicates that they were likely missed in previous studies. Here, we report that correlation strength is distinct from sparseness (choristers are not simply broadly tuned neurons), that choristers are located in non-granular output layers, and that correlated activity predicts human visual search efficiency. These counterintuitive results suggest that a redundant correlational structure supports efficient processing and behavior. PMID:25610392

Sparse dictionary learning of resting state fMRI networks.

PubMed

Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

2012-07-02

Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

Predicting drug side-effect profiles: a chemical fragment-based approach

PubMed Central

2011-01-01

Background Drug side-effects, or adverse drug reactions, have become a major public health concern. It is one of the main causes of failure in the process of drug development, and of drug withdrawal once they have reached the market. Therefore, in silico prediction of potential side-effects early in the drug discovery process, before reaching the clinical stages, is of great interest to improve this long and expensive process and to provide new efficient and safe therapies for patients. Results In the present work, we propose a new method to predict potential side-effects of drug candidate molecules based on their chemical structures, applicable on large molecular databanks. A unique feature of the proposed method is its ability to extract correlated sets of chemical substructures (or chemical fragments) and side-effects. This is made possible using sparse canonical correlation analysis (SCCA). In the results, we show the usefulness of the proposed method by predicting 1385 side-effects in the SIDER database from the chemical structures of 888 approved drugs. These predictions are performed with simultaneous extraction of correlated ensembles formed by a set of chemical substructures shared by drugs that are likely to have a set of side-effects. We also conduct a comprehensive side-effect prediction for many uncharacterized drug molecules stored in DrugBank, and were able to confirm interesting predictions using independent source of information. Conclusions The proposed method is expected to be useful in various stages of the drug development process. PMID:21586169

An integrated brain-behavior model for working memory.

PubMed

Moser, D A; Doucet, G E; Ing, A; Dima, D; Schumann, G; Bilder, R M; Frangou, S

2017-12-05

Working memory (WM) is a central construct in cognitive neuroscience because it comprises mechanisms of active information maintenance and cognitive control that underpin most complex cognitive behavior. Individual variation in WM has been associated with multiple behavioral and health features including demographic characteristics, cognitive and physical traits and lifestyle choices. In this context, we used sparse canonical correlation analyses (sCCAs) to determine the covariation between brain imaging metrics of WM-network activation and connectivity and nonimaging measures relating to sensorimotor processing, affective and nonaffective cognition, mental health and personality, physical health and lifestyle choices derived from 823 healthy participants derived from the Human Connectome Project. We conducted sCCAs at two levels: a global level, testing the overall association between the entire imaging and behavioral-health data sets; and a modular level, testing associations between subsets of the two data sets. The behavioral-health and neuroimaging data sets showed significant interdependency. Variables with positive correlation to the neuroimaging variate represented higher physical endurance and fluid intelligence as well as better function in multiple higher-order cognitive domains. Negatively correlated variables represented indicators of suboptimal cardiovascular and metabolic control and lifestyle choices such as alcohol and nicotine use. These results underscore the importance of accounting for behavioral-health factors in neuroimaging studies of WM and provide a neuroscience-informed framework for personalized and public health interventions to promote and maintain the integrity of the WM network.Molecular Psychiatry advance online publication, 5 December 2017; doi:10.1038/mp.2017.247.

Inference for High-dimensional Differential Correlation Matrices.

PubMed

Cai, T Tony; Zhang, Anru

2016-01-01

Motivated by differential co-expression analysis in genomics, we consider in this paper estimation and testing of high-dimensional differential correlation matrices. An adaptive thresholding procedure is introduced and theoretical guarantees are given. Minimax rate of convergence is established and the proposed estimator is shown to be adaptively rate-optimal over collections of paired correlation matrices with approximately sparse differences. Simulation results show that the procedure significantly outperforms two other natural methods that are based on separate estimation of the individual correlation matrices. The procedure is also illustrated through an analysis of a breast cancer dataset, which provides evidence at the gene co-expression level that several genes, of which a subset has been previously verified, are associated with the breast cancer. Hypothesis testing on the differential correlation matrices is also considered. A test, which is particularly well suited for testing against sparse alternatives, is introduced. In addition, other related problems, including estimation of a single sparse correlation matrix, estimation of the differential covariance matrices, and estimation of the differential cross-correlation matrices, are also discussed.

Sparse brain network using penalized linear regression

NASA Astrophysics Data System (ADS)

Lee, Hyekyoung; Lee, Dong Soo; Kang, Hyejin; Kim, Boong-Nyun; Chung, Moo K.

2011-03-01

Sparse partial correlation is a useful connectivity measure for brain networks when it is difficult to compute the exact partial correlation in the small-n large-p setting. In this paper, we formulate the problem of estimating partial correlation as a sparse linear regression with a l1-norm penalty. The method is applied to brain network consisting of parcellated regions of interest (ROIs), which are obtained from FDG-PET images of the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. To validate the results, we check their reproducibilities of the obtained brain networks by the leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.

A Technology Integration Education (TIE) Model for Millennial Preservice Teachers: Exploring the Canonical Correlation Relationships among Attitudes, Subjective Norms, Perceived Behavioral Controls, Motivation, and Technological, Pedagogical, and Content Knowledge (TPACK) Competencies

ERIC Educational Resources Information Center

Holland, Denise D.; Piper, Randy T.

2016-01-01

Intellectual goods can follow the same pattern as physical goods with the product life cycle of birth, growth, maturity, and decline. For the intellectual good of technological, pedagogical, and content knowledge (TPACK), its birth began with Shulman (1986, 1987). Canonical correlation analysis (CCA) was used to test the relationships among five…

Thermodynamic characterization of synchronization-optimized oscillator networks

NASA Astrophysics Data System (ADS)

Yanagita, Tatsuo; Ichinomiya, Takashi

2014-12-01

We consider a canonical ensemble of synchronization-optimized networks of identical oscillators under external noise. By performing a Markov chain Monte Carlo simulation using the Kirchhoff index, i.e., the sum of the inverse eigenvalues of the Laplacian matrix (as a graph Hamiltonian of the network), we construct more than 1 000 different synchronization-optimized networks. We then show that the transition from star to core-periphery structure depends on the connectivity of the network, and is characterized by the node degree variance of the synchronization-optimized ensemble. We find that thermodynamic properties such as heat capacity show anomalies for sparse networks.

Use of Canonical Analysis in Cropley's "A Five-Year Longitudinal Study of the Validity of Creativity Tests"

ERIC Educational Resources Information Center

Jordan, Lawrence A.

1975-01-01

Calls attention to several errors in a recent application of canonical correlation analysis. The reanalysis contradicts Cropley's conclusion that "creativity tests can be said to possess reasonable and encouraging long-range predictive validity." (Author/SDH)

Two-particle correlation function and dihadron correlation approach

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

Vechernin, V. V., E-mail: v.vechernin@spbu.ru; Ivanov, K. O.; Neverov, D. I.

It is shown that, in the case of asymmetric nuclear interactions, the application of the traditional dihadron correlation approach to determining a two-particle correlation function C may lead to a form distorted in relation to the canonical pair correlation function {sub C}{sup 2}. This result was obtained both by means of exact analytic calculations of correlation functions within a simple string model for proton–nucleus and deuteron–nucleus collisions and by means of Monte Carlo simulations based on employing the HIJING event generator. It is also shown that the method based on studying multiplicity correlations in two narrow observation windows separated inmore » rapidity makes it possible to determine correctly the canonical pair correlation function C{sub 2} for all cases, including the case where the rapidity distribution of product particles is not uniform.« less

Inference for High-dimensional Differential Correlation Matrices *

PubMed Central

Cai, T. Tony; Zhang, Anru

2015-01-01

Motivated by differential co-expression analysis in genomics, we consider in this paper estimation and testing of high-dimensional differential correlation matrices. An adaptive thresholding procedure is introduced and theoretical guarantees are given. Minimax rate of convergence is established and the proposed estimator is shown to be adaptively rate-optimal over collections of paired correlation matrices with approximately sparse differences. Simulation results show that the procedure significantly outperforms two other natural methods that are based on separate estimation of the individual correlation matrices. The procedure is also illustrated through an analysis of a breast cancer dataset, which provides evidence at the gene co-expression level that several genes, of which a subset has been previously verified, are associated with the breast cancer. Hypothesis testing on the differential correlation matrices is also considered. A test, which is particularly well suited for testing against sparse alternatives, is introduced. In addition, other related problems, including estimation of a single sparse correlation matrix, estimation of the differential covariance matrices, and estimation of the differential cross-correlation matrices, are also discussed. PMID:26500380

Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data.

PubMed

Nielsen, Allan Aasbjerg

2002-01-01

This paper describes two- and multiset canonical correlations analysis (CCA) for data fusion, multisource, multiset, or multitemporal exploratory data analysis. These techniques transform multivariate multiset data into new orthogonal variables called canonical variates (CVs) which, when applied in remote sensing, exhibit ever-decreasing similarity (as expressed by correlation measures) over sets consisting of 1) spectral variables at fixed points in time (R-mode analysis), or 2) temporal variables with fixed wavelengths (T-mode analysis). The CVs are invariant to linear and affine transformations of the original variables within sets which means, for example, that the R-mode CVs are insensitive to changes over time in offset and gain in a measuring device. In a case study, CVs are calculated from Landsat Thematic Mapper (TM) data with six spectral bands over six consecutive years. Both Rand T-mode CVs clearly exhibit the desired characteristic: they show maximum similarity for the low-order canonical variates and minimum similarity for the high-order canonical variates. These characteristics are seen both visually and in objective measures. The results from the multiset CCA R- and T-mode analyses are very different. This difference is ascribed to the noise structure in the data. The CCA methods are related to partial least squares (PLS) methods. This paper very briefly describes multiset CCA-based multiset PLS. Also, the CCA methods can be applied as multivariate extensions to empirical orthogonal functions (EOF) techniques. Multiset CCA is well-suited for inclusion in geographical information systems (GIS).

Spatial information outflow from the hippocampal circuit: distributed spatial coding and phase precession in the subiculum.

PubMed

Kim, Steve M; Ganguli, Surya; Frank, Loren M

2012-08-22

Hippocampal place cells convey spatial information through a combination of spatially selective firing and theta phase precession. The way in which this information influences regions like the subiculum that receive input from the hippocampus remains unclear. The subiculum receives direct inputs from area CA1 of the hippocampus and sends divergent output projections to many other parts of the brain, so we examined the firing patterns of rat subicular neurons. We found a substantial transformation in the subicular code for space from sparse to dense firing rate representations along a proximal-distal anatomical gradient: neurons in the proximal subiculum are more similar to canonical, sparsely firing hippocampal place cells, whereas neurons in the distal subiculum have higher firing rates and more distributed spatial firing patterns. Using information theory, we found that the more distributed spatial representation in the subiculum carries, on average, more information about spatial location and context than the sparse spatial representation in CA1. Remarkably, despite the disparate firing rate properties of subicular neurons, we found that neurons at all proximal-distal locations exhibit robust theta phase precession, with similar spiking oscillation frequencies as neurons in area CA1. Our findings suggest that the subiculum is specialized to compress sparse hippocampal spatial codes into highly informative distributed codes suitable for efficient communication to other brain regions. Moreover, despite this substantial compression, the subiculum maintains finer scale temporal properties that may allow it to participate in oscillatory phase coding and spike timing-dependent plasticity in coordination with other regions of the hippocampal circuit.

A Comparison of Three Multivariate Models for Estimating Test Battery Reliability.

ERIC Educational Resources Information Center

Wood, Terry M.; Safrit, Margaret J.

1987-01-01

A comparison of three multivariate models (canonical reliability model, maximum generalizability model, canonical correlation model) for estimating test battery reliability indicated that the maximum generalizability model showed the least degree of bias, smallest errors in estimation, and the greatest relative efficiency across all experimental…

A New Ensemble Canonical Correlation Prediction Scheme for Seasonal Precipitation

NASA Technical Reports Server (NTRS)

Kim, Kyu-Myong; Lau, William K. M.; Li, Guilong; Shen, Samuel S. P.; Lau, William K. M. (Technical Monitor)

2001-01-01

Department of Mathematical Sciences, University of Alberta, Edmonton, Canada This paper describes the fundamental theory of the ensemble canonical correlation (ECC) algorithm for the seasonal climate forecasting. The algorithm is a statistical regression sch eme based on maximal correlation between the predictor and predictand. The prediction error is estimated by a spectral method using the basis of empirical orthogonal functions. The ECC algorithm treats the predictors and predictands as continuous fields and is an improvement from the traditional canonical correlation prediction. The improvements include the use of area-factor, estimation of prediction error, and the optimal ensemble of multiple forecasts. The ECC is applied to the seasonal forecasting over various parts of the world. The example presented here is for the North America precipitation. The predictor is the sea surface temperature (SST) from different ocean basins. The Climate Prediction Center's reconstructed SST (1951-1999) is used as the predictor's historical data. The optimally interpolated global monthly precipitation is used as the predictand?s historical data. Our forecast experiments show that the ECC algorithm renders very high skill and the optimal ensemble is very important to the high value.

Canonical correlation analysis of synchronous neural interactions and cognitive deficits in Alzheimer's dementia

NASA Astrophysics Data System (ADS)

Karageorgiou, Elissaios; Lewis, Scott M.; Riley McCarten, J.; Leuthold, Arthur C.; Hemmy, Laura S.; McPherson, Susan E.; Rottunda, Susan J.; Rubins, David M.; Georgopoulos, Apostolos P.

2012-10-01

In previous work (Georgopoulos et al 2007 J. Neural Eng. 4 349-55) we reported on the use of magnetoencephalographic (MEG) synchronous neural interactions (SNI) as a functional biomarker in Alzheimer's dementia (AD) diagnosis. Here we report on the application of canonical correlation analysis to investigate the relations between SNI and cognitive neuropsychological (NP) domains in AD patients. First, we performed individual correlations between each SNI and each NP, which provided an initial link between SNI and specific cognitive tests. Next, we performed factor analysis on each set, followed by a canonical correlation analysis between the derived SNI and NP factors. This last analysis optimally associated the entire MEG signal with cognitive function. The results revealed that SNI as a whole were mostly associated with memory and language, and, slightly less, executive function, processing speed and visuospatial abilities, thus differentiating functions subserved by the frontoparietal and the temporal cortices. These findings provide a direct interpretation of the information carried by the SNI and set the basis for identifying specific neural disease phenotypes according to cognitive deficits.

Correlations of occupation numbers in the canonical ensemble and application to a Bose-Einstein condensate in a one-dimensional harmonic trap

NASA Astrophysics Data System (ADS)

Giraud, Olivier; Grabsch, Aurélien; Texier, Christophe

2018-05-01

We study statistical properties of N noninteracting identical bosons or fermions in the canonical ensemble. We derive several general representations for the p -point correlation function of occupation numbers n1⋯np ¯. We demonstrate that it can be expressed as a ratio of two p ×p determinants involving the (canonical) mean occupations n1¯, ..., np¯, which can themselves be conveniently expressed in terms of the k -body partition functions (with k ≤N ). We draw some connection with the theory of symmetric functions and obtain an expression of the correlation function in terms of Schur functions. Our findings are illustrated by revisiting the problem of Bose-Einstein condensation in a one-dimensional harmonic trap, for which we get analytical results. We get the moments of the occupation numbers and the correlation between ground-state and excited-state occupancies. In the temperature regime dominated by quantum correlations, the distribution of the ground-state occupancy is shown to be a truncated Gumbel law. The Gumbel law, describing extreme-value statistics, is obtained when the temperature is much smaller than the Bose-Einstein temperature.

Science laboratory behavior strategies of students relative to performance in and attitude to laboratory work

NASA Astrophysics Data System (ADS)

Okebukola, Peter Akinsola

The relationship between science laboratory behavior strategies of students and performance in and attitude to laboratory work was investigated in an observational study of 160 laboratory sessions involving 600 class five (eleventh grade) biology students. Zero-order correlations between the behavior strategies and outcome measures reveal a set of low to strong relationships. Transmitting information, listening and nonlesson related behaviors exhibited low correlations with practical skills and the attitude measure. The correlations between manipulating apparatus and observation with practical skills measures were found to be strong. Multiple correlation analysis revealed that the behaviors of students in the laboratories observed accounted for a large percentage of the variance in the scores on manipulative skills and a low percentage on interpretation of data, responsibility, initiative, and work habits. One significant canonical correlation emerged. The loadings on this canonical variate indicate that the practical skills measures, i.e., planning and design, manipulative skills and conduct of experiments, observation and recording of data, and attitude to laboratory work made primary contributions to the canonical relationship. Suggestions as to how students can be encouraged to go beyond cookbook-like laboratories and develop a more favorable attitude to laboratory work are made.

Multivariate analysis of meat production traits in Murciano-Granadina goat kids.

PubMed

Zurita-Herrera, P; Delgado, J V; Argüello, A; Camacho, M E

2011-07-01

Growth, carcass quality, and meat quality data from Murciano-Granadina kids (n=61) raised under three different systems were collected. Canonical discriminatory analysis and cluster analysis of the entire meat production process and its stages were performed using the rearing systems as grouping criteria. All comparisons resulted in significant differences and indicated the existence of three products with different quality characteristics as a result of the three rearing systems. Differences among groups were greater when comparing carcass and meat qualities as compared with growth differences. The paired analyses of canonical correlations among groups of variables integrated in growth, carcass and meat quality, resulted in all being statistically significant, pointing out the canonical correlation coefficient between carcass quality and meat quality. Copyright © 2011 Elsevier Ltd. All rights reserved.

Droughts in Amazonia: Spatiotemporal Variability, Teleconnections, and Seasonal Predictions

NASA Astrophysics Data System (ADS)

Lima, Carlos H. R.; AghaKouchak, Amir

2017-12-01

Most Amazonia drought studies have focused on rainfall deficits and their impact on river discharges, while the analysis of other important driver variables, such as temperature and soil moisture, has attracted less attention. Here we try to better understand the spatiotemporal dynamics of Amazonia droughts and associated climate teleconnections as characterized by the Palmer Drought Severity Index (PDSI), which integrates information from rainfall deficit, temperature anomalies, and soil moisture capacity. The results reveal that Amazonia droughts are most related to one dominant pattern across the entire region, followed by two seesaw kind of patterns: north-south and east-west. The main two modes are correlated with sea surface temperature (SST) anomalies in the tropical Pacific and Atlantic oceans. The teleconnections associated with global SST are then used to build a seasonal forecast model for PDSI over Amazonia based on predictors obtained from a sparse canonical correlation analysis approach. A unique feature of the presented drought prediction method is using only a few number of predictors to avoid excessive noise in the predictor space. Cross-validated results show correlations between observed and predicted spatial average PDSI up to 0.60 and 0.45 for lead times of 5 and 9 months, respectively. To the best of our knowledge, this is the first study in the region that, based on cross-validation results, leads to appreciable forecast skills for lead times beyond 4 months. This is a step forward in better understanding the dynamics of Amazonia droughts and improving risk assessment and management, through improved drought forecasting.

A canonical correlation neural network for multicollinearity and functional data.

PubMed

Gou, Zhenkun; Fyfe, Colin

2004-03-01

We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing Partial Least Squares regression (at one extreme) to Canonical Correlation Analysis (at the other)and every intermediate operation between the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, we develop a second penalty term which acts on such data as a smoother in that the resulting weight vectors are much smoother and more interpretable than the weights without the robustification term. We illustrate our algorithms on both artificial and real data.

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

PubMed Central

Brunton, Steven L.; Proctor, Joshua L.; Kutz, J. Nathan

2016-01-01

Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing. PMID:27035946

Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

PubMed

Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

2016-04-12

Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.

The relationship between procrastination, learning strategies and statistics anxiety among Iranian college students: a canonical correlation analysis.

PubMed

Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

2012-01-01

Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction.

[Non-linear canonical correlation analysis between anthropometric indicators and multiple metabolic abnormalities].

PubMed

Fu, Xiaoli; Liu, Li; Ping, Zhiguang; Li, Linlin

2013-09-01

To define the general correlation between anthropometric indicators and multiple metabolic abnormalities, and to put forward some particular suggestions for the prevention of multiple metabolic abnormalities. A random cluster sampling was carried out in one county of Henan Province. Questionnaire, physical examination and biochemical tests were admitted to the adult inhabitants. Non-linear canonical correlation analysis (NLCCA) was applied with OVERALS of SPSS 13.0. The coefficients of canonical correlation and multiple correlation were calculated. The plot of centroids labeled by variables showed the correlation among various indicators. In total, 2,914 objects were investigated. It included 1,134 (38.9%) males and 1,780 (61.1%) females (60.0%). The average age was (50.58 +/- 13.70) years old. The fitting result of NLCCA were as follows: the loss of 0.577 accounting for 28.8% of the total variation was relatively small, and indicated that the two sets of variables of this study, namely sets of biochemical indicators (including serum total cholesterol, total triglyceride, high-density lipoprotein cholesterol, low density lipoprotein cholesterol and fasting plasma glucose) and sets of others (including gender, BMI and waist circumference) were closely related and often changed synchronously. Multivariate correlation coefficient showed that internal indicators of the above two sets were closely related respectively and often showed the multiple anomalies of the same set. The diagram of the center of gravity of the association of various indicators showed that the symptoms of metabolic abnormalities increased with age. Women were more liable to have metabolic abnormalities. Overweight and obese people often suffer multiple metabolic disorders. Waist circumference was positively correlated with metabolic abnormalities. (1) Biochemical indicators and anthropometric often change in combination. (2) Much attention should be paid to older people especially middle-aged or older men and older women in primary prevention. (3) Overweight and abdominal obesity can be considered the sensitive predictive indicator of multiple metabolic abnormalities. (4) Nonlinear canonical correlation and center of gravity Figure had the advantage of analyze the correlation between multiple sets of variables.

A canonical correlation analysis on the relationship between functional fitness and health-related quality of life in older adults.

PubMed

Chung, Pak-Kwong; Zhao, Yanan; Liu, Jing-Dong; Quach, Binh

This study aimed to explore the relationship between the functional fitness (FF) and health-related quality of life (HRQoL) in older adults, and to identify the key subdimensions of FF and HRQoL influencing their overall relationship. This cross-sectional study was performed among 851 independent community members (65-84 years; men=402). The Senior Fitness Test and the Short Form 36 Health Survey were used to measure FF and HRQoL, respectively. A canonical correlation analysis was conducted using seven fitness variables as predictors of eight HRQoL variables to examine the relationship between FF and HRQoL. The overall FF was positively correlated with the overall HRQoL in both men (canonical correlation=0.350) and women (canonical correlation=0.456). The up-and-go and 2-min step contributed the most to FF, and physical functioning contributed the most to HRQOL among men. Conversely, the up-and-go and 30-s chair stand contributed the most to FF, and physical functioning contributed the most to HRQoL in women. There were positive and moderate relationships between overall FF and overall HRQOL in older adults. The FF has a significant influence on HRQoL, particularly physical functioning. The main FF components influencing the relationship between FF and HRQoL in men are balance and agility and aerobic endurance, whereas in women they are balance and agility and lower extremity muscle strength. Results from this study facilitate comprehensively understanding the relationship between FF and HRQoL, and generating critical insight into HRQoL improvement from the perspective of FF enhancement. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Sparsely sampling the sky: Regular vs. random sampling

NASA Astrophysics Data System (ADS)

Paykari, P.; Pires, S.; Starck, J.-L.; Jaffe, A. H.

2015-09-01

Aims: The next generation of galaxy surveys, aiming to observe millions of galaxies, are expensive both in time and money. This raises questions regarding the optimal investment of this time and money for future surveys. In a previous work, we have shown that a sparse sampling strategy could be a powerful substitute for the - usually favoured - contiguous observation of the sky. In our previous paper, regular sparse sampling was investigated, where the sparse observed patches were regularly distributed on the sky. The regularity of the mask introduces a periodic pattern in the window function, which induces periodic correlations at specific scales. Methods: In this paper, we use a Bayesian experimental design to investigate a "random" sparse sampling approach, where the observed patches are randomly distributed over the total sparsely sampled area. Results: We find that in this setting, the induced correlation is evenly distributed amongst all scales as there is no preferred scale in the window function. Conclusions: This is desirable when we are interested in any specific scale in the galaxy power spectrum, such as the matter-radiation equality scale. As the figure of merit shows, however, there is no preference between regular and random sampling to constrain the overall galaxy power spectrum and the cosmological parameters.

ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES.

PubMed

Fan, Jianqing; Rigollet, Philippe; Wang, Weichen

High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓ r norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics.

ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES

PubMed Central

Fan, Jianqing; Rigollet, Philippe; Wang, Weichen

2016-01-01

High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓr norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics. PMID:26806986

Social inequality, lifestyles and health - a non-linear canonical correlation analysis based on the approach of Pierre Bourdieu.

PubMed

Grosse Frie, Kirstin; Janssen, Christian

2009-01-01

Based on the theoretical and empirical approach of Pierre Bourdieu, a multivariate non-linear method is introduced as an alternative way to analyse the complex relationships between social determinants and health. The analysis is based on face-to-face interviews with 695 randomly selected respondents aged 30 to 59. Variables regarding socio-economic status, life circumstances, lifestyles, health-related behaviour and health were chosen for the analysis. In order to determine whether the respondents can be differentiated and described based on these variables, a non-linear canonical correlation analysis (OVERALS) was performed. The results can be described on three dimensions; Eigenvalues add up to the fit of 1.444, which can be interpreted as approximately 50 % of explained variance. The three-dimensional space illustrates correspondences between variables and provides a framework for interpretation based on latent dimensions, which can be described by age, education, income and gender. Using non-linear canonical correlation analysis, health characteristics can be analysed in conjunction with socio-economic conditions and lifestyles. Based on Bourdieus theoretical approach, the complex correlations between these variables can be more substantially interpreted and presented.

Sparse models for correlative and integrative analysis of imaging and genetic data

PubMed Central

Lin, Dongdong; Cao, Hongbao; Calhoun, Vince D.

2014-01-01

The development of advanced medical imaging technologies and high-throughput genomic measurements has enhanced our ability to understand their interplay as well as their relationship with human behavior by integrating these two types of datasets. However, the high dimensionality and heterogeneity of these datasets presents a challenge to conventional statistical methods; there is a high demand for the development of both correlative and integrative analysis approaches. Here, we review our recent work on developing sparse representation based approaches to address this challenge. We show how sparse models are applied to the correlation and integration of imaging and genetic data for biomarker identification. We present examples on how these approaches are used for the detection of risk genes and classification of complex diseases such as schizophrenia. Finally, we discuss future directions on the integration of multiple imaging and genomic datasets including their interactions such as epistasis. PMID:25218561

Onset of Speech-Like Vocalizations in Infants with Down Syndrome.

ERIC Educational Resources Information Center

Lynch, Michael P.; And Others

1995-01-01

Evaluation of canonical babbling of 13 infants with Down syndrome found that age of onset of babbling was approximately 2 months later and less stable than that of 27 typically developing infants. Age at onset of canonical babbling for Down syndrome infants was correlated with scores at 27 months on the Early Social-Communication Scales.…

Relationship between organisational commitment and burnout syndrome: a canonical correlation approach.

PubMed

Enginyurt, Ozgur; Cankaya, Soner; Aksay, Kadir; Tunc, Taner; Koc, Bozkurt; Bas, Orhan; Ozer, Erdal

2016-04-01

Objective Burnout syndrome can significantly reduce the performance of health workers. Although many factors have been identified as antecedents of burnout, few studies have investigated the role of organisational commitment in its development. The purpose of the present study was to examine the relationships between subdimensions of burnout syndrome (emotional exhaustion, depersonalisation and personal accomplishment) and subdimensions of organisational commitment (affective commitment, continuance commitment and normative commitment). Methods The present study was a cross-sectional survey of physicians and other healthcare employees working in the Ministry of Health Ordu University Education and Research Hospital. The sample consisted of 486 healthcare workers. Data were collected using the Maslach Burnout Inventory and the Organisation Commitment Scale, and were analysed using the canonical correlation approach. Results The first of three canonical correlation coefficients between pairs of canonical variables (Ui , burnout syndrome and Vi, organisational commitment) was found to be statistically significant. Emotional exhaustion was found to contribute most towards the explanatory capacity of canonical variables estimated from the subdimensions of burnout syndrome, whereas affective commitment provided the largest contribution towards the explanatory capacity of canonical variables estimated from the subdimensions of organisational commitment. Conclusions The results of the present study indicate that affective commitment is the primary determinant of burnout syndrome in healthcare professionals. What is known about the topic? Organisational commitment and burnout syndrome are the most important criteria in predicting health workforce performance. An increasing number of studies in recent years have clearly indicated the field's continued relevance and importance. Conversely, canonical correlation analysis (CCA) is a technique for describing the relationship between two variable sets simultaneously to produce both structural and spatial meaning. What does this paper add? To our knowledge, CCA has not been used to determine the relationships between burnout and organisational commitment of physicians and other healthcare staff. Accordingly, the present study adds information regarding the relationship between burnout and organisational commitment variables determined using CCA. This analysis is used to describe the relationship between two variable sets simultaneously and allows for an easy method of interpretation. What are the implications for practitioners? Burnout syndrome is a major threat to both the health workforce and its organisations. In addition, it affects the quality and effectiveness of health care. Thus, the findings of the present study offer a solid foundation from which actions to decrease burnout levels in healthcare professionals can be implemented by successfully increasing levels of organisational commitment.

Motivators and barriers to participating in health promotion behaviors in Black men.

PubMed

Calvert, Wilma J; Isaac-Savage, E Paulette

2013-08-01

There is limited research examining the health promotion behaviors (HPBs) of low-income Black men. This study examined the relationship between HPBs, and motivators and barriers to participating in these behaviors in Black men (N = 107), aged 21 to 56. Using descriptive statistics, more than 96% of the participants reported they were motivated because of the desire to be healthy. Canonical correlation analysis and conditional random forest were used to determine the importance of individual motivators and barriers. Canonical correlation analysis yielded one interpretable canonical variate that explained 39.5% of the variance in sets of motivators and barriers, and health promotion lifestyle variables. Men with fewer motivators and more barriers took less responsibility for their health, participated in less physical activity, and reported less spiritual growth. Having too many things to do and not knowing what to do best predicted participation in HPBs.

The Relationship Between Procrastination, Learning Strategies and Statistics Anxiety Among Iranian College Students: A Canonical Correlation Analysis

PubMed Central

Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

2012-01-01

Objective: Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. Methods: A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Results: Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. Conclusion: These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction. PMID:24644468

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.

PubMed

Deligianni, Fani; Centeno, Maria; Carmichael, David W; Clayden, Jonathan D

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

PubMed Central

Deligianni, Fani; Centeno, Maria; Carmichael, David W.; Clayden, Jonathan D.

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity. PMID:25221467

The Application of a Technique for Vector Correlation to Problems in Meteorology and Oceanography.

NASA Astrophysics Data System (ADS)

Breaker, L. C.; Gemmill, W. H.; Crosby, D. S.

1994-11-01

In a recent study, Crosby et al. proposed a definition for vector correlation that has not been commonly used in meteorology or oceanography. This definition has both a firm theoretical basis and a rather complete set of desirable statistical properties. In this study, the authors apply the definition to practical problems arising in meteorology and oceanography. In the first of two case studies, vector correlations were calculated between subsurface currents for five locations along the southeastern shore of Lake Erie. Vector correlations for one sample size were calculated for all current meter combinations, first including the seiche frequency and then with the seiche frequency removed. Removal of the seiche frequency, which was easily detected in the current spectra, had only a small effect on the vector correlations. Under reasonable assumptions, the vector correlations were in most cases statistically significant and revealed considerable fine structure in the vector correlation sequences. In some cases, major variations in vector correlation coincided with changes in surface wind. The vector correlations for the various current meter combinations decreased rapidly with increasing spatial separation. For one current meter combination, canonical correlations were also calculated; the first canonical correlation tended to retain the underlying trend, whereas the second canonical correlation retained the peaks in the vector correlations.In the second case study, vector correlations were calculated between marine surface winds derived from the National Meteorological Center's Global Data Assimilation System and observed winds acquired from the network of National Data Buoy Center buoys that are located off the continental United States and in the Gulf of Alaska. Results of this comparison indicated that 1) there was a significant decrease in correlation between the predicted and observed winds with increasing forecast interval out to 72 h, 2) the technique provides a sensitive indicator for detecting bad buoy reports, and 3) there was no obvious seasonal cycle in the monthly vector correlations for the period of observation.

Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination.

PubMed

Lin, Andrew C; Bygrave, Alexei M; de Calignon, Alix; Lee, Tzumin; Miesenböck, Gero

2014-04-01

Sparse coding may be a general strategy of neural systems for augmenting memory capacity. In Drosophila melanogaster, sparse odor coding by the Kenyon cells of the mushroom body is thought to generate a large number of precisely addressable locations for the storage of odor-specific memories. However, it remains untested how sparse coding relates to behavioral performance. Here we demonstrate that sparseness is controlled by a negative feedback circuit between Kenyon cells and the GABAergic anterior paired lateral (APL) neuron. Systematic activation and blockade of each leg of this feedback circuit showed that Kenyon cells activated APL and APL inhibited Kenyon cells. Disrupting the Kenyon cell-APL feedback loop decreased the sparseness of Kenyon cell odor responses, increased inter-odor correlations and prevented flies from learning to discriminate similar, but not dissimilar, odors. These results suggest that feedback inhibition suppresses Kenyon cell activity to maintain sparse, decorrelated odor coding and thus the odor specificity of memories.

Examination of the relationships between environmental exposures to volatile organic compounds and biochemical liver tests: application of canonical correlation analysis

PubMed Central

Liu, Jing; Drane, Wanzer; Liu, Xuefeng; Wu, Tiejian

2009-01-01

This study was to explore the relationships between personal exposure to ten volatile organic compounds (VOCs) and biochemical liver tests with the application of canonical correlation analysis. Data from a subsample of the 1999–2000 National Health and Nutrition Examination Survey were used. Serum albumin, total bilirubin (TB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), and γ-glutamyl transferase (GGT) served as the outcome variables. Personal exposures to benzene, chloroform, ethylbenzene, tetrachloroethene, toluene, trichloroethene, o-xylene, m-, p-xylene, 1,4-dichlorobenzene, and methyl tert-butyl ether (MTBE) were assessed through the use of passive exposure monitors worn by study participants. The first two canonical correlations were 0.3218 and 0.2575, suggesting a positive correlation mainly between the six VOCs (benzene, ethylbenzene, toluene, o-xylene, m-, p-xylene, and MTBE) and the three biochemical liver tests (albumin, ALP, and GGT) and a positive correlation mainly between the two VOCs (1,4-dichlorobenzene and tetrachloroethene) and the two biochemical liver tests (LDH and TB). Subsequent multiple linear regressions show that exposure to benzene, toluene, or MTBE was associated with serum albumin, while exposure to tetrachloroethene was associated with LDH and total bilirubin. In conclusion, exposure to certain VOCs as a group or individually may influence certain biochemical liver test results in the general population. PMID:19117555

Examination of the relationships between environmental exposures to volatile organic compounds and biochemical liver tests: application of canonical correlation analysis.

PubMed

Liu, Jing; Drane, Wanzer; Liu, Xuefeng; Wu, Tiejian

2009-02-01

This study was to explore the relationships between personal exposure to 10 volatile organic compounds (VOCs) and biochemical liver tests with the application of canonical correlation analysis. Data from a subsample of the 1999-2000 National Health and Nutrition Examination Survey were used. Serum albumin, total bilirubin (TB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT) served as the outcome variables. Personal exposures to benzene, chloroform, ethylbenzene, tetrachloroethene, toluene, trichloroethene, o-xylene, m-,p-xylene, 1,4-dichlorobenzene, and methyl tert-butyl ether (MTBE) were assessed through the use of passive exposure monitors worn by study participants. The first two canonical correlations were 0.3218 and 0.2575, suggesting a positive correlation mainly between the six VOCs (benzene, ethylbenzene, toluene, o-xylene, m-,p-xylene, and MTBE) and the three biochemical liver tests (albumin, ALP, and GGT) and a positive correlation mainly between the two VOCs (1,4-dichlorobenzene and tetrachloroethene) and the two biochemical liver tests (LDH and TB). Subsequent multiple linear regressions show that exposure to benzene, toluene, or MTBE was associated with serum albumin, while exposure to tetrachloroethene was associated with LDH and total bilirubin. In conclusion, exposure to certain VOCs as a group or individually may influence certain biochemical liver test results in the general population.

Infant vocalizations and the early diagnosis of severe hearing impairment.

PubMed

Eilers, R E; Oller, D K

1994-02-01

To determine whether late onset of canonical babbling could be used as a criterion to determine risk of hearing impairment, we obtained vocalization samples longitudinally from 94 infants with normal hearing and 37 infants with severe to profound hearing impairment. Parents were instructed to report the onset of canonical babbling (the production of well-formed syllables such as "da," "na," "bee," "yaya"). Verification that the infants were producing canonical syllables was collected in laboratory audio recordings. Infants with normal hearing produced canonical vocalizations before 11 months of age (range, 3 to 10 months; mode, 7 months); infants who were deaf failed to produce canonical syllables until 11 months of age or older, often well into the third year of life (range, 11 to 49 months; mode, 24 months). The correlation between age at onset of the canonical stage and age at auditory amplification was 0.68, indicating that early identification and fitting of hearing aids is of significant benefit to infants learning language. The fact that there is no overlap in the distribution of the onset of canonical babbling between infants with normal hearing and infants with hearing impairment means that the failure of otherwise healthy infants to produce canonical syllables before 11 months of age should be considered a serious risk factor for hearing impairment and, when observed, should result in immediate referral for audiologic evaluation.

Kanerva's sparse distributed memory with multiple hamming thresholds

NASA Technical Reports Server (NTRS)

Pohja, Seppo; Kaski, Kimmo

1992-01-01

If the stored input patterns of Kanerva's Sparse Distributed Memory (SDM) are highly correlated, utilization of the storage capacity is very low compared to the case of uniformly distributed random input patterns. We consider a variation of SDM that has a better storage capacity utilization for correlated input patterns. This approach uses a separate selection threshold for each physical storage address or hard location. The selection of the hard locations for reading or writing can be done in parallel of which SDM implementations can benefit.

Correlation between Gini index and mobility in a stochastic kinetic model of economic exchange

NASA Astrophysics Data System (ADS)

Bertotti, Maria Letizia; Chattopadhyay, Amit K.; Modanese, Giovanni

Starting from a class of stochastically driven kinetic models of economic exchange, here we present results highlighting the correlation of the Gini inequality index with the social mobility rate, close to dynamical equilibrium. Except for the "canonical-additive case", our numerical results consistently indicate negative values of the correlation coefficient, in agreement with empirical evidence. This confirms that growing inequality is not conducive to social mobility which then requires an "external source" to sustain its dynamics. On the other hand, the sign of the correlation between inequality and total income in the canonical ensemble depends on the way wealth enters or leaves the system. At a technical level, the approach involves a generalization of a stochastic dynamical system formulation, that further paves the way for a probabilistic formulation of perturbed economic exchange models.

Urban slum structure: integrating socioeconomic and land cover data to model slum evolution in Salvador, Brazil.

PubMed

Hacker, Kathryn P; Seto, Karen C; Costa, Federico; Corburn, Jason; Reis, Mitermayer G; Ko, Albert I; Diuk-Wasser, Maria A

2013-10-20

The expansion of urban slums is a key challenge for public and social policy in the 21st century. The heterogeneous and dynamic nature of slum communities limits the use of rigid slum definitions. A systematic and flexible approach to characterize, delineate and model urban slum structure at an operational resolution is essential to plan, deploy, and monitor interventions at the local and national level. We modeled the multi-dimensional structure of urban slums in the city of Salvador, a city of 3 million inhabitants in Brazil, by integrating census-derived socioeconomic variables and remotely-sensed land cover variables. We assessed the correlation between the two sets of variables using canonical correlation analysis, identified land cover proxies for the socioeconomic variables, and produced an integrated map of deprivation in Salvador at 30 m × 30 m resolution. The canonical analysis identified three significant ordination axes that described the structure of Salvador census tracts according to land cover and socioeconomic features. The first canonical axis captured a gradient from crowded, low-income communities with corrugated roof housing to higher-income communities. The second canonical axis discriminated among socioeconomic variables characterizing the most marginalized census tracts, those without access to sanitation or piped water. The third canonical axis accounted for the least amount of variation, but discriminated between high-income areas with white-painted or tiled roofs from lower-income areas. Our approach captures the socioeconomic and land cover heterogeneity within and between slum settlements and identifies the most marginalized communities in a large, complex urban setting. These findings indicate that changes in the canonical scores for slum areas can be used to track their evolution and to monitor the impact of development programs such as slum upgrading.

Urban slum structure: integrating socioeconomic and land cover data to model slum evolution in Salvador, Brazil

PubMed Central

2013-01-01

Background The expansion of urban slums is a key challenge for public and social policy in the 21st century. The heterogeneous and dynamic nature of slum communities limits the use of rigid slum definitions. A systematic and flexible approach to characterize, delineate and model urban slum structure at an operational resolution is essential to plan, deploy, and monitor interventions at the local and national level. Methods We modeled the multi-dimensional structure of urban slums in the city of Salvador, a city of 3 million inhabitants in Brazil, by integrating census-derived socioeconomic variables and remotely-sensed land cover variables. We assessed the correlation between the two sets of variables using canonical correlation analysis, identified land cover proxies for the socioeconomic variables, and produced an integrated map of deprivation in Salvador at 30 m × 30 m resolution. Results The canonical analysis identified three significant ordination axes that described the structure of Salvador census tracts according to land cover and socioeconomic features. The first canonical axis captured a gradient from crowded, low-income communities with corrugated roof housing to higher-income communities. The second canonical axis discriminated among socioeconomic variables characterizing the most marginalized census tracts, those without access to sanitation or piped water. The third canonical axis accounted for the least amount of variation, but discriminated between high-income areas with white-painted or tiled roofs from lower-income areas. Conclusions Our approach captures the socioeconomic and land cover heterogeneity within and between slum settlements and identifies the most marginalized communities in a large, complex urban setting. These findings indicate that changes in the canonical scores for slum areas can be used to track their evolution and to monitor the impact of development programs such as slum upgrading. PMID:24138776

An atomistic fingerprint algorithm for learning ab initio molecular force fields

NASA Astrophysics Data System (ADS)

Tang, Yu-Hang; Zhang, Dongkun; Karniadakis, George Em

2018-01-01

Molecular fingerprints, i.e., feature vectors describing atomistic neighborhood configurations, is an important abstraction and a key ingredient for data-driven modeling of potential energy surface and interatomic force. In this paper, we present the density-encoded canonically aligned fingerprint algorithm, which is robust and efficient, for fitting per-atom scalar and vector quantities. The fingerprint is essentially a continuous density field formed through the superimposition of smoothing kernels centered on the atoms. Rotational invariance of the fingerprint is achieved by aligning, for each fingerprint instance, the neighboring atoms onto a local canonical coordinate frame computed from a kernel minisum optimization procedure. We show that this approach is superior over principal components analysis-based methods especially when the atomistic neighborhood is sparse and/or contains symmetry. We propose that the "distance" between the density fields be measured using a volume integral of their pointwise difference. This can be efficiently computed using optimal quadrature rules, which only require discrete sampling at a small number of grid points. We also experiment on the choice of weight functions for constructing the density fields and characterize their performance for fitting interatomic potentials. The applicability of the fingerprint is demonstrated through a set of benchmark problems.

Linear-scaling implementation of molecular response theory in self-consistent field electronic-structure theory.

PubMed

Coriani, Sonia; Høst, Stinne; Jansík, Branislav; Thøgersen, Lea; Olsen, Jeppe; Jørgensen, Poul; Reine, Simen; Pawłowski, Filip; Helgaker, Trygve; Sałek, Paweł

2007-04-21

A linear-scaling implementation of Hartree-Fock and Kohn-Sham self-consistent field theories for the calculation of frequency-dependent molecular response properties and excitation energies is presented, based on a nonredundant exponential parametrization of the one-electron density matrix in the atomic-orbital basis, avoiding the use of canonical orbitals. The response equations are solved iteratively, by an atomic-orbital subspace method equivalent to that of molecular-orbital theory. Important features of the subspace method are the use of paired trial vectors (to preserve the algebraic structure of the response equations), a nondiagonal preconditioner (for rapid convergence), and the generation of good initial guesses (for robust solution). As a result, the performance of the iterative method is the same as in canonical molecular-orbital theory, with five to ten iterations needed for convergence. As in traditional direct Hartree-Fock and Kohn-Sham theories, the calculations are dominated by the construction of the effective Fock/Kohn-Sham matrix, once in each iteration. Linear complexity is achieved by using sparse-matrix algebra, as illustrated in calculations of excitation energies and frequency-dependent polarizabilities of polyalanine peptides containing up to 1400 atoms.

Canonical correlation analysis to identify the semen characteristics used to forecast the freeze survival of Curimba (Prochilodus lineatus) spermatozoa.

PubMed

Carvalho, A F S; Murgas, L D S; Ferreira-Machad, M R; Andrade, E S; Felizardo, V O; Allaman, I B; de Paula, F G

　　OBJECTIVE: To identify which sperm characteristics were able to predict more accurately the quality of curimba (Prochilodus lineatus) semen upon freezing using canonical correlation analysis. Eleven fish breeders with initial mean weight of 705.21 ± 111 g were used. For cryopreservation, 200 µL of semen were taken from each animal and diluted in the cryoprotectant solution (10% dimethyl sulfoxide and 5% Beltsville Thawing Solution Minitub) in a 1:4 ratio and placed into 0.5-mL straws. Sperm characteristics (motility, sperm abnormalities, total antioxidant activity and lipid peroxidation) were evaluated. A randomized block design with duplicate samples per treatment (fresh and frozen semen) was used. The block factor was the animals, and the experimental unit the ejaculates. Canonical correlation was used to evaluate the association between sperm characteristics of fresh semen and thawed semen. There was a significant association (P = 0.10) among the variables measured in fresh semen with the variables measured in thawed semen, and 78.6% of the difference observed in the thawed semen can be attributed to variation of variables measured in fresh semen. Sperm motility, motility duration and antioxidant activity of the thawed semen showed an inverse relationship with those of the fresh semen; whereas the minor sperm abnormalities, major sperm abnormalities and lipid peroxidation showed a direct relationship with those of the fresh semen. Only the rate and motility duration of the thawed semen presented high correlation (-0.63 and -0.73, respectively) with the canonical variable represented by the sperm characteristics of fresh semen. The rate and motility duration of fresh semen may be used to predict the quality of the thawed sperm in Prochilodus lineatus.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

NASA Astrophysics Data System (ADS)

Hyman, J.; Aldrich, G. A.; Viswanathan, H. S.; Makedonska, N.; Karra, S.

2016-12-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semi-correlation, and non-correlation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same.We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

Grid-based lattice summation of electrostatic potentials by assembled rank-structured tensor approximation

NASA Astrophysics Data System (ADS)

Khoromskaia, Venera; Khoromskij, Boris N.

2014-12-01

Our recent method for low-rank tensor representation of sums of the arbitrarily positioned electrostatic potentials discretized on a 3D Cartesian grid reduces the 3D tensor summation to operations involving only 1D vectors however retaining the linear complexity scaling in the number of potentials. Here, we introduce and study a novel tensor approach for fast and accurate assembled summation of a large number of lattice-allocated potentials represented on 3D N × N × N grid with the computational requirements only weakly dependent on the number of summed potentials. It is based on the assembled low-rank canonical tensor representations of the collected potentials using pointwise sums of shifted canonical vectors representing the single generating function, say the Newton kernel. For a sum of electrostatic potentials over L × L × L lattice embedded in a box the required storage scales linearly in the 1D grid-size, O(N) , while the numerical cost is estimated by O(NL) . For periodic boundary conditions, the storage demand remains proportional to the 1D grid-size of a unit cell, n = N / L, while the numerical cost reduces to O(N) , that outperforms the FFT-based Ewald-type summation algorithms of complexity O(N3 log N) . The complexity in the grid parameter N can be reduced even to the logarithmic scale O(log N) by using data-sparse representation of canonical N-vectors via the quantics tensor approximation. For justification, we prove an upper bound on the quantics ranks for the canonical vectors in the overall lattice sum. The presented approach is beneficial in applications which require further functional calculus with the lattice potential, say, scalar product with a function, integration or differentiation, which can be performed easily in tensor arithmetics on large 3D grids with 1D cost. Numerical tests illustrate the performance of the tensor summation method and confirm the estimated bounds on the tensor ranks.

SparseMaps—A systematic infrastructure for reduced-scaling electronic structure methods. III. Linear-scaling multireference domain-based pair natural orbital N-electron valence perturbation theory

NASA Astrophysics Data System (ADS)

Guo, Yang; Sivalingam, Kantharuban; Valeev, Edward F.; Neese, Frank

2016-03-01

Multi-reference (MR) electronic structure methods, such as MR configuration interaction or MR perturbation theory, can provide reliable energies and properties for many molecular phenomena like bond breaking, excited states, transition states or magnetic properties of transition metal complexes and clusters. However, owing to their inherent complexity, most MR methods are still too computationally expensive for large systems. Therefore the development of more computationally attractive MR approaches is necessary to enable routine application for large-scale chemical systems. Among the state-of-the-art MR methods, second-order N-electron valence state perturbation theory (NEVPT2) is an efficient, size-consistent, and intruder-state-free method. However, there are still two important bottlenecks in practical applications of NEVPT2 to large systems: (a) the high computational cost of NEVPT2 for large molecules, even with moderate active spaces and (b) the prohibitive cost for treating large active spaces. In this work, we address problem (a) by developing a linear scaling "partially contracted" NEVPT2 method. This development uses the idea of domain-based local pair natural orbitals (DLPNOs) to form a highly efficient algorithm. As shown previously in the framework of single-reference methods, the DLPNO concept leads to an enormous reduction in computational effort while at the same time providing high accuracy (approaching 99.9% of the correlation energy), robustness, and black-box character. In the DLPNO approach, the virtual space is spanned by pair natural orbitals that are expanded in terms of projected atomic orbitals in large orbital domains, while the inactive space is spanned by localized orbitals. The active orbitals are left untouched. Our implementation features a highly efficient "electron pair prescreening" that skips the negligible inactive pairs. The surviving pairs are treated using the partially contracted NEVPT2 formalism. A detailed comparison between the partial and strong contraction schemes is made, with conclusions that discourage the strong contraction scheme as a basis for local correlation methods due to its non-invariance with respect to rotations in the inactive and external subspaces. A minimal set of conservatively chosen truncation thresholds controls the accuracy of the method. With the default thresholds, about 99.9% of the canonical partially contracted NEVPT2 correlation energy is recovered while the crossover of the computational cost with the already very efficient canonical method occurs reasonably early; in linear chain type compounds at a chain length of around 80 atoms. Calculations are reported for systems with more than 300 atoms and 5400 basis functions.

The association between indicators of health and housing in people with Parkinson's disease.

PubMed

Nilsson, Maria H; Ullén, Susann; Ekström, Henrik; Iwarsson, Susanne

2016-07-27

There are knowledge gaps about the life situation for people ageing with Parkinson's disease (PD), with virtually no understanding of home and health dynamics. Therefore, the aim of the present study was to explore the association between aspects of health and objective as well as perceived housing in people with PD. Participants were recruited from three hospitals in the region of Skåne in southern Sweden. The sample for the present study included 231 (62 % men) participants with PD, with a mean age of 75 (min-max, 45-93) years. The data collection procedure included a self-administered postal survey and a subsequent home visit where structured interviews, observations and clinical assessments were administered. To study the association between aspects of health and housing canonical correlation was applied. Twelve variables (6 in the health and 6 in the housing set) were included. This corresponds to about 20 individuals per variable and is considered sufficient to accurately interpret the largest (i.e., first) canonical correlation. The analysis between the health variables and housing variables set yielded two significant pairs of variates with the canonical correlations 0.68 (p < 0.0001) and 0.33 (p = 0.0112), respectively. For the first pair of variates the canonical R(2) was 0.46. The results showed that external control beliefs and behavioral aspects of meaning of home contributed the most to the housing variate, whereas difficulties/dependence in activities of daily living (ADL) and functional limitations contributed the most to the health variate. Although a significant relationship was found for the second canonical correlation, the shared variance between the two variates was considerably lower; R(2) = 0.11. This study suggests that people with PD who have more functional limitations, difficulties in ADL and are more dependent perceive their homes as less meaningful from a behavioral perspective. Moreover, they tend to rely on external influences managing their housing situation. With this kind of knowledge at hand, health care and social services professionals are in a better position to observe and efficiently address problems related to health and housing among people with PD.

Doubly Nonparametric Sparse Nonnegative Matrix Factorization Based on Dependent Indian Buffet Processes.

PubMed

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Xu, Richard Yi Da; Luo, Xiangfeng

2018-05-01

Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP). We apply a correlation function for the generation of two stick weights associated with each column pair of factor matrices while still maintaining their respective marginal distribution specified by IBP. As a consequence, the generation of two factor matrices will be columnwise correlated. Under this framework, two classes of correlation function are proposed: 1) using bivariate Beta distribution and 2) using Copula function. Compared with the single IBP-based NMF, this paper jointly makes two factor matrices nonparametric and sparse, which could be applied to broader scenarios, such as co-clustering. This paper is seen to be much more flexible than Gaussian process-based and hierarchial Beta process-based dIBPs in terms of allowing the two corresponding binary matrix columns to have greater variations in their nonzero entries. Our experiments on synthetic data show the merits of this paper compared with the state-of-the-art models in respect of factorization efficiency, sparsity, and flexibility. Experiments on real-world data sets demonstrate the efficiency of this paper in document-word co-clustering tasks.

A canonical correlation analysis based EMG classification algorithm for eliminating electrode shift effect.

PubMed

Zhe Fan; Zhong Wang; Guanglin Li; Ruomei Wang

2016-08-01

Motion classification system based on surface Electromyography (sEMG) pattern recognition has achieved good results in experimental condition. But it is still a challenge for clinical implement and practical application. Many factors contribute to the difficulty of clinical use of the EMG based dexterous control. The most obvious and important is the noise in the EMG signal caused by electrode shift, muscle fatigue, motion artifact, inherent instability of signal and biological signals such as Electrocardiogram. In this paper, a novel method based on Canonical Correlation Analysis (CCA) was developed to eliminate the reduction of classification accuracy caused by electrode shift. The average classification accuracy of our method were above 95% for the healthy subjects. In the process, we validated the influence of electrode shift on motion classification accuracy and discovered the strong correlation with correlation coefficient of >0.9 between shift position data and normal position data.

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets.

PubMed

Madrigal, Pedro

2017-03-01

Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomic science, as it allows both to evaluate reproducibility of biological or technical replicates, and to compare different datasets to identify their potential correlations. Here we present fCCAC, an application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We show how this method differs from other measures of correlation, and exemplify how it can reveal shared covariance between histone modifications and DNA binding proteins, such as the relationship between the H3K4me3 chromatin mark and its epigenetic writers and readers. An R/Bioconductor package is available at http://bioconductor.org/packages/fCCAC/ . pmb59@cam.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Path integral approach to the Wigner representation of canonical density operators for discrete systems coupled to harmonic baths.

PubMed

Montoya-Castillo, Andrés; Reichman, David R

2017-01-14

We derive a semi-analytical form for the Wigner transform for the canonical density operator of a discrete system coupled to a harmonic bath based on the path integral expansion of the Boltzmann factor. The introduction of this simple and controllable approach allows for the exact rendering of the canonical distribution and permits systematic convergence of static properties with respect to the number of path integral steps. In addition, the expressions derived here provide an exact and facile interface with quasi- and semi-classical dynamical methods, which enables the direct calculation of equilibrium time correlation functions within a wide array of approaches. We demonstrate that the present method represents a practical path for the calculation of thermodynamic data for the spin-boson and related systems. We illustrate the power of the present approach by detailing the improvement of the quality of Ehrenfest theory for the correlation function C zz (t)=Re⟨σ z (0)σ z (t)⟩ for the spin-boson model with systematic convergence to the exact sampling function. Importantly, the numerically exact nature of the scheme presented here and its compatibility with semiclassical methods allows for the systematic testing of commonly used approximations for the Wigner-transformed canonical density.

Sparse representation based SAR vehicle recognition along with aspect angle.

PubMed

Xing, Xiangwei; Ji, Kefeng; Zou, Huanxin; Sun, Jixiang

2014-01-01

As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA), in which the correlation between the vehicle's aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA) feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle's aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

Numerosity but not texture-density discrimination correlates with math ability in children.

PubMed

Anobile, Giovanni; Castaldi, Elisa; Turi, Marco; Tinelli, Francesca; Burr, David C

2016-08-01

Considerable recent work suggests that mathematical abilities in children correlate with the ability to estimate numerosity. Does math correlate only with numerosity estimation, or also with other similar tasks? We measured discrimination thresholds of school-age (6- to 12.5-years-old) children in 3 tasks: numerosity of patterns of relatively sparse, segregatable items (24 dots); numerosity of very dense textured patterns (250 dots); and discrimination of direction of motion. Thresholds in all tasks improved with age, but at different rates, implying the action of different mechanisms: In particular, in young children, thresholds were lower for sparse than textured patterns (the opposite of adults), suggesting earlier maturation of numerosity mechanisms. Importantly, numerosity thresholds for sparse stimuli correlated strongly with math skills, even after controlling for the influence of age, gender and nonverbal IQ. However, neither motion-direction discrimination nor numerosity discrimination of texture patterns showed a significant correlation with math abilities. These results provide further evidence that numerosity and texture-density are perceived by independent neural mechanisms, which develop at different rates; and importantly, only numerosity mechanisms are related to math. As developmental dyscalculia is characterized by a profound deficit in discriminating numerosity, it is fundamental to understand the mechanism behind the discrimination. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Joint Blind Source Separation by Multi-set Canonical Correlation Analysis

PubMed Central

Li, Yi-Ou; Adalı, Tülay; Wang, Wei; Calhoun, Vince D

2009-01-01

In this work, we introduce a simple and effective scheme to achieve joint blind source separation (BSS) of multiple datasets using multi-set canonical correlation analysis (M-CCA) [1]. We first propose a generative model of joint BSS based on the correlation of latent sources within and between datasets. We specify source separability conditions, and show that, when the conditions are satisfied, the group of corresponding sources from each dataset can be jointly extracted by M-CCA through maximization of correlation among the extracted sources. We compare source separation performance of the M-CCA scheme with other joint BSS methods and demonstrate the superior performance of the M-CCA scheme in achieving joint BSS for a large number of datasets, group of corresponding sources with heterogeneous correlation values, and complex-valued sources with circular and non-circular distributions. We apply M-CCA to analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects and show its utility in estimating meaningful brain activations from a visuomotor task. PMID:20221319

Canonical correlation analysis of infant's size at birth and maternal factors: a study in rural northwest Bangladesh.

PubMed

Kabir, Alamgir; Merrill, Rebecca D; Shamim, Abu Ahmed; Klemn, Rolf D W; Labrique, Alain B; Christian, Parul; West, Keith P; Nasser, Mohammed

2014-01-01

This analysis was conducted to explore the association between 5 birth size measurements (weight, length and head, chest and mid-upper arm [MUAC] circumferences) as dependent variables and 10 maternal factors as independent variables using canonical correlation analysis (CCA). CCA considers simultaneously sets of dependent and independent variables and, thus, generates a substantially reduced type 1 error. Data were from women delivering a singleton live birth (n = 14,506) while participating in a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural Bangladesh. The first canonical correlation was 0.42 (P<0.001), demonstrating a moderate positive correlation mainly between the 5 birth size measurements and 5 maternal factors (preterm delivery, early pregnancy MUAC, infant sex, age and parity). A significant interaction between infant sex and preterm delivery on birth size was also revealed from the score plot. Thirteen percent of birth size variability was explained by the composite score of the maternal factors (Redundancy, RY/X = 0.131). Given an ability to accommodate numerous relationships and reduce complexities of multiple comparisons, CCA identified the 5 maternal variables able to predict birth size in this rural Bangladesh setting. CCA may offer an efficient, practical and inclusive approach to assessing the association between two sets of variables, addressing the innate complexity of interactions.

Tensor-guided fitting of subduction slab depths

USGS Publications Warehouse

Bazargani, Farhad; Hayes, Gavin P.

2013-01-01

Geophysical measurements are often acquired at scattered locations in space. Therefore, interpolating or fitting the sparsely sampled data as a uniform function of space (a procedure commonly known as gridding) is a ubiquitous problem in geophysics. Most gridding methods require a model of spatial correlation for data. This spatial correlation model can often be inferred from some sort of secondary information, which may also be sparsely sampled in space. In this paper, we present a new method to model the geometry of a subducting slab in which we use a data‐fitting approach to address the problem. Earthquakes and active‐source seismic surveys provide estimates of depths of subducting slabs but only at scattered locations. In addition to estimates of depths from earthquake locations, focal mechanisms of subduction zone earthquakes also provide estimates of the strikes of the subducting slab on which they occur. We use these spatially sparse strike samples and the Earth’s curved surface geometry to infer a model for spatial correlation that guides a blended neighbor interpolation of slab depths. We then modify the interpolation method to account for the uncertainties associated with the depth estimates.

Lymphotoxin β receptor activation promotes bladder cancer in a nuclear factor-κB-dependent manner.

PubMed

Shen, Mo; Duan, Xiuzhi; Zhou, Ping; Zhou, Wu; Wu, Xiuling; Xu, Siqi; Chen, Yuhua; Tao, Zhihua

2015-02-01

Bladder cancer (BCa) is the most common tumor of the urinary system. Chronic inflammation in the papillary urothelial neoplasm of low malignant potential (PUNLMP)may contribute to carcinogenesis, including that of BCa, via poorly understood mechanisms. In this study, we show that the lymphotoxin β receptor (LTβR) is upregulated in BCa via activation of the canonical and non-canonical nuclear factor-κB (NF-κB) pathways. The mRNA expression of LTβR in 81 BCa, 10 chronic cystitis and 23 healthy bladder mucosa tissues was investigated by reverse transcription-fluorescent quantitative polymerase chain reaction (RT-FQ-PCR), and protein expression was studied in 73 BCa, 30 cystitis and 15 healthy paraffin-embedded tissue sections by immunohistochemistry. Both LTβR mRNA and protein were upregulated in BCa and cystitis compared to the healthy group (P<0.05). The mRNA level of the downstream NF-κB canonical pathway p65 gene and of the non-canonical pathway RelB gene were higher in the BCa and cystitis groups compared to the healthy one. The level of phosphorylated p65 (p-p65) protein of the canonical NF-κB pathway and that of p52, a protein of the non-canonical NF-κB pathway, were also higher in the BCa and cystitis group compared to the healthy group. The levels of these proteins significantly correlated to the pathological grade, clinical stage and lymph node metastasis of BCa patients (P<0.05). In addition, there was a positive correlation between LTβR and NF-κB pathway proteins. Thus, LTβR signaling may be involved in promoting BCa through the NF-κB pathway, and which may represent the molecular link between inflammation and BCa.

Factor Analysis and Counseling Research

ERIC Educational Resources Information Center

Weiss, David J.

1970-01-01

Topics discussed include factor analysis versus cluster analysis, analysis of Q correlation matrices, ipsativity and factor analysis, and tests for the significance of a correlation matrix prior to application of factor analytic techniques. Techniques for factor extraction discussed include principal components, canonical factor analysis, alpha…

Dimensions of Intuition: First-Round Validation Studies

ERIC Educational Resources Information Center

Vrugtman, Rosanne

2009-01-01

This study utilized confirmatory factor analysis (CFA), canonical correlation analysis (CCA), regression analysis (RA), and correlation analysis (CA) for first-round validation of the researcher's Dimensions of Intuition (DOI) instrument. The DOI examined 25 personal characteristics and situations purportedly predictive of intuition. Data was…

Motivation and Burnout in Professional Rugby Players

ERIC Educational Resources Information Center

Cresswell, Scott L.; Eklund, Robert C.

2005-01-01

The authors examined relationships among burnout and motivational types differing in self-determination, using simple correlational analyses as well as more sophisticated canonical correlation analyses allowing for the simultaneous examination of multiple independent and dependent variables. The authors hypothesized that: (a) motivation low in…

Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long

2001-01-01

This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

Emotional distress and dysfunctional illness perception are associated with low mental and physical quality of life in Chinese breast cancer patients.

PubMed

Tang, Lili; Fritzsche, Kurt; Leonhart, Rainer; Pang, Ying; Li, Jinjiang; Song, Lili; Fischer, Irmela; Koch, Maike; Wuensch, Alexander; Mewes, Ricarda; Schaefert, Rainer

2017-12-01

To evaluate the relationship between quality of life (QOL) and physical as well as psychological variables in Chinese breast cancer patients. This multicenter cross-sectional study enrolled 254 Chinese breast cancer patients in different stages and treatment phases. They answered standard instruments assessing QOL (EORTC), somatic symptom severity (PHQ-15), depression (PHQ-9), anxiety (GAD-7), health-related anxiety (WI-7), illness perception (BIPQ), and sense of coherence (SOC-9). Canonical correlation was applied to identify the strongest correlates between the physical, emotional and social QOL scales and the physical and psychological variables. In our sample, a low global QOL was significantly associated with the following physical and psychological variables: symptom-related disability (Karnofsky Index) (r = .211, p < .01), somatic symptom severity (r = -.391, p < .001), depression (r = -.488, p < .001), anxiety (r = -.439, p < .001), health-related anxiety (r = -.398, p < .001), dysfunctional illness perception (r = -.411, p < .001), and sense of coherence (r = .371, p < .001). In the canonical correlation analysis, high somatic symptom severity, depression, anxiety, dysfunctional illness perception, and low sense of coherence showed the strongest correlations with low physical, emotional and social functioning. The first three significant canonical correlations between these two sets of variables were .78, .56, and .45. QOL in Chinese breast cancer patients is strongly associated with psychological factors. Our results suggest that Chinese physicians and nurses should incorporate these factors into their care for women with breast cancer to improve patients' QOL.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

NASA Astrophysics Data System (ADS)

Hyman, J. D.; Aldrich, G.; Viswanathan, H.; Makedonska, N.; Karra, S.

2016-08-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

An efficient dictionary learning algorithm and its application to 3-D medical image denoising.

PubMed

Li, Shutao; Fang, Leyuan; Yin, Haitao

2012-02-01

In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a way to apply this algorithm to 3-D medical image denoising. Our learning approach is composed of two main parts: sparse coding and dictionary updating. On the sparse coding stage, an efficient algorithm named multiple clusters pursuit (MCP) is proposed. The MCP first applies a dictionary structuring strategy to cluster the atoms with high coherence together, and then employs a multiple-selection strategy to select several competitive atoms at each iteration. These two strategies can greatly reduce the computation complexity of the MCP and assist it to obtain better sparse solution. On the dictionary updating stage, the alternating optimization that efficiently approximates the singular value decomposition is introduced. Furthermore, in the 3-D medical image denoising application, a joint 3-D operation is proposed for taking the learning capabilities of the presented algorithm to simultaneously capture the correlations within each slice and correlations across the nearby slices, thereby obtaining better denoising results. The experiments on both synthetically generated data and real 3-D medical images demonstrate that the proposed approach has superior performance compared to some well-known methods. © 2011 IEEE

Perturbative triples correction for local pair natural orbital based explicitly correlated CCSD(F12*) using Laplace transformation techniques.

PubMed

Schmitz, Gunnar; Hättig, Christof

2016-12-21

We present an implementation of pair natural orbital coupled cluster singles and doubles with perturbative triples, PNO-CCSD(T), which avoids the quasi-canonical triples approximation (T0) where couplings due to off-diagonal Fock matrix elements are neglected. A numerical Laplace transformation of the canonical expression for the perturbative (T) triples correction is used to avoid an I/O and storage bottleneck for the triples amplitudes. Results for a test set of reaction energies show that only very few Laplace grid points are needed to obtain converged energy differences and that PNO-CCSD(T) is a more robust approximation than PNO-CCSD(T0) with a reduced mean absolute deviation from canonical CCSD(T) results. We combine the PNO-based (T) triples correction with the explicitly correlated PNO-CCSD(F12*) method and investigate the use of specialized F12-PNOs in the conventional triples correction. We find that no significant additional errors are introduced and that PNO-CCSD(F12*)(T) can be applied in a black box manner.

Recent applications of THERMUS

NASA Astrophysics Data System (ADS)

Wheaton, S.; Hauer, M.

2011-12-01

Some of the most recent applications of the statistical-thermal model package, THERMUS, are reviewed. These applications focus on fluctuation and correlation observables in an ideal particle and anti-particle gas in limited momentum space segments, as well as in a hadron resonance gas. In the case of the latter, a Monte Carlo event generator, utilising THERMUS functionality and assuming thermal production of hadrons, is discussed. The system under consideration is sampled grand canonically in the Boltzmann approximation. A re-weighting scheme is then introduced to account for conservation of charges (baryon number, strangeness, electric charge) and energy and momentum, effectively allowing for extrapolation of grand canonical results to the micro canonical limit. The approach utilised in this and other applications suggests improvements to existing THERMUS calculations.

An approach for generating trajectory-based dynamics which conserves the canonical distribution in the phase space formulation of quantum mechanics. II. Thermal correlation functions.

PubMed

Liu, Jian; Miller, William H

2011-03-14

We show the exact expression of the quantum mechanical time correlation function in the phase space formulation of quantum mechanics. The trajectory-based dynamics that conserves the quantum canonical distribution-equilibrium Liouville dynamics (ELD) proposed in Paper I is then used to approximately evaluate the exact expression. It gives exact thermal correlation functions (of even nonlinear operators, i.e., nonlinear functions of position or momentum operators) in the classical, high temperature, and harmonic limits. Various methods have been presented for the implementation of ELD. Numerical tests of the ELD approach in the Wigner or Husimi phase space have been made for a harmonic oscillator and two strongly anharmonic model problems, for each potential autocorrelation functions of both linear and nonlinear operators have been calculated. It suggests ELD can be a potentially useful approach for describing quantum effects for complex systems in condense phase.

A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong

2001-01-01

This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

On event-based optical flow detection

PubMed Central

Brosch, Tobias; Tschechne, Stephan; Neumann, Heiko

2015-01-01

Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER). Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency). A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations. PMID:25941470

Correlated Hopping in the 1D Falicov--Kimball Model

NASA Astrophysics Data System (ADS)

Gajek, Z.; Lemanski, R.

2001-10-01

Ground state phase diagrams in the canonical ensemble of the one-dimensional Falicov-Kimball Model (FKM) with the correlated hopping are presented for several values of the model parameters. As compare to the conventional FKM, the diagrams exhibit a loss of the particle--hole symmetry.

Digital Correlation In Laser-Speckle Velocimetry

NASA Technical Reports Server (NTRS)

Gilbert, John A.; Mathys, Donald R.

1992-01-01

Periodic recording helps to eliminate spurious results. Improved digital-correlation process extracts velocity field of two-dimensional flow from laser-speckle images of seed particles distributed sparsely in flow. Method which involves digital correlation of images recorded at unequal intervals, completely automated and has potential to be fastest yet.

Group-sparse representation with dictionary learning for medical image denoising and fusion.

PubMed

Li, Shutao; Yin, Haitao; Fang, Leyuan

2012-12-01

Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity

PubMed Central

Day, Ryan; Lennox, Kristin P.; Sukhanov, Paul; Dahl, David B.; Vannucci, Marina; Tsai, Jerry

2011-01-01

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/. PMID:22028638

Adolescents' Faith Commitments as Correlates of Their Involvement in Christian Service

ERIC Educational Resources Information Center

Nagy, Andrea; Ostrander, Raymond; Kijai, Jimmy; Matthews, John

2017-01-01

This study sought to determine the relationship between adolescents' involvement in service to others and their commitment to religious values and Seventh-day Adventist beliefs. Canonical correlation indicated that adolescents' involvement in service to others is significantly related to their commitment to religious values and beliefs. Results…

Joint sparse representation for robust multimodal biometrics recognition.

PubMed

Shekhar, Sumit; Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama

2014-01-01

Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.

SparseMaps—A systematic infrastructure for reduced-scaling electronic structure methods. III. Linear-scaling multireference domain-based pair natural orbital N-electron valence perturbation theory

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

Guo, Yang; Sivalingam, Kantharuban; Neese, Frank, E-mail: Frank.Neese@cec.mpg.de

2016-03-07

Multi-reference (MR) electronic structure methods, such as MR configuration interaction or MR perturbation theory, can provide reliable energies and properties for many molecular phenomena like bond breaking, excited states, transition states or magnetic properties of transition metal complexes and clusters. However, owing to their inherent complexity, most MR methods are still too computationally expensive for large systems. Therefore the development of more computationally attractive MR approaches is necessary to enable routine application for large-scale chemical systems. Among the state-of-the-art MR methods, second-order N-electron valence state perturbation theory (NEVPT2) is an efficient, size-consistent, and intruder-state-free method. However, there are still twomore » important bottlenecks in practical applications of NEVPT2 to large systems: (a) the high computational cost of NEVPT2 for large molecules, even with moderate active spaces and (b) the prohibitive cost for treating large active spaces. In this work, we address problem (a) by developing a linear scaling “partially contracted” NEVPT2 method. This development uses the idea of domain-based local pair natural orbitals (DLPNOs) to form a highly efficient algorithm. As shown previously in the framework of single-reference methods, the DLPNO concept leads to an enormous reduction in computational effort while at the same time providing high accuracy (approaching 99.9% of the correlation energy), robustness, and black-box character. In the DLPNO approach, the virtual space is spanned by pair natural orbitals that are expanded in terms of projected atomic orbitals in large orbital domains, while the inactive space is spanned by localized orbitals. The active orbitals are left untouched. Our implementation features a highly efficient “electron pair prescreening” that skips the negligible inactive pairs. The surviving pairs are treated using the partially contracted NEVPT2 formalism. A detailed comparison between the partial and strong contraction schemes is made, with conclusions that discourage the strong contraction scheme as a basis for local correlation methods due to its non-invariance with respect to rotations in the inactive and external subspaces. A minimal set of conservatively chosen truncation thresholds controls the accuracy of the method. With the default thresholds, about 99.9% of the canonical partially contracted NEVPT2 correlation energy is recovered while the crossover of the computational cost with the already very efficient canonical method occurs reasonably early; in linear chain type compounds at a chain length of around 80 atoms. Calculations are reported for systems with more than 300 atoms and 5400 basis functions.« less

The Ecology of Early Childhood Risk: A Canonical Correlation Analysis of Children’s Adjustment, Family, and Community Context in a High-Risk Sample

PubMed Central

Aiyer, Sophie M.; Wilson, Melvin N.; Shaw, Daniel S.; Dishion, Thomas J.

2013-01-01

The ecology of the emergence of psycho-pathology in early childhood is often approached by the analysis of a limited number of contextual risk factors. In the present study, we provide a comprehensive analysis of ecological risk by conducting a canonical correlation analysis of 13 risk factors at child age 2 and seven narrow-band scales of internalizing and externalizing problem behaviors at child age 4, using a sample of 364 geographically and ethnically diverse, disadvantaged primary caregivers, alternative caregivers, and preschool-age children. Participants were recruited from Special Supplemental Nutrition Program for Women, Infants, and Children sites and were screened for family risk. Canonical correlation analysis revealed that (1) a first latent combination of family and individual risks of caregivers predicted combinations of child emotional and behavioral problems, and that (2) a second latent combination of contextual and structural risks predicted child somatic complaints. Specifically, (1) the combination of chaotic home, conflict with child, parental depression, and parenting hassles predicted a co-occurrence of internalizing and externalizing behaviors, and (2) the combination of father absence, perceived discrimination, neighborhood danger, and fewer children living in the home predicted child somatic complaints. The research findings are discussed in terms of the development of psychopathology, as well as the potential prevention needs of families in high-risk contexts. PMID:23700232

Multidimensional correlation among plan complexity, quality and deliverability parameters for volumetric-modulated arc therapy using canonical correlation analysis.

PubMed

Shen, Lanxiao; Chen, Shan; Zhu, Xiaoyang; Han, Ce; Zheng, Xiaomin; Deng, Zhenxiang; Zhou, Yongqiang; Gong, Changfei; Xie, Congying; Jin, Xiance

2018-03-01

A multidimensional exploratory statistical method, canonical correlation analysis (CCA), was applied to evaluate the impact of complexity parameters on the plan quality and deliverability of volumetric-modulated arc therapy (VMAT) and to determine parameters in the generation of an ideal VMAT plan. Canonical correlations among complexity, quality and deliverability parameters of VMAT, as well as the contribution weights of different parameters were investigated with 71 two-arc VMAT nasopharyngeal cancer (NPC) patients, and further verified with 28 one-arc VMAT prostate cancer patients. The average MU and MU per control point (MU/CP) for two-arc VMAT plans were 702.6 ± 55.7 and 3.9 ± 0.3 versus 504.6 ± 99.2 and 5.6 ± 1.1 for one-arc VMAT plans, respectively. The individual volume-based 3D gamma passing rates of clinical target volume (γCTV) and planning target volume (γPTV) for NPC and prostate cancer patients were 85.7% ± 9.0% vs 92.6% ± 7.8%, and 88.0% ± 7.6% vs 91.2% ± 7.7%, respectively. Plan complexity parameters of NPC patients were correlated with plan quality (P = 0.047) and individual volume-based 3D gamma indices γ(IV) (P = 0.01), in which, MU/CP and segment area (SA) per control point (SA/CP) were weighted highly in correlation with γ(IV) , and SA/CP, percentage of CPs with SA < 5 × 5 cm2 (%SA < 5 × 5 cm2) and PTV volume were weighted highly in correlation with plan quality with coefficients of 0.98, 0.68 and -0.99, respectively. Further verification with one-arc VMAT plans demonstrated similar results. In conclusion, MU, SA-related parameters and PTV volume were found to have strong effects on the plan quality and deliverability.

Registration of prone and supine CT colonography scans using correlation optimized warping and canonical correlation analysis

PubMed Central

Wang, Shijun; Yao, Jianhua; Liu, Jiamin; Petrick, Nicholas; Van Uitert, Robert L.; Periaswamy, Senthil; Summers, Ronald M.

2009-01-01

Purpose: In computed tomographic colonography (CTC), a patient will be scanned twice—Once supine and once prone—to improve the sensitivity for polyp detection. To assist radiologists in CTC reading, in this paper we propose an automated method for colon registration from supine and prone CTC scans. Methods: We propose a new colon centerline registration method for prone and supine CTC scans using correlation optimized warping (COW) and canonical correlation analysis (CCA) based on the anatomical structure of the colon. Four anatomical salient points on the colon are first automatically distinguished. Then correlation optimized warping is applied to the segments defined by the anatomical landmarks to improve the global registration based on local correlation of segments. The COW method was modified by embedding canonical correlation analysis to allow multiple features along the colon centerline to be used in our implementation. Results: We tested the COW algorithm on a CTC data set of 39 patients with 39 polyps (19 training and 20 test cases) to verify the effectiveness of the proposed COW registration method. Experimental results on the test set show that the COW method significantly reduces the average estimation error in a polyp location between supine and prone scans by 67.6%, from 46.27±52.97 to 14.98 mm±11.41 mm, compared to the normalized distance along the colon centerline algorithm (p<0.01). Conclusions: The proposed COW algorithm is more accurate for the colon centerline registration compared to the normalized distance along the colon centerline method and the dynamic time warping method. Comparison results showed that the feature combination of z-coordinate and curvature achieved lowest registration error compared to the other feature combinations used by COW. The proposed method is tolerant to centerline errors because anatomical landmarks help prevent the propagation of errors across the entire colon centerline. PMID:20095272

Registration of prone and supine CT colonography scans using correlation optimized warping and canonical correlation analysis

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

Wang Shijun; Yao Jianhua; Liu Jiamin

Purpose: In computed tomographic colonography (CTC), a patient will be scanned twice--Once supine and once prone--to improve the sensitivity for polyp detection. To assist radiologists in CTC reading, in this paper we propose an automated method for colon registration from supine and prone CTC scans. Methods: We propose a new colon centerline registration method for prone and supine CTC scans using correlation optimized warping (COW) and canonical correlation analysis (CCA) based on the anatomical structure of the colon. Four anatomical salient points on the colon are first automatically distinguished. Then correlation optimized warping is applied to the segments defined bymore » the anatomical landmarks to improve the global registration based on local correlation of segments. The COW method was modified by embedding canonical correlation analysis to allow multiple features along the colon centerline to be used in our implementation. Results: We tested the COW algorithm on a CTC data set of 39 patients with 39 polyps (19 training and 20 test cases) to verify the effectiveness of the proposed COW registration method. Experimental results on the test set show that the COW method significantly reduces the average estimation error in a polyp location between supine and prone scans by 67.6%, from 46.27{+-}52.97 to 14.98 mm{+-}11.41 mm, compared to the normalized distance along the colon centerline algorithm (p<0.01). Conclusions: The proposed COW algorithm is more accurate for the colon centerline registration compared to the normalized distance along the colon centerline method and the dynamic time warping method. Comparison results showed that the feature combination of z-coordinate and curvature achieved lowest registration error compared to the other feature combinations used by COW. The proposed method is tolerant to centerline errors because anatomical landmarks help prevent the propagation of errors across the entire colon centerline.« less

Multidimensional correlation among plan complexity, quality and deliverability parameters for volumetric-modulated arc therapy using canonical correlation analysis

PubMed Central

Shen, Lanxiao; Chen, Shan; Zhu, Xiaoyang; Han, Ce; Zheng, Xiaomin; Deng, Zhenxiang; Zhou, Yongqiang; Gong, Changfei; Jin, Xiance

2018-01-01

Abstract A multidimensional exploratory statistical method, canonical correlation analysis (CCA), was applied to evaluate the impact of complexity parameters on the plan quality and deliverability of volumetric-modulated arc therapy (VMAT) and to determine parameters in the generation of an ideal VMAT plan. Canonical correlations among complexity, quality and deliverability parameters of VMAT, as well as the contribution weights of different parameters were investigated with 71 two-arc VMAT nasopharyngeal cancer (NPC) patients, and further verified with 28 one-arc VMAT prostate cancer patients. The average MU and MU per control point (MU/CP) for two-arc VMAT plans were 702.6 ± 55.7 and 3.9 ± 0.3 versus 504.6 ± 99.2 and 5.6 ± 1.1 for one-arc VMAT plans, respectively. The individual volume-based 3D gamma passing rates of clinical target volume (γCTV) and planning target volume (γPTV) for NPC and prostate cancer patients were 85.7% ± 9.0% vs 92.6% ± 7.8%, and 88.0% ± 7.6% vs 91.2% ± 7.7%, respectively. Plan complexity parameters of NPC patients were correlated with plan quality (P = 0.047) and individual volume-based 3D gamma indices γ(IV) (P = 0.01), in which, MU/CP and segment area (SA) per control point (SA/CP) were weighted highly in correlation with γ(IV) , and SA/CP, percentage of CPs with SA < 5 × 5 cm2 (%SA < 5 × 5 cm2) and PTV volume were weighted highly in correlation with plan quality with coefficients of 0.98, 0.68 and −0.99, respectively. Further verification with one-arc VMAT plans demonstrated similar results. In conclusion, MU, SA-related parameters and PTV volume were found to have strong effects on the plan quality and deliverability. PMID:29415196

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

DOE PAGES

Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.; ...

2016-08-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

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

Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

Sparse Modeling of Human Actions from Motion Imagery

DTIC Science & Technology

2011-09-02

is here developed. Spatio-temporal features that char- acterize local changes in the image are rst extracted. This is followed by the learning of a...video comes from the optimal sparse linear com- bination of the learned basis vectors (action primitives) representing the actions. A low...computational cost deep-layer model learning the inter- class correlations of the data is added for increasing discriminative power. In spite of its simplicity

Sparsity-Based Representation for Classification Algorithms and Comparison Results for Transient Acoustic Signals

DTIC Science & Technology

2016-05-01

large but correlated noise and signal interference (i.e., low -rank interference). Another contribution is the implementation of deep learning...representation, low rank, deep learning 52 Tung-Duong Tran-Luu 301-394-3082Unclassified Unclassified Unclassified UU ii Approved for public release; distribution...Classification of Acoustic Transients 6 3.2 Joint Sparse Representation with Low -Rank Interference 7 3.3 Simultaneous Group-and-Joint Sparse Representation

Spatially Correlated Sparse MIMO Channel Path Delay Estimation in Scattering Environments Based on Signal Subspace Tracking

PubMed Central

Chargé, Pascal; Bazzi, Oussama; Ding, Yuehua

2018-01-01

A parametric scheme for spatially correlated sparse multiple-input multiple-output (MIMO) channel path delay estimation in scattering environments is presented in this paper. In MIMO outdoor communication scenarios, channel impulse responses (CIRs) of different transmit–receive antenna pairs are often supposed to be sparse due to a few significant scatterers, and share a common sparse pattern, such that path delays are assumed to be equal for every transmit–receive antenna pair. In some existing works, an exact common support condition is exploited, where the path delays are considered equal for every transmit–receive antenna pair, meanwhile ignoring the influence of scattering. A more realistic channel model is proposed in this paper, where due to scatterers in the environment, the received signals are modeled as clusters of multi-rays around a nominal or mean time delay at different antenna elements, resulting in a non-strictly exact common support phenomenon. A method for estimating the channel mean path delays is then derived based on the subspace approach, and the tracking of the effective dimension of the signal subspace that changes due to the wireless environment. The proposed method shows an improved channel mean path delays estimation performance in comparison with the conventional estimation methods. PMID:29734797

Spatially Correlated Sparse MIMO Channel Path Delay Estimation in Scattering Environments Based on Signal Subspace Tracking.

PubMed

Mohydeen, Ali; Chargé, Pascal; Wang, Yide; Bazzi, Oussama; Ding, Yuehua

2018-05-06

A parametric scheme for spatially correlated sparse multiple-input multiple-output (MIMO) channel path delay estimation in scattering environments is presented in this paper. In MIMO outdoor communication scenarios, channel impulse responses (CIRs) of different transmit⁻receive antenna pairs are often supposed to be sparse due to a few significant scatterers, and share a common sparse pattern, such that path delays are assumed to be equal for every transmit⁻receive antenna pair. In some existing works, an exact common support condition is exploited, where the path delays are considered equal for every transmit⁻receive antenna pair, meanwhile ignoring the influence of scattering. A more realistic channel model is proposed in this paper, where due to scatterers in the environment, the received signals are modeled as clusters of multi-rays around a nominal or mean time delay at different antenna elements, resulting in a non-strictly exact common support phenomenon. A method for estimating the channel mean path delays is then derived based on the subspace approach, and the tracking of the effective dimension of the signal subspace that changes due to the wireless environment. The proposed method shows an improved channel mean path delays estimation performance in comparison with the conventional estimation methods.

Sparse distributed memory overview

NASA Technical Reports Server (NTRS)

Raugh, Mike

1990-01-01

The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking

PubMed Central

Qu, Shiru

2016-01-01

Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness. PMID:27630710

Freezing behavior as a response to sexual visual stimuli as demonstrated by posturography.

PubMed

Mouras, Harold; Lelard, Thierry; Ahmaidi, Said; Godefroy, Olivier; Krystkowiak, Pierre

2015-01-01

Posturographic changes in motivational conditions remain largely unexplored in the context of embodied cognition. Over the last decade, sexual motivation has been used as a good canonical working model to study motivated social interactions. The objective of this study was to explore posturographic variations in response to visual sexual videos as compared to neutral videos. Our results support demonstration of a freezing-type response in response to sexually explicit stimuli compared to other conditions, as demonstrated by significantly decreased standard deviations for (i) the center of pressure displacement along the mediolateral and anteroposterior axes and (ii) center of pressure's displacement surface. These results support the complexity of the motor correlates of sexual motivation considered to be a canonical functional context to study the motor correlates of motivated social interactions.

Pressures, Stresses, Anxieties, and On-Job Safety of the School Superintendent.

ERIC Educational Resources Information Center

Chand, Krishan

Identification of the causes of job stress for public school superintendents, with a focus on personal-experiential and task variables, is the purpose of this study. Methodology involved a mail survey of 1,531 randomly selected superintendents. Canonical correlation analysis (CCA) and multiple regression correlation (MCR) analysis were used to…

Effect of correlations on controllability transition in network control

PubMed Central

Nie, Sen; Wang, Xu-Wen; Wang, Bing-Hong; Jiang, Luo-Luo

2016-01-01

The network control problem has recently attracted an increasing amount of attention, owing to concerns including the avoidance of cascading failures of power-grids and the management of ecological networks. It has been proven that numerical control can be achieved if the number of control inputs exceeds a certain transition point. In the present study, we investigate the effect of degree correlation on the numerical controllability in networks whose topological structures are reconstructed from both real and modeling systems, and we find that the transition point of the number of control inputs depends strongly on the degree correlation in both undirected and directed networks with moderately sparse links. More interestingly, the effect of the degree correlation on the transition point cannot be observed in dense networks for numerical controllability, which contrasts with the corresponding result for structural controllability. In particular, for directed random networks and scale-free networks, the influence of the degree correlation is determined by the types of correlations. Our approach provides an understanding of control problems in complex sparse networks. PMID:27063294

Cell Assembly Dynamics of Sparsely-Connected Inhibitory Networks: A Simple Model for the Collective Activity of Striatal Projection Neurons.

PubMed

Angulo-Garcia, David; Berke, Joshua D; Torcini, Alessandro

2016-02-01

Striatal projection neurons form a sparsely-connected inhibitory network, and this arrangement may be essential for the appropriate temporal organization of behavior. Here we show that a simplified, sparse inhibitory network of Leaky-Integrate-and-Fire neurons can reproduce some key features of striatal population activity, as observed in brain slices. In particular we develop a new metric to determine the conditions under which sparse inhibitory networks form anti-correlated cell assemblies with time-varying activity of individual cells. We find that under these conditions the network displays an input-specific sequence of cell assembly switching, that effectively discriminates similar inputs. Our results support the proposal that GABAergic connections between striatal projection neurons allow stimulus-selective, temporally-extended sequential activation of cell assemblies. Furthermore, we help to show how altered intrastriatal GABAergic signaling may produce aberrant network-level information processing in disorders such as Parkinson's and Huntington's diseases.

FINAL REPORT (MILESTONE DATE 9/30/11) FOR SUBCONTRACT NO. B594099 NUMERICAL METHODS FOR LARGE-SCALE DATA FACTORIZATION

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

De Sterck, H

2011-10-18

The following work has been performed by PI Hans De Sterck and graduate student Manda Winlaw for the required tasks 1-5 (as listed in the Statement of Work). Graduate student Manda Winlaw has visited LLNL January 31-March 11, 2011 and May 23-August 19, 2010, working with Van Henson and Mike O'Hara on non-negative matrix factorizations (NMF). She has investigated the dense subgraph clustering algorithm from 'Finding Dense Subgraphs for Sparse Undirected, Directed, and Bipartite Graphs' by Chen and Saad, testing this method on several term-document matrices and adapting it to cluster based on the rank of the subgraphs instead ofmore » the density. Manda Winlaw was awarded a first prize in the annual LLNL summer student poster competition for a poster on her NMF research. PI Hans De Sterck has developed a new adaptive algebraic multigrid algorithm for computing a few dominant or minimal singular triplets of sparse rectangular matrices. This work builds on adaptive algebraic multigrid methods that were further developed by the PI and collaborators (including Sanders and Henson) for Markov chains. The method also combines and extends existing multigrid algorithms for the symmetric eigenproblem. The PI has visited LLNL February 22-25, 2011, and has given a CASC seminar 'Algebraic Multigrid for the Singular Value Problem' on this work on February 23, 2011. During his visit, he has discussed this work and related topics with Van Henson, Geoffrey Sanders, Panayot Vassilevski, and others. He has tested the algorithm on PDE matrices and on a term-document matrix, with promising initial results. Manda Winlaw has also started to work, with O'Hara, on estimating probability distributions over undirected graph edges. The goal is to estimate probabilistic models from sets of undirected graph edges for the purpose of prediction, anomaly detection and support to supervised learning. Graduate student Manda Winlaw is writing a paper on the results obtained with O'Hara which will be submitted some time later in 2011 to a data mining conference. PI Hans De Sterck has developed a new optimization algorithm for canonical tensor approximation, formulating an extension of the nonlinear GMRES method to optimization problems. Numerical results for tensors with up to 8 modes show that this new method is efficient for sparse and dense tensors. He has written a paper on this which has been submitted to the SIAM Journal on Scientific Computing. PI Hans De Sterck has further developed his new optimization algorithm for canonical tensor approximation, formulating an extension in terms of steepest-descent preconditioning, which makes the approach generally applicable for nonlinear optimization. He has written a paper on this extension which has been submitted to Numerical Linear Algebra with Applications.« less

New insights into transcription fidelity: thermal stability of non-canonical structures in template DNA regulates transcriptional arrest, pause, and slippage.

PubMed

Tateishi-Karimata, Hisae; Isono, Noburu; Sugimoto, Naoki

2014-01-01

The thermal stability and topology of non-canonical structures of G-quadruplexes and hairpins in template DNA were investigated, and the effect of non-canonical structures on transcription fidelity was evaluated quantitatively. We designed ten template DNAs: A linear sequence that does not have significant higher-order structure, three sequences that form hairpin structures, and six sequences that form G-quadruplex structures with different stabilities. Templates with non-canonical structures induced the production of an arrested, a slipped, and a full-length transcript, whereas the linear sequence produced only a full-length transcript. The efficiency of production for run-off transcripts (full-length and slipped transcripts) from templates that formed the non-canonical structures was lower than that from the linear. G-quadruplex structures were more effective inhibitors of full-length product formation than were hairpin structure even when the stability of the G-quadruplex in an aqueous solution was the same as that of the hairpin. We considered that intra-polymerase conditions may differentially affect the stability of non-canonical structures. The values of transcription efficiencies of run-off or arrest transcripts were correlated with stabilities of non-canonical structures in the intra-polymerase condition mimicked by 20 wt% polyethylene glycol (PEG). Transcriptional arrest was induced when the stability of the G-quadruplex structure (-ΔG°37) in the presence of 20 wt% PEG was more than 8.2 kcal mol(-1). Thus, values of stability in the presence of 20 wt% PEG are an important indicator of transcription perturbation. Our results further our understanding of the impact of template structure on the transcription process and may guide logical design of transcription-regulating drugs.

New Insights into Transcription Fidelity: Thermal Stability of Non-Canonical Structures in Template DNA Regulates Transcriptional Arrest, Pause, and Slippage

PubMed Central

Tateishi-Karimata, Hisae; Isono, Noburu; Sugimoto, Naoki

2014-01-01

The thermal stability and topology of non-canonical structures of G-quadruplexes and hairpins in template DNA were investigated, and the effect of non-canonical structures on transcription fidelity was evaluated quantitatively. We designed ten template DNAs: A linear sequence that does not have significant higher-order structure, three sequences that form hairpin structures, and six sequences that form G-quadruplex structures with different stabilities. Templates with non-canonical structures induced the production of an arrested, a slipped, and a full-length transcript, whereas the linear sequence produced only a full-length transcript. The efficiency of production for run-off transcripts (full-length and slipped transcripts) from templates that formed the non-canonical structures was lower than that from the linear. G-quadruplex structures were more effective inhibitors of full-length product formation than were hairpin structure even when the stability of the G-quadruplex in an aqueous solution was the same as that of the hairpin. We considered that intra-polymerase conditions may differentially affect the stability of non-canonical structures. The values of transcription efficiencies of run-off or arrest transcripts were correlated with stabilities of non-canonical structures in the intra-polymerase condition mimicked by 20 wt% polyethylene glycol (PEG). Transcriptional arrest was induced when the stability of the G-quadruplex structure (−ΔGo 37) in the presence of 20 wt% PEG was more than 8.2 kcal mol−1. Thus, values of stability in the presence of 20 wt% PEG are an important indicator of transcription perturbation. Our results further our understanding of the impact of template structure on the transcription process and may guide logical design of transcription-regulating drugs. PMID:24594642

Immunological memory is associative

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

Smith, D.J.; Forrest, S.; Perelson, A.S.

1996-12-31

The purpose of this paper is to show that immunological memory is an associative and robust memory that belongs to the class of sparse distributed memories. This class of memories derives its associative and robust nature by sparsely sampling the input space and distributing the data among many independent agents. Other members of this class include a model of the cerebellar cortex and Sparse Distributed Memory (SDM). First we present a simplified account of the immune response and immunological memory. Next we present SDM, and then we show the correlations between immunological memory and SDM. Finally, we show how associativemore » recall in the immune response can be both beneficial and detrimental to the fitness of an individual.« less

[The correlations of somatic, dermatoglyphic and psychological characteristics in the structure of general human constitution from the standpoint of systemic approach].

PubMed

Negasheva, M A

2008-01-01

669 young men and women aged 16-23 years were examined using a program including the measurements of 40 body, head and face parameters, fingerprinting and determination of personal psychological characteristics. On the basis of the study of the correlations between the different groups of characteristics, the evidence was obtained that supports the concept of a relative autonomy of the morpho-functional systems as an essential condition for the integrity of the organism as a whole. The coefficients of canonical correlation were calculated between the somatic and dermatoglyphic features (R=0.3), somatic sizes and psychological personality characteristics (R=0.4), psychological characteristics and the dermatoglyphic indices (R=0.4). An original model is suggested that describes the correlations of somatic, dermatoglyphic and psychological features in the structure of general human constitution on the basis of statistically significant canonical correlations (revealed by the author) and that takes in consideration the degree of the influence of genetic and social-economic complex of factors (on the basis of the literature data) on the development and formation of the investigated systems of characteristics.

Genetic Introgression and the Survival of Florida Panther Kittens

PubMed Central

Hostetler, Jeffrey A.; Onorato, David P.; Nichols, James D.; Johnson, Warren E.; Roelke, Melody E.; O’Brien, Stephen J.; Jansen, Deborah; Oli, Madan K.

2010-01-01

Estimates of survival for the young of a species are critical for population models. These models can often be improved by determining the effects of management actions and population abundance on this demographic parameter. We used multiple sources of data collected during 1982-2008 and a live recapture-dead recovery modeling framework to estimate and model survival of Florida panther (Puma concolor coryi) kittens (age 0 – 1 year). Overall, annual survival of Florida panther kittens was 0.323 ± 0.071 (SE), which was lower than estimates used in previous population models. In 1995, female pumas from Texas (P. c. stanleyana) were released into occupied panther range as part of an intentional introgression program to restore genetic variability. We found that kitten survival generally increased with degree of admixture: F1 admixed and backcrossed to Texas kittens survived better than canonical Florida panther and backcrossed to canonical kittens. Average heterozygosity positively influenced kitten and older panther survival, whereas index of panther abundance negatively influenced kitten survival. Our results provide strong evidence for the positive population-level impact of genetic introgression on Florida panthers. Our approach to integrate data from multiple sources was effective at improving robustness as well as precision of estimates of Florida panther kitten survival, and can be useful in estimating vital rates for other elusive species with sparse data. PMID:21113436

Genetic introgression and the survival of Florida panther kittens

USGS Publications Warehouse

Hostetler, Jeffrey A.; Onorato, David P.; Nichols, James D.; Johnson, Warren E.; Roelke, Melody E.; O'Brien, Stephen J.; Jansen, Deborah; Oli, Madan K.

2010-01-01

Estimates of survival for the young of a species are critical for population models. These models can often be improved by determining the effects of management actions and population abundance on this demographic parameter. We used multiple sources of data collected during 1982–2008 and a live-recapture dead-recovery modeling framework to estimate and model survival of Florida panther (Puma concolor coryi) kittens (age 0–1 year). Overall, annual survival of Florida panther kittens was 0.323 ± 0.071 (SE), which was lower than estimates used in previous population models. In 1995, female pumas from Texas (P. c. stanleyana) were released into occupied panther range as part of an intentional introgression program to restore genetic variability. We found that kitten survival generally increased with degree of admixture: F1 admixed and backcrossed to Texas kittens survived better than canonical Florida panther and backcrossed to canonical kittens. Average heterozygosity positively influenced kitten and older panther survival, whereas index of panther abundance negatively influenced kitten survival. Our results provide strong evidence for the positive population-level impact of genetic introgression on Florida panthers. Our approach to integrate data from multiple sources was effective at improving robustness as well as precision of estimates of Florida panther kitten survival, and can be useful in estimating vital rates for other elusive species with sparse data.

Analytical gradients for tensor hyper-contracted MP2 and SOS-MP2 on graphical processing units

DOE PAGES

Song, Chenchen; Martinez, Todd J.

2017-08-29

Analytic energy gradients for tensor hyper-contraction (THC) are derived and implemented for second-order Møller-Plesset perturbation theory (MP2), with and without the scaled-opposite-spin (SOS)-MP2 approximation. By exploiting the THC factorization, the formal scaling of MP2 and SOS-MP2 gradient calculations with respect to system size is reduced to quartic and cubic, respectively. An efficient implementation has been developed that utilizes both graphics processing units and sparse tensor techniques exploiting spatial sparsity of the atomic orbitals. THC-MP2 has been applied to both geometry optimization and ab initio molecular dynamics (AIMD) simulations. Furthermore, the resulting energy conservation in micro-canonical AIMD demonstrates that the implementationmore » provides accurate nuclear gradients with respect to the THC-MP2 potential energy surfaces.« less

Analytical gradients for tensor hyper-contracted MP2 and SOS-MP2 on graphical processing units

NASA Astrophysics Data System (ADS)

Song, Chenchen; Martínez, Todd J.

2017-10-01

Analytic energy gradients for tensor hyper-contraction (THC) are derived and implemented for second-order Møller-Plesset perturbation theory (MP2), with and without the scaled-opposite-spin (SOS)-MP2 approximation. By exploiting the THC factorization, the formal scaling of MP2 and SOS-MP2 gradient calculations with respect to system size is reduced to quartic and cubic, respectively. An efficient implementation has been developed that utilizes both graphics processing units and sparse tensor techniques exploiting spatial sparsity of the atomic orbitals. THC-MP2 has been applied to both geometry optimization and ab initio molecular dynamics (AIMD) simulations. The resulting energy conservation in micro-canonical AIMD demonstrates that the implementation provides accurate nuclear gradients with respect to the THC-MP2 potential energy surfaces.

Analytical gradients for tensor hyper-contracted MP2 and SOS-MP2 on graphical processing units

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

Song, Chenchen; Martinez, Todd J.

Analytic energy gradients for tensor hyper-contraction (THC) are derived and implemented for second-order Møller-Plesset perturbation theory (MP2), with and without the scaled-opposite-spin (SOS)-MP2 approximation. By exploiting the THC factorization, the formal scaling of MP2 and SOS-MP2 gradient calculations with respect to system size is reduced to quartic and cubic, respectively. An efficient implementation has been developed that utilizes both graphics processing units and sparse tensor techniques exploiting spatial sparsity of the atomic orbitals. THC-MP2 has been applied to both geometry optimization and ab initio molecular dynamics (AIMD) simulations. Furthermore, the resulting energy conservation in micro-canonical AIMD demonstrates that the implementationmore » provides accurate nuclear gradients with respect to the THC-MP2 potential energy surfaces.« less

Vanishing-Overhead Linear-Scaling Random Phase Approximation by Cholesky Decomposition and an Attenuated Coulomb-Metric.

PubMed

Luenser, Arne; Schurkus, Henry F; Ochsenfeld, Christian

2017-04-11

A reformulation of the random phase approximation within the resolution-of-the-identity (RI) scheme is presented, that is competitive to canonical molecular orbital RI-RPA already for small- to medium-sized molecules. For electronically sparse systems drastic speedups due to the reduced scaling behavior compared to the molecular orbital formulation are demonstrated. Our reformulation is based on two ideas, which are independently useful: First, a Cholesky decomposition of density matrices that reduces the scaling with basis set size for a fixed-size molecule by one order, leading to massive performance improvements. Second, replacement of the overlap RI metric used in the original AO-RPA by an attenuated Coulomb metric. Accuracy is significantly improved compared to the overlap metric, while locality and sparsity of the integrals are retained, as is the effective linear scaling behavior.

Treating Subvalence Correlation Effects in Domain Based Pair Natural Orbital Coupled Cluster Calculations: An Out-of-the-Box Approach.

PubMed

Bistoni, Giovanni; Riplinger, Christoph; Minenkov, Yury; Cavallo, Luigi; Auer, Alexander A; Neese, Frank

2017-07-11

The validity of the main approximations used in canonical and domain based pair natural orbital coupled cluster methods (CCSD(T) and DLPNO-CCSD(T), respectively) in standard chemical applications is discussed. In particular, we investigate the dependence of the results on the number of electrons included in the correlation treatment in frozen-core (FC) calculations and on the main threshold governing the accuracy of DLPNO all-electron (AE) calculations. Initially, scalar relativistic orbital energies for the ground state of the atoms from Li to Rn in the periodic table are calculated. An energy criterion is used for determining the orbitals that can be excluded from the correlation treatment in FC coupled cluster calculations without significant loss of accuracy. The heterolytic dissociation energy (HDE) of a series of metal compounds (LiF, NaF, AlF 3 , CaF 2 , CuF, GaF 3 , YF 3 , AgF, InF 3 , HfF 4 , and AuF) is calculated at the canonical CCSD(T) level, and the dependence of the results on the number of correlated electrons is investigated. Although for many of the studied reactions subvalence correlation effects contribute significantly to the HDE, the use of an energy criterion permits a conservative definition of the size of the core, allowing FC calculations to be performed in a black-box fashion while retaining chemical accuracy. A comparison of the CCSD and the DLPNO-CCSD methods in describing the core-core, core-valence, and valence-valence components of the correlation energy is given. It is found that more conservative thresholds must be used for electron pairs containing at least one core electron in order to achieve high accuracy in AE DLPNO-CCSD calculations relative to FC calculations. With the new settings, the DLPNO-CCSD method reproduces canonical CCSD results in both AE and FC calculations with the same accuracy.

Nonlinear circuits for naturalistic visual motion estimation

PubMed Central

Fitzgerald, James E; Clark, Damon A

2015-01-01

Many animals use visual signals to estimate motion. Canonical models suppose that animals estimate motion by cross-correlating pairs of spatiotemporally separated visual signals, but recent experiments indicate that humans and flies perceive motion from higher-order correlations that signify motion in natural environments. Here we show how biologically plausible processing motifs in neural circuits could be tuned to extract this information. We emphasize how known aspects of Drosophila's visual circuitry could embody this tuning and predict fly behavior. We find that segregating motion signals into ON/OFF channels can enhance estimation accuracy by accounting for natural light/dark asymmetries. Furthermore, a diversity of inputs to motion detecting neurons can provide access to more complex higher-order correlations. Collectively, these results illustrate how non-canonical computations improve motion estimation with naturalistic inputs. This argues that the complexity of the fly's motion computations, implemented in its elaborate circuits, represents a valuable feature of its visual motion estimator. DOI: http://dx.doi.org/10.7554/eLife.09123.001 PMID:26499494

Correlates of adiposity among Latino preschool children

USDA-ARS?s Scientific Manuscript database

Childhood obesity is at record high levels in the US and disproportionately affects Latino children; however, studies examining Latino preschool children's obesity-related risk factors are sparse. This study determined correlates of Latino preschoolers' (ages 3-5 years) adiposity to inform future ob...

Freezing Behavior as a Response to Sexual Visual Stimuli as Demonstrated by Posturography

PubMed Central

Mouras, Harold; Lelard, Thierry; Ahmaidi, Said; Godefroy, Olivier; Krystkowiak, Pierre

2015-01-01

Posturographic changes in motivational conditions remain largely unexplored in the context of embodied cognition. Over the last decade, sexual motivation has been used as a good canonical working model to study motivated social interactions. The objective of this study was to explore posturographic variations in response to visual sexual videos as compared to neutral videos. Our results support demonstration of a freezing-type response in response to sexually explicit stimuli compared to other conditions, as demonstrated by significantly decreased standard deviations for (i) the center of pressure displacement along the mediolateral and anteroposterior axes and (ii) center of pressure’s displacement surface. These results support the complexity of the motor correlates of sexual motivation considered to be a canonical functional context to study the motor correlates of motivated social interactions. PMID:25992571

Gustave Caillebotte, French impressionism, and mere exposure.

PubMed

Cutting, James E

2003-06-01

Gustave Caillebotte was a painter, a collector of some of his colleagues' most renowned works, and a major force in the creation of the late 19th century French Impressionist canon. Six studies are presented as a naturalistic investigation of the effects of mere exposure to images in his collection and to those matched to them. The probabilities of cultural exposure to the 132 stimulus images were indexed by the frequencies of their separate appearances in Cornell University library books--a total of 4,232 times in 980 different books. Across the studies, adult preferences were correlated with differences in image frequencies, but not with recognition, complexity, or prototypicality judgments; children's preferences were not correlated with frequency. Prior cultural exposure also interacted with experimental exposure in predictable ways. The results suggest that mere exposure helps to maintain an artistic canon.

A third-generation density-functional-theory-based method for calculating canonical molecular orbitals of large molecules.

PubMed

Hirano, Toshiyuki; Sato, Fumitoshi

2014-07-28

We used grid-free modified Cholesky decomposition (CD) to develop a density-functional-theory (DFT)-based method for calculating the canonical molecular orbitals (CMOs) of large molecules. Our method can be used to calculate standard CMOs, analytically compute exchange-correlation terms, and maximise the capacity of next-generation supercomputers. Cholesky vectors were first analytically downscaled using low-rank pivoted CD and CD with adaptive metric (CDAM). The obtained Cholesky vectors were distributed and stored on each computer node in a parallel computer, and the Coulomb, Fock exchange, and pure exchange-correlation terms were calculated by multiplying the Cholesky vectors without evaluating molecular integrals in self-consistent field iterations. Our method enables DFT and massively distributed memory parallel computers to be used in order to very efficiently calculate the CMOs of large molecules.

Paradoxical correlation between signal in functional magnetic resonance imaging and deoxygenated haemoglobin content in capillaries: a new theoretical explanation

NASA Astrophysics Data System (ADS)

Yamamoto, Toru; Kato, Toshinori

2002-04-01

Signal increases in functional magnetic resonance imaging (fMRI) are believed to be a result of decreased paramagnetic deoxygenated haemoglobin (deoxyHb) content in the neural activation area. However, discrepancies in this canonical blood oxygenation level dependent (BOLD) theory have been pointed out in studies using optical techniques, which directly measure haemoglobin changes. To explain the discrepancies, we developed a new theory bridging magnetic resonance (MR) signal and haemoglobin changes. We focused on capillary influences, which have been neglected in most previous fMRI studies and performed a combined fMRI and near-infrared spectroscopy (NIRS) study using a language task. Paradoxically, both the MR signal and deoxyHb content increased in Broca's area. On the other hand, fMRI activation in the auditory area near large veins correlated with a mirror-image decrease in deoxyHb and increase in oxygenated haemoglobin (oxyHb), in agreement with canonical BOLD theory. All fMRI signal changes correlated consistently with changes in oxyHb, the diamagnetism of which is insensitive to MR. We concluded that the discrepancy with the canonical BOLD theory is caused by the fact that the BOLD theory ignores the effect of the capillaries. Our theory explains the paradoxical phenomena of the oxyHb and deoxyHb contributions to the MR signal and gives a new insight into the precise haemodynamics of activation by analysing fMRI and NIRS data.

IQ-domain GTPase-activating protein 1 promotes the malignant phenotype of invasive ductal breast carcinoma via canonical Wnt pathway.

PubMed

Zhao, Huan-Yu; Han, Yang; Wang, Jian; Yang, Lian-He; Zheng, Xiao-Ying; Du, Jiang; Wu, Guang-Ping; Wang, En-Hua

2017-06-01

IQ-domain GTPase-activating protein 1 is a scaffolding protein with multidomain which plays a role in modulating dishevelled (Dvl) nuclear translocation in canonical Wnt pathway. However, the biological function and mechanism of IQ-domain GTPase-activating protein 1 in invasive ductal carcinoma (IDC) remain unknown. In this study, we found that IQ-domain GTPase-activating protein 1 expression was elevated in invasive ductal carcinoma, which was positively correlated with tumor grade, lymphatic metastasis, and poor prognosis. Coexpression of IQ-domain GTPase-activating protein 1 and Dvl in the nucleus and cytoplasm of invasive ductal carcinoma was significantly correlated but not in the membrane. Postoperative survival in the patients with their coexpression in the nucleus and cytoplasm was obviously lower than that without coexpression. The positive expression rates of c-myc and cyclin D1 were significantly higher in the patients with nuclear coexpression of Dvl and IQ-domain GTPase-activating protein 1 than that with cytoplasmic coexpression, correlating with poor prognosis. IQ-domain GTPase-activating protein 1 significantly enhanced cell proliferation and invasion in invasive ductal carcinoma cell lines by interacting with Dvl in cytoplasm to promote Dvl nuclear translocation so as to upregulate the expression of c-myc and cyclin D1. Collectively, our data suggest that IQ-domain GTPase-activating protein 1 may promote the malignant phenotype of invasive ductal carcinoma via canonical Wnt signaling, and it could be used as a potential prognostic biomarker for breast cancer patients.

Balanced increases in selectivity and tolerance produce constant sparseness along the ventral visual stream

PubMed Central

Rust, Nicole C.; DiCarlo, James J.

2012-01-01

While popular accounts suggest that neurons along the ventral visual processing stream become increasingly selective for particular objects, this appears at odds with the fact that inferior temporal cortical (IT) neurons are broadly tuned. To explore this apparent contradiction, we compared processing in two ventral stream stages (V4 and IT) in the rhesus macaque monkey. We confirmed that IT neurons are indeed more selective for conjunctions of visual features than V4 neurons, and that this increase in feature conjunction selectivity is accompanied by an increase in tolerance (“invariance”) to identity-preserving transformations (e.g. shifting, scaling) of those features. We report here that V4 and IT neurons are, on average, tightly matched in their tuning breadth for natural images (“sparseness”), and that the average V4 or IT neuron will produce a robust firing rate response (over 50% of its peak observed firing rate) to ~10% of all natural images. We also observed that sparseness was positively correlated with conjunction selectivity and negatively correlated with tolerance within both V4 and IT, consistent with selectivity-building and invariance-building computations that offset one another to produce sparseness. Our results imply that the conjunction-selectivity-building and invariance-building computations necessary to support object recognition are implemented in a balanced fashion to maintain sparseness at each stage of processing. PMID:22836252

Weight-Loss Expectancies, Relative Weight, and Symptoms of Bulimia in Young Women.

ERIC Educational Resources Information Center

Thombs, Dennis L.; And Others

1996-01-01

A canonical correlation analysis of various weight concerns in a sample of college women revealed that strong expectations of weight loss benefits and a high relative body weight were positively correlated with the four major symptoms of bulimia. Expectations of increased self-worth and social confidence were linked to eating problems. (RJM)

Smart Grid Integrity Attacks: Characterizations and Countermeasures

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

Annarita Giani; Eilyan Bitar; Miles McQueen

2011-10-01

Real power injections at loads and generators, and real power flows on selected lines in a transmission network are monitored, transmitted over a SCADA network to the system operator, and used in state estimation algorithms to make dispatch, re-balance and other energy management system [EMS] decisions. Coordinated cyber attacks of power meter readings can be arranged to be undetectable by any bad data detection algorithm. These unobservable attacks present a serious threat to grid operations. Of particular interest are sparse attacks that involve the compromise of a modest number of meter readings. An efficient algorithm to find all unobservable attacksmore » [under standard DC load flow approximations] involving the compromise of exactly two power injection meters and an arbitrary number of power meters on lines is presented. This requires O(n2m) flops for a power system with n buses and m line meters. If all lines are metered, there exist canonical forms that characterize all 3, 4, and 5-sparse unobservable attacks. These can be quickly detected in power systems using standard graph algorithms. Known secure phase measurement units [PMUs] can be used as countermeasures against an arbitrary collection of cyber attacks. Finding the minimum number of necessary PMUs is NP-hard. It is shown that p + 1 PMUs at carefully chosen buses are sufficient to neutralize a collection of p cyber attacks.« less

Improved Estimation and Interpretation of Correlations in Neural Circuits

PubMed Central

Yatsenko, Dimitri; Josić, Krešimir; Ecker, Alexander S.; Froudarakis, Emmanouil; Cotton, R. James; Tolias, Andreas S.

2015-01-01

Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150–350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive ‘excitatory’ interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative ‘inhibitory’ interactions were less selective. Because of its superior performance, this ‘sparse+latent’ estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix. PMID:25826696

Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

NASA Astrophysics Data System (ADS)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.

Heavy metals relationship with water and size-fractionated sediments in rivers using canonical correlation analysis (CCA) case study, rivers of south western Caspian Sea.

PubMed

Vosoogh, Ali; Saeedi, Mohsen; Lak, Raziyeh

2016-11-01

Some pollutants can qualitatively affect aquatic freshwater such as rivers, and heavy metals are one of the most important pollutants in aquatic fresh waters. Heavy metals can be found in the form of components dissolved in these waters or in compounds with suspended particles and surface sediments. It can be said that heavy metals are in equilibrium between water and sediment. In this study, the amount of heavy metals is determined in water and different sizes of sediment. To obtain the relationship between heavy metals in water and size-fractionated sediments, a canonical correlation analysis (CCA) was utilized in rivers of the southwestern Caspian Sea. In this research, a case study was carried out on 18 sampling stations in nine rivers. In the first step, the concentrations of heavy metals (Cu, Zn, Cr, Fe, Mn, Pb, Ni, and Cd) were determined in water and size-fractionated sediment samples. Water sampling sites were classified by hierarchical cluster analysis (HCA) utilizing squared Euclidean distance with Ward's method. In addition, for interpreting the obtained results and the relationships between the concentration of heavy metals in the tested river water and sample sediments, canonical correlation analysis (CCA) was utilized. The rivers were grouped into two classes (those having no pollution and those having low pollution) based on the HCA results obtained for river water samples. CCA results found numerous relationships between rivers in Iran's Guilan province and their size-fractionated sediments samples. The heavy metals of sediments with 0.038 to 0.125 mm size in diameter are slightly correlated with those of water samples.

Deep Learning Predicts Correlation between a Functional Signature of Higher Visual Areas and Sparse Firing of Neurons.

PubMed

Zhuang, Chengxu; Wang, Yulong; Yamins, Daniel; Hu, Xiaolin

2017-01-01

Visual information in the visual cortex is processed in a hierarchical manner. Recent studies show that higher visual areas, such as V2, V3, and V4, respond more vigorously to images with naturalistic higher-order statistics than to images lacking them. This property is a functional signature of higher areas, as it is much weaker or even absent in the primary visual cortex (V1). However, the mechanism underlying this signature remains elusive. We studied this problem using computational models. In several typical hierarchical visual models including the AlexNet, VggNet, and SHMAX, this signature was found to be prominent in higher layers but much weaker in lower layers. By changing both the model structure and experimental settings, we found that the signature strongly correlated with sparse firing of units in higher layers but not with any other factors, including model structure, training algorithm (supervised or unsupervised), receptive field size, and property of training stimuli. The results suggest an important role of sparse neuronal activity underlying this special feature of higher visual areas.

Deep Learning Predicts Correlation between a Functional Signature of Higher Visual Areas and Sparse Firing of Neurons

PubMed Central

Zhuang, Chengxu; Wang, Yulong; Yamins, Daniel; Hu, Xiaolin

2017-01-01

Visual information in the visual cortex is processed in a hierarchical manner. Recent studies show that higher visual areas, such as V2, V3, and V4, respond more vigorously to images with naturalistic higher-order statistics than to images lacking them. This property is a functional signature of higher areas, as it is much weaker or even absent in the primary visual cortex (V1). However, the mechanism underlying this signature remains elusive. We studied this problem using computational models. In several typical hierarchical visual models including the AlexNet, VggNet, and SHMAX, this signature was found to be prominent in higher layers but much weaker in lower layers. By changing both the model structure and experimental settings, we found that the signature strongly correlated with sparse firing of units in higher layers but not with any other factors, including model structure, training algorithm (supervised or unsupervised), receptive field size, and property of training stimuli. The results suggest an important role of sparse neuronal activity underlying this special feature of higher visual areas. PMID:29163117

Measurements of the Canonical Helicity Evolution of a Gyrating Kinked Flux Rope

NASA Astrophysics Data System (ADS)

von der Linden, J.; Sears, J.; Intrator, T.; You, S.

2017-12-01

Magnetic structures in the solar corona and planetary magnetospheres are often modelled as magnetic flux ropes governed by magnetohydrodynamics (MHD); however, inside these structures, as exhibited in reconnection, conversions between magnetic and kinetic energies occur over a wide range of scales. Flux ropes based on the flux of canonical momentum circulation extend the flux rope concept to include effects of finite particle momentum and present the distinct advantage of reconciling all plasma regimes - e.g. kinetic, two-fluid, and MHD - with the topological concept of helicity: twists, writhes, and linkages. This presentation shows the first visualization and analysis of the 3D dynamics of canonical flux ropes and their relative helicity evolution from laboratory measurements. Ion and electron canonical flux ropes are visualized from a dataset of Mach, triple, and Ḃ probe measurements at over 10,000 spatial locations of a gyrating kinked flux rope. The flux ropes co-gyrate with the peak density and electron temperature in and out of a measurement volume. The electron and ion canonical flux ropes twist with opposite handedness and the ion flux ropes writhe around the electron flux ropes. The relative cross helicity between the magnetic and ion flow vorticity flux ropes dominates the relative ion canonical helicity and is anti-correlated with the relative magnetic helicity. The 3D nature of the kink and a reverse eddy current affect the helicity evolution. This work is supported by DOE Grant DE-SC0010340 and the DOE Office of Science Graduate Student Research Program and prepared in part by LLNL under Contract DE-AC52-07NA27344. LLNL-ABS-735426

El Niño-Southern Oscillation-time scale covariation of sea surface salinity and freshwater flux in the western tropical and northern subtropical Pacific

NASA Astrophysics Data System (ADS)

Nagano, Akira; Hasegawa, Takuya; Ueki, Iwao; Ando, Kentaro

2017-07-01

We examined the covariation of sea surface salinity (SSS) and freshwater flux in the western tropical and northern subtropical Pacific on the El Niño-Southern Oscillation time scale, using a canonical correlation analysis of monthly data between 2001 and 2013. The dominant covariation, i.e., the first canonical mode, has large positive and negative amplitudes in regions east of the Philippines and New Guinea, respectively, and reaches peaks in autumn to winter of El Niño years. The positive SSS anomaly east of the Philippines is advected to the Kuroshio Extension region. We found that the second canonical mode is another coupled variation with localized amplitudes of SSS under the atmospheric convergence zones in winter to spring of La Niña years. However, the negative SSS anomaly is annihilated possibly by the evaporation in the subtropical region.

Measures and models for angular correlation and angular-linear correlation. [correlation of random variables

NASA Technical Reports Server (NTRS)

Johnson, R. A.; Wehrly, T.

1976-01-01

Population models for dependence between two angular measurements and for dependence between an angular and a linear observation are proposed. The method of canonical correlations first leads to new population and sample measures of dependence in this latter situation. An example relating wind direction to the level of a pollutant is given. Next, applied to pairs of angular measurements, the method yields previously proposed sample measures in some special cases and a new sample measure in general.

A physiologically motivated sparse, compact, and smooth (SCS) approach to EEG source localization.

PubMed

Cao, Cheng; Akalin Acar, Zeynep; Kreutz-Delgado, Kenneth; Makeig, Scott

2012-01-01

Here, we introduce a novel approach to the EEG inverse problem based on the assumption that principal cortical sources of multi-channel EEG recordings may be assumed to be spatially sparse, compact, and smooth (SCS). To enforce these characteristics of solutions to the EEG inverse problem, we propose a correlation-variance model which factors a cortical source space covariance matrix into the multiplication of a pre-given correlation coefficient matrix and the square root of the diagonal variance matrix learned from the data under a Bayesian learning framework. We tested the SCS method using simulated EEG data with various SNR and applied it to a real ECOG data set. We compare the results of SCS to those of an established SBL algorithm.

Role of Frontotemporal Fiber Tract Integrity in Task-Switching Performance of Healthy Controls and Patients with Temporal Lobe Epilepsy

PubMed Central

Kucukboyaci, N. Erkut; Girard, H.M.; Hagler, D.J.; Kuperman, J.; Tecoma, E.S.; Iragui, V.J.; Halgren, E.; McDonald, C.R.

2012-01-01

The objective of this study is to investigate the relationships among frontotemporal fiber tract compromise and task-switching performance in healthy controls and patients with temporal lobe epilepsy (TLE). We performed diffusion tensor imaging (DTI) on 30 controls and 32 patients with TLE (15 left TLE). Fractional anisotropy (FA) was calculated for four fiber tracts [uncinate fasciculus (UncF), arcuate fasciculus (ArcF), dorsal cingulum (CING), and inferior fronto-occipital fasciculus (IFOF)]. Participants completed the Trail Making Test-B (TMT-B) and Verbal Fluency Category Switching (VFCS) test. Multivariate analyses of variances (MANOVAs) were performed to investigate group differences in fiber FA and set-shifting performances. Canonical correlations were used to examine the overall patterns of structural-cognitive relationships and were followed by within-group bivariate correlations. We found a significant canonical correlation between fiber FA and task-switching performance. In controls, TMT-B correlated with left IFOF, whereas VFCS correlated with FA of left ArcF and left UncF. These correlations were not significant in patients with TLE. We report significant correlations between frontotemporal fiber tract integrity and set-shifting performance in healthy controls that appear to be absent or attenuated in patients with TLE. These findings suggest a breakdown of typical structure-function relationships in TLE that may reflect aberrant developmental or degenerative processes. PMID:22014246

Hierarchical classification in high dimensional numerous class cases

NASA Technical Reports Server (NTRS)

Kim, Byungyong; Landgrebe, D. A.

1990-01-01

As progress in new sensor technology continues, increasingly high resolution imaging sensors are being developed. These sensors give more detailed and complex data for each picture element and greatly increase the dimensionality of data over past systems. Three methods for designing a decision tree classifier are discussed: a top down approach, a bottom up approach, and a hybrid approach. Three feature extraction techniques are implemented. Canonical and extended canonical techniques are mainly dependent upon the mean difference between two classes. An autocorrelation technique is dependent upon the correlation differences. The mathematical relationship between sample size, dimensionality, and risk value is derived.

Exploring the Replicability of a Study's Results: Bootstrap Statistics for the Multivariate Case.

ERIC Educational Resources Information Center

Thompson, Bruce

1995-01-01

Use of the bootstrap method in a canonical correlation analysis to evaluate the replicability of a study's results is illustrated. More confidence may be vested in research results that replicate. (SLD)

Sparse PCA with Oracle Property.

PubMed

Gu, Quanquan; Wang, Zhaoran; Liu, Han

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

Sparse PCA with Oracle Property

PubMed Central

Gu, Quanquan; Wang, Zhaoran; Liu, Han

2014-01-01

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

The Use of Canonical Correlation Analysis to Assess the Relationship Between Executive Functioning and Verbal Memory in Older Adults

PubMed Central

Moreira, Pedro Silva; Santos, Nadine Correia; Sousa, Nuno

2015-01-01

Executive functioning (EF), which is considered to govern complex cognition, and verbal memory (VM) are constructs assumed to be related. However, it is not known the magnitude of the association between EF and VM, and how sociodemographic and psychological factors may affect this relationship, including in normal aging. In this study, we assessed different EF and VM parameters, via a battery of neurocognitive/psychological tests, and performed a Canonical Correlation Analysis (CCA) to explore the connection between these constructs, in a sample of middle-aged and older healthy individuals without cognitive impairment (N = 563, 50+ years of age). The analysis revealed a positive and moderate association between EF and VM independently of gender, age, education, global cognitive performance level, and mood. These results confirm that EF presents a significant association with VM performance. PMID:28138465

The integrated model of sport confidence: a canonical correlation and mediational analysis.

PubMed

Koehn, Stefan; Pearce, Alan J; Morris, Tony

2013-12-01

The main purpose of the study was to examine crucial parts of Vealey's (2001) integrated framework hypothesizing that sport confidence is a mediating variable between sources of sport confidence (including achievement, self-regulation, and social climate) and athletes' affect in competition. The sample consisted of 386 athletes, who completed the Sources of Sport Confidence Questionnaire, Trait Sport Confidence Inventory, and Dispositional Flow Scale-2. Canonical correlation analysis revealed a confidence-achievement dimension underlying flow. Bias-corrected bootstrap confidence intervals in AMOS 20.0 were used in examining mediation effects between source domains and dispositional flow. Results showed that sport confidence partially mediated the relationship between achievement and self-regulation domains and flow, whereas no significant mediation was found for social climate. On a subscale level, full mediation models emerged for achievement and flow dimensions of challenge-skills balance, clear goals, and concentration on the task at hand.

Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging.

PubMed

Bilenko, Natalia Y; Gallant, Jack L

2016-01-01

In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model.

Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging

PubMed Central

Bilenko, Natalia Y.; Gallant, Jack L.

2016-01-01

In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model. PMID:27920675

Strongly contracted canonical transformation theory

NASA Astrophysics Data System (ADS)

Neuscamman, Eric; Yanai, Takeshi; Chan, Garnet Kin-Lic

2010-01-01

Canonical transformation (CT) theory describes dynamic correlation in multireference systems with large active spaces. Here we discuss CT theory's intruder state problem and why our previous approach of overlap matrix truncation becomes infeasible for sufficiently large active spaces. We propose the use of strongly and weakly contracted excitation operators as alternatives for dealing with intruder states in CT theory. The performance of these operators is evaluated for the H2O, N2, and NiO molecules, with comparisons made to complete active space second order perturbation theory and Davidson-corrected multireference configuration interaction theory. Finally, using a combination of strongly contracted CT theory and orbital-optimized density matrix renormalization group theory, we evaluate the singlet-triplet gap of free base porphin using an active space containing all 24 out-of-plane 2p orbitals. Modeling dynamic correlation with an active space of this size is currently only possible using CT theory.

Improved Spectral Calculations for Discrete Schrődinger Operators

NASA Astrophysics Data System (ADS)

Puelz, Charles

This work details an O(n2) algorithm for computing spectra of discrete Schrődinger operators with periodic potentials. Spectra of these objects enhance our understanding of fundamental aperiodic physical systems and contain rich theoretical structure of interest to the mathematical community. Previous work on the Harper model led to an O(n2) algorithm relying on properties not satisfied by other aperiodic operators. Physicists working with the Fibonacci Hamiltonian, a popular quasicrystal model, have instead used a problematic dynamical map approach or a sluggish O(n3) procedure for their calculations. The algorithm presented in this work, a blend of well-established eigenvalue/vector algorithms, provides researchers with a more robust computational tool of general utility. Application to the Fibonacci Hamiltonian in the sparsely studied intermediate coupling regime reveals structure in canonical coverings of the spectrum that will prove useful in motivating conjectures regarding band combinatorics and fractal dimensions.

Robust Small Target Co-Detection from Airborne Infrared Image Sequences.

PubMed

Gao, Jingli; Wen, Chenglin; Liu, Meiqin

2017-09-29

In this paper, a novel infrared target co-detection model combining the self-correlation features of backgrounds and the commonality features of targets in the spatio-temporal domain is proposed to detect small targets in a sequence of infrared images with complex backgrounds. Firstly, a dense target extraction model based on nonlinear weights is proposed, which can better suppress background of images and enhance small targets than weights of singular values. Secondly, a sparse target extraction model based on entry-wise weighted robust principal component analysis is proposed. The entry-wise weight adaptively incorporates structural prior in terms of local weighted entropy, thus, it can extract real targets accurately and suppress background clutters efficiently. Finally, the commonality of targets in the spatio-temporal domain are used to construct target refinement model for false alarms suppression and target confirmation. Since real targets could appear in both of the dense and sparse reconstruction maps of a single frame, and form trajectories after tracklet association of consecutive frames, the location correlation of the dense and sparse reconstruction maps for a single frame and tracklet association of the location correlation maps for successive frames have strong ability to discriminate between small targets and background clutters. Experimental results demonstrate that the proposed small target co-detection method can not only suppress background clutters effectively, but also detect targets accurately even if with target-like interference.

Decoding the auditory brain with canonical component analysis.

PubMed

de Cheveigné, Alain; Wong, Daniel D E; Di Liberto, Giovanni M; Hjortkjær, Jens; Slaney, Malcolm; Lalor, Edmund

2018-05-15

The relation between a stimulus and the evoked brain response can shed light on perceptual processes within the brain. Signals derived from this relation can also be harnessed to control external devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated "decoding" strategies are needed to address continuous stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both the stimulus and the response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves observations of the relation between stimulus and response. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Classification of multipartite entanglement via negativity fonts

NASA Astrophysics Data System (ADS)

Sharma, S. Shelly; Sharma, N. K.

2012-04-01

Partial transposition of state operator is a well-known tool to detect quantum correlations between two parts of a composite system. In this paper, the global partial transpose (GPT) is linked to conceptually multipartite underlying structures in a state—the negativity fonts. If K-way negativity fonts with nonzero determinants exist, then selective partial transposition of a pure state, involving K of the N qubits (K⩽N), yields an operator with negative eigenvalues, identifying K-body correlations in the state. Expansion of GPT in terms of K-way partially transposed (KPT) operators reveals the nature of intricate intrinsic correlations in the state. Classification criteria for multipartite entangled states based on the underlying structure of global partial transpose of canonical state are proposed. The number of N-partite entanglement types for an N-qubit system is found to be 2N-1-N+2, while the number of major entanglement classes is 2N-1-1. Major classes for three- and four-qubit states are listed. Subclasses are determined by the number and type of negativity fonts in canonical states.

Factors associated with the nutritional status of children less than 5 years of age

PubMed Central

Miglioli, Teresa Cristina; Fonseca, Vania Matos; Gomes, Saint Clair; da Silva, Katia Silveira; de Lira, Pedro Israel Cabral; Batista, Malaquias

2015-01-01

OBJECTIVE To analyze if the nutritional status of children aged less than five years is related to the biological conditions of their mothers, environmental and socioeconomic factors, and access to health services and social programs. METHODS This cross-sectional population-based study analyzed 664 mothers and 790 children using canonical correlation analysis. Dependent variables were characteristics of the children (weight/age, height/age, BMI/age, hemoglobin, and retinol serum levels). Independent variables were those related to the mothers’ nutritional status (BMI, hemoglobin, and retinol serum levels), age, environmental and socioeconomic factors and access to health service and social programs. A < 0.05 significance level was adopted to select the interpreted canonical functions (CF) and ± 0.40 as canonical load value of the analyzed variables. RESULTS Three canonical functions were selected, concentrating 89.9% of the variability of the relationship among the groups. In the first canonical function, weight/age (-0.73) and height/age (-0.99) of the children were directly related to the mother’s height (-0.82), prenatal appointments (-0.43), geographical area of the residence (-0.41), and household income per capita (-0.42). Inverse relationship between the variables related to the children and people/room (0.44) showed that the larger the number of people/room, the poorer their nutritional status. Rural residents were found to have the worse nutritional conditions. In the second canonical function, the BMI of the mother (-0.48) was related to BMI/age and retinol of the children, indicating that as women gained weight so did their children. Underweight women tended to have children with vitamin A deficiency. In the third canonical function, hemoglobin (-0.72) and retinol serum levels (-0.40) of the children were directly related to the mother’s hemoglobin levels (-0.43). CONCLUSIONS Mothers and children were associated concerning anemia, vitamin A deficiency and anthropometric markers. Living in rural areas is a determining factor for the families health status. PMID:26398874

Cities as selective land predators? A lesson on urban growth, deregulated planning and sprawl containment.

PubMed

Colantoni, Andrea; Grigoriadis, Efstathios; Sateriano, Adele; Venanzoni, Giuseppe; Salvati, Luca

2016-03-01

The present study investigates changes in the use of land caused by the expansion of an informal city in the Mediterranean region (Athens, Greece) and it proposes a simplified methodology to assess selective land take at the scale of municipalities. The amount of land take over twenty years (1987-2007) for cropland, sparsely vegetated areas and natural land was compared with the surface area of the respective class at the beginning of the study period (1987). Indicators of selective land take by class were correlated with socioeconomic indicators at the scale of municipalities to verify the influence of the local context and the impact of urban planning on land take processes. Evidence indicates that urban expansion into fringe land consumes primarily cropland and sparse vegetation in the case of the Athens' metropolitan region. Cropland and sparse vegetation were consumed proportionally more than the respective availability in 16 municipalities out of 60. Agricultural land take was positively correlated with population density and growth rate, rate of participation to the job market and road density. Sparse vegetation land take was observed in municipalities with predominance of high density settlements. As a result of second-home expansion in coastal municipalities, natural land was converted to urban use in proportion to the availability in the landscape. Urban planning seems to have a limited impact on selective land take. Copyright © 2015 Elsevier B.V. All rights reserved.

Eigenanatomy on Fractional Anisotropy Imaging Provides White Matter Anatomical Features Discriminating Between Alzheimer's Disease and Late Onset Bipolar Disorder.

PubMed

Besga, Ariadna; Chyzhyk, Darya; González-Ortega, Itxaso; Savio, Alexandre; Ayerdi, Borja; Echeveste, Jon; Graña, Manuel; González-Pinto, Ana

2016-01-01

Late Onset Bipolar Disorder (LOBD) is the arousal of Bipolar Disorder (BD) at old age (>60) without any previous history of disorders. LOBD is often difficult to distinguish from degenerative dementias, such as Alzheimer Disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence is increasing due to population aging. Biomarkers extracted from blood plasma are not discriminant because both pathologies share pathophysiological features related to neuroinflammation, therefore we look for anatomical features highly correlated with blood biomarkers that allow accurate diagnosis prediction. This may shed some light on the basic biological mechanisms leading to one or another disease. Moreover, accurate diagnosis is needed to select the best personalized treatment. We look for white matter features which are correlated with blood plasma biomarkers (inflammatory and neurotrophic) discriminating LOBD from AD. A sample of healthy controls (HC) (n=19), AD patients (n=35), and BD patients (n=24) has been recruited at the Alava University Hospital. Plasma biomarkers have been obtained at recruitment time. Diffusion weighted (DWI) magnetic resonance imaging (MRI) are obtained for each subject. DWI is preprocessed to obtain diffusion tensor imaging (DTI) data, which is reduced to fractional anisotropy (FA) data. In the selection phase, eigenanatomy finds FA eigenvolumes maximally correlated with plasma biomarkers by partial sparse canonical correlation analysis (PSCCAN). In the analysis phase, we take the eigenvolume projection coefficients as the classification features, carrying out cross-validation of support vector machine (SVM) to obtain discrimination power of each biomarker effects. The John Hopkins Universtiy white matter atlas is used to provide anatomical localizations of the detected feature clusters. Classification results show that one specific biomarker of oxidative stress (malondialdehyde MDA) gives the best classification performance ( accuracy 85%, F-score 86%, sensitivity, and specificity 87%, ) in the discrimination of AD and LOBD. Discriminating features appear to be localized in the posterior limb of the internal capsule and superior corona radiata. It is feasible to support contrast diagnosis among LOBD and AD by means of predictive classifiers based on eigenanatomy features computed from FA imaging correlated to plasma biomarkers. In addition, white matter eigenanatomy localizations offer some new avenues to assess the differential pathophysiology of LOBD and AD.

Studying Canonical Analysis: A Reply to Thorndike's Comments

ERIC Educational Resources Information Center

Barcikowski, Robert S.; Stevens, James P.

1976-01-01

This article is a rejoinder to TM 502 249. Each of Thorndike's comments are examined. A possible solution to the large number of subjects necessary for stable weights and variate-variable correlations using ridge regression procedures is suggested. (RC)

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

PubMed

Cleophas, Ton J

2016-01-01

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P = 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method.

Quantum canonical ensemble: A projection operator approach

NASA Astrophysics Data System (ADS)

Magnus, Wim; Lemmens, Lucien; Brosens, Fons

2017-09-01

Knowing the exact number of particles N, and taking this knowledge into account, the quantum canonical ensemble imposes a constraint on the occupation number operators. The constraint particularly hampers the systematic calculation of the partition function and any relevant thermodynamic expectation value for arbitrary but fixed N. On the other hand, fixing only the average number of particles, one may remove the above constraint and simply factorize the traces in Fock space into traces over single-particle states. As is well known, that would be the strategy of the grand-canonical ensemble which, however, comes with an additional Lagrange multiplier to impose the average number of particles. The appearance of this multiplier can be avoided by invoking a projection operator that enables a constraint-free computation of the partition function and its derived quantities in the canonical ensemble, at the price of an angular or contour integration. Introduced in the recent past to handle various issues related to particle-number projected statistics, the projection operator approach proves beneficial to a wide variety of problems in condensed matter physics for which the canonical ensemble offers a natural and appropriate environment. In this light, we present a systematic treatment of the canonical ensemble that embeds the projection operator into the formalism of second quantization while explicitly fixing N, the very number of particles rather than the average. Being applicable to both bosonic and fermionic systems in arbitrary dimensions, transparent integral representations are provided for the partition function ZN and the Helmholtz free energy FN as well as for two- and four-point correlation functions. The chemical potential is not a Lagrange multiplier regulating the average particle number but can be extracted from FN+1 -FN, as illustrated for a two-dimensional fermion gas.

ℓ0 -based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation

NASA Astrophysics Data System (ADS)

Xu, Xia; Shi, Zhenwei; Pan, Bin

2018-07-01

Sparse unmixing aims at recovering pure materials from hyperpspectral images and estimating their abundance fractions. Sparse unmixing is actually ℓ0 problem which is NP-h ard, and a relaxation is often used. In this paper, we attempt to deal with ℓ0 problem directly via a multi-objective based method, which is a non-convex manner. The characteristics of hyperspectral images are integrated into the proposed method, which leads to a new spectra and multi-objective based sparse unmixing method (SMoSU). In order to solve the ℓ0 norm optimization problem, the spectral library is encoded in a binary vector, and a bit-wise flipping strategy is used to generate new individuals in the evolution process. However, a multi-objective method usually produces a number of non-dominated solutions, while sparse unmixing requires a single solution. How to make the final decision for sparse unmixing is challenging. To handle this problem, we integrate the spectral characteristic of hyperspectral images into SMoSU. By considering the spectral correlation in hyperspectral data, we improve the Tchebycheff decomposition function in SMoSU via a new regularization item. This regularization item is able to enforce the individual divergence in the evolution process of SMoSU. In this way, the diversity and convergence of population is further balanced, which is beneficial to the concentration of individuals. In the experiments part, three synthetic datasets and one real-world data are used to analyse the effectiveness of SMoSU, and several state-of-art sparse unmixing algorithms are compared.

P-cadherin regulates human hair growth and cycling via canonical Wnt signaling and transforming growth factor-β2.

PubMed

Samuelov, Liat; Sprecher, Eli; Tsuruta, Daisuke; Bíró, Tamás; Kloepper, Jennifer E; Paus, Ralf

2012-10-01

P-cadherin is a key component of epithelial adherens junctions, and it is prominently expressed in the hair follicle (HF) matrix. Loss-of-function mutations in CDH3, which encodes P-cadherin, result in hypotrichosis with juvenile macular dystrophy (HJMD), an autosomal recessive disorder featuring sparse and short hair. Here, we attempted to recapitulate some aspects of HJMD in vitro by transfecting normal, organ-cultured human scalp HFs with lipofectamine and CDH3-specific or scrambled control siRNAs. As in HJMD patients, P-cadherin silencing inhibited hair shaft growth, prematurely induced HF regression (catagen), and inhibited hair matrix keratinocyte proliferation. In situ, membrane β-catenin expression and transcription of the β-catenin target gene, axin2, were significantly reduced, whereas glycogen synthase kinase 3 β (GSK3β) and phospho-β-catenin immunoreactivity were increased. These effects were partially reversed by inhibiting GSK3β. P-cadherin silencing reduced the expression of the anagen-promoting growth factor, IGF-1, whereas that of transforming growth factor β 2 (TGFβ2; catagen promoter) was enhanced. Neutralizing TGFβ antagonized the catagen-promoting effects of P-cadherin silencing. In summary, we introduce human HFs as an attractive preclinical model for studying the functions of P-cadherin in human epithelial biology and pathology. This model demonstrates that cadherins can be successfully knocked down in an intact human organ in vitro, and shows that P-cadherin is needed for anagen maintenance by regulating canonical Wnt signaling and suppressing TGFβ2.

Life skills and subjective well-being of people with disabilities: a canonical correlation analysis.

PubMed

da Silva Cardoso, Elizabeth; Blalock, Kacie; Allen, Chase A; Chan, Fong; Rubin, Stanford E

2004-12-01

This study examined the canonical relationships between a set of life skill variables and a set of subjective well-being variables among a national sample of vocational rehabilitation clients in the USA. Self-direction, work tolerance, general employability, and self-care were related to physical, family and social, and financial well-being. This analysis also found that communication skill is related to family and social well-being, while psychological well-being is not related to any life skills in the set. The results showed that vocational rehabilitation services aimed to improve life functioning will lead to an improvement in subjective quality of life.

Reweighted mass center based object-oriented sparse subspace clustering for hyperspectral images

NASA Astrophysics Data System (ADS)

Zhai, Han; Zhang, Hongyan; Zhang, Liangpei; Li, Pingxiang

2016-10-01

Considering the inevitable obstacles faced by the pixel-based clustering methods, such as salt-and-pepper noise, high computational complexity, and the lack of spatial information, a reweighted mass center based object-oriented sparse subspace clustering (RMC-OOSSC) algorithm for hyperspectral images (HSIs) is proposed. First, the mean-shift segmentation method is utilized to oversegment the HSI to obtain meaningful objects. Second, a distance reweighted mass center learning model is presented to extract the representative and discriminative features for each object. Third, assuming that all the objects are sampled from a union of subspaces, it is natural to apply the SSC algorithm to the HSI. Faced with the high correlation among the hyperspectral objects, a weighting scheme is adopted to ensure that the highly correlated objects are preferred in the procedure of sparse representation, to reduce the representation errors. Two widely used hyperspectral datasets were utilized to test the performance of the proposed RMC-OOSSC algorithm, obtaining high clustering accuracies (overall accuracy) of 71.98% and 89.57%, respectively. The experimental results show that the proposed method clearly improves the clustering performance with respect to the other state-of-the-art clustering methods, and it significantly reduces the computational time.

Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome.

PubMed

Morine, Melissa J; McMonagle, Jolene; Toomey, Sinead; Reynolds, Clare M; Moloney, Aidan P; Gormley, Isobel C; Gaora, Peadar O; Roche, Helen M

2010-10-07

Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p < 0.05), followed by muscle (601 genes) and adipose (16 genes). Results from modified GSEA showed that the high-CLA beef diet affected diverse biological processes across the three tissues, and that the majority of pathway changes reached significance only with the bi-directional test. Combining the liver tissue microarray results with plasma marker data revealed 110 CLA-sensitive genes showing strong canonical correlation with one or more plasma markers of metabolic health, and 9 significantly overrepresented pathways among this set; each of these pathways was also significantly changed by the high-CLA diet. Closer inspection of two of these pathways--selenoamino acid metabolism and steroid biosynthesis--illustrated clear diet-sensitive changes in constituent genes, as well as strong correlations between gene expression and plasma markers of metabolic syndrome independent of the dietary effect. Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of analysis has the potential to generate novel transcriptome-based biomarkers of disease.

Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome

PubMed Central

2010-01-01

Background Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Results Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p < 0.05), followed by muscle (601 genes) and adipose (16 genes). Results from modified GSEA showed that the high-CLA beef diet affected diverse biological processes across the three tissues, and that the majority of pathway changes reached significance only with the bi-directional test. Combining the liver tissue microarray results with plasma marker data revealed 110 CLA-sensitive genes showing strong canonical correlation with one or more plasma markers of metabolic health, and 9 significantly overrepresented pathways among this set; each of these pathways was also significantly changed by the high-CLA diet. Closer inspection of two of these pathways - selenoamino acid metabolism and steroid biosynthesis - illustrated clear diet-sensitive changes in constituent genes, as well as strong correlations between gene expression and plasma markers of metabolic syndrome independent of the dietary effect. Conclusion Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of analysis has the potential to generate novel transcriptome-based biomarkers of disease. PMID:20929581

The structural, connectomic and network covariance of the human brain.

PubMed

Irimia, Andrei; Van Horn, John D

2013-02-01

Though it is widely appreciated that complex structural, functional and morphological relationships exist between distinct areas of the human cerebral cortex, the extent to which such relationships coincide remains insufficiently appreciated. Here we determine the extent to which correlations between brain regions are modulated by either structural, connectomic or network-theoretic properties using a structural neuroimaging data set of magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) volumes acquired from N=110 healthy human adults. To identify the linear relationships between all available pairs of regions, we use canonical correlation analysis to test whether a statistically significant correlation exists between each pair of cortical parcels as quantified via structural, connectomic or network-theoretic measures. In addition to this, we investigate (1) how each group of canonical variables (whether structural, connectomic or network-theoretic) contributes to the overall correlation and, additionally, (2) whether each individual variable makes a significant contribution to the test of the omnibus null hypothesis according to which no correlation between regions exists across subjects. We find that, although region-to-region correlations are extensively modulated by structural and connectomic measures, there are appreciable differences in how these two groups of measures drive inter-regional correlation patterns. Additionally, our results indicate that the network-theoretic properties of the cortex are strong modulators of region-to-region covariance. Our findings are useful for understanding the structural and connectomic relationship between various parts of the brain, and can inform theoretical and computational models of cortical information processing. Published by Elsevier Inc.

Isolation and characterization of novel RECK tumor suppressor gene splice variants

PubMed Central

Trombetta-Lima, Marina; Winnischofer, Sheila Maria Brochado; Demasi, Marcos Angelo Almeida; Filho, Renato Astorino; Carreira, Ana Claudia Oliveira; Wei, Beiyang; de Assis Ribas, Thais; Konig, Michelle Silberspitz; Bowman-Colin, Christian; Oba-Shinjo, Sueli Mieko; Marie, Suely Kazue Nagahashi; Stetler-Stevenson, William; Sogayar, Mari Cleide

2015-01-01

Glioblastoma multiforme is the most common and lethal of the central nervous system glial-derived tumors. RECK suppresses tumor invasion by negatively regulating at least three members of the matrix metalloproteinase family: MMP-9, MMP-2, and MT1-MMP. A positive correlation has been observed between the abundance of RECK expression in tumor samples and a more favorable prognosis for patients with several types of tumors. In the present study, novel alternatively spliced variants of the RECK gene: RECK-B and RECK-I were isolated by RT-PCR and sequenced. The expression levels and profiles of these alternative RECK transcripts, as well as canonical RECK were determined in tissue samples of malignant astrocytomas of different grades and in a normal tissue RNA panel by qRT-PCR. Our results show that higher canonical RECK expression, accompanied by a higher canonical to alternative transcript expression ratio, positively correlates with higher overall survival rate after chemotherapeutic treatment of GBM patients. U87MG and T98G cells over-expressing the RECK-B alternative variant display higher anchorage-independent clonal growth and do not display modulation of, respectively, MMP-2 and MMP-9 expression. Our findings suggest that RECK transcript variants might have opposite roles in GBM biology and the ratio of their expression levels may be informative for the prognostic outcome of GBM patients. PMID:26431549

Formulation of state projected centroid molecular dynamics: Microcanonical ensemble and connection to the Wigner distribution.

PubMed

Orr, Lindsay; Hernández de la Peña, Lisandro; Roy, Pierre-Nicholas

2017-06-07

A derivation of quantum statistical mechanics based on the concept of a Feynman path centroid is presented for the case of generalized density operators using the projected density operator formalism of Blinov and Roy [J. Chem. Phys. 115, 7822-7831 (2001)]. The resulting centroid densities, centroid symbols, and centroid correlation functions are formulated and analyzed in the context of the canonical equilibrium picture of Jang and Voth [J. Chem. Phys. 111, 2357-2370 (1999)]. The case where the density operator projects onto a particular energy eigenstate of the system is discussed, and it is shown that one can extract microcanonical dynamical information from double Kubo transformed correlation functions. It is also shown that the proposed projection operator approach can be used to formally connect the centroid and Wigner phase-space distributions in the zero reciprocal temperature β limit. A Centroid Molecular Dynamics (CMD) approximation to the state-projected exact quantum dynamics is proposed and proven to be exact in the harmonic limit. The state projected CMD method is also tested numerically for a quartic oscillator and a double-well potential and found to be more accurate than canonical CMD. In the case of a ground state projection, this method can resolve tunnelling splittings of the double well problem in the higher barrier regime where canonical CMD fails. Finally, the state-projected CMD framework is cast in a path integral form.

Formulation of state projected centroid molecular dynamics: Microcanonical ensemble and connection to the Wigner distribution

NASA Astrophysics Data System (ADS)

Orr, Lindsay; Hernández de la Peña, Lisandro; Roy, Pierre-Nicholas

2017-06-01

A derivation of quantum statistical mechanics based on the concept of a Feynman path centroid is presented for the case of generalized density operators using the projected density operator formalism of Blinov and Roy [J. Chem. Phys. 115, 7822-7831 (2001)]. The resulting centroid densities, centroid symbols, and centroid correlation functions are formulated and analyzed in the context of the canonical equilibrium picture of Jang and Voth [J. Chem. Phys. 111, 2357-2370 (1999)]. The case where the density operator projects onto a particular energy eigenstate of the system is discussed, and it is shown that one can extract microcanonical dynamical information from double Kubo transformed correlation functions. It is also shown that the proposed projection operator approach can be used to formally connect the centroid and Wigner phase-space distributions in the zero reciprocal temperature β limit. A Centroid Molecular Dynamics (CMD) approximation to the state-projected exact quantum dynamics is proposed and proven to be exact in the harmonic limit. The state projected CMD method is also tested numerically for a quartic oscillator and a double-well potential and found to be more accurate than canonical CMD. In the case of a ground state projection, this method can resolve tunnelling splittings of the double well problem in the higher barrier regime where canonical CMD fails. Finally, the state-projected CMD framework is cast in a path integral form.

Mandibular trabecular bone as fracture indicator in 80-year-old men and women.

PubMed

Hassani-Nejad, Azar; Ahlqwist, Margareta; Hakeberg, Magnus; Jonasson, Grethe

2013-12-01

The objective of the present study was to compare assessments of the mandibular bone as fracture risk indicators for 277 men and women. The mandibular trabecular bone was evaluated in periapical radiographs, using a visual index, as dense, mixed dense and sparse, or sparse. Bone texture was analysed using a computer-based method in which the number of transitions from trabeculae to intertrabecular spaces was calculated. The sum of the sizes and intensities of the spaces between the trabeculae was calculated using Jaw-X software. Women had a statistically significantly greater number of fractures and a higher frequency of sparse mandibular bone. The OR for having suffered a fracture with visually sparse trabecular bone was highest for the male group (OR = 5.55) and lowest for the female group (OR = 3.35). For bone texture as an indicator of previous fracture, the OR was significant for the female group (OR = 2.61) but not for the male group, whereas the Jaw-X calculations did not differentiate between fractured and non-fractured groups. In conclusion, all bone-quality assessments showed that women had a higher incidence of sparse trabecular bone than did men. Only the methods of visual assessment and trabecular texture were significantly correlated with previous bone fractures. © 2013 Eur J Oral Sci.

The application of low-rank and sparse decomposition method in the field of climatology

NASA Astrophysics Data System (ADS)

Gupta, Nitika; Bhaskaran, Prasad K.

2018-04-01

The present study reports a low-rank and sparse decomposition method that separates the mean and the variability of a climate data field. Until now, the application of this technique was limited only in areas such as image processing, web data ranking, and bioinformatics data analysis. In climate science, this method exactly separates the original data into a set of low-rank and sparse components, wherein the low-rank components depict the linearly correlated dataset (expected or mean behavior), and the sparse component represents the variation or perturbation in the dataset from its mean behavior. The study attempts to verify the efficacy of this proposed technique in the field of climatology with two examples of real world. The first example attempts this technique on the maximum wind-speed (MWS) data for the Indian Ocean (IO) region. The study brings to light a decadal reversal pattern in the MWS for the North Indian Ocean (NIO) during the months of June, July, and August (JJA). The second example deals with the sea surface temperature (SST) data for the Bay of Bengal region that exhibits a distinct pattern in the sparse component. The study highlights the importance of the proposed technique used for interpretation and visualization of climate data.

Interpreting medium ring canonical conformers by a triangular plane tessellation of the macrocycle

NASA Astrophysics Data System (ADS)

Khalili, Pegah; Barnett, Christopher B.; Naidoo, Kevin J.

2013-05-01

Cyclic conformational coordinates are essential for the distinction of molecular ring conformers as the use of Cremer-Pople coordinates have illustrated for five- and six-membered rings. Here, by tessellating medium rings into triangular planes and using the relative angles made between triangular planes we are able to assign macrocyclic pucker conformations into canonical pucker conformers such as chairs, boats, etc. We show that the definition is straightforward compared with other methods popularly used for small rings and that it is computationally simple to implement for complex macrocyclic rings. These cyclic conformational coordinates directly couple to the motion of individual nodes of a ring. Therefore, they are useful for correlating the physical properties of macrocycles with their ring pucker and measuring the dynamic ring conformational behavior. We illustrate the triangular tessellation, assignment, and pucker analysis on 7- and 8-membered rings. Sets of canonical states are given for cycloheptane and cyclooctane that have been previously experimentally analysed.

Fast sparsely synchronized brain rhythms in a scale-free neural network

NASA Astrophysics Data System (ADS)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D . For small D , full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D

Estimation for sparse vegetation information in desertification region based on Tiangong-1 hyperspectral image.

PubMed

Wu, Jun-Jun; Gao, Zhi-Hai; Li, Zeng-Yuan; Wang, Hong-Yan; Pang, Yong; Sun, Bin; Li, Chang-Long; Li, Xu-Zhi; Zhang, Jiu-Xing

2014-03-01

In order to estimate the sparse vegetation information accurately in desertification region, taking southeast of Sunite Right Banner, Inner Mongolia, as the test site and Tiangong-1 hyperspectral image as the main data, sparse vegetation coverage and biomass were retrieved based on normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), combined with the field investigation data. Then the advantages and disadvantages between them were compared. Firstly, the correlation between vegetation indexes and vegetation coverage under different bands combination was analyzed, as well as the biomass. Secondly, the best bands combination was determined when the maximum correlation coefficient turned up between vegetation indexes (VI) and vegetation parameters. It showed that the maximum correlation coefficient between vegetation parameters and NDVI could reach as high as 0.7, while that of SAVI could nearly reach 0.8. The center wavelength of red band in the best bands combination for NDVI was 630nm, and that of the near infrared (NIR) band was 910 nm. Whereas, when the center wavelength was 620 and 920 nm respectively, they were the best combination for SAVI. Finally, the linear regression models were established to retrieve vegetation coverage and biomass based on Tiangong-1 VIs. R2 of all models was more than 0.5, while that of the model based on SAVI was higher than that based on NDVI, especially, the R2 of vegetation coverage retrieve model based on SAVI was as high as 0.59. By intersection validation, the standard errors RMSE based on SAVI models were lower than that of the model based on NDVI. The results showed that the abundant spectral information of Tiangong-1 hyperspectral image can reflect the actual vegetaion condition effectively, and SAVI can estimate the sparse vegetation information more accurately than NDVI in desertification region.

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

Sparse Logistic Regression for Diagnosis of Liver Fibrosis in Rat by Using SCAD-Penalized Likelihood

PubMed Central

Yan, Fang-Rong; Lin, Jin-Guan; Liu, Yu

2011-01-01

The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis. PMID:21716672

Multi-objective based spectral unmixing for hyperspectral images

NASA Astrophysics Data System (ADS)

Xu, Xia; Shi, Zhenwei

2017-02-01

Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.

Joint sparse coding based spatial pyramid matching for classification of color medical image.

PubMed

Shi, Jun; Li, Yi; Zhu, Jie; Sun, Haojie; Cai, Yin

2015-04-01

Although color medical images are important in clinical practice, they are usually converted to grayscale for further processing in pattern recognition, resulting in loss of rich color information. The sparse coding based linear spatial pyramid matching (ScSPM) and its variants are popular for grayscale image classification, but cannot extract color information. In this paper, we propose a joint sparse coding based SPM (JScSPM) method for the classification of color medical images. A joint dictionary can represent both the color information in each color channel and the correlation between channels. Consequently, the joint sparse codes calculated from a joint dictionary can carry color information, and therefore this method can easily transform a feature descriptor originally designed for grayscale images to a color descriptor. A color hepatocellular carcinoma histological image dataset was used to evaluate the performance of the proposed JScSPM algorithm. Experimental results show that JScSPM provides significant improvements as compared with the majority voting based ScSPM and the original ScSPM for color medical image classification. Copyright © 2014 Elsevier Ltd. All rights reserved.

Matched field localization based on CS-MUSIC algorithm

NASA Astrophysics Data System (ADS)

Guo, Shuangle; Tang, Ruichun; Peng, Linhui; Ji, Xiaopeng

2016-04-01

The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered. A matched field localization algorithm based on CS-MUSIC (Compressive Sensing Multiple Signal Classification) is proposed based on the sparse mathematical model of the underwater positioning. The signal matrix is calculated through the SVD (Singular Value Decomposition) of the observation matrix. The observation matrix in the sparse mathematical model is replaced by the signal matrix, and a new concise sparse mathematical model is obtained, which means not only the scale of the localization problem but also the noise level is reduced; then the new sparse mathematical model is solved by the CS-MUSIC algorithm which is a combination of CS (Compressive Sensing) method and MUSIC (Multiple Signal Classification) method. The algorithm proposed in this paper can overcome effectively the difficulties caused by correlated sources and shortness of snapshots, and it can also reduce the time complexity and noise level of the localization problem by using the SVD of the observation matrix when the number of snapshots is large, which will be proved in this paper.

Cortico-striatal language pathways dynamically adjust for syntactic complexity: A computational study.

PubMed

Szalisznyó, Krisztina; Silverstein, David; Teichmann, Marc; Duffau, Hugues; Smits, Anja

2017-01-01

A growing body of literature supports a key role of fronto-striatal circuits in language perception. It is now known that the striatum plays a role in engaging attentional resources and linguistic rule computation while also serving phonological short-term memory capabilities. The ventral semantic and the dorsal phonological stream dichotomy assumed for spoken language processing also seems to play a role in cortico-striatal perception. Based on recent studies that correlate deep Broca-striatal pathways with complex syntax performance, we used a previously developed computational model of frontal-striatal syntax circuits and hypothesized that different parallel language pathways may contribute to canonical and non-canonical sentence comprehension separately. We modified and further analyzed a thematic role assignment task and corresponding reservoir computing model of language circuits, as previously developed by Dominey and coworkers. We examined the models performance under various parameter regimes, by influencing how fast the presented language input decays and altering the temporal dynamics of activated word representations. This enabled us to quantify canonical and non-canonical sentence comprehension abilities. The modeling results suggest that separate cortico-cortical and cortico-striatal circuits may be recruited differently for processing syntactically more difficult and less complicated sentences. Alternatively, a single circuit would need to dynamically and adaptively adjust to syntactic complexity. Copyright © 2016. Published by Elsevier Inc.

Stable micron-scale holes are a general feature of canonical holins

PubMed Central

Savva, Christos G.; Dewey, Jill S.; Moussa, Samir H.; To, Kam H.; Holzenburg, Andreas; Young, Ry

2014-01-01

Summary At a programmed time in phage infection cycles, canonical holins suddenly trigger to cause lethal damage to the cytoplasmic membrane, resulting in the cessation of respiration and the non-specific release of pre-folded, fully active endolysins to the periplasm. For the paradigm holin S105 of lambda, triggering is correlated with the formation of micron-scale membrane holes, visible as interruptions in the bilayer in cryo-electron microscopic images and tomographic reconstructions. Here we report that the size distribution of the holes is stable for long periods after triggering. Moreover, early triggering caused by an early lysis allele of S105 formed approximately the same number of holes, but the lesions were significantly smaller. In contrast, early triggering prematurely induced by energy poisons resulted in many fewer visible holes, consistent with previous sizing studies. Importantly, the unrelated canonical holins P2 Y and T4 T were found to cause the formation of holes of approximately the same size and number as for lambda. In contrast, no such lesions were visible after triggering of the pinholin S2168. These results generalize the hole formation phenomenon for canonical holins. A model is presented suggesting the unprecedentedly large size of these holes is related to the timing mechanism. PMID:24164554

Stable micron-scale holes are a general feature of canonical holins.

PubMed

Savva, Christos G; Dewey, Jill S; Moussa, Samir H; To, Kam H; Holzenburg, Andreas; Young, Ry

2014-01-01

At a programmed time in phage infection cycles, canonical holins suddenly trigger to cause lethal damage to the cytoplasmic membrane, resulting in the cessation of respiration and the non-specific release of pre-folded, fully active endolysins to the periplasm. For the paradigm holin S105 of lambda, triggering is correlated with the formation of micron-scale membrane holes, visible as interruptions in the bilayer in cryo-electron microscopic images and tomographic reconstructions. Here we report that the size distribution of the holes is stable for long periods after triggering. Moreover, early triggering caused by an early lysis allele of S105 formed approximately the same number of holes, but the lesions were significantly smaller. In contrast, early triggering prematurely induced by energy poisons resulted in many fewer visible holes, consistent with previous sizing studies. Importantly, the unrelated canonical holins P2 Y and T4 T were found to cause the formation of holes of approximately the same size and number as for lambda. In contrast, no such lesions were visible after triggering of the pinholin S(21) 68. These results generalize the hole formation phenomenon for canonical holins. A model is presented suggesting the unprecedentedly large size of these holes is related to the timing mechanism. © 2013 John Wiley & Sons Ltd.

System Model Network for Adipose Tissue Signatures Related to Weight Changes in Response to Calorie Restriction and Subsequent Weight Maintenance

PubMed Central

Montastier, Emilie; Villa-Vialaneix, Nathalie; Caspar-Bauguil, Sylvie; Hlavaty, Petr; Tvrzicka, Eva; Gonzalez, Ignacio; Saris, Wim H. M.; Langin, Dominique; Kunesova, Marie; Viguerie, Nathalie

2015-01-01

Nutrigenomics investigates relationships between nutrients and all genome-encoded molecular entities. This holistic approach requires systems biology to scrutinize the effects of diet on tissue biology. To decipher the adipose tissue (AT) response to diet induced weight changes we focused on key molecular (lipids and transcripts) AT species during a longitudinal dietary intervention. To obtain a systems model, a network approach was used to combine all sets of variables (bio-clinical, fatty acids and mRNA levels) and get an overview of their interactions. AT fatty acids and mRNA levels were quantified in 135 obese women at baseline, after an 8-week low calorie diet (LCD) and after 6 months of ad libitum weight maintenance diet (WMD). After LCD, individuals were stratified a posteriori according to weight change during WMD. A 3 steps approach was used to infer a global model involving the 3 sets of variables. It consisted in inferring intra-omic networks with sparse partial correlations and inter-omic networks with regularized canonical correlation analysis and finally combining the obtained omic-specific network in a single global model. The resulting networks were analyzed using node clustering, systematic important node extraction and cluster comparisons. Overall, AT showed both constant and phase-specific biological signatures in response to dietary intervention. AT from women regaining weight displayed growth factors, angiogenesis and proliferation signaling signatures, suggesting unfavorable tissue hyperplasia. By contrast, after LCD a strong positive relationship between AT myristoleic acid (a fatty acid with low AT level) content and de novo lipogenesis mRNAs was found. This relationship was also observed, after WMD, in the group of women that continued to lose weight. This original system biology approach provides novel insight in the AT response to weight control by highlighting the central role of myristoleic acid that may account for the beneficial effects of weight loss. PMID:25590576

RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint

PubMed Central

Piao, Xinglin; Zhang, Yong; Li, Tingshu; Hu, Yongli; Liu, Hao; Zhang, Ke; Ge, Yun

2016-01-01

The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or temporal correlation existing in fingerprint data and measurement data, which is helpful for improving localization accuracy. In this paper, we propose an RSS fingerprint-based indoor localization method by integrating the spatio-temporal constraints into the sparse representation model. The proposed model utilizes the inherent spatial correlation of fingerprint data in the fingerprint matching and uses the temporal continuity of the RSS measurement data in the localization phase. Experiments on the simulated data and the localization tests in the real scenes show that the proposed method improves the localization accuracy and stability effectively compared with state-of-the-art indoor localization methods. PMID:27827882

Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface

NASA Astrophysics Data System (ADS)

Chen, Xiaogang; Wang, Yijun; Gao, Shangkai; Jung, Tzyy-Ping; Gao, Xiaorong

2015-08-01

Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ˜33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min-1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

The Propagation of Movement Variability in Time: A Methodological Approach for Discrete Movements with Multiple Degrees of Freedom.

PubMed

Krüger, Melanie; Straube, Andreas; Eggert, Thomas

2017-01-01

In recent years, theory-building in motor neuroscience and our understanding of the synergistic control of the redundant human motor system has significantly profited from the emergence of a range of different mathematical approaches to analyze the structure of movement variability. Approaches such as the Uncontrolled Manifold method or the Noise-Tolerance-Covariance decomposition method allow to detect and interpret changes in movement coordination due to e.g., learning, external task constraints or disease, by analyzing the structure of within-subject, inter-trial movement variability. Whereas, for cyclical movements (e.g., locomotion), mathematical approaches exist to investigate the propagation of movement variability in time (e.g., time series analysis), similar approaches are missing for discrete, goal-directed movements, such as reaching. Here, we propose canonical correlation analysis as a suitable method to analyze the propagation of within-subject variability across different time points during the execution of discrete movements. While similar analyses have already been applied for discrete movements with only one degree of freedom (DoF; e.g., Pearson's product-moment correlation), canonical correlation analysis allows to evaluate the coupling of inter-trial variability across different time points along the movement trajectory for multiple DoF-effector systems, such as the arm. The theoretical analysis is illustrated by empirical data from a study on reaching movements under normal and disturbed proprioception. The results show increased movement duration, decreased movement amplitude, as well as altered movement coordination under ischemia, which results in a reduced complexity of movement control. Movement endpoint variability is not increased under ischemia. This suggests that healthy adults are able to immediately and efficiently adjust the control of complex reaching movements to compensate for the loss of proprioceptive information. Further, it is shown that, by using canonical correlation analysis, alterations in movement coordination that indicate changes in the control strategy concerning the use of motor redundancy can be detected, which represents an important methodical advance in the context of neuromechanics.

Sparsistency and Rates of Convergence in Large Covariance Matrix Estimation.

PubMed

Lam, Clifford; Fan, Jianqing

2009-01-01

This paper studies the sparsistency and rates of convergence for estimating sparse covariance and precision matrices based on penalized likelihood with nonconvex penalty functions. Here, sparsistency refers to the property that all parameters that are zero are actually estimated as zero with probability tending to one. Depending on the case of applications, sparsity priori may occur on the covariance matrix, its inverse or its Cholesky decomposition. We study these three sparsity exploration problems under a unified framework with a general penalty function. We show that the rates of convergence for these problems under the Frobenius norm are of order (s(n) log p(n)/n)(1/2), where s(n) is the number of nonzero elements, p(n) is the size of the covariance matrix and n is the sample size. This explicitly spells out the contribution of high-dimensionality is merely of a logarithmic factor. The conditions on the rate with which the tuning parameter λ(n) goes to 0 have been made explicit and compared under different penalties. As a result, for the L(1)-penalty, to guarantee the sparsistency and optimal rate of convergence, the number of nonzero elements should be small: sn'=O(pn) at most, among O(pn2) parameters, for estimating sparse covariance or correlation matrix, sparse precision or inverse correlation matrix or sparse Cholesky factor, where sn' is the number of the nonzero elements on the off-diagonal entries. On the other hand, using the SCAD or hard-thresholding penalty functions, there is no such a restriction.

Model Identification of Integrated ARMA Processes

ERIC Educational Resources Information Center

Stadnytska, Tetiana; Braun, Simone; Werner, Joachim

2008-01-01

This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…

Global Optimality of the Successive Maxbet Algorithm.

ERIC Educational Resources Information Center

Hanafi, Mohamed; ten Berge, Jos M. F.

2003-01-01

It is known that the Maxbet algorithm, which is an alternative to the method of generalized canonical correlation analysis and Procrustes analysis, may converge to local maxima. Discusses an eigenvalue criterion that is sufficient, but not necessary, for global optimality of the successive Maxbet algorithm. (SLD)

MULTIVARIATE ANALYSIS OF MACROINVERTEBRATE ASSEMBLAGES TO DETERMINE IMPACTS ON ROCKY MOUNTAIN STREAM ECOSYSTEMS

EPA Science Inventory

Using reduncancy (RDA) and canonical correlation analysis (CCA) we assessed relationships between chemical and physical characteristics and periphyton at 105 stream sites sampled by REMAP in the mineral belt of the southern Rockies ecoregion in Colorado. We contrasted results ob...

A Feature Fusion Based Forecasting Model for Financial Time Series

PubMed Central

Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie

2014-01-01

Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455

Oblique rotaton in canonical correlation analysis reformulated as maximizing the generalized coefficient of determination.

PubMed

Satomura, Hironori; Adachi, Kohei

2013-07-01

To facilitate the interpretation of canonical correlation analysis (CCA) solutions, procedures have been proposed in which CCA solutions are orthogonally rotated to a simple structure. In this paper, we consider oblique rotation for CCA to provide solutions that are much easier to interpret, though only orthogonal rotation is allowed in the existing formulations of CCA. Our task is thus to reformulate CCA so that its solutions have the freedom of oblique rotation. Such a task can be achieved using Yanai's (Jpn. J. Behaviormetrics 1:46-54, 1974; J. Jpn. Stat. Soc. 11:43-53, 1981) generalized coefficient of determination for the objective function to be maximized in CCA. The resulting solutions are proved to include the existing orthogonal ones as special cases and to be rotated obliquely without affecting the objective function value, where ten Berge's (Psychometrika 48:519-523, 1983) theorems on suborthonormal matrices are used. A real data example demonstrates that the proposed oblique rotation can provide simple, easily interpreted CCA solutions.

Using canonical correlation analysis to identify environmental attitude groups: considerations for national forest planning in the southwestern U.S.

PubMed

Prera, Alejandro J; Grimsrud, Kristine M; Thacher, Jennifer A; McCollum, Dan W; Berrens, Robert P

2014-10-01

As public land management agencies pursue region-specific resource management plans, with meaningful consideration of public attitudes and values, there is a need to characterize the complex mix of environmental attitudes in a diverse population. The contribution of this investigation is to make use of a unique household, mail/internet survey data set collected in 2007 in the Southwestern United States (Region 3 of the U.S. Forest Service). With over 5,800 survey responses to a set of 25 Public Land Value statements, canonical correlation analysis is able to identify 7 statistically distinct environmental attitudinal groups. We also examine the effect of expected changes in regional demographics on overall environmental attitudes, which may help guide in the development of socially acceptable long-term forest management policies. Results show significant support for conservationist management policies and passive environmental values, as well as a greater role for stakeholder groups in generating consensus for current and future forest management policies.

Reconstruction of 7T-Like Images From 3T MRI

PubMed Central

Bahrami, Khosro; Shi, Feng; Zong, Xiaopeng; Shin, Hae Won; An, Hongyu

2016-01-01

In the recent MRI scanning, ultra-high-field (7T) MR imaging provides higher resolution and better tissue contrast compared to routine 3T MRI, which may help in more accurate and early brain diseases diagnosis. However, currently, 7T MRI scanners are more expensive and less available at clinical and research centers. These motivate us to propose a method for the reconstruction of images close to the quality of 7T MRI, called 7T-like images, from 3T MRI, to improve the quality in terms of resolution and contrast. By doing so, the post-processing tasks, such as tissue segmentation, can be done more accurately and brain tissues details can be seen with higher resolution and contrast. To do this, we have acquired a unique dataset which includes paired 3T and 7T images scanned from same subjects, and then propose a hierarchical reconstruction based on group sparsity in a novel multi-level Canonical Correlation Analysis (CCA) space, to improve the quality of 3T MR image to be 7T-like MRI. First, overlapping patches are extracted from the input 3T MR image. Then, by extracting the most similar patches from all the aligned 3T and 7T images in the training set, the paired 3T and 7T dictionaries are constructed for each patch. It is worth noting that, for the training, we use pairs of 3T and 7T MR images from each training subject. Then, we propose multi-level CCA to map the paired 3T and 7T patch sets to a common space to increase their correlations. In such space, each input 3T MRI patch is sparsely represented by the 3T dictionary and then the obtained sparse coefficients are used together with the corresponding 7T dictionary to reconstruct the 7T-like patch. Also, to have the structural consistency between adjacent patches, the group sparsity is employed. This reconstruction is performed with changing patch sizes in a hierarchical framework. Experiments have been done using 13 subjects with both 3T and 7T MR images. The results show that our method outperforms previous methods and is able to recover better structural details. Also, to place our proposed method in a medical application context, we evaluated the influence of post-processing methods such as brain tissue segmentation on the reconstructed 7T-like MR images. Results show that our 7T-like images lead to higher accuracy in segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and skull, compared to segmentation of 3T MR images. PMID:27046894

Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

NASA Astrophysics Data System (ADS)

Cannon, A. J.; Hsieh, W. W.

2008-02-01

Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pearson product-moment correlation in the double-barreled network is replaced by the biweight midcorrelation, and the mean squared error (mse) in the inverse mapping networks can be replaced by the mean absolute error (mae). Robust variants of NLCCA are demonstrated on a synthetic dataset and are used to forecast sea surface temperatures in the tropical Pacific Ocean based on the sea level pressure field. Results suggest that adoption of the biweight midcorrelation can lead to improved performance, especially when a strong, common event exists in both predictor/predictand datasets. Replacing the mse by the mae leads to improved performance on the synthetic dataset, but not on the climate dataset except at the longest lead time, which suggests that the appropriate cost function for the inverse mapping networks is more problem dependent.

Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal.

PubMed

Roy, Vandana; Shukla, Shailja; Shukla, Piyush Kumar; Rawat, Paresh

2017-01-01

The motion generated at the capturing time of electro-encephalography (EEG) signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA) for removing artifacts along with ensemble empirical mode decomposition (EEMD) and wavelet transform (WT). A new approach is proposed to further analyse and improve the filtering performance and reduce the filter computation time under highly noisy environment. This new approach of CCA is based on Gaussian elimination method which is used for calculating the correlation coefficients using backslash operation and is designed for EEG signal motion artifact removal. Gaussian elimination is used for solving linear equation to calculate Eigen values which reduces the computation cost of the CCA method. This novel proposed method is tested against currently available artifact removal techniques using EEMD-CCA and wavelet transform. The performance is tested on synthetic and real EEG signal data. The proposed artifact removal technique is evaluated using efficiency matrices such as del signal to noise ratio (DSNR), lambda ( λ ), root mean square error (RMSE), elapsed time, and ROC parameters. The results indicate suitablity of the proposed algorithm for use as a supplement to algorithms currently in use.

Artificial neural network does better spatiotemporal compressive sampling

NASA Astrophysics Data System (ADS)

Lee, Soo-Young; Hsu, Charles; Szu, Harold

2012-06-01

Spatiotemporal sparseness is generated naturally by human visual system based on artificial neural network modeling of associative memory. Sparseness means nothing more and nothing less than the compressive sensing achieves merely the information concentration. To concentrate the information, one uses the spatial correlation or spatial FFT or DWT or the best of all adaptive wavelet transform (cf. NUS, Shen Shawei). However, higher dimensional spatiotemporal information concentration, the mathematics can not do as flexible as a living human sensory system. The reason is obviously for survival reasons. The rest of the story is given in the paper.

Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

NASA Astrophysics Data System (ADS)

Wu, Juan; Bai, Min

2018-05-01

We propose to apply a novel incoherent dictionary learning (IDL) algorithm for regularizing the least-squares inversion in seismic imaging. The IDL is proposed to overcome the drawback of traditional dictionary learning algorithm in losing partial texture information. Firstly, the noisy image is divided into overlapped image patches, and some random patches are extracted for dictionary learning. Then, we apply the IDL technology to minimize the coherency between atoms during dictionary learning. Finally, the sparse representation problem is solved by a sparse coding algorithm, and image is restored by those sparse coefficients. By reducing the correlation among atoms, it is possible to preserve most of the small-scale features in the image while removing much of the long-wavelength noise. The application of the IDL method to regularization of seismic images from least-squares reverse time migration shows successful performance.

Synthesis and Characterization of Novel Anchorlipids for Tethered Bilayer Lipid Membranes.

PubMed

Andersson, Jakob; Knobloch, Jacqueline J; Perkins, Michael V; Holt, Stephen A; Köper, Ingo

2017-05-09

Tethered bilayer lipid membranes are versatile solid-supported model membrane systems. Core to these systems is an anchorlipid that covalently links a lipid bilayer to a support. The molecular structure of these lipids can have a significant impact on the properties of the resulting bilayer. Here, the synthesis of anchorlipids containing ester groups in the tethering part is described. The lipids are used to form bilayer membranes, and the resulting structures are compared with membranes formed using conventional anchorlipids or sparsely tethered membranes. All membranes showed good electrical sealing properties; the disulphide-terminated anchorlipids could be used in a sparsely tethered system without significantly reducing the sealing properties of the lipid bilayers. The sparsely tethered systems also allowed for higher ion transport across the membrane, which is in good correlation with higher hydration of the spacer region as seen by neutron scattering.

Statistical manifestation of quantum correlations via disequilibrium

NASA Astrophysics Data System (ADS)

Pennini, F.; Plastino, A.

2017-12-01

The statistical notion of disequilibrium (D) was introduced by López-Ruiz, Mancini, and Calbet (LMC) (1995) [1] more than 20 years ago. D measures the amount of ;correlational structure; of a system. We wish to use D to analyze one of the simplest types of quantum correlations, those present in gaseous systems due to symmetry considerations. To this end we extend the LMC formalism to the grand canonical environment and show that D displays distinctive behaviors for simple gases, that allow for interesting insights into their structural properties.

Relationship among side channels, fish assemblages, and environmental gradients in the unimpounded Upper Mississippi River

USGS Publications Warehouse

Barko, V.A.; Herzog, D.P.

2003-01-01

We analyzed fish abundance and environmental data collected over nine years from six side channels of the unimpounded upper Mississippi River between river km 46.7 and 128.7. A partial canonical correspondence analysis revealed differences in fish assemblages and environmental factors correlated with the six side channels. Fishes correlated with open side channels represented large river species tolerant of current and/or turbidity. Fishes correlated with closed side channels represented assemblages preferring either moderate to low turbidity/current or pools.

Reading Ability as a Predictor of Academic Procrastination among African American Graduate Students

ERIC Educational Resources Information Center

Collins, Kathleen M. T.; Onwuegbuzie, Anthony J.; Jiao, Qun G.

2008-01-01

The present study examined the relationship between reading ability (i.e., reading comprehension and reading vocabulary) and academic procrastination among 120 African American graduate students. A canonical correlation analysis revealed statistically significant and practically significant multivariate relationships between these two reading…

A Perspective on Quercus Life History Characteristics and Forest Diturbance

Treesearch

Richard P. Guyette; Rose-Marie Muzika; John Kabrick; Michael C. Stambaugh

2004-01-01

Plant strategy theory suggests that life history characteristics reflect growth and reproductive adaptations to environmental disturbance. Species characteristics and abundance should correspond to predictions based on competitive ability and maximizing fitness in a given disturbance environment. A significant canonical correlation between oak growth attributes (height...

Multidimensional Relationships in the WAIS-R Subscales and Demographic Variables.

ERIC Educational Resources Information Center

Chastain, Robert L.; Joe, George W.

This study attempts to integrate and extend previous research by multivariate investigation to determine multidimensional relationships among both the Wechsler Adult Intelligence Scale-Revised (WAIS-R) subscales and the demographic variables for the 1981 WAIS-R standardization sample. Canonical correlation with orthogonal rotation of composite…

Statistical field theory with constraints: Application to critical Casimir forces in the canonical ensemble.

PubMed

Gross, Markus; Gambassi, Andrea; Dietrich, S

2017-08-01

The effect of imposing a constraint on a fluctuating scalar order parameter field in a system of finite volume is studied within statistical field theory. The canonical ensemble, corresponding to a fixed total integrated order parameter (e.g., the total number of particles), is obtained as a special case of the theory. A perturbative expansion is developed which allows one to systematically determine the constraint-induced finite-volume corrections to the free energy and to correlation functions. In particular, we focus on the Landau-Ginzburg model in a film geometry (i.e., in a rectangular parallelepiped with a small aspect ratio) with periodic, Dirichlet, or Neumann boundary conditions in the transverse direction and periodic boundary conditions in the remaining, lateral directions. Within the expansion in terms of ε=4-d, where d is the spatial dimension of the bulk, the finite-size contribution to the free energy of the confined system and the associated critical Casimir force are calculated to leading order in ε and are compared to the corresponding expressions for an unconstrained (grand canonical) system. The constraint restricts the fluctuations within the system and it accordingly modifies the residual finite-size free energy. The resulting critical Casimir force is shown to depend on whether it is defined by assuming a fixed transverse area or a fixed total volume. In the former case, the constraint is typically found to significantly enhance the attractive character of the force as compared to the grand canonical case. In contrast to the grand canonical Casimir force, which, for supercritical temperatures, vanishes in the limit of thick films, in the canonical case with fixed transverse area the critical Casimir force attains for thick films a negative value for all boundary conditions studied here. Typically, the dependence of the critical Casimir force both on the temperaturelike and on the fieldlike scaling variables is different in the two ensembles.

Statistical field theory with constraints: Application to critical Casimir forces in the canonical ensemble

NASA Astrophysics Data System (ADS)

Gross, Markus; Gambassi, Andrea; Dietrich, S.

2017-08-01

The effect of imposing a constraint on a fluctuating scalar order parameter field in a system of finite volume is studied within statistical field theory. The canonical ensemble, corresponding to a fixed total integrated order parameter (e.g., the total number of particles), is obtained as a special case of the theory. A perturbative expansion is developed which allows one to systematically determine the constraint-induced finite-volume corrections to the free energy and to correlation functions. In particular, we focus on the Landau-Ginzburg model in a film geometry (i.e., in a rectangular parallelepiped with a small aspect ratio) with periodic, Dirichlet, or Neumann boundary conditions in the transverse direction and periodic boundary conditions in the remaining, lateral directions. Within the expansion in terms of ɛ =4 -d , where d is the spatial dimension of the bulk, the finite-size contribution to the free energy of the confined system and the associated critical Casimir force are calculated to leading order in ɛ and are compared to the corresponding expressions for an unconstrained (grand canonical) system. The constraint restricts the fluctuations within the system and it accordingly modifies the residual finite-size free energy. The resulting critical Casimir force is shown to depend on whether it is defined by assuming a fixed transverse area or a fixed total volume. In the former case, the constraint is typically found to significantly enhance the attractive character of the force as compared to the grand canonical case. In contrast to the grand canonical Casimir force, which, for supercritical temperatures, vanishes in the limit of thick films, in the canonical case with fixed transverse area the critical Casimir force attains for thick films a negative value for all boundary conditions studied here. Typically, the dependence of the critical Casimir force both on the temperaturelike and on the fieldlike scaling variables is different in the two ensembles.

Ensemble Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts

NASA Technical Reports Server (NTRS)

Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.

2001-01-01

This paper presents preliminary results of an ensemble canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.

Fast sparsely synchronized brain rhythms in a scale-free neural network.

PubMed

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D

Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization.

PubMed

Yuan, Yuan; Lin, Jianzhe; Wang, Qi

2016-12-01

Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.

Cross-correlation matrix analysis of Chinese and American bank stocks in subprime crisis

NASA Astrophysics Data System (ADS)

Zhu, Shi-Zhao; Li, Xin-Li; Nie, Sen; Zhang, Wen-Qing; Yu, Gao-Feng; Han, Xiao-Pu; Wang, Bing-Hong

2015-05-01

In order to study the universality of the interactions among different markets, we analyze the cross-correlation matrix of the price of the Chinese and American bank stocks. We then find that the stock prices of the emerging market are more correlated than that of the developed market. Considering that the values of the components for the eigenvector may be positive or negative, we analyze the differences between two markets in combination with the endogenous and exogenous events which influence the financial markets. We find that the sparse pattern of components of eigenvectors out of the threshold value has no change in American bank stocks before and after the subprime crisis. However, it changes from sparse to dense for Chinese bank stocks. By using the threshold value to exclude the external factors, we simulate the interactions in financial markets. Project supported by the National Natural Science Foundation of China (Grant Nos. 11275186, 91024026, and FOM2014OF001) and the University of Shanghai for Science and Technology (USST) of Humanities and Social Sciences, China (Grant Nos. USST13XSZ05 and 11YJA790231).

Auditory-Phonetic Projection and Lexical Structure in the Recognition of Sine-Wave Words

ERIC Educational Resources Information Center

Remez, Robert E.; Dubowski, Kathryn R.; Broder, Robin S.; Davids, Morgana L.; Grossman, Yael S.; Moskalenko, Marina; Pardo, Jennifer S.; Hasbun, Sara Maria

2011-01-01

Speech remains intelligible despite the elimination of canonical acoustic correlates of phonemes from the spectrum. A portion of this perceptual flexibility can be attributed to modulation sensitivity in the auditory-to-phonetic projection, although signal-independent properties of lexical neighborhoods also affect intelligibility in utterances…

Elementary Students' Scientific Epistemological Beliefs in Relation to Socio-Economic Status and Gender

ERIC Educational Resources Information Center

Ozkal, Kudret; Tekkaya, Ceren; Sungur, Semra; Cakiroglu, Jale; Cakiroglu, Erdinc

2010-01-01

This study investigated students' scientific epistemological beliefs in relation to socio-economic status (SES) and gender. Data were obtained from 1,152 eight grade Turkish elementary school students using Scientific Epistemological Beliefs instrument. Canonical correlation analysis indicated that students with a working mother and educated…

The Relationship between Occupational Interests and Values

ERIC Educational Resources Information Center

Smith, Thomas J.; Campbell, Cynthia

2009-01-01

A values characterization of the RIASEC occupational interest categories was developed using the U.S. Department of Labor's O*NET occupational data. Values profile plots were constructed for each interest category, then correspondence analysis and canonical correlation were carried out to assess the relationship between the interest and values…

Five-Factor Model of Personality and Career Exploration

ERIC Educational Resources Information Center

Reed, Mary Beth; Bruch, Monroe A.; Haase, Richard F.

2004-01-01

This study investigates whether the dimensions of the five-factor model (FFM) of personality are related to specific career exploration variables. Based on the FFM, predictions were made about the relevance of particular traits to career exploration variables. Results from a canonical correlation analysis showed that variable loadings on three…

Civic Engagement in College Students: Connections between Involvement and Attitudes

ERIC Educational Resources Information Center

O'Leary, Lisa S.

2014-01-01

This chapter describes how canonical correlation was used in conjunction with an item response theory model to address the relationship between college students' civic engagement involvement and attitudes as undergraduates. The constructs of interest were students' participation in civic, political, and expressive activities, as well as…

An Extension of Dominance Analysis to Canonical Correlation Analysis

ERIC Educational Resources Information Center

Huo, Yan; Budescu, David V.

2009-01-01

Dominance analysis (Budescu, 1993) offers a general framework for determination of relative importance of predictors in univariate and multivariate multiple regression models. This approach relies on pairwise comparisons of the contribution of predictors in all relevant subset models. In this article we extend dominance analysis to canonical…

Inference on the Ranks of the Canonical Correlation Matrices for Elliptically Symmetric Populations.

DTIC Science & Technology

1985-05-01

robust estimates of the covariance matrix, the reader is referred to Devlin, Gnanadesikan and Kettenring (1975) and Maronna (1976). Murihead and...contoured distributions. J. Multivariate Anal. 11, 368-385. 6. DEVLIN, S.J. GNANADESIKAN , R. and KETTENRING, J. (1975). Robust estima- tion and outlier

Controls on sinuosity in the sparsely vegetated Fossálar River, southern Iceland

NASA Astrophysics Data System (ADS)

Ielpi, Alessandro

2017-06-01

Vegetation exerts strong controls on fluvial sinuosity, providing bank stability and buffering surface runoff. These controls are manifest in densely vegetated landscapes, whereas sparsely vegetated fluvial systems have been so far overlooked. This study integrates remote sensing and gauging records of the meandering to wandering Fossálar River, a relatively steep-sloped (< 2.5%) Icelandic river featuring well-developed point bars (79%-85% of total active bar surface) despite the lack of thick, arborescent vegetation. Over four decades, fluctuations in the sinuosity index (1.15-1.43) and vegetation cover (63%-83%) are not significantly correlated (r = 0.28, p > 0.05), suggesting that relationships between the two are mediated by intervening variables and uncertain lag times. By comparison, discharge regime and fluvial planform show direct correlation over monthly to yearly time scales, with stable discharge stages accompanying the accretion of meander bends and peak floods related to destructive point-bar reworking. Rapid planform change is aided by the unconsolidated nature of unrooted alluvial banks, with recorded rates of lateral channel-belt migration averaging 18 m/yr. Valley confinement and channel mobility also control the geometry and evolution of individual point bars, with the highest degree of spatial geomorphic variability recorded in low-gradient stretches where lateral migration is unimpeded. Point bars in the Fossálar River display morphometric values comparable to those of other sparsely vegetated rivers, suggesting shared scalar properties. This conjecture prompts the need for more sophisticated integrations between remote sensing and gauging records on modern rivers lacking widespread plant life. While a large volume of experimental and field-based work maintains that thick vegetation has a critical role in limiting braiding, thus favouring sinuosity, this study demonstrates the stronger controls of discharge regime and alluvial morphology on sparsely vegetated sinuous rivers.

Loss of FTO antagonises Wnt signaling and leads to developmental defects associated with ciliopathies.

PubMed

Osborn, Daniel P S; Roccasecca, Rosa Maria; McMurray, Fiona; Hernandez-Hernandez, Victor; Mukherjee, Sriparna; Barroso, Inês; Stemple, Derek; Cox, Roger; Beales, Philip L; Christou-Savina, Sonia

2014-01-01

Common intronic variants in the Human fat mass and obesity-associated gene (FTO) are found to be associated with an increased risk of obesity. Overexpression of FTO correlates with increased food intake and obesity, whilst loss-of-function results in lethality and severe developmental defects. Despite intense scientific discussions around the role of FTO in energy metabolism, the function of FTO during development remains undefined. Here, we show that loss of Fto leads to developmental defects such as growth retardation, craniofacial dysmorphism and aberrant neural crest cells migration in Zebrafish. We find that the important developmental pathway, Wnt, is compromised in the absence of FTO, both in vivo (zebrafish) and in vitro (Fto(-/-) MEFs and HEK293T). Canonical Wnt signalling is down regulated by abrogated β-Catenin translocation to the nucleus whilst non-canonical Wnt/Ca(2+) pathway is activated via its key signal mediators CaMKII and PKCδ. Moreover, we demonstrate that loss of Fto results in short, absent or disorganised cilia leading to situs inversus, renal cystogenesis, neural crest cell defects and microcephaly in Zebrafish. Congruently, Fto knockout mice display aberrant tissue specific cilia. These data identify FTO as a protein-regulator of the balanced activation between canonical and non-canonical branches of the Wnt pathway. Furthermore, we present the first evidence that FTO plays a role in development and cilia formation/function.

ClaRNA: a classifier of contacts in RNA 3D structures based on a comparative analysis of various classification schemes

PubMed Central

Waleń, Tomasz; Chojnowski, Grzegorz; Gierski, Przemysław; Bujnicki, Janusz M.

2014-01-01

The understanding of folding and function of RNA molecules depends on the identification and classification of interactions between ribonucleotide residues. We developed a new method named ClaRNA for computational classification of contacts in RNA 3D structures. Unique features of the program are the ability to identify imperfect contacts and to process coarse-grained models. Each doublet of spatially close ribonucleotide residues in a query structure is compared to clusters of reference doublets obtained by analysis of a large number of experimentally determined RNA structures, and assigned a score that describes its similarity to one or more known types of contacts, including pairing, stacking, base–phosphate and base–ribose interactions. The accuracy of ClaRNA is 0.997 for canonical base pairs, 0.983 for non-canonical pairs and 0.961 for stacking interactions. The generalized squared correlation coefficient (GC2) for ClaRNA is 0.969 for canonical base pairs, 0.638 for non-canonical pairs and 0.824 for stacking interactions. The classifier can be easily extended to include new types of spatial relationships between pairs or larger assemblies of nucleotide residues. ClaRNA is freely available via a web server that includes an extensive set of tools for processing and visualizing structural information about RNA molecules. PMID:25159614

Sparse orthogonal population representation of spatial context in the retrosplenial cortex.

PubMed

Mao, Dun; Kandler, Steffen; McNaughton, Bruce L; Bonin, Vincent

2017-08-15

Sparse orthogonal coding is a key feature of hippocampal neural activity, which is believed to increase episodic memory capacity and to assist in navigation. Some retrosplenial cortex (RSC) neurons convey distributed spatial and navigational signals, but place-field representations such as observed in the hippocampus have not been reported. Combining cellular Ca 2+ imaging in RSC of mice with a head-fixed locomotion assay, we identified a population of RSC neurons, located predominantly in superficial layers, whose ensemble activity closely resembles that of hippocampal CA1 place cells during the same task. Like CA1 place cells, these RSC neurons fire in sequences during movement, and show narrowly tuned firing fields that form a sparse, orthogonal code correlated with location. RSC 'place' cell activity is robust to environmental manipulations, showing partial remapping similar to that observed in CA1. This population code for spatial context may assist the RSC in its role in memory and/or navigation.Neurons in the retrosplenial cortex (RSC) encode spatial and navigational signals. Here the authors use calcium imaging to show that, similar to the hippocampus, RSC neurons also encode place cell-like activity in a sparse orthogonal representation, partially anchored to the allocentric cues on the linear track.

Sparse Coding of Natural Human Motion Yields Eigenmotions Consistent Across People

NASA Astrophysics Data System (ADS)

Thomik, Andreas; Faisal, A. Aldo

2015-03-01

Providing a precise mathematical description of the structure of natural human movement is a challenging problem. We use a data-driven approach to seek a generative model of movement capturing the underlying simplicity of spatial and temporal structure of behaviour observed in daily life. In perception, the analysis of natural scenes has shown that sparse codes of such scenes are information theoretic efficient descriptors with direct neuronal correlates. Translating from perception to action, we identify a generative model of movement generation by the human motor system. Using wearable full-hand motion capture, we measure the digit movement of the human hand in daily life. We learn a dictionary of ``eigenmotions'' which we use for sparse encoding of the movement data. We show that the dictionaries are generally well preserved across subjects with small deviations accounting for individuality of the person and variability in tasks. Further, the dictionary elements represent motions which can naturally describe hand movements. Our findings suggest the motor system can compose complex movement behaviours out of the spatially and temporally sparse activation of ``eigenmotion'' neurons, and is consistent with data on grasp-type specificity of specialised neurons in the premotor cortex. Andreas is supported by the Luxemburg Research Fund (1229297).

Sparse coding can predict primary visual cortex receptive field changes induced by abnormal visual input.

PubMed

Hunt, Jonathan J; Dayan, Peter; Goodhill, Geoffrey J

2013-01-01

Receptive fields acquired through unsupervised learning of sparse representations of natural scenes have similar properties to primary visual cortex (V1) simple cell receptive fields. However, what drives in vivo development of receptive fields remains controversial. The strongest evidence for the importance of sensory experience in visual development comes from receptive field changes in animals reared with abnormal visual input. However, most sparse coding accounts have considered only normal visual input and the development of monocular receptive fields. Here, we applied three sparse coding models to binocular receptive field development across six abnormal rearing conditions. In every condition, the changes in receptive field properties previously observed experimentally were matched to a similar and highly faithful degree by all the models, suggesting that early sensory development can indeed be understood in terms of an impetus towards sparsity. As previously predicted in the literature, we found that asymmetries in inter-ocular correlation across orientations lead to orientation-specific binocular receptive fields. Finally we used our models to design a novel stimulus that, if present during rearing, is predicted by the sparsity principle to lead robustly to radically abnormal receptive fields.

Sparse Coding Can Predict Primary Visual Cortex Receptive Field Changes Induced by Abnormal Visual Input

PubMed Central

Hunt, Jonathan J.; Dayan, Peter; Goodhill, Geoffrey J.

2013-01-01

Receptive fields acquired through unsupervised learning of sparse representations of natural scenes have similar properties to primary visual cortex (V1) simple cell receptive fields. However, what drives in vivo development of receptive fields remains controversial. The strongest evidence for the importance of sensory experience in visual development comes from receptive field changes in animals reared with abnormal visual input. However, most sparse coding accounts have considered only normal visual input and the development of monocular receptive fields. Here, we applied three sparse coding models to binocular receptive field development across six abnormal rearing conditions. In every condition, the changes in receptive field properties previously observed experimentally were matched to a similar and highly faithful degree by all the models, suggesting that early sensory development can indeed be understood in terms of an impetus towards sparsity. As previously predicted in the literature, we found that asymmetries in inter-ocular correlation across orientations lead to orientation-specific binocular receptive fields. Finally we used our models to design a novel stimulus that, if present during rearing, is predicted by the sparsity principle to lead robustly to radically abnormal receptive fields. PMID:23675290

In vivo quantitative evaluation of vascular parameters for angiogenesis based on sparse principal component analysis and aggregated boosted trees

NASA Astrophysics Data System (ADS)

Zhao, Fengjun; Liu, Junting; Qu, Xiaochao; Xu, Xianhui; Chen, Xueli; Yang, Xiang; Cao, Feng; Liang, Jimin; Tian, Jie

2014-12-01

To solve the multicollinearity issue and unequal contribution of vascular parameters for the quantification of angiogenesis, we developed a quantification evaluation method of vascular parameters for angiogenesis based on in vivo micro-CT imaging of hindlimb ischemic model mice. Taking vascular volume as the ground truth parameter, nine vascular parameters were first assembled into sparse principal components (PCs) to reduce the multicolinearity issue. Aggregated boosted trees (ABTs) were then employed to analyze the importance of vascular parameters for the quantification of angiogenesis via the loadings of sparse PCs. The results demonstrated that vascular volume was mainly characterized by vascular area, vascular junction, connectivity density, segment number and vascular length, which indicated they were the key vascular parameters for the quantification of angiogenesis. The proposed quantitative evaluation method was compared with both the ABTs directly using the nine vascular parameters and Pearson correlation, which were consistent. In contrast to the ABTs directly using the vascular parameters, the proposed method can select all the key vascular parameters simultaneously, because all the key vascular parameters were assembled into the sparse PCs with the highest relative importance.

Load Adaptation of Lamellipodial Actin Networks.

PubMed

Mueller, Jan; Szep, Gregory; Nemethova, Maria; de Vries, Ingrid; Lieber, Arnon D; Winkler, Christoph; Kruse, Karsten; Small, J Victor; Schmeiser, Christian; Keren, Kinneret; Hauschild, Robert; Sixt, Michael

2017-09-21

Actin filaments polymerizing against membranes power endocytosis, vesicular traffic, and cell motility. In vitro reconstitution studies suggest that the structure and the dynamics of actin networks respond to mechanical forces. We demonstrate that lamellipodial actin of migrating cells responds to mechanical load when membrane tension is modulated. In a steady state, migrating cell filaments assume the canonical dendritic geometry, defined by Arp2/3-generated 70° branch points. Increased tension triggers a dense network with a broadened range of angles, whereas decreased tension causes a shift to a sparse configuration dominated by filaments growing perpendicularly to the plasma membrane. We show that these responses emerge from the geometry of branched actin: when load per filament decreases, elongation speed increases and perpendicular filaments gradually outcompete others because they polymerize the shortest distance to the membrane, where they are protected from capping. This network-intrinsic geometrical adaptation mechanism tunes protrusive force in response to mechanical load. Copyright © 2017 Elsevier Inc. All rights reserved.

Identification of individual coherent sets associated with flow trajectories using coherent structure coloring

NASA Astrophysics Data System (ADS)

Schlueter-Kuck, Kristy L.; Dabiri, John O.

2017-09-01

We present a method for identifying the coherent structures associated with individual Lagrangian flow trajectories even where only sparse particle trajectory data are available. The method, based on techniques in spectral graph theory, uses the Coherent Structure Coloring vector and associated eigenvectors to analyze the distance in higher-dimensional eigenspace between a selected reference trajectory and other tracer trajectories in the flow. By analyzing this distance metric in a hierarchical clustering, the coherent structure of which the reference particle is a member can be identified. This algorithm is proven successful in identifying coherent structures of varying complexities in canonical unsteady flows. Additionally, the method is able to assess the relative coherence of the associated structure in comparison to the surrounding flow. Although the method is demonstrated here in the context of fluid flow kinematics, the generality of the approach allows for its potential application to other unsupervised clustering problems in dynamical systems such as neuronal activity, gene expression, or social networks.

Extraction of the brachialis muscle activity using HD-sEMG technique and canonical correlation analysis.

PubMed

Al Harrach, M; Afsharipour, B; Boudaoud, S; Carriou, V; Marin, F; Merletti, R

2016-08-01

The Brachialis (BR) is placed under the Biceps Brachii (BB) deep in the upper arm. Therefore, the detection of the corresponding surface Electromyogram (sEMG) is a complex task. The BR is an important elbow flexor, but it is usually not considered in the sEMG based force estimation process. The aim of this study was to attempt to separate the two sEMG activities of the BR and the BB by using a High Density sEMG (HD-sEMG) grid placed at the upper arm and Canonical Component Analysis (CCA) technique. For this purpose, we recorded sEMG signals from seven subjects with two 8 × 4 electrode grids placed over BB and BR. Four isometric voluntary contraction levels were recorded (5, 10, 30 and 50 %MVC) for 90° elbow angle. Then using CCA and image processing tools the sources of each muscle activity were separated. Finally, the corresponding sEMG signals were reconstructed using the remaining canonical components in order to retrieve the activity of the BB and the BR muscles.

Free Fermions and the Classical Compact Groups

NASA Astrophysics Data System (ADS)

Cunden, Fabio Deelan; Mezzadri, Francesco; O'Connell, Neil

2018-06-01

There is a close connection between the ground state of non-interacting fermions in a box with classical (absorbing, reflecting, and periodic) boundary conditions and the eigenvalue statistics of the classical compact groups. The associated determinantal point processes can be extended in two natural directions: (i) we consider the full family of admissible quantum boundary conditions (i.e., self-adjoint extensions) for the Laplacian on a bounded interval, and the corresponding projection correlation kernels; (ii) we construct the grand canonical extensions at finite temperature of the projection kernels, interpolating from Poisson to random matrix eigenvalue statistics. The scaling limits in the bulk and at the edges are studied in a unified framework, and the question of universality is addressed. Whether the finite temperature determinantal processes correspond to the eigenvalue statistics of some matrix models is, a priori, not obvious. We complete the picture by constructing a finite temperature extension of the Haar measure on the classical compact groups. The eigenvalue statistics of the resulting grand canonical matrix models (of random size) corresponds exactly to the grand canonical measure of free fermions with classical boundary conditions.

Neural Correlates of Intolerance of Uncertainty in Clinical Disorders.

PubMed

Wever, Mirjam; Smeets, Paul; Sternheim, Lot

2015-01-01

Intolerance of uncertainty is a key contributor to anxiety-related disorders. Recent studies highlight its importance in other clinical disorders. The link between its clinical presentation and the underlying neural correlates remains unclear. This review summarizes the emerging literature on the neural correlates of intolerance of uncertainty. In conclusion, studies focusing on the neural correlates of this construct are sparse, and findings are inconsistent across disorders. Future research should identify neural correlates of intolerance of uncertainty in more detail. This may unravel the neurobiology of a wide variety of clinical disorders and pave the way for novel therapeutic targets.

Concentration-dependent and configuration-dependent interactions of monovalent ions with an RNA tetraloop

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

Miner, Jacob Carlson; Garcia, Angel Enrique

Monovalent salt solutions have strongly coupled interactions with biopolymers, from large polyelectrolytes to small RNA oligomers. High salt concentrations have been known to induce transitions in the structure of RNA, producing non-canonical configurations and even driving RNA to precipitate out of solution. Using all-atom molecular dynamics simulations, we model a monovalent salt species (KCL) at high concentrations (0.1–3m) and calculate the equilibrium distributions of water and ions around a small tetraloop-forming RNA oligomer in a variety of structural arrangements: folded A-RNA (canonical) and Z-RNA (non-canonical) tetraloops and unfolded configurations. From these data, we calculate the ion preferential binding coefficients andmore » Donnan coefficients for the RNA oligomer as a function of concentration and structure. We find that cation accumulation is highest around non-canonical Z-RNA configurations at concentrations below 0.5m, while unfolded configurations accumulate the most co-ions in all concentrations. By contrast, canonical A-RNA structures consistently show the lowest accumulations for all ion species. Water distributions vary markedly with RNA configuration but show little dependency on KCL concentration. Based on Donnan coefficient calculations, the net charge of the solution at the surface of the RNA decreases linearly as a function of salt concentration and becomes net-neutral near 2.5–3m KCL for folded configurations, while unfolded configurations still show a positive solution charge. Our findings show that all-atom molecular dynamics can describe the equilibrium distributions of monovalent salt in the presence of small RNA oligomers at KCL concentrations where ion correlation effects become important. Furthermore, these results provide valuable insights into the distributions of water and ions near the RNA oligomer surface as a function of structural configuration.« less

Concentration-dependent and configuration-dependent interactions of monovalent ions with an RNA tetraloop

DOE PAGES

Miner, Jacob Carlson; Garcia, Angel Enrique

2018-05-29

Monovalent salt solutions have strongly coupled interactions with biopolymers, from large polyelectrolytes to small RNA oligomers. High salt concentrations have been known to induce transitions in the structure of RNA, producing non-canonical configurations and even driving RNA to precipitate out of solution. Using all-atom molecular dynamics simulations, we model a monovalent salt species (KCL) at high concentrations (0.1–3m) and calculate the equilibrium distributions of water and ions around a small tetraloop-forming RNA oligomer in a variety of structural arrangements: folded A-RNA (canonical) and Z-RNA (non-canonical) tetraloops and unfolded configurations. From these data, we calculate the ion preferential binding coefficients andmore » Donnan coefficients for the RNA oligomer as a function of concentration and structure. We find that cation accumulation is highest around non-canonical Z-RNA configurations at concentrations below 0.5m, while unfolded configurations accumulate the most co-ions in all concentrations. By contrast, canonical A-RNA structures consistently show the lowest accumulations for all ion species. Water distributions vary markedly with RNA configuration but show little dependency on KCL concentration. Based on Donnan coefficient calculations, the net charge of the solution at the surface of the RNA decreases linearly as a function of salt concentration and becomes net-neutral near 2.5–3m KCL for folded configurations, while unfolded configurations still show a positive solution charge. Our findings show that all-atom molecular dynamics can describe the equilibrium distributions of monovalent salt in the presence of small RNA oligomers at KCL concentrations where ion correlation effects become important. Furthermore, these results provide valuable insights into the distributions of water and ions near the RNA oligomer surface as a function of structural configuration.« less

Concentration-dependent and configuration-dependent interactions of monovalent ions with an RNA tetraloop

NASA Astrophysics Data System (ADS)

Miner, Jacob Carlson; García, Angel Enrique

2018-06-01

Monovalent salt solutions have strongly coupled interactions with biopolymers, from large polyelectrolytes to small RNA oligomers. High salt concentrations have been known to induce transitions in the structure of RNA, producing non-canonical configurations and even driving RNA to precipitate out of solution. Using all-atom molecular dynamics simulations, we model a monovalent salt species (KCL) at high concentrations (0.1-3m) and calculate the equilibrium distributions of water and ions around a small tetraloop-forming RNA oligomer in a variety of structural arrangements: folded A-RNA (canonical) and Z-RNA (non-canonical) tetraloops and unfolded configurations. From these data, we calculate the ion preferential binding coefficients and Donnan coefficients for the RNA oligomer as a function of concentration and structure. We find that cation accumulation is highest around non-canonical Z-RNA configurations at concentrations below 0.5m, while unfolded configurations accumulate the most co-ions in all concentrations. By contrast, canonical A-RNA structures consistently show the lowest accumulations for all ion species. Water distributions vary markedly with RNA configuration but show little dependency on KCL concentration. Based on Donnan coefficient calculations, the net charge of the solution at the surface of the RNA decreases linearly as a function of salt concentration and becomes net-neutral near 2.5-3m KCL for folded configurations, while unfolded configurations still show a positive solution charge. Our findings show that all-atom molecular dynamics can describe the equilibrium distributions of monovalent salt in the presence of small RNA oligomers at KCL concentrations where ion correlation effects become important. Furthermore, these results provide valuable insights into the distributions of water and ions near the RNA oligomer surface as a function of structural configuration.

Concentration-dependent and configuration-dependent interactions of monovalent ions with an RNA tetraloop.

PubMed

Miner, Jacob Carlson; García, Angel Enrique

2018-06-14

Monovalent salt solutions have strongly coupled interactions with biopolymers, from large polyelectrolytes to small RNA oligomers. High salt concentrations have been known to induce transitions in the structure of RNA, producing non-canonical configurations and even driving RNA to precipitate out of solution. Using all-atom molecular dynamics simulations, we model a monovalent salt species (KCL) at high concentrations (0.1-3m) and calculate the equilibrium distributions of water and ions around a small tetraloop-forming RNA oligomer in a variety of structural arrangements: folded A-RNA (canonical) and Z-RNA (non-canonical) tetraloops and unfolded configurations. From these data, we calculate the ion preferential binding coefficients and Donnan coefficients for the RNA oligomer as a function of concentration and structure. We find that cation accumulation is highest around non-canonical Z-RNA configurations at concentrations below 0.5m, while unfolded configurations accumulate the most co-ions in all concentrations. By contrast, canonical A-RNA structures consistently show the lowest accumulations for all ion species. Water distributions vary markedly with RNA configuration but show little dependency on KCL concentration. Based on Donnan coefficient calculations, the net charge of the solution at the surface of the RNA decreases linearly as a function of salt concentration and becomes net-neutral near 2.5-3m KCL for folded configurations, while unfolded configurations still show a positive solution charge. Our findings show that all-atom molecular dynamics can describe the equilibrium distributions of monovalent salt in the presence of small RNA oligomers at KCL concentrations where ion correlation effects become important. Furthermore, these results provide valuable insights into the distributions of water and ions near the RNA oligomer surface as a function of structural configuration.

Time-Frequency Based Instantaneous Frequency Estimation of Sparse Signals from an Incomplete Set of Samples

DTIC Science & Technology

2014-06-17

100 0 2 4 Wigner distribution 0 50 100 0 0.5 1 Auto-correlation function 0 50 100 0 2 4 L- Wigner distribution 0 50 100 0 0.5 1 Auto-correlation function ...bilinear or higher order autocorrelation functions will increase the number of missing samples, the analysis shows that accurate instantaneous...frequency estimation can be achieved even if we deal with only few samples, as long as the auto-correlation function is properly chosen to coincide with

Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.

PubMed

Lee, Young-Beom; Lee, Jeonghyeon; Tak, Sungho; Lee, Kangjoo; Na, Duk L; Seo, Sang Won; Jeong, Yong; Ye, Jong Chul

2016-01-15

Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease. Copyright © 2015 Elsevier Inc. All rights reserved.

Combining DCQGMP-Based Sparse Decomposition and MPDR Beamformer for Multi-Type Interferences Mitigation for GNSS Receivers.

PubMed

Guo, Qiang; Qi, Liangang

2017-04-10

In the coexistence of multiple types of interfering signals, the performance of interference suppression methods based on time and frequency domains is degraded seriously, and the technique using an antenna array requires a large enough size and huge hardware costs. To combat multi-type interferences better for GNSS receivers, this paper proposes a cascaded multi-type interferences mitigation method combining improved double chain quantum genetic matching pursuit (DCQGMP)-based sparse decomposition and an MPDR beamformer. The key idea behind the proposed method is that the multiple types of interfering signals can be excised by taking advantage of their sparse features in different domains. In the first stage, the single-tone (multi-tone) and linear chirp interfering signals are canceled by sparse decomposition according to their sparsity in the over-complete dictionary. In order to improve the timeliness of matching pursuit (MP)-based sparse decomposition, a DCQGMP is introduced by combining an improved double chain quantum genetic algorithm (DCQGA) and the MP algorithm, and the DCQGMP algorithm is extended to handle the multi-channel signals according to the correlation among the signals in different channels. In the second stage, the minimum power distortionless response (MPDR) beamformer is utilized to nullify the residuary interferences (e.g., wideband Gaussian noise interferences). Several simulation results show that the proposed method can not only improve the interference mitigation degree of freedom (DoF) of the array antenna, but also effectively deal with the interference arriving from the same direction with the GNSS signal, which can be sparse represented in the over-complete dictionary. Moreover, it does not bring serious distortions into the navigation signal.

Combining DCQGMP-Based Sparse Decomposition and MPDR Beamformer for Multi-Type Interferences Mitigation for GNSS Receivers

PubMed Central

Guo, Qiang; Qi, Liangang

2017-01-01

In the coexistence of multiple types of interfering signals, the performance of interference suppression methods based on time and frequency domains is degraded seriously, and the technique using an antenna array requires a large enough size and huge hardware costs. To combat multi-type interferences better for GNSS receivers, this paper proposes a cascaded multi-type interferences mitigation method combining improved double chain quantum genetic matching pursuit (DCQGMP)-based sparse decomposition and an MPDR beamformer. The key idea behind the proposed method is that the multiple types of interfering signals can be excised by taking advantage of their sparse features in different domains. In the first stage, the single-tone (multi-tone) and linear chirp interfering signals are canceled by sparse decomposition according to their sparsity in the over-complete dictionary. In order to improve the timeliness of matching pursuit (MP)-based sparse decomposition, a DCQGMP is introduced by combining an improved double chain quantum genetic algorithm (DCQGA) and the MP algorithm, and the DCQGMP algorithm is extended to handle the multi-channel signals according to the correlation among the signals in different channels. In the second stage, the minimum power distortionless response (MPDR) beamformer is utilized to nullify the residuary interferences (e.g., wideband Gaussian noise interferences). Several simulation results show that the proposed method can not only improve the interference mitigation degree of freedom (DoF) of the array antenna, but also effectively deal with the interference arriving from the same direction with the GNSS signal, which can be sparse represented in the over-complete dictionary. Moreover, it does not bring serious distortions into the navigation signal. PMID:28394290

Effect of Climate Change on Vegetation Phenology of Different Land Cover Types on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Cheng, M.; Jin, J.

2017-12-01

Vegetation phenology is one of the most sensitive bio-indicators of climate change, and it has received increasing interests in the context of global warming. As one of the most sensitive areas to global change, the Tibetan Plateau is a unique region to study the trends in vegetation phenology in response to climate change because of its unique vegetation composition, climate features and low-level human disturbance. Although some studies have aroused wide controversies about the actual plant phenology patterns in the Tibetan Plateau, yet the reasons remain unclear. In particular, the phenology characteristics of sparse herbaceous or sparse shrub and evergreen forest that are mostly located in the northwest and southeast of the Tibetan Plateau remain less studied. In this study, the spatio-temporal patterns of the start (SOS), end (EOS) and length (LOS) of the vegetation growing season for six vegetation types in the Tibetan Plateau, including evergreen broadleaf forests, evergreen coniferous forests, evergreen shrub, meadow, steppe and sparse herbaceous or sparse shrub, were quantified from 1982 to 2014 using NOAA/AVHRR NDVI data set at a spatial resolution of 0.05°×0.05° and 7-day intervals using NDVI relative change rate threshold and sixth order polynomial fit models. Assisted with the monthly precipitation and temperature data, the relative effects of changing climates on the variability of phenology were also examined. Diverse phenological changes were observed for different land cover types, with an advancing start of growing season (SOS), delaying end of growing season (EOS) and increasing length of growing season (LOS) in the eastern Tibetan Plateau where meadow was the dominant vegetation type, but with the opposite changes in the steppe and sparse herbaceous or sparse shrub regions which are mostly located in the northwestern and western edges of the Tibetan Plateau. Correlation analysis indicated that sufficient preseason precipitation may delay the SOS of evergreen forests in the southeastern Plateau and advance the SOS of steppe and sparse herbaceous or sparse shrub in relatively arid areas, while the advance of SOS in meadow areas could be related to higher preseason temperature.

Correlations, soliton modes, and non-Hermitian linear mode transmutation in the one-dimensional noisy Burgers equation.

PubMed

Fogedby, Hans C

2003-08-01

Using the previously developed canonical phase space approach applied to the noisy Burgers equation in one dimension, we discuss in detail the growth morphology in terms of nonlinear soliton modes and superimposed linear modes. We moreover analyze the non-Hermitian character of the linear mode spectrum and the associated dynamical pinning, and mode transmutation from diffusive to propagating behavior induced by the solitons. We discuss the anomalous diffusion of growth modes, switching and pathways, correlations in the multisoliton sector, and in detail the correlations and scaling properties in the two-soliton sector.

Association between somatotypes and blood pressure in an adult Chuvasha population.

PubMed

Kalichman, Leonid; Livshits, Gregory; Kobyliansky, Eugene

2004-01-01

The relationship between blood pressure (BP) and anthropometrical characteristics has been indeed examined extensively, but only a few studies have investigated any connection of somatotypes to BP. to evaluate the association between BP and various anthropometrical characteristics, including components of somatotypes (using the methods of Heath and Carter and of Deriabin). The study sample comprised 783 males aged 18-89 years and 720 females aged 18-90 years, all residents of the Chuvasha, Russian Federation. We used multiple regression, Pearson's and canonical correlation analyses. Significant correlations (r = 0.19-0.28, p < 0.05) were obtained between BP and anthropometric characteristics associated with body compositions and somatotypes. The most impressive were the canonical correlations between BP and somatotype components derived according to Heath and Carter (0.275), and according to Deriabin's (0.333) method. Different body types were highly significantly (P < 0.001) associated with systolic and diastolic BP. Individuals of robust physique (with high endomorphy and mesomorphy) showed high mean values of systolic and diastolic BP, whereas the smallest persons had the lowest BP values. These findings suggest the existence of common physiological paths in the development of body physique and blood pressure regulation and may possibly be indicative of the involvement of pleiotropic genetic and/or epigenetic mechanisms in this regulation.

The Effectiveness of Using Limited Gauge Measurements for Bias Adjustment of Satellite-Based Precipitation Estimation over Saudi Arabia

NASA Astrophysics Data System (ADS)

Alharbi, Raied; Hsu, Kuolin; Sorooshian, Soroosh; Braithwaite, Dan

2018-01-01

Precipitation is a key input variable for hydrological and climate studies. Rain gauges are capable of providing reliable precipitation measurements at point scale. However, the uncertainty of rain measurements increases when the rain gauge network is sparse. Satellite -based precipitation estimations appear to be an alternative source of precipitation measurements, but they are influenced by systematic bias. In this study, a method for removing the bias from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping, climate classification, and inverse-weighted distance method. Daily PERSIANN-CCS is selected to test the capability of the method for removing the bias over Saudi Arabia during the period of 2010 to 2016. The first six years (2010 - 2015) are calibrated years and 2016 is used for validation. The results show that the yearly correlation coefficient was enhanced by 12%, the yearly mean bias was reduced by 93% during validated year. Root mean square error was reduced by 73% during validated year. The correlation coefficient, the mean bias, and the root mean square error show that the proposed method removes the bias on PERSIANN-CCS effectively that the method can be applied to other regions where the rain gauge network is sparse.

Allozyme markers in breeding zone designation

Treesearch

R. D. Westfall; M. T. Conkle

1992-01-01

Early studies of allozyme variation in plant populations suggested that allelic frequencies in some loci vary by geography. Since then, the expectation that allozymes might be useful in describing geographic patterns has generally not been borne out by single locus analyses, except on the broadest scale. Multi-locus analyses reveal the converse: canonical correlation...

The Contributions of Emotional Intelligence and Social Support for Adaptive Career Progress among Italian Youth

ERIC Educational Resources Information Center

Di Fabio, Annamaria; Kenny, Maureen E.

2015-01-01

Drawing from career construction and positive youth development perspectives, this study explores, among 254 Italian high school students, the relationship between emotional intelligence (EI) and support from friends and teachers with indices of adaptive career development. Results from the full canonical correlational model revealed that…

The Relationship between the Big-Five Model of Personality and Self-Regulated Learning Strategies

ERIC Educational Resources Information Center

Bidjerano, Temi; Dai, David Yun

2007-01-01

The study examined the relationship between the big-five model of personality and the use of self-regulated learning strategies. Measures of self-regulated learning strategies and big-five personality traits were administered to a sample of undergraduate students. Results from canonical correlation analysis indicated an overlap between the…

Investigating the Relationship between Conceptual and Procedural Errors in the Domain of Probability Problem-Solving.

ERIC Educational Resources Information Center

O'Connell, Ann Aileen

The relationships among types of errors observed during probability problem solving were studied. Subjects were 50 graduate students in an introductory probability and statistics course. Errors were classified as text comprehension, conceptual, procedural, and arithmetic. Canonical correlation analysis was conducted on the frequencies of specific…

Another View of the Relation of Environment to Mental Abilities: A Reply

ERIC Educational Resources Information Center

Marjoribanks, Kevin

1974-01-01

Harris and McArthur concluded that Marjoribanks' measure of the learning environment of the home was not differentially related to mental ability test performance. It is argued, here, that a canonical correlation analysis provides a better explanation of the data than Harris' and McArthur's interbattery factor analysis. (Author/SE)

Word Processing in Children with Autism Spectrum Disorders: Evidence from Event-Related Potentials

ERIC Educational Resources Information Center

Sandbank, Michael; Yoder, Paul; Key, Alexandra P.

2017-01-01

Purpose: This investigation was conducted to determine whether young children with autism spectrum disorders exhibited a canonical neural response to word stimuli and whether putative event-related potential (ERP) measures of word processing were correlated with a concurrent measure of receptive language. Additional exploratory analyses were used…

Parent Characteristics, Economic Stress and Neighborhood Context as Predictors of Parent Involvement in Preschool Children's Education

ERIC Educational Resources Information Center

Waanders, Christine; Mendez, Julia L.; Downer, Jason T.

2007-01-01

This study examines factors related to three dimensions of parent involvement in preschool: school-based involvement, home-based involvement, and the parent-teacher relationship. Participants were 154 predominantly African American parents recruited from two Head Start programs. Results of bivariate and canonical correlation analyses support the…

Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study.

PubMed

Al-Shargie, Fares; Tang, Tong Boon; Kiguchi, Masashi

2017-05-01

This paper presents an investigation about the effects of mental stress on prefrontal cortex (PFC) subregions using simultaneous measurement of functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) signals. The aim is to explore canonical correlation analysis (CCA) technique to study the relationship among the bi-modality signals in mental stress assessment, and how we could fuse the signals for better accuracy in stress detection. Twenty-five male healthy subjects participated in the study while performing mental arithmetic task under control and stress (under time pressure with negative feedback) conditions. The fusion of brain signals acquired by fNIRS-EEG was performed at feature-level using CCA by maximizing the inter-subject covariance across modalities. The CCA result discovered the associations across the modalities and estimated the components responsible for these associations. The experiment results showed that mental stress experienced by this cohort of subjects is subregion specific and localized to the right ventrolateral PFC subregion. These suggest the right ventrolateral PFC as a suitable candidate region to extract biomarkers as performance indicators of neurofeedback training in stress coping.

A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval.

PubMed

Cai, Jia; Tang, Yi

2018-02-01

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Applications of temporal kernel canonical correlation analysis in adherence studies.

PubMed

John, Majnu; Lencz, Todd; Ferbinteanu, Janina; Gallego, Juan A; Robinson, Delbert G

2017-10-01

Adherence to medication is often measured as a continuous outcome but analyzed as a dichotomous outcome due to lack of appropriate tools. In this paper, we illustrate the use of the temporal kernel canonical correlation analysis (tkCCA) as a method to analyze adherence measurements and symptom levels on a continuous scale. The tkCCA is a novel method developed for studying the relationship between neural signals and hemodynamic response detected by functional MRI during spontaneous activity. Although the tkCCA is a powerful tool, it has not been utilized outside the application that it was originally developed for. In this paper, we simulate time series of symptoms and adherence levels for patients with a hypothetical brain disorder and show how the tkCCA can be used to understand the relationship between them. We also examine, via simulations, the behavior of the tkCCA under various missing value mechanisms and imputation methods. Finally, we apply the tkCCA to a real data example of psychotic symptoms and adherence levels obtained from a study based on subjects with a first episode of schizophrenia, schizophreniform or schizoaffective disorder.

Similarity in temperament between mother and offspring rhesus monkeys: Sex differences and the role of monoamine oxidase-A and serotonin transporter promoter polymorphism genotypes

PubMed Central

Sullivan, Erin C.; Mendoza, Sally P.; Capitanio, John P.

2011-01-01

Temperament is usually considered biologically based and largely inherited, however the environment can shape the development of temperament. Allelic variation may confer differential sensitivity to early environment resulting in variations in temperament. Here we explore the relationship between measures of temperament in mothers and their first-born offspring and the role of genetic sensitivity in establishing the strength of these associations. Temperament ratings were conducted on 3-4 month old rhesus monkeys after a 25-hour biobehavioral assessment. Factor analysis revealed a four factor structure of temperament. Females assessed as infants have reproduced and their offspring have also been evaluated through the standardized testing paradigm. Canonical correlation analysis revealed statistically significant associations between factor scores of mothers and sons, but not mothers and daughters. Further, offspring possessing the high activity, “low risk”, alleles of the rhMAOA-LPR or rh5-HTTLPR showed statistically significant canonical correlations, whereas those possessing other alleles did not, suggesting differential genetic sensitivity to the normative early experience of maternal temperament. PMID:21866539

The evaluation of rainfall influence on combined sewer overflows characteristics: the Berlin case study.

PubMed

Sandoval, S; Torres, A; Pawlowsky-Reusing, E; Riechel, M; Caradot, N

2013-01-01

The present study aims to explore the relationship between rainfall variables and water quality/quantity characteristics of combined sewer overflows (CSOs), by the use of multivariate statistical methods and online measurements at a principal CSO outlet in Berlin (Germany). Canonical correlation results showed that the maximum and average rainfall intensities are the most influential variables to describe CSO water quantity and pollutant loads whereas the duration of the rainfall event and the rain depth seem to be the most influential variables to describe CSO pollutant concentrations. The analysis of partial least squares (PLS) regression models confirms the findings of the canonical correlation and highlights three main influences of rainfall on CSO characteristics: (i) CSO water quantity characteristics are mainly influenced by the maximal rainfall intensities, (ii) CSO pollutant concentrations were found to be mostly associated with duration of the rainfall and (iii) pollutant loads seemed to be principally influenced by dry weather duration before the rainfall event. The prediction quality of PLS models is rather low (R² < 0.6) but results can be useful to explore qualitatively the influence of rainfall on CSO characteristics.

Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals.

PubMed

Singh, Anurag; Dandapat, Samarendra

2017-04-01

In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems.

Emergence of ultrafast sparsely synchronized rhythms and their responses to external stimuli in an inhomogeneous small-world complex neuronal network.

PubMed

Kim, Sang-Yoon; Lim, Woochang

2017-09-01

We consider an inhomogeneous small-world network (SWN) composed of inhibitory short-range (SR) and long-range (LR) interneurons, and investigate the effect of network architecture on emergence of synchronized brain rhythms by varying the fraction of LR interneurons p long . The betweenness centralities of the LR and SR interneurons (characterizing the potentiality in controlling communication between other interneurons) are distinctly different. Hence, in view of the betweenness, SWNs we consider are inhomogeneous, unlike the "canonical" Watts-Strogatz SWN with nearly the same betweenness centralities. For small p long , the load of communication traffic is much concentrated on a few LR interneurons. However, as p long is increased, the number of LR connections (coming from LR interneurons) increases, and then the load of communication traffic is less concentrated on LR interneurons, which leads to better efficiency of global communication between interneurons. Sparsely synchronized rhythms are thus found to emerge when passing a small critical value p long (c) (≃0.16). The population frequency of the sparsely synchronized rhythm is ultrafast (higher than 100 Hz), while the mean firing rate of individual interneurons is much lower (∼30 Hz) due to stochastic and intermittent neural discharges. These dynamical behaviors in the inhomogeneous SWN are also compared with those in the homogeneous Watts-Strogatz SWN, in connection with their network topologies. Particularly, we note that the main difference between the two types of SWNs lies in the distribution of betweenness centralities. Unlike the case of the Watts-Strogatz SWN, dynamical responses to external stimuli vary depending on the type of stimulated interneurons in the inhomogeneous SWN. We consider two cases of external time-periodic stimuli applied to sub-populations of the LR and SR interneurons, respectively. Dynamical responses (such as synchronization suppression and enhancement) to these two cases of stimuli are studied and discussed in relation to the betweenness centralities of stimulated interneurons, representing the effectiveness for transfer of stimulation effect in the whole network. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems

PubMed Central

Smith, Jason F.; Pillai, Ajay; Chen, Kewei; Horwitz, Barry

2012-01-01

Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need to be addressed. The issues are discussed within the framework of linear dynamic systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a “node” in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an “instantaneous” connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis. PMID:22279430

Prediction of subjective ratings of emotional pictures by EEG features

NASA Astrophysics Data System (ADS)

McFarland, Dennis J.; Parvaz, Muhammad A.; Sarnacki, William A.; Goldstein, Rita Z.; Wolpaw, Jonathan R.

2017-02-01

Objective. Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli. Approach. To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings. Main results. Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization. Significance. The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.

Is the technical performance of young soccer players influenced by hormonal status, sexual maturity, anthropometric profile, and physical performance?

PubMed

Moreira, Alexandre; Massa, Marcelo; Thiengo, Carlos R; Rodrigues Lopes, Rafael Alan; Lima, Marcelo R; Vaeyens, Roel; Barbosa, Wesley P; Aoki, Marcelo S

2017-12-01

The aim of this study was to examine the influence of hormonal status, anthropometric profile, sexual maturity level, and physical performance on the technical abilities of 40 young male soccer players during small-sided games (SSGs). Anthropometric profiling, saliva sampling, sexual maturity assessment (Tanner scale), and physical performance tests (Yo-Yo and vertical jumps) were conducted two weeks prior to the SSGs. Salivary testosterone was determined by the enzyme-linked immunosorbent assay method. Technical performance was determined by the frequency of actions during SSGs. Principal component analyses identified four technical actions of importance: total number of passes, effectiveness, goal attempts, and total tackles. A multivariate canonical correlation analysis was then employed to verify the prediction of a multiple dependent variables set (composed of four technical actions) from an independent set of variables, composed of testosterone concentration, stage of pubic hair and genitalia development, vertical jumps and Yo-Yo performance. A moderate-to-large relationship between the technical performance set and the independent set was observed. The canonical correlation was 0.75 with a canonical R 2 of 0.45. The highest structure coefficient in the technical performance set was observed for tackles (0.77), while testosterone presented the highest structure coefficient (0.75) for the variables of the independent set. The current data suggest that the selected independent set of variables might be useful in predicting SSG performance in young soccer players. Coaches should be aware that physical development plays a key role in technical performance to avoid decision-making mistakes during the selection of young players.

Patient engagement: an investigation at a primary care clinic

PubMed Central

Gill, Preetinder Singh

2013-01-01

Background Engaged employees are an asset to any organization. They are instrumental in ensuring good commercial outcomes through continuous innovation and incremental improvement. A health care facility is similar to a regular work setting in many ways. A health care provider and a patient have roles akin to a team leader and a team member/stakeholder, respectively. Hence it can be argued that the concept of employee engagement can be applied to patients in health care settings in order to improve health outcomes. Methods Patient engagement data were collected using a survey instrument from a primary care clinic in the northern Indian state of Punjab. Canonical correlation equations were formulated to identify combinations which were strongly related to each other. In addition, the cause-effect relationship between patient engagement and patient-perceived health outcomes was described using structural equation modeling. Results Canonical correlation analysis showed that the first set of canonical variables had a fairly strong relationship, ie, a magnitude > 0.80 at the 95% confidence interval, for five dimensions of patient engagement. Structural equation modeling analysis yielded a β ≥ 0.10 and a Student’s t statistic ≥ 2.96 for these five dimensions. The threshold Student’s t statistic was 1.99. Hence it was found the β values were significant at the 95% confidence interval for all census regions. Conclusion A scaled reliable survey instrument was developed to measured patient engagement. Better patient engagement is associated with better patient-perceived health outcomes. This study provides preliminary evidence that patient engagement has a causal relationship with patient-perceived health outcomes. PMID:23515133

Similarity regularized sparse group lasso for cup to disc ratio computation.

PubMed

Cheng, Jun; Zhang, Zhuo; Tao, Dacheng; Wong, Damon Wing Kee; Liu, Jiang; Baskaran, Mani; Aung, Tin; Wong, Tien Yin

2017-08-01

Automatic cup to disc ratio (CDR) computation from color fundus images has shown to be promising for glaucoma detection. Over the past decade, many algorithms have been proposed. In this paper, we first review the recent work in the area and then present a novel similarity-regularized sparse group lasso method for automated CDR estimation. The proposed method reconstructs the testing disc image based on a set of reference disc images by integrating the similarity between testing and the reference disc images with the sparse group lasso constraints. The reconstruction coefficients are then used to estimate the CDR of the testing image. The proposed method has been validated using 650 images with manually annotated CDRs. Experimental results show an average CDR error of 0.0616 and a correlation coefficient of 0.7, outperforming other methods. The areas under curve in the diagnostic test reach 0.843 and 0.837 when manual and automatically segmented discs are used respectively, better than other methods as well.

Interpretation of the Precision Matrix and Its Application in Estimating Sparse Brain Connectivity during Sleep Spindles from Human Electrocorticography Recordings

PubMed Central

Das, Anup; Sampson, Aaron L.; Lainscsek, Claudia; Muller, Lyle; Lin, Wutu; Doyle, John C.; Cash, Sydney S.; Halgren, Eric; Sejnowski, Terrence J.

2017-01-01

The correlation method from brain imaging has been used to estimate functional connectivity in the human brain. However, brain regions might show very high correlation even when the two regions are not directly connected due to the strong interaction of the two regions with common input from a third region. One previously proposed solution to this problem is to use a sparse regularized inverse covariance matrix or precision matrix (SRPM) assuming that the connectivity structure is sparse. This method yields partial correlations to measure strong direct interactions between pairs of regions while simultaneously removing the influence of the rest of the regions, thus identifying regions that are conditionally independent. To test our methods, we first demonstrated conditions under which the SRPM method could indeed find the true physical connection between a pair of nodes for a spring-mass example and an RC circuit example. The recovery of the connectivity structure using the SRPM method can be explained by energy models using the Boltzmann distribution. We then demonstrated the application of the SRPM method for estimating brain connectivity during stage 2 sleep spindles from human electrocorticography (ECoG) recordings using an 8 × 8 electrode array. The ECoG recordings that we analyzed were from a 32-year-old male patient with long-standing pharmaco-resistant left temporal lobe complex partial epilepsy. Sleep spindles were automatically detected using delay differential analysis and then analyzed with SRPM and the Louvain method for community detection. We found spatially localized brain networks within and between neighboring cortical areas during spindles, in contrast to the case when sleep spindles were not present. PMID:28095202

[Job stress of nursing aides in Swiss nursing homes : Nonlinear canonical analysis].

PubMed

Ziegler, A; Bernet, M; Metzenthin, P; Conca, A; Hahn, S

2016-08-01

Due to demographic changes, the demand for care in nursing homes for the elderly and infirmed is growing. At the same time nursing staff shortages are also increasing. Nursing aides are the primary care providers and comprise the largest staff group in Swiss nursing homes. They are exposed to various forms of job stress, which threaten job retention. The aim of this study was to discover which features of the work situation and which personal characteristics of the nursing aides were related to the workload. Data from nursing aides in Swiss nursing homes were investigated through a secondary analysis of a national quantitative cross-sectional study, using descriptive statistics and a nonlinear canonical correlation analysis. A total of 1054 nursing aides were included in the secondary analysis, 94.6 % of whom were women between the ages of 42 and 61 years. The job stress most frequently mentioned in the descriptive analysis, almost 60 % of the participants referred to it, was staff shortage. The nonlinear canonical correlation analysis revealed that many job strains are caused by social and organizational issues. In particular, a lack of support from supervisors was associated with staff not feeling appreciated. These job strains correlated with a high level of responsibility, the feeling of being unable to work independently and a feeling of being exploited. These strains were predominant in the nursing aides between 32 and 51 years old who had part time jobs but workloads of 80-90 %. Middle-aged nursing aides who worked to 80-90 % are particularly at risk to resign from the position prematurely. Measures need to be mainly implemented in the social and organizational areas. It can be assumed that a targeted individual support, recognition and promotion of nursing aides may decrease the level of job strain.

Coping styles used by sexual minority men who experience intimate partner violence.

PubMed

Goldberg-Looney, Lisa D; Perrin, Paul B; Snipes, Daniel J; Calton, Jenna M

2016-12-01

This study examined the coping styles used by sexual minority men who have experienced intimate partner violence, including sexual, emotional and physical victimisation, as well as physical injury. Although sexual minority men experience intimate partner violence at least as often as do heterosexuals, there is currently limited knowledge of intimate partner violence in this community or resources for sexual minority men who experience intimate partner violence. Cross-sectional design. Sexual minority men (N = 89) were recruited as part of a national online survey and completed questionnaires assessing lifetime experiences of intimate partner violence as well as various coping strategies. In terms of intimate partner violence, 34·8% of participants reported having been targets of sexual abuse, 38·2% targets of physical abuse, 69·7% targets of psychological abuse and 28·1% had experienced an injury as a result of intimate partner violence during their lifetime. Canonical correlation analyses found that intimate partner violence victimisation explained 32·5% of the variance in adaptive and 31·4% of the variance in maladaptive coping behaviours. In the adaptive coping canonical correlation, standardised loadings suggested that sexual minority men who experienced intimate partner violence resulting in injury were more likely to use religious coping, but less likely to use planning coping. In the maladaptive coping canonical correlation, sexual minority men who had been targets of intimate partner sexual victimisation and intimate partner violence resulting in injury tended to engage in increased behavioural disengagement coping. This study revealed several coping behaviours that are more or less likely as the severity of different forms of intimate partner violence increases. The identification of these coping styles could be applied to the development and modification of evidence-based interventions to foster effective and discourage ineffective coping styles, thereby improving outcomes for sexual minority men who experience intimate partner violence. © 2016 John Wiley & Sons Ltd.

The canonical semantic network supports residual language function in chronic post-stroke aphasia

PubMed Central

Griffis, Joseph C.; Nenert, Rodolphe; Allendorfer, Jane B.; Vannest, Jennifer; Holland, Scott; Dietz, Aimee; Szaflarski, Jerzy P.

2016-01-01

Current theories of language recovery after stroke are limited by a reliance on small studies. Here, we aimed to test predictions of current theory and resolve inconsistencies regarding right hemispheric contributions to long-term recovery. We first defined the canonical semantic network in 43 healthy controls. Then, in a group of 43 patients with chronic post-stroke aphasia, we tested whether activity in this network predicted performance on measures of semantic comprehension, naming, and fluency while controlling for lesion volume effects. Canonical network activation accounted for 22–33% of the variance in language test scores. Whole-brain analyses corroborated these findings, and revealed a core set of regions showing positive relationships to all language measures. We next evaluated the relationship between activation magnitudes in left and right hemispheric portions of the network, and characterized how right hemispheric activation related to the extent of left hemispheric damage. Activation magnitudes in each hemispheric network were strongly correlated, but four right frontal regions showed heightened activity in patients with large lesions. Activity in two of these regions (inferior frontal gyrus pars opercularis and supplementary motor area) was associated with better language abilities in patients with larger lesions, but poorer language abilities in patients with smaller lesions. Our results indicate that bilateral language networks support language processing after stroke, and that right hemispheric activations related to extensive left hemisphere damage occur outside of the canonical semantic network and differentially relate to behavior depending on the extent of left hemispheric damage. PMID:27981674

Emergence of superconductivity in the canonical heavy-electron metal YbRh₂Si₂.

PubMed

Schuberth, Erwin; Tippmann, Marc; Steinke, Lucia; Lausberg, Stefan; Steppke, Alexander; Brando, Manuel; Krellner, Cornelius; Geibel, Christoph; Yu, Rong; Si, Qimiao; Steglich, Frank

2016-01-29

The smooth disappearance of antiferromagnetic order in strongly correlated metals commonly furnishes the development of unconventional superconductivity. The canonical heavy-electron compound YbRh2Si2 seems to represent an apparent exception from this quantum critical paradigm in that it is not a superconductor at temperature T ≥ 10 millikelvin (mK). Here we report magnetic and calorimetric measurements on YbRh2Si2, down to temperatures as low as T ≈ 1 mK. The data reveal the development of nuclear antiferromagnetic order slightly above 2 mK and of heavy-electron superconductivity almost concomitantly with this order. Our results demonstrate that superconductivity in the vicinity of quantum criticality is a general phenomenon. Copyright © 2016, American Association for the Advancement of Science.

Astronaut Preflight Cardiovascular Variables Associated with Vascular Compliance are Highly Correlated with Post-Flight Eye Outcome Measures in the Visual Impairment Intracranial Pressure (VIIP) Syndrome Following Long Duration Spaceflight

NASA Technical Reports Server (NTRS)

Otto, Christian; Ploutz-Snyder, R.

2015-01-01

The detection of the first VIIP case occurred in 2005, and adequate eye outcome measures were available for 31 (67.4%) of the 46 long duration US crewmembers who had flown on the ISS since its first crewed mission in 2000. Therefore, this analysis is limited to a subgroup (22 males and 9 females). A "cardiovascular profile" for each astronaut was compiled by examining twelve individual parameters; eleven of these were preflight variables: systolic blood pressure, pulse pressure, body mass index, percentage body fat, LDL, HDL, triglycerides, use of anti-lipid medication, fasting serum glucose, and maximal oxygen uptake in ml/kg. Each of these variables was averaged across three preflight annual physical exams. Astronaut age prior to the long duration mission, and inflight salt intake was also included in the analysis. The group of cardiovascular variables for each crew member was compared with seven VIIP eye outcome variables collected during the immediate post-flight period: anterior-posterior axial length of the globe measured by ultrasound and optical biometry; optic nerve sheath diameter, optic nerve diameter, and optic nerve to sheath ratio- each measured by ultrasound and magnetic resonance imaging (MRI), intraocular pressure (IOP), change in manifest refraction, mean retinal nerve fiber layer (RNFL) on optical coherence tomography (OCT), and RNFL of the inferior and superior retinal quadrants. Since most of the VIIP eye outcome measures were added sequentially beginning in 2005, as knowledge of the syndrome improved, data were unavailable for 22.0% of the outcome measurements. To address the missing data, we employed multivariate multiple imputation techniques with predictive mean matching methods to accumulate 200 separate imputed datasets for analysis. We were able to impute data for the 22.0% of missing VIIP eye outcomes. We then applied Rubin's rules for collapsing the statistical results across our 200 multiply imputed data sets to assess the canonical correlation between the eye outcomes and the twelve astronaut cardiovascular variables available for all 31 subjects. Results: A highly significant canonical correlation was observed among the canonical solutions (p<.00001), with an average best canonical correlation of.97. The results suggest a strong association between astronauts' measures of cardiovascular health and the seven eye outcomes of the VIIP syndrome used in this analysis. Furthermore, the "joint test" revealed a significant difference in cardiovascular profile between male and female astronauts (Prob > F = 0.00001). Overall, female astronauts demonstrated a significantly healthier cardiovascular status. Individually, the female astronauts had significantly healthier profiles on seven of twelve cardiovascular variables than the men (p values ranging from <0.0001 to <0.05). Male astronauts did not demonstrate significantly healthier values on any of the twelve cardiovascular variables measured

Health, well-being, and psychopathology in a clinical population: Structure and discriminant validity of Mental Health Continuum Short Form (MHC-SF).

PubMed

van Erp Taalman Kip, Rogier M; Hutschemaekers, Giel J M

2018-03-30

The literature suggests a distinction between illness (negative health) and the ability to cope with challenges such as illness (positive health). The two continua model of mental health distinguishes psychiatric symptoms (illness) from well-being (positive health). Well-being consists of hedonic, eudaimonic, and social well-being, constituting one factor that is moderately correlated with psychopathology in the general population. In a mental health care population, we examined whether the three dimensions of well-being are distinguishable and whether well-being is also moderately correlated with symptoms. A representative sample of 1,069 patients (63% female, 47% male; mean age: 42 years) voluntarily completed the Mental Health Continuum-Short Form (MHC-SF), a 14-item test that assesses three components of well-being. Confirmatory factor analysis revealed a model with strong correlations between the three subscales of the MHC-SF, indicating poor discriminant validity. Furthermore, the MHC-SF was strongly correlated (r = -.71) with the symptomatic distress scale of the OQ-45. Exploratory factor analysis permitted a two-factor solution, providing support for the two continua model of mental health. However, the explained variance of the second factor (well-being) was meager in comparison with the first factor (psychopathology). The results of a canonic correlation did not confirm the two continua model, and only a model with one common canonical factor was significant. For patients with clinical levels of psychopathology, the level of well-being and psychopathology correlate much higher than in the general population. Well-being and psychopathology are so entwined that the supposed distinction should be seriously questioned. © 2018 Wiley Periodicals, Inc.

NELasso: Group-Sparse Modeling for Characterizing Relations Among Named Entities in News Articles.

PubMed

Tariq, Amara; Karim, Asim; Foroosh, Hassan

2017-10-01

Named entities such as people, locations, and organizations play a vital role in characterizing online content. They often reflect information of interest and are frequently used in search queries. Although named entities can be detected reliably from textual content, extracting relations among them is more challenging, yet useful in various applications (e.g., news recommending systems). In this paper, we present a novel model and system for learning semantic relations among named entities from collections of news articles. We model each named entity occurrence with sparse structured logistic regression, and consider the words (predictors) to be grouped based on background semantics. This sparse group LASSO approach forces the weights of word groups that do not influence the prediction towards zero. The resulting sparse structure is utilized for defining the type and strength of relations. Our unsupervised system yields a named entities' network where each relation is typed, quantified, and characterized in context. These relations are the key to understanding news material over time and customizing newsfeeds for readers. Extensive evaluation of our system on articles from TIME magazine and BBC News shows that the learned relations correlate with static semantic relatedness measures like WLM, and capture the evolving relationships among named entities over time.

The self-trapping transition in the non-half-filled strongly correlated extended Holstein-Hubbard model in two-dimensions

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

Sankar, I. V., E-mail: ivshankar27@gmail.com; Chatterjee, Ashok, E-mail: ivshankar27@gmail.com

2014-04-24

The two-dimensional extended Holstein-Hubbard model (EHH) has been considered at strong correlation regime in the non-half-filled band case to understand the self-trapping transition of electrons in strongly correlated electron system. We have used the method of optimized canonical transformations to transform an EHH model into an effective extended Hubbard (EEH) model. In the strong on-site correlation limit an EH model can be transformed into a t-J model which is finally solved using Hartree-Fock approximation (HFA). We found that, for non-half-filled band case, the transition is abrupt in the adiabatic region whereas it is continuous in the anti-adiabatic region.

Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function.

PubMed

Lefort-Besnard, Jérémy; Bassett, Danielle S; Smallwood, Jonathan; Margulies, Daniel S; Derntl, Birgit; Gruber, Oliver; Aleman, Andre; Jardri, Renaud; Varoquaux, Gaël; Thirion, Bertrand; Eickhoff, Simon B; Bzdok, Danilo

2018-02-01

Schizophrenia is a devastating mental disease with an apparent disruption in the highly associative default mode network (DMN). Interplay between this canonical network and others probably contributes to goal-directed behavior so its disturbance is a candidate neural fingerprint underlying schizophrenia psychopathology. Previous research has reported both hyperconnectivity and hypoconnectivity within the DMN, and both increased and decreased DMN coupling with the multimodal saliency network (SN) and dorsal attention network (DAN). This study systematically revisited network disruption in patients with schizophrenia using data-derived network atlases and multivariate pattern-learning algorithms in a multisite dataset (n = 325). Resting-state fluctuations in unconstrained brain states were used to estimate functional connectivity, and local volume differences between individuals were used to estimate structural co-occurrence within and between the DMN, SN, and DAN. In brain structure and function, sparse inverse covariance estimates of network coupling were used to characterize healthy participants and patients with schizophrenia, and to identify statistically significant group differences. Evidence did not confirm that the backbone of the DMN was the primary driver of brain dysfunction in schizophrenia. Instead, functional and structural aberrations were frequently located outside of the DMN core, such as in the anterior temporoparietal junction and precuneus. Additionally, functional covariation analyses highlighted dysfunctional DMN-DAN coupling, while structural covariation results highlighted aberrant DMN-SN coupling. Our findings reframe the role of the DMN core and its relation to canonical networks in schizophrenia. We thus underline the importance of large-scale neural interactions as effective biomarkers and indicators of how to tailor psychiatric care to single patients. © 2017 Wiley Periodicals, Inc.

Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity.

PubMed

Shi, Weiwei; Bugrim, Andrej; Nikolsky, Yuri; Nikolskya, Tatiana; Brennan, Richard J

2008-01-01

ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of predictive genomic investigations.

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

PubMed

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

2016-01-29

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

Potential coupling effects of ammonia-oxidizing and anaerobic ammonium-oxidizing bacteria on completely autotrophic nitrogen removal over nitrite biofilm formation induced by the second messenger cyclic diguanylate.

PubMed

Wang, Chao; Liu, Sitong; Xu, Xiaochen; Zhao, Chuanqi; Yang, Fenglin; Wang, Dong

2017-05-01

The objective of this study was to investigate the influence of extracellular polymeric substance (EPS) on the coupling effects between ammonia-oxidizing bacteria (AOB) and anaerobic ammonium-oxidizing (anammox) bacteria for the completely autotrophic nitrogen removal over nitrite (CANON) biofilm formation in a moving bed biofilm reactor (MBBR). Analysis of the quantity of EPS and cyclic diguanylate (c-di-GMP) confirmed that the contents of polysaccharides and c-di-GMP were correlated in the AOB sludge, anammox sludge, and CANON biofilm. The anammox sludge secreted more EPS (especially polysaccharides) than AOB with a markedly higher c-di-GMP content, which could be used by the bacteria to regulate the synthesis of exopolysaccharides that are ultimately used as a fixation matrix, for the adhesion of biomass. Indeed, increased intracellular c-di-GMP concentrations in the anammox sludge enhanced the regulation of polysaccharides to promote the adhesion of AOB and formation of the CANON biofilm. Overall, the results of this study provide new comprehensive information regarding the coupling effects of AOB and anammox bacteria for the nitrogen removal process.

Eukaryotic tRNAs fingerprint invertebrates vis-à-vis vertebrates.

PubMed

Mitra, Sanga; Das, Pijush; Samadder, Arpa; Das, Smarajit; Betai, Rupal; Chakrabarti, Jayprokas

2015-01-01

During translation, aminoacyl-tRNA synthetases recognize the identities of the tRNAs to charge them with their respective amino acids. The conserved identities of 58,244 eukaryotic tRNAs of 24 invertebrates and 45 vertebrates in genomic tRNA database were analyzed and their novel features extracted. The internal promoter sequences, namely, A-Box and B-Box, were investigated and evidence gathered that the intervention of optional nucleotides at 17a and 17b correlated with the optimal length of the A-Box. The presence of canonical transcription terminator sequences at the immediate vicinity of tRNA genes was ventured. Even though non-canonical introns had been reported in red alga, green alga, and nucleomorph so far, fairly motivating evidence of their existence emerged in tRNA genes of other eukaryotes. Non-canonical introns were seen to interfere with the internal promoters in two cases, questioning their transcription fidelity. In a first of its kind, phylogenetic constructs based on tRNA molecules delineated and built the trees of the vast and diverse invertebrates and vertebrates. Finally, two tRNA models representing the invertebrates and the vertebrates were drawn, by isolating the dominant consensus in the positional fluctuations of nucleotide compositions.

A Canonical Correlation Analysis of Social Capital and Knowledge Exchange for Virtual Members of IT Teams

ERIC Educational Resources Information Center

Dutton Feliu, Genevieve

2018-01-01

Social capital theory conceptualized social capital as key to connecting team members into the flow of valued resources and activities, with knowledge deemed one of the most valuable of these resources. Yet, the literature found teams struggle to effectively share knowledge. This quantitative survey-based study assessed the interrelationship…

Canonical Correlation Analysis on Life Stress and Learning Burnout of College Students in Taiwan

ERIC Educational Resources Information Center

Huang, Yun-Chen; Lin, Shu Hui

2010-01-01

With rapid social changes, stress in life has increased significantly for everyone including college students. Understanding life stress among college students and how it may affect learning burnout has become an important concern for education researchers. The purposes of this study were (1) to assess the current status and factor structures of…

Measures of Writing Ability of Fourth, Fifth, and Sixth Grade Children.

ERIC Educational Resources Information Center

Simpson, George Franklin

The purpose of this canonical and multiple correlation study of measures of writing ability was to develop a valid weighted index of writing ability to replace the single measures now being used to evaluate elementary English programs. The subjects, 134 pupils from each fourth, fifth, and sixth grade level of Broadway and Dunham schools in Maple…

The Association between Learning and Learning Style in Instructional Marketing Games

ERIC Educational Resources Information Center

Garber, Lawrence L., Jr.; Hyatt, Eva M.; Boya, Unal O.; Ausherman, Babs

2012-01-01

To understand how learners of respective types respond to marketing games, a joint space generated by canonical correlation analysis is used to recreate Kolb's learning style-type plot and locate business students as points within it according to their learning style types. Two hundred twenty-three undergraduate students played The Marketing Game!…

Avian community composition and habitat importance in the Rio Grande corridor of New Mexico

Treesearch

David A. Leal; Raymond A. Meyer; Bruce C. Thompson

1996-01-01

We investigated avian species richness and abundance within vegetation communities of the Rio Grande Corridor of New Mexico during spring, summer, and fall 1992 and 1993. A subset of 64 transects, for which all bird and vegetation variables were available, representing 16 composite vegetation community types were subjected to canonical correlation analysis to...

The Role of Reinforcement Sensitivity in the Development of Childhood Personality

ERIC Educational Resources Information Center

Slobodskaya, Helena R.; Kuznetsova, Valeriya B.

2013-01-01

The study examined the contribution of reinforcement sensitivity to childhood personality at three levels of the hierarchical structure, mid-level traits, the Big Five and two higher-order factors, and the moderating role of sex and age in a sample of 3-18-year-olds. The canonical correlation analyses indicated that reinforcement sensitivity and…

Statistics Anxiety and Worry: The Roles of Worry Beliefs, Negative Problem Orientation, and Cognitive Avoidance

ERIC Educational Resources Information Center

Williams, Amanda S.

2015-01-01

Statistics anxiety is a common problem for graduate students. This study explores the multivariate relationship between a set of worry-related variables and six types of statistics anxiety. Canonical correlation analysis indicates a significant relationship between the two sets of variables. Findings suggest that students who are more intolerant…

Discovering the Listener within Us: The Impact of Trait-Like Personality Variables and Communicator Styles on Preferences for Listening Style

ERIC Educational Resources Information Center

Villaume, William A.; Bodie, Graham D.

2007-01-01

Extending past research, the present study provides an initial examination of the relationship between trait-like personality variables, communicator style, and individual listening preferences. A series of canonical correlations were run to ascertain to what degree certain communication preferences and trait-like personality variables are related…

A Method of Relating General Circulation Model Simulated Climate to the Observed Local Climate. Part I: Seasonal Statistics.

NASA Astrophysics Data System (ADS)

Karl, Thomas R.; Wang, Wei-Chyung; Schlesinger, Michael E.; Knight, Richard W.; Portman, David

1990-10-01

Important surface observations such as the daily maximum and minimum temperature, daily precipitation, and cloud ceilings often have localized characteristics that are difficult to reproduce with the current resolution and the physical parameterizations in state-of-the-art General Circulation climate Models (GCMs). Many of the difficulties can be partially attributed to mismatches in scale, local topography. regional geography and boundary conditions between models and surface-based observations. Here, we present a method, called climatological projection by model statistics (CPMS), to relate GCM grid-point flee-atmosphere statistics, the predictors, to these important local surface observations. The method can be viewed as a generalization of the model output statistics (MOS) and perfect prog (PP) procedures used in numerical weather prediction (NWP) models. It consists of the application of three statistical methods: 1) principle component analysis (FICA), 2) canonical correlation, and 3) inflated regression analysis. The PCA reduces the redundancy of the predictors The canonical correlation is used to develop simultaneous relationships between linear combinations of the predictors, the canonical variables, and the surface-based observations. Finally, inflated regression is used to relate the important canonical variables to each of the surface-based observed variables.We demonstrate that even an early version of the Oregon State University two-level atmospheric GCM (with prescribed sea surface temperature) produces free-atmosphere statistics than can, when standardized using the model's internal means and variances (the MOS-like version of CPMS), closely approximate the observed local climate. When the model data are standardized by the observed free-atmosphere means and variances (the PP version of CPMS), however, the model does not reproduce the observed surface climate as well. Our results indicate that in the MOS-like version of CPMS the differences between the output of a ten-year GCM control run and the surface-based observations are often smaller than the differences between the observations of two ten-year periods. Such positive results suggest that GCMs may already contain important climatological information that can be used to infer the local climate.

Path Models of Vocational Calling in Christian College Students

ERIC Educational Resources Information Center

Phillips, Sheri L.

2011-01-01

In the Christian college environment, students are encouraged to understand their vocational calling, yet quantitative research on how college students conceptualize calling is sparse. This correlational study extends the research literature significantly by empirically examining variables that affect sense of vocational calling in 270 college…

Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum.

PubMed

Ponzi, Adam; Wickens, Jeff

2010-04-28

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

Local receptive field constrained stacked sparse autoencoder for classification of hyperspectral images.

PubMed

Wan, Xiaoqing; Zhao, Chunhui

2017-06-01

As a competitive machine learning algorithm, the stacked sparse autoencoder (SSA) has achieved outstanding popularity in exploiting high-level features for classification of hyperspectral images (HSIs). In general, in the SSA architecture, the nodes between adjacent layers are fully connected and need to be iteratively fine-tuned during the pretraining stage; however, the nodes of previous layers further away may be less likely to have a dense correlation to the given node of subsequent layers. Therefore, to reduce the classification error and increase the learning rate, this paper proposes the general framework of locally connected SSA; that is, the biologically inspired local receptive field (LRF) constrained SSA architecture is employed to simultaneously characterize the local correlations of spectral features and extract high-level feature representations of hyperspectral data. In addition, the appropriate receptive field constraint is concurrently updated by measuring the spatial distances from the neighbor nodes to the corresponding node. Finally, the efficient random forest classifier is cascaded to the last hidden layer of the SSA architecture as a benchmark classifier. Experimental results on two real HSI datasets demonstrate that the proposed hierarchical LRF constrained stacked sparse autoencoder and random forest (SSARF) provides encouraging results with respect to other contrastive methods, for instance, the improvements of overall accuracy in a range of 0.72%-10.87% for the Indian Pines dataset and 0.74%-7.90% for the Kennedy Space Center dataset; moreover, it generates lower running time compared with the result provided by similar SSARF based methodology.

Partitioning evapotranspiration in sparsely vegetated rangeland using a portable chamber

USGS Publications Warehouse

Stannard, David I.; Weltz, Mark A.

2006-01-01

A portable chamber was used to separate evapotranspiration (ET) from a sparse, mixed‐species shrub canopy in southeastern Arizona, United States, into vegetation and soil components. Chamber measurements were made of ET from the five dominant species, and from bare soil, on 3 days during the monsoon season when the soil surface was dry. The chamber measurements were assembled into landscape ET using a simple geometric model of the vegetated land surface. Chamber estimates of landscape ET were well correlated with, but about 26% greater than, simultaneous eddy‐correlation measurements. Excessive air speed inside the chamber appears to be the primary cause of the overestimate. Overall, transpiration accounted for 84% of landscape ET, and bare soil evaporation for 16%. Desert zinnia, a small (∼0.1 m high) but abundant species, was the greatest water user, both per unit area of shrub and of landscape. Partitioning of ETinto components varied as a function of air temperature and shallow soil moisture. Transpiration from shorter species was more highly correlated with air temperature whereas transpiration from taller species was more highly correlated with shallow soil moisture. Application of these results to a full drying cycle between rainfalls at a similar site suggests that during the monsoon, ET at such sites may be about equally partitioned between transpiration and bare soil evaporation.

A Bayesian Missing Data Framework for Generalized Multiple Outcome Mixed Treatment Comparisons

ERIC Educational Resources Information Center

Hong, Hwanhee; Chu, Haitao; Zhang, Jing; Carlin, Bradley P.

2016-01-01

Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta-analysis, comparing only two…

Fragile X Syndrome: An Aging Perspective

ERIC Educational Resources Information Center

Schneider, Andrea; Ligsay, Andrew; Hagerman, Randi J.

2013-01-01

Cognitive and behavioral correlates of molecular variations related to the FMR1 gene have been studied rather extensively, but research about the long-term outcome in individuals with fragile X spectrum disorders remains sparse. In this review, we present an overview of aging research and recent findings in regard to cellular and clinical…

Gene-to-metabolite network for biosynthesis of lignans in MeJA-elicited Isatis indigotica hairy root cultures

PubMed Central

Chen, Ruibing; Li, Qing; Tan, Hexin; Chen, Junfeng; Xiao, Ying; Ma, Ruifang; Gao, Shouhong; Zerbe, Philipp; Chen, Wansheng; Zhang, Lei

2015-01-01

Root and leaf tissue of Isatis indigotica shows notable anti-viral efficacy, and are widely used as “Banlangen” and “Daqingye” in traditional Chinese medicine. The plants' pharmacological activity is attributed to phenylpropanoids, especially a group of lignan metabolites. However, the biosynthesis of lignans in I. indigotica remains opaque. This study describes the discovery and analysis of biosynthetic genes and AP2/ERF-type transcription factors involved in lignan biosynthesis in I. indigotica. MeJA treatment revealed differential expression of three genes involved in phenylpropanoid backbone biosynthesis (IiPAL, IiC4H, Ii4CL), five genes involved in lignan biosynthesis (IiCAD, IiC3H, IiCCR, IiDIR, and IiPLR), and 112 putative AP2/ERF transcription factors. In addition, four intermediates of lariciresinol biosynthesis were found to be induced. Based on these results, a canonical correlation analysis using Pearson's correlation coefficient was performed to construct gene-to-metabolite networks and identify putative key genes and rate-limiting reactions in lignan biosynthesis. Over-expression of IiC3H, identified as a key pathway gene, was used for metabolic engineering of I. indigotica hairy roots, and resulted in an increase in lariciresinol production. These findings illustrate the utility of canonical correlation analysis for the discovery and metabolic engineering of key metabolic genes in plants. PMID:26579184

A Wearable EEG-HEG-HRV Multimodal System With Simultaneous Monitoring of tES for Mental Health Management.

PubMed

Ha, Unsoo; Lee, Yongsu; Kim, Hyunki; Roh, Taehwan; Bae, Joonsung; Kim, Changhyeon; Yoo, Hoi-Jun

2015-12-01

A multimodal mental management system in the shape of the wearable headband and earplugs is proposed to monitor electroencephalography (EEG), hemoencephalography (HEG) and heart rate variability (HRV) for accurate mental health monitoring. It enables simultaneous transcranial electrical stimulation (tES) together with real-time monitoring. The total weight of the proposed system is less than 200 g. The multi-loop low-noise amplifier (MLLNA) achieves over 130 dB CMRR for EEG sensing and the capacitive correlated-double sampling transimpedance amplifier (CCTIA) has low-noise characteristics for HEG and HRV sensing. Measured three-physiology domains such as neural, vascular and autonomic domain signals are combined with canonical correlation analysis (CCA) and temporal kernel canonical correlation analysis (tkCCA) algorithm to find the neural-vascular-autonomic coupling. It supports highly accurate classification with the 19% maximum improvement with multimodal monitoring. For the multi-channel stimulation functionality, after-effects maximization monitoring and sympathetic nerve disorder monitoring, the stimulator is designed as reconfigurable. The 3.37 × 2.25 mm(2) chip has 2-channel EEG sensor front-end, 2-channel NIRS sensor front-end, NIRS current driver to drive dual-wavelength VCSEL and 6-b DAC current source for tES mode. It dissipates 24 mW with 2 mA stimulation current and 5 mA NIRS driver current.

Technological innovation and its effect on public health in the United States

PubMed Central

Gill, Preetinder Singh

2013-01-01

Background Good public health ensures an efficient work force. Organizations can ensure a prominent position on the global stage by staying on the leading edge of technological development. Public health and technological innovation are vital elements of prosperous economies. It is important to understand how these elements affect each other. This research study explored and described the relationship between these two critical elements/constructs. Methods Indicators representing technological innovation and public health were identified. Indicator data from 2000 to 2009 were collected from various US federal government sources, for the four US Census regions. The four US Census regions were then compared in terms of these indicators. Canonical correlation equations were formulated to identify combinations of the indicators that are strongly related to each other. Additionally, the cause–effect relationship between public health and technological innovation was described using the structural equation modeling technique. Results The four US Census regions ranked differently in terms of both type of indicators in a statistically significant manner. The canonical correlation analysis showed that the first set of canonical variables had a fairly strong relationship, with a magnitude > 0.65 at the 95% confidence interval, for all census regions. Structural equation modeling analysis provided β < −0.69 and Student’s t statistic > 12.98, for all census regions. The threshold Student’s t statistic was 1.98. Hence, it was found that the β values were significant at the 95% confidence interval, for all census regions. Discussion The results of the study showed that better technological innovation indicator scores were associated with better public health indicator scores. Furthermore, the study provided preliminary evidence that technological innovation shares causal relation with public health. PMID:23378771

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 3. Variable selection in classification.

PubMed

Ballabio, Davide; Consonni, Viviana; Mauri, Andrea; Todeschini, Roberto

2010-01-11

In multivariate regression and classification issues variable selection is an important procedure used to select an optimal subset of variables with the aim of producing more parsimonious and eventually more predictive models. Variable selection is often necessary when dealing with methodologies that produce thousands of variables, such as Quantitative Structure-Activity Relationships (QSARs) and highly dimensional analytical procedures. In this paper a novel method for variable selection for classification purposes is introduced. This method exploits the recently proposed Canonical Measure of Correlation between two sets of variables (CMC index). The CMC index is in this case calculated for two specific sets of variables, the former being comprised of the independent variables and the latter of the unfolded class matrix. The CMC values, calculated by considering one variable at a time, can be sorted and a ranking of the variables on the basis of their class discrimination capabilities results. Alternatively, CMC index can be calculated for all the possible combinations of variables and the variable subset with the maximal CMC can be selected, but this procedure is computationally more demanding and classification performance of the selected subset is not always the best one. The effectiveness of the CMC index in selecting variables with discriminative ability was compared with that of other well-known strategies for variable selection, such as the Wilks' Lambda, the VIP index based on the Partial Least Squares-Discriminant Analysis, and the selection provided by classification trees. A variable Forward Selection based on the CMC index was finally used in conjunction of Linear Discriminant Analysis. This approach was tested on several chemical data sets. Obtained results were encouraging.

Late-phase synthesis of IκBα insulates the TLR4-activated canonical NF-κB pathway from noncanonical NF-κB signaling in macrophages

PubMed Central

Mukherjee, Tapas; Taye, Nandaraj; Vijayaragavan, Bharath; Chattopadhyay, Samit; Gomes, James; Basak, Soumen

2017-01-01

The nuclear factor κB (NF-κB) transcription factors coordinate the inflammatory immune response during microbial infection. Pathogenic substances engage canonical NF-κB signaling through the heterodimer RelA:p50, which is subjected to rapid negative feedback by inhibitor of κBα (IκBα). The noncanonical NF-κB pathway is required for the differentiation of immune cells; however, crosstalk between both pathways can occur. Concomitantly activated noncanonical signaling generates p52 from the p100 precursor. The synthesis of p100 is induced by canonical signaling, leading to formation of the late-acting RelA:p52 heterodimer. This crosstalk prolongs inflammatory RelA activity in epithelial cells to ensure pathogen clearance. We found that the Toll-like receptor 4 (TLR4)–activated canonical NF-κB signaling pathway is insulated from lymphotoxin β receptor (LTβR)–induced noncanonical signaling in mouse macrophage cell lines. Combined computational and biochemical studies indicated that the extent of NF-κB–responsive expression of Nfkbia, which encodes IκBα, inversely correlated with crosstalk. The Nfkbia promoter showed enhanced responsiveness to NF-κB activation in macrophages compared to that in fibroblasts. We found that this hyperresponsive promoter engaged the RelA:p52 dimer generated during costimulation of macrophages through TLR4 and LTβR to trigger synthesis of IκBα at late time points, which prevented the late-acting RelA crosstalk response. Together, these data suggest that despite the presence of identical signaling networks in cells of diverse lineages, emergent crosstalk between signaling pathways is subject to cell type–specific regulation. We propose that the insulation of canonical and noncanonical NF-κB pathways limits the deleterious effects of macrophage-mediated inflammation. PMID:27923915

Parallel Prebiotic Origin of Canonical and Non-Canonical Purine Nucleosides

NASA Astrophysics Data System (ADS)

Becker, S.; Carell, T.

2017-07-01

RNA of all living organisms is highly modified. It is unclear if these non-canonical bases are ancestors of an early Earth or biological inventions. We investigated a prebiotic pathway that leads to canonical and non-canonical purine nucleosides.

The Pathway Coexpression Network: Revealing pathway relationships

PubMed Central

Tanzi, Rudolph E.

2018-01-01

A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer’s Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/. PMID:29554099

Development of a selective left-hemispheric fronto-temporal network for processing syntactic complexity in language comprehension.

PubMed

Xiao, Yaqiong; Friederici, Angela D; Margulies, Daniel S; Brauer, Jens

2016-03-01

The development of language comprehension abilities in childhood is closely related to the maturation of the brain, especially the ability to process syntactically complex sentences. Recent studies proposed that the fronto-temporal connection within left perisylvian regions, supporting the processing of syntactically complex sentences, is still immature at preschool age. In the current study, resting state functional magnetic resonance imaging data were acquired from typically developing 5-year-old children and adults to shed further light on the brain functional development. Children additionally performed a behavioral syntactic comprehension test outside the scanner. The amplitude of low-frequency fluctuations was analyzed in order to identify the functional correlation networks of language-relevant brain regions. Results showed an intrahemispheric correlation between left inferior frontal gyrus (IFG) and left posterior superior temporal sulcus (pSTS) in adults, whereas an interhemispheric correlation between left IFG and its right-hemispheric homolog was predominant in children. Correlation analysis between resting-state functional connectivity and sentence processing performance in 5-year-olds revealed that local connectivity within the left IFG is associated with competence of processing syntactically simple canonical sentences, while long-range connectivity between IFG and pSTS in left hemisphere is associated with competence of processing syntactically relatively more complex non-canonical sentences. The present developmental data suggest that a selective left fronto-temporal connectivity network for processing complex syntax is already in functional connection at the age of 5 years when measured in a non-task situation. The correlational findings provide new insight into the relationship between intrinsic functional connectivity and syntactic language abilities in preschool children. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Analysis of the Effects of the Commander’s Battle Positioning on Unit Combat Performance

DTIC Science & Technology

1991-03-01

Analysis ......... .. 58 Logistic Regression Analysis ......... .. 61 Canonical Correlation Analysis ........ .. 62 Descriminant Analysis...entails classifying objects into two or more distinct groups, or responses. Dillon defines descriminant analysis as "deriving linear combinations of the...object given it’s predictor variables. The second objective is, through analysis of the parameters of the descriminant functions, determine those

Forest structure estimation and pattern exploration from discrete return lidar in subalpine forests of the Central Rockies

Treesearch

K. R. Sherrill; M. A. Lefsky; J. B. Bradford; M. G. Ryan

2008-01-01

This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory...

Forest structure estimation and pattern exploration from discrete-return lidar in subalpine forests of the central Rockies

Treesearch

K.R. Sherrill; M.A. Lefsky; J.B. Bradford; M.G. Ryan

2008-01-01

This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory...

Corrigendum to "Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data"

Treesearch

Andrew T. Hudak; Nicholas L. Crookston; Jeffrey S. Evans; David E. hall; Michael J. Falkowski

2009-01-01

The authors regret that an error was discovered in the code within the R software package, yaImpute (Crookston & Finley, 2008), which led to incorrect results reported in the above article. The Most Similar Neighbor (MSN) method computes the distance between reference observations and target observations in a projected space defined using canonical correlation...

A Canonical Correlation Analysis of the Influence of Social Comparison, Gender, and Grade Level on the Multidimensional Self-Concepts of Gifted Adolescents

ERIC Educational Resources Information Center

Rinn, Anne N.; Jamieson, Kelly M.; Gross, Candace M.; McQueen, Kand S.

2009-01-01

This study examines the effects of social comparison, gender, and grade level on gifted adolescents' multidimensional self-concept. Participants include 248 gifted adolescents who had completed the sixth through tenth grade during the previous academic year. Multidimensional self-concept was measured using the Self Description Questionnaire II…

Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study

PubMed Central

Al-Shargie, Fares; Tang, Tong Boon; Kiguchi, Masashi

2017-01-01

This paper presents an investigation about the effects of mental stress on prefrontal cortex (PFC) subregions using simultaneous measurement of functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) signals. The aim is to explore canonical correlation analysis (CCA) technique to study the relationship among the bi-modality signals in mental stress assessment, and how we could fuse the signals for better accuracy in stress detection. Twenty-five male healthy subjects participated in the study while performing mental arithmetic task under control and stress (under time pressure with negative feedback) conditions. The fusion of brain signals acquired by fNIRS-EEG was performed at feature-level using CCA by maximizing the inter-subject covariance across modalities. The CCA result discovered the associations across the modalities and estimated the components responsible for these associations. The experiment results showed that mental stress experienced by this cohort of subjects is subregion specific and localized to the right ventrolateral PFC subregion. These suggest the right ventrolateral PFC as a suitable candidate region to extract biomarkers as performance indicators of neurofeedback training in stress coping. PMID:28663892

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

PubMed

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

PubMed Central

Huang, Chih-Sheng; Yang, Wen-Yu; Chuang, Chun-Hsiang; Wang, Yu-Kai

2018-01-01

Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research. PMID:29599950

A Multifunctional Sensor in Ternary Solution Using Canonical Correlations for Variable Links Assessment

PubMed Central

Liu, Dan; Wang, Qisong; Liu, Xin; Niu, Ruixin; Zhang, Yan; Sun, Jinwei

2016-01-01

Accurately measuring the oil content and salt content of crude oil is very important for both estimating oil reserves and predicting the lifetime of an oil well. There are some problems with the current methods such as high cost, low precision, and difficulties in operation. To solve these problems, we present a multifunctional sensor, which applies, respectively, conductivity method and ultrasound method to measure the contents of oil, water, and salt. Based on cross sensitivity theory, these two transducers are ideally integrated for simplifying the structure. A concentration test of ternary solutions is carried out to testify its effectiveness, and then Canonical Correlation Analysis is applied to evaluate the data. From the perspective of statistics, the sensor inputs, for instance, oil concentration, salt concentration, and temperature, are closely related to its outputs including output voltage and time of flight of ultrasound wave, which further identify the correctness of the sensing theory and the feasibility of the integrated design. Combined with reconstruction algorithms, the sensor can realize the content measurement of the solution precisely. The potential development of the proposed sensor and method in the aspect of online test for crude oil is of important reference and practical value. PMID:27775640

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

PubMed Central

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

Are neoclassical canons valid for southern Chinese faces?

PubMed

Jayaratne, Yasas S N; Deutsch, Curtis K; McGrath, Colman P J; Zwahlen, Roger A

2012-01-01

Proportions derived from neoclassical canons, initially described by Renaissance sculptors and painters, are still being employed as aesthetic guidelines during the clinical assessment of the facial morphology. 1. to determine the applicability of neoclassical canons for Southern Chinese faces and 2. to explore gender differences in relation to the applicability of the neoclassical canons and their variants. 3-D photographs acquired from 103 young adults (51 males and 52 females) without facial dysmorphology were used to test applicability of four neoclassical canons. Standard anthropometric measurements that determine the facial canons were made on these 3-D images. The validity of the canons as well as their different variants were quantified. The neoclassical cannons seldom applied to these individuals, and facial three-section and orbital canons did not apply at all. The orbitonasal canon was most frequently applicable, with a frequency of 19%. Significant sexual dimorphism was found relative to the prevalence of the variants of facial three-section and orbitonasal canons. The neoclassical canons did not appear to apply to our sample when rigorous quantitative measurements were employed. Thus, they should not be used as esthetic goals for craniofacial surgical interventions.

Linear-scaling method for calculating nuclear magnetic resonance chemical shifts using gauge-including atomic orbitals within Hartree-Fock and density-functional theory.

PubMed

Kussmann, Jörg; Ochsenfeld, Christian

2007-08-07

Details of a new density matrix-based formulation for calculating nuclear magnetic resonance chemical shifts at both Hartree-Fock and density functional theory levels are presented. For systems with a nonvanishing highest occupied molecular orbital-lowest unoccupied molecular orbital gap, the method allows us to reduce the asymptotic scaling order of the computational effort from cubic to linear, so that molecular systems with 1000 and more atoms can be tackled with today's computers. The key feature is a reformulation of the coupled-perturbed self-consistent field (CPSCF) theory in terms of the one-particle density matrix (D-CPSCF), which avoids entirely the use of canonical MOs. By means of a direct solution for the required perturbed density matrices and the adaptation of linear-scaling integral contraction schemes, the overall scaling of the computational effort is reduced to linear. A particular focus of our formulation is to ensure numerical stability when sparse-algebra routines are used to obtain an overall linear-scaling behavior.

Infrared moving small target detection based on saliency extraction and image sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Xiaomin; Ren, Kan; Gao, Jin; Li, Chaowei; Gu, Guohua; Wan, Minjie

2016-10-01

Moving small target detection in infrared image is a crucial technique of infrared search and tracking system. This paper present a novel small target detection technique based on frequency-domain saliency extraction and image sparse representation. First, we exploit the features of Fourier spectrum image and magnitude spectrum of Fourier transform to make a rough extract of saliency regions and use a threshold segmentation system to classify the regions which look salient from the background, which gives us a binary image as result. Second, a new patch-image model and over-complete dictionary were introduced to the detection system, then the infrared small target detection was converted into a problem solving and optimization process of patch-image information reconstruction based on sparse representation. More specifically, the test image and binary image can be decomposed into some image patches follow certain rules. We select the target potential area according to the binary patch-image which contains salient region information, then exploit the over-complete infrared small target dictionary to reconstruct the test image blocks which may contain targets. The coefficients of target image patch satisfy sparse features. Finally, for image sequence, Euclidean distance was used to reduce false alarm ratio and increase the detection accuracy of moving small targets in infrared images due to the target position correlation between frames.

Dynamic Textures Modeling via Joint Video Dictionary Learning.

PubMed

Wei, Xian; Li, Yuanxiang; Shen, Hao; Chen, Fang; Kleinsteuber, Martin; Wang, Zhongfeng

2017-04-06

Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DT) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint video dictionary learning (JVDL), to model a video adaptively. By treating the sparse coefficients of image frames over a learned dictionary as the underlying "states", we learn an efficient and robust linear transition matrix between two adjacent frames of sparse events in time series. Hence, a dynamic scene sequence is represented by an appropriate transition matrix associated with a dictionary. In order to ensure the stability of JVDL, we impose several constraints on such transition matrix and dictionary. The developed framework is able to capture the dynamics of a moving scene by exploring both sparse properties and the temporal correlations of consecutive video frames. Moreover, such learned JVDL parameters can be used for various DT applications, such as DT synthesis and recognition. Experimental results demonstrate the strong competitiveness of the proposed JVDL approach in comparison with state-of-the-art video representation methods. Especially, it performs significantly better in dealing with DT synthesis and recognition on heavily corrupted data.

Pair natural orbital and canonical coupled cluster reaction enthalpies involving light to heavy alkali and alkaline earth metals: the importance of sub-valence correlation.

PubMed

Minenkov, Yury; Bistoni, Giovanni; Riplinger, Christoph; Auer, Alexander A; Neese, Frank; Cavallo, Luigi

2017-04-05

In this work, we tested canonical and domain based pair natural orbital coupled cluster methods (CCSD(T) and DLPNO-CCSD(T), respectively) for a set of 32 ligand exchange and association/dissociation reaction enthalpies involving ionic complexes of Li, Be, Na, Mg, Ca, Sr, Ba and Pb(ii). Two strategies were investigated: in the former, only valence electrons were included in the correlation treatment, giving rise to the computationally very efficient FC (frozen core) approach; in the latter, all non-ECP electrons were included in the correlation treatment, giving rise to the AE (all electron) approach. Apart from reactions involving Li and Be, the FC approach resulted in non-homogeneous performance. The FC approach leads to very small errors (<2 kcal mol -1 ) for some reactions of Na, Mg, Ca, Sr, Ba and Pb, while for a few reactions of Ca and Ba deviations up to 40 kcal mol -1 have been obtained. Large errors are both due to artificial mixing of the core (sub-valence) orbitals of metals and the valence orbitals of oxygen and halogens in the molecular orbitals treated as core, and due to neglecting core-core and core-valence correlation effects. These large errors are reduced to a few kcal mol -1 if the AE approach is used or the sub-valence orbitals of metals are included in the correlation treatment. On the technical side, the CCSD(T) and DLPNO-CCSD(T) results differ by a fraction of kcal mol -1 , indicating the latter method as the perfect choice when the CPU efficiency is essential. For completely black-box applications, as requested in catalysis or thermochemical calculations, we recommend the DLPNO-CCSD(T) method with all electrons that are not covered by effective core potentials included in the correlation treatment and correlation-consistent polarized core valence basis sets of cc-pwCVQZ(-PP) quality.

Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory models. [Sparse, Distributed Memory

NASA Technical Reports Server (NTRS)

Keeler, James D.

1988-01-01

The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used here, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.

Contemporary ultrasonic signal processing approaches for nondestructive evaluation of multilayered structures

NASA Astrophysics Data System (ADS)

Zhang, Guang-Ming; Harvey, David M.

2012-03-01

Various signal processing techniques have been used for the enhancement of defect detection and defect characterisation. Cross-correlation, filtering, autoregressive analysis, deconvolution, neural network, wavelet transform and sparse signal representations have all been applied in attempts to analyse ultrasonic signals. In ultrasonic nondestructive evaluation (NDE) applications, a large number of materials have multilayered structures. NDE of multilayered structures leads to some specific problems, such as penetration, echo overlap, high attenuation and low signal-to-noise ratio. The signals recorded from a multilayered structure are a class of very special signals comprised of limited echoes. Such signals can be assumed to have a sparse representation in a proper signal dictionary. Recently, a number of digital signal processing techniques have been developed by exploiting the sparse constraint. This paper presents a review of research to date, showing the up-to-date developments of signal processing techniques made in ultrasonic NDE. A few typical ultrasonic signal processing techniques used for NDE of multilayered structures are elaborated. The practical applications and limitations of different signal processing methods in ultrasonic NDE of multilayered structures are analysed.

Three-dimensional image authentication scheme using sparse phase information in double random phase encoded integral imaging.

PubMed

Yi, Faliu; Jeoung, Yousun; Moon, Inkyu

2017-05-20

In recent years, many studies have focused on authentication of two-dimensional (2D) images using double random phase encryption techniques. However, there has been little research on three-dimensional (3D) imaging systems, such as integral imaging, for 3D image authentication. We propose a 3D image authentication scheme based on a double random phase integral imaging method. All of the 2D elemental images captured through integral imaging are encrypted with a double random phase encoding algorithm and only partial phase information is reserved. All the amplitude and other miscellaneous phase information in the encrypted elemental images is discarded. Nevertheless, we demonstrate that 3D images from integral imaging can be authenticated at different depths using a nonlinear correlation method. The proposed 3D image authentication algorithm can provide enhanced information security because the decrypted 2D elemental images from the sparse phase cannot be easily observed by the naked eye. Additionally, using sparse phase images without any amplitude information can greatly reduce data storage costs and aid in image compression and data transmission.

Beauty is in the efficient coding of the beholder.

PubMed

Renoult, Julien P; Bovet, Jeanne; Raymond, Michel

2016-03-01

Sexual ornaments are often assumed to be indicators of mate quality. Yet it remains poorly known how certain ornaments are chosen before any coevolutionary race makes them indicative. Perceptual biases have been proposed to play this role, but known biases are mostly restricted to a specific taxon, which precludes evaluating their general importance in sexual selection. Here we identify a potentially universal perceptual bias in mate choice. We used an algorithm that models the sparseness of the activity of simple cells in the primary visual cortex (or V1) of humans when coding images of female faces. Sparseness was found positively correlated with attractiveness as rated by men and explained up to 17% of variance in attractiveness. Because V1 is adapted to process signals from natural scenes, in general, not faces specifically, our results indicate that attractiveness for female faces is influenced by a visual bias. Sparseness and more generally efficient neural coding are ubiquitous, occurring in various animals and sensory modalities, suggesting that the influence of efficient coding on mate choice can be widespread in animals.

Population coding in sparsely connected networks of noisy neurons.

PubMed

Tripp, Bryan P; Orchard, Jeff

2012-01-01

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons.

Development and Implementation of GPS Correlator Structures in MATLAB and Simulink with Focus on SDR Applications: Implementation of a Standard GPS Correlator Architecture (Baseline) Implementation of the MIT Quicksynch Sparse Algorithm Development and Implementation of Parallel Circular Correlator Constructs

DTIC Science & Technology

2014-05-01

software is available for a wide variety of operating systems , including Unix, FreeBSD, Linux, Solaris, Novell NetWare, OS X, Microsoft Windows, OS/2, TPF...Word for Xenix systems . Subsequent versions were later written for several other platforms including IBM PCs running DOS (1983), Apple Macintosh ...this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204

A Comparison between Model Base Hardconstrain, Bandlimited, and Sparse-Spike Seismic Inversion: New Insights for CBM Reservoir Modelling on Muara Enim Formation, South Sumatra

NASA Astrophysics Data System (ADS)

Mohamad Noor, Faris; Adipta, Agra

2018-03-01

Coal Bed Methane (CBM) as a newly developed resource in Indonesia is one of the alternatives to relieve Indonesia’s dependencies on conventional energies. Coal resource of Muara Enim Formation is known as one of the prolific reservoirs in South Sumatra Basin. Seismic inversion and well analysis are done to determine the coal seam characteristics of Muara Enim Formation. This research uses three inversion methods, which are: model base hard- constrain, bandlimited, and sparse-spike inversion. Each type of seismic inversion has its own advantages to display the coal seam and its characteristic. Interpretation result from the analysis data shows that the Muara Enim coal seam has 20 (API) gamma ray value, 1 (gr/cc) – 1.4 (gr/cc) from density log, and low AI cutoff value range between 5000-6400 (m/s)*(g/cc). The distribution of coal seam is laterally thinning northwest to southeast. Coal seam is seen biasedly on model base hard constraint inversion and discontinued on band-limited inversion which isn’t similar to the geological model. The appropriate AI inversion is sparse spike inversion which has 0.884757 value from cross plot inversion as the best correlation value among the chosen inversion methods. Sparse Spike inversion its self-has high amplitude as a proper tool to identify coal seam continuity which commonly appears as a thin layer. Cross-sectional sparse spike inversion shows that there are possible new boreholes in CDP 3662-3722, CDP 3586-3622, and CDP 4004-4148 which is seen in seismic data as a thick coal seam.

Solving large tomographic linear systems: size reduction and error estimation

NASA Astrophysics Data System (ADS)

Voronin, Sergey; Mikesell, Dylan; Slezak, Inna; Nolet, Guust

2014-10-01

We present a new approach to reduce a sparse, linear system of equations associated with tomographic inverse problems. We begin by making a modification to the commonly used compressed sparse-row format, whereby our format is tailored to the sparse structure of finite-frequency (volume) sensitivity kernels in seismic tomography. Next, we cluster the sparse matrix rows to divide a large matrix into smaller subsets representing ray paths that are geographically close. Singular value decomposition of each subset allows us to project the data onto a subspace associated with the largest eigenvalues of the subset. After projection we reject those data that have a signal-to-noise ratio (SNR) below a chosen threshold. Clustering in this way assures that the sparse nature of the system is minimally affected by the projection. Moreover, our approach allows for a precise estimation of the noise affecting the data while also giving us the ability to identify outliers. We illustrate the method by reducing large matrices computed for global tomographic systems with cross-correlation body wave delays, as well as with surface wave phase velocity anomalies. For a massive matrix computed for 3.7 million Rayleigh wave phase velocity measurements, imposing a threshold of 1 for the SNR, we condensed the matrix size from 1103 to 63 Gbyte. For a global data set of multiple-frequency P wave delays from 60 well-distributed deep earthquakes we obtain a reduction to 5.9 per cent. This type of reduction allows one to avoid loss of information due to underparametrizing models. Alternatively, if data have to be rejected to fit the system into computer memory, it assures that the most important data are preserved.

Comparative Analyses of DNA Methylation and Sequence Evolution Using Nasonia Genomes

PubMed Central

Park, Jungsun; Peng, Zuogang; Zeng, Jia; Elango, Navin; Park, Taesung; Wheeler, Dave; Werren, John H.; Yi, Soojin V.

2011-01-01

The functional and evolutionary significance of DNA methylation in insect genomes remains to be resolved. Nasonia is well situated for comparative analyses of DNA methylation and genome evolution, since the genomes of a moderately distant outgroup species as well as closely related sibling species are available. Using direct sequencing of bisulfite-converted DNA, we uncovered a substantial level of DNA methylation in 17 of 18 Nasonia vitripennis genes and a strong correlation between methylation level and CpG depletion. Notably, in the sex-determining locus transformer, the exon that is alternatively spliced between the sexes is heavily methylated in both males and females, whereas other exons are only sparsely methylated. Orthologous genes of the honeybee and Nasonia show highly similar relative levels of CpG depletion, despite ∼190 My divergence. Densely and sparsely methylated genes in these species also exhibit similar functional enrichments. We found that the degree of CpG depletion is negatively correlated with substitution rates between closely related Nasonia species for synonymous, nonsynonymous, and intron sites. This suggests that mutation rates increase with decreasing levels of germ line methylation. Thus, DNA methylation is prevalent in the Nasonia genome, may participate in regulatory processes such as sex determination and alternative splicing, and is correlated with several aspects of genome and sequence evolution. PMID:21693438

Are Neoclassical Canons Valid for Southern Chinese Faces?

PubMed Central

Jayaratne, Yasas S. N.; Deutsch, Curtis K.; McGrath, Colman P. J.; Zwahlen, Roger A.

2012-01-01

Background Proportions derived from neoclassical canons, initially described by Renaissance sculptors and painters, are still being employed as aesthetic guidelines during the clinical assessment of the facial morphology. Objective 1. to determine the applicability of neoclassical canons for Southern Chinese faces and 2. to explore gender differences in relation to the applicability of the neoclassical canons and their variants. Methodology 3-D photographs acquired from 103 young adults (51 males and 52 females) without facial dysmorphology were used to test applicability of four neoclassical canons. Standard anthropometric measurements that determine the facial canons were made on these 3-D images. The validity of the canons as well as their different variants were quantified. Principal Findings The neoclassical cannons seldom applied to these individuals, and facial three-section and orbital canons did not apply at all. The orbitonasal canon was most frequently applicable, with a frequency of 19%. Significant sexual dimorphism was found relative to the prevalence of the variants of facial three-section and orbitonasal canons. Conclusion The neoclassical canons did not appear to apply to our sample when rigorous quantitative measurements were employed. Thus, they should not be used as esthetic goals for craniofacial surgical interventions. PMID:23285105

Spatial correlation of auroral zone geomagnetic variations

NASA Astrophysics Data System (ADS)

Jackel, B. J.; Davalos, A.

2016-12-01

Magnetic field perturbations in the auroral zone are produced by a combination of distant ionospheric and local ground induced currents. Spatial and temporal structure of these currents is scientifically interesting and can also have a significant influence on critical infrastructure.Ground-based magnetometer networks are an essential tool for studying these phenomena, with the existing complement of instruments in Canada providing extended local time coverage. In this study we examine the spatial correlation between magnetic field observations over a range of scale lengths. Principal component and canonical correlation analysis are used to quantify relationships between multiple sites. Results could be used to optimize network configurations, validate computational models, and improve methods for empirical interpolation.

Correlates of depression in bipolar disorder

PubMed Central

Moore, Paul J.; Little, Max A.; McSharry, Patrick E.; Goodwin, Guy M.; Geddes, John R.

2014-01-01

We analyse time series from 100 patients with bipolar disorder for correlates of depression symptoms. As the sampling interval is non-uniform, we quantify the extent of missing and irregular data using new measures of compliance and continuity. We find that uniformity of response is negatively correlated with the standard deviation of sleep ratings (ρ = –0.26, p = 0.01). To investigate the correlation structure of the time series themselves, we apply the Edelson–Krolik method for correlation estimation. We examine the correlation between depression symptoms for a subset of patients and find that self-reported measures of sleep and appetite/weight show a lower average correlation than other symptoms. Using surrogate time series as a reference dataset, we find no evidence that depression is correlated between patients, though we note a possible loss of information from sparse sampling. PMID:24352942

General Relativity without paradigm of space-time covariance, and resolution of the problem of time

NASA Astrophysics Data System (ADS)

Soo, Chopin; Yu, Hoi-Lai

2014-01-01

The framework of a theory of gravity from the quantum to the classical regime is presented. The paradigm shift from full space-time covariance to spatial diffeomorphism invariance, together with clean decomposition of the canonical structure, yield transparent physical dynamics and a resolution of the problem of time. The deep divide between quantum mechanics and conventional canonical formulations of quantum gravity is overcome with a Schrödinger equation for quantum geometrodynamics that describes evolution in intrinsic time. Unitary time development with gauge-invariant temporal ordering is also viable. All Kuchar observables become physical; and classical space-time, with direct correlation between its proper times and intrinsic time intervals, emerges from constructive interference. The framework not only yields a physical Hamiltonian for Einstein's theory, but also prompts natural extensions and improvements towards a well behaved quantum theory of gravity. It is a consistent canonical scheme to discuss Horava-Lifshitz theories with intrinsic time evolution, and of the many possible alternatives that respect 3-covariance (rather than the more restrictive 4-covariance of Einstein's theory), Horava's "detailed balance" form of the Hamiltonian constraint is essentially pinned down by this framework. Issues in quantum gravity that depend on radiative corrections and the rigorous definition and regularization of the Hamiltonian operator are not addressed in this work.

Spatiotemporal Co-variability of Surface Climate for Renewable Energy across the Contiguous United States: Role of the North Atlantic Subtropical High

NASA Astrophysics Data System (ADS)

Doering, K.; Steinschneider, S.

2017-12-01

The variability of renewable energy supply and drivers of demand across space and time largely determines the energy balance within power systems with a high penetration of renewable technologies. This study examines the joint spatiotemporal variability of summertime climate linked to renewable energy production (precipitation, wind speeds, insolation) and energy demand (temperature) across the contiguous United States (CONUS) between 1948 and 2015. Canonical correlation analysis is used to identify the major modes of joint variability between summer wind speeds and precipitation and related patterns of insolation and temperature. Canonical variates are then related to circulation anomalies to identify common drivers of the joint modes of climate variability. Results show that the first two modes of joint variability between summer wind speeds and precipitation exhibit pan-US dipole patterns with centers of action located in the eastern and central CONUS. Temperature and insolation also exhibit related US-wide dipoles. The relationship between canonical variates and lower-tropospheric geopotential height indicates that these modes are related to variability in the North Atlantic subtropical high (NASH). This insight can inform optimal strategies for siting renewables in an interconnected electric grid, and has implications for the impacts of climate variability and change on renewable energy systems.

Probing eudesmane cation-π interactions in catalysis by aristolochene synthase with non-canonical amino acids.

PubMed

Faraldos, Juan A; Antonczak, Alicja K; González, Verónica; Fullerton, Rebecca; Tippmann, Eric M; Allemann, Rudolf K

2011-09-07

Stabilization of the reaction intermediate eudesmane cation (3) through interaction with Trp 334 during catalysis by aristolochene synthase from Penicillium roqueforti was investigated by site-directed incorporation of proteinogenic and non-canonical aromatic amino acids. The amount of germacrene A (2) generated by the mutant enzymes served as a measure of the stabilization of 3. 2 is a neutral intermediate, from which 3 is formed during PR-AS catalysis by protonation of the C6,C7 double bond. The replacement of Trp 334 with para-substituted phenylalanines of increasing electron-withdrawing properties led to a progressive accumulation of 2 that showed a good correlation with the interaction energies of simple cations such as Na(+) with substituted benzenes. These results provide compelling evidence for the stabilizing role played by Trp 334 in aristolochene synthase catalysis for the energetically demanding transformation of 2 to 3.

Using canonical correlation analysis to identify environmental attitude groups: Considerations for national forest planning in the southwestern U.S

Treesearch

Alejandro J. Prera; Kristine M. Grimsrud; Jennifer A. Thacher; Dan W. McCollum; Robert P. Berrens

2014-01-01

As public land management agencies pursue region-specific resource management plans, with meaningful consideration of public attitudes and values, there is a need to characterize the complex mix of environmental attitudes in a diverse population. The contribution of this investigation is to make use of a unique household, mail/ internet survey data set collected in...

Electronic Structure at Oxide Interfaces

DTIC Science & Technology

2014-06-01

of materials with desired correlated electron properties such as ferromagnetism with a high Curie temperature, high transition temperature...approximation and therefore the canonical Mott picture is unable to account for the insulating behavior of these materials . We resolve this apparent...the two materials . LaTiO3 shows insulating behavior with a small excitation gap set by Ti d-d transitions and a wide energy separation between Ti d

Relationships between Work Values, Socio-Educational and Work Experiences, and Vocational Aspirations of 6th, 9th, 10th, and 12th Graders

ERIC Educational Resources Information Center

Wijting, Jan P.; And Others

1977-01-01

Using a cross-sectional design and canonical regression analysis, the differential relationships between work values and social, educational, and occupational experiences and aspirations were examined for boys and girls in grades 6, 9, 10, and 12. Support was found for predicted differences in work-values correlates across grades and between…

Three-dimensional facial analyses of Indian and Malaysian women.

PubMed

Kusugal, Preethi; Ruttonji, Zarir; Gowda, Roopa; Rajpurohit, Ladusingh; Lad, Pritam; Ritu

2015-01-01

Facial measurements serve as a valuable tool in the treatment planning of maxillofacial rehabilitation, orthodontic treatment, and orthognathic surgeries. The esthetic guidelines of face are still based on neoclassical canons, which were used in the ancient art. These canons are considered to be highly subjective, and there is ample evidence in the literature, which raises such questions as whether or not these canons can be applied for the modern population. This study was carried out to analyze the facial features of Indian and Malaysian women by using three-dimensional (3D) scanner and thus determine the prevalence of neoclassical facial esthetic canons in both the groups. The study was carried out on 60 women in the age range of 18-25 years, out of whom 30 were Indian and 30 Malaysian. As many as 16 facial measurements were taken by using a noncontact 3D scanner. Unpaired t-test was used for comparison of facial measurements between Indian and Malaysian females. Two-tailed Fisher exact test was used to determine the prevalence of neoclassical canons. Orbital Canon was prevalent in 80% of Malaysian women; the same was found only in 16% of Indian women (P = 0.00013). About 43% of Malaysian women exhibited orbitonasal canon (P = 0.0470) whereas nasoaural canon was prevalent in 73% of Malaysian and 33% of Indian women (P = 0.0068). Orbital, orbitonasal, and nasoaural canon were more prevalent in Malaysian women. Facial profile canon, nasooral, and nasofacial canons were not seen in either group. Though some canons provide guidelines in esthetic analyses of face, complete reliance on these canons is not justifiable.

Entropy of the Bose-Einstein-condensate ground state: Correlation versus ground-state entropy

NASA Astrophysics Data System (ADS)

Kim, Moochan B.; Svidzinsky, Anatoly; Agarwal, Girish S.; Scully, Marlan O.

2018-01-01

Calculation of the entropy of an ideal Bose-Einstein condensate (BEC) in a three-dimensional trap reveals unusual, previously unrecognized, features of the canonical ensemble. It is found that, for any temperature, the entropy of the Bose gas is equal to the entropy of the excited particles although the entropy of the particles in the ground state is nonzero. We explain this by considering the correlations between the ground-state particles and particles in the excited states. These correlations lead to a correlation entropy which is exactly equal to the contribution from the ground state. The correlations themselves arise from the fact that we have a fixed number of particles obeying quantum statistics. We present results for correlation functions between the ground and excited states in a Bose gas, so as to clarify the role of fluctuations in the system. We also report the sub-Poissonian nature of the ground-state fluctuations.

Defining the Relationship of Student Achievement Between STEM Subjects Through Canonical Correlation Analysis of 2011 Trends in International Mathematics and Science Study (TIMSS) Data

NASA Astrophysics Data System (ADS)

O'Neal, Melissa Jean

Canonical correlation analysis was used to analyze data from Trends in International Mathematics and Science Study (TIMSS) 2011 achievement databases encompassing information from fourth/eighth grades. Student achievement in life science/biology was correlated with achievement in mathematics and other sciences across three analytical areas: mathematics and science student performance, achievement in cognitive domains, and achievement in content domains. Strong correlations between student achievement in life science/biology with achievement in mathematics and overall science occurred for both high- and low-performing education systems. Hence, partial emphases on the inter-subject connections did not always lead to a better student learning outcome in STEM education. In addition, student achievement in life science/biology was positively correlated with achievement in mathematics and science cognitive domains; these patterns held true for correlations of life science/biology with mathematics as well as other sciences. The importance of linking student learning experiences between and within STEM domains to support high performance on TIMSS assessments was indicated by correlations of moderate strength (57 TIMSS assessments was indicated by correlations of moderate strength (57 < r < 85) stronger correlations (73 < r < 97) between life science/biology and other science domains. Results demonstrated the foundational nature of STEM knowledge at the fourth grade level, and established the importance of strong interconnections among life science/biology, mathematics, and other sciences. At the eighth grade level, students who built increasing levels of cognitive complexity upon firm foundations were prepared for successful learning throughout their educational careers. The results from this investigation promote a holistic design of school learning opportunities to improve student achievement in life science/biology and other science, technology, engineering, and mathematics (STEM) subjects at the elementary and middle school levels. While the curriculum can vary from combined STEM subjects to separated mathematics or science courses, both professional learning communities (PLC) for teachers and problem-based learning (PBL) for learners can be strengthened through new knowledge construction beyond the traditional boundaries of each subject. It is the knowledge transfer across subjects that breaks barriers of future STEM discoveries to improve STEM education outcomes.

Variational 3D-PIV with sparse descriptors

NASA Astrophysics Data System (ADS)

Lasinger, Katrin; Vogel, Christoph; Pock, Thomas; Schindler, Konrad

2018-06-01

3D particle imaging velocimetry (3D-PIV) aims to recover the flow field in a volume of fluid, which has been seeded with tracer particles and observed from multiple camera viewpoints. The first step of 3D-PIV is to reconstruct the 3D locations of the tracer particles from synchronous views of the volume. We propose a new method for iterative particle reconstruction, in which the locations and intensities of all particles are inferred in one joint energy minimization. The energy function is designed to penalize deviations between the reconstructed 3D particles and the image evidence, while at the same time aiming for a sparse set of particles. We find that the new method, without any post-processing, achieves significantly cleaner particle volumes than a conventional, tomographic MART reconstruction, and can handle a wide range of particle densities. The second step of 3D-PIV is to then recover the dense motion field from two consecutive particle reconstructions. We propose a variational model, which makes it possible to directly include physical properties, such as incompressibility and viscosity, in the estimation of the motion field. To further exploit the sparse nature of the input data, we propose a novel, compact descriptor of the local particle layout. Hence, we avoid the memory-intensive storage of high-resolution intensity volumes. Our framework is generic and allows for a variety of different data costs (correlation measures) and regularizers. We quantitatively evaluate it with both the sum of squared differences and the normalized cross-correlation, respectively with both a hard and a soft version of the incompressibility constraint.

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

PubMed

Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

PubMed

Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

2015-01-01

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection

PubMed Central

Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

2015-01-01

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks. PMID:26496370

Spectral fingerprints of large-scale neuronal interactions.

PubMed

Siegel, Markus; Donner, Tobias H; Engel, Andreas K

2012-01-11

Cognition results from interactions among functionally specialized but widely distributed brain regions; however, neuroscience has so far largely focused on characterizing the function of individual brain regions and neurons therein. Here we discuss recent studies that have instead investigated the interactions between brain regions during cognitive processes by assessing correlations between neuronal oscillations in different regions of the primate cerebral cortex. These studies have opened a new window onto the large-scale circuit mechanisms underlying sensorimotor decision-making and top-down attention. We propose that frequency-specific neuronal correlations in large-scale cortical networks may be 'fingerprints' of canonical neuronal computations underlying cognitive processes.

Rigorous proof for the nonlocal correlation function in the transverse Ising model with ring frustration.

PubMed

Dong, Jian-Jun; Zheng, Zhen-Yu; Li, Peng

2018-01-01

An unusual correlation function was conjectured by Campostrini et al. [Phys. Rev. E 91, 042123 (2015)PLEEE81539-375510.1103/PhysRevE.91.042123] for the ground state of a transverse Ising chain with geometrical frustration. Later, we provided a rigorous proof for it and demonstrated its nonlocal nature based on an evaluation of a Toeplitz determinant in the thermodynamic limit [J. Stat. Mech. (2016) 11310210.1088/1742-5468/2016/11/113102]. In this paper, we further prove that all the low excited energy states forming the gapless kink phase share the same asymptotic correlation function with the ground state. As a consequence, the thermal correlation function almost remains constant at low temperatures if one assumes a canonical ensemble.

Effects of biomotor structures on performance of competitive gymnastics elements in elementary school male sixth-graders.

PubMed

Delas, Suncica; Zagorac, Nebojsa; Katić, Ratko

2008-06-01

In order to identify the biomotor systems that determine performance of competitive gymnastics elements in elementary school male sixth-graders, factor structures of morphological characteristics and basic motor abilities were determined first, followed by relations of the morphological-motor system factors obtained with a set of criterion variables evaluating specific motor skills in competitive gymnastics in 110 male children aged 12 years +/- 3 months. Factor analysis of 17 morphological measures produced three morphological factors: factor of mesoectoendomorphy (general morphological factor) and factor of pronounced endomorphy, i.e. excessive adipose tissue, along with low skeleton longitudinality. Factor analysis of 16 motor variables yielded four motor factors: factor of general motoricity; factor integrating leg flexibility and arm explosiveness; factor juxtaposing body flexibility and repetitive leg strength; and factor predominantly defining leg movement frequency. Three significant canonical correlations, i.e. linear combinations, explained the association between the set of six latent variables of the morphological and basic motor system, and five variables assessing the knowledge in competitive gymnastics. The first canonical linear combination was based on the favorable and predominant impact of the general motor factor (a system integrating leg explosiveness, whole body coordination, relative arm and trunk strength, and arm movement frequency), along with unfavorable effect of morphological factors on the gymnastics elements performance, squat vault and handstand in particular The relation of the second pair of canonical factors pointed to the effects of leg flexibility and arm explosiveness on the cartwheel and backward pullover mount performance, whereas the relation of the third pair of canonical factors showed a favorable impact of the general morphological factor and leg movement frequency regulator on the forward shoulderkip from increase, cartwheel and handstand performance.

Effects of biomotor structures on performance of competitive gymnastics elements in elementary school female sixth-graders.

PubMed

Delas, Suncica; Babin, Josip; Katić, Ratko

2007-12-01

In order to identify biomotor systems that determine performance of competitive gymnastics elements in elementary school female sixth-graders, factor structures of morphological characteristics and basic motor abilities were determined first, followed by relations of the morphological-motor system factors obtained with a set of criterion variables evaluating specific motor skills in competitive gymnastics in 126 female children aged 12 years +/- 3 months. Factor analysis of 17 morphological measures yielded three morphological factors: factor of mesoendomorphy and/or adipose body voluminosity; factor of longitudinal body dimensionality; and factor of transverse arm dimensionality. Factor analysis of 16 motor variables produced four motor factors: general motoricity factor (motor system); general speed factor; factor of explosive strength of throwing type (arm explosiveness); and factor of arm and leg flexibility. Three significant canonical correlations, i.e. linear combinations, explained the association between the set of seven latent variables of the morphological and basic motor system, and five variables evaluating the knowledge in competitive gymnastics. The first canonical linear combination was based on a favorable and predominant impact of the general motor factor (a system integrating whole body coordination, leg explosiveness, relative arm strength, arm movement frequency and body flexibility) on performance of gymnastics elements, cartwheel, handstand and backward pullover mount in particular, and to a lesser extent front scale and double leg pirouette for 180 degrees. The relation of the second pair of canonical factors additionally explained the role of transverse dimensionality of arm skeleton, arm flexibility and explosiveness in performing cartwheel and squat vault, whereas the relation of the third pair of canonical factors explained the unfavorable impact of adipose voluminosity on the performance of squat vault and backward pullover mount.

Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis.

PubMed

Somers, Ben; Bertrand, Alexander

2016-12-01

Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

[Study on rules of TCM syndrome in patients with dyslipidemia and its objectization].

PubMed

Lei, Yan; Wang, Zhen-Hua; Liu, Jian-Gang

2007-12-01

To explore the rules of TCM syndrome in patients with dyslipidemia and its relation with C-reactive protein (CRP), homocysteine (Hcy), carotid ultrasonic picture, blood lipids and blood viscosity. From 152 recruited patients symptoms and physical signs (including figures of tongue and pulse) were selected and analyzed in grading and quantifying by factor analysis. At the same time, blood lipids, CRP, Hcy, carotid ultrasonic picture and blood viscosity were detected to conduct a canonical correlation analysis for exploring the relationship between different TCM syndromes and their corresponding physical and/or chemical indexes. Five types of TCM syndrome obtained by factor analysis were syndrome of Shen-yin deficiency (I), Pi-qi deficiency (II), turbid-phlegm impediment (III), blood stasis (IV), and phlegm-blood block (V). By canonical correlation analysis, they were characterized with: Type I, high levels of CRP and blood viscosity; Type II, high level of very low-density lipoprotein cholesterol (VLDL-C); Type III, high level of total cholesterol (TC) and low level of high-density lipoprotein cholesterol (HDL-C); and Type V, high level of Hcy. The five syndrome types frequently found in patients with dyslipidemia are syndrome of Shen-yin deficiency, Pi-qi deficiency, turbid-phlegm impediment, blood stasis, and phlegm-blood block. Different syndrome has its own correlation with some corresponding physical and/or chemical laboratory indexes, the issue provides new evidences for the objectification of TCM syndromes in patients with dyslipidemia.

Influence of Environmental Factors on Essential Oil Variability in Origanum compactum Benth. Growing Wild in Morocco.

PubMed

Aboukhalid, Kaoutar; Al Faiz, Chaouki; Douaik, Ahmed; Bakha, Mohamed; Kursa, Karolina; Agacka-Mołdoch, Monika; Machon, Nathalie; Tomi, Félix; Lamiri, Abdeslam

2017-09-01

The present study aimed to evaluate the influence of environmental factors on essential oils (EOs) composition of Origanum compactum populations sampled all over the distribution area of the species in Morocco, and to determine the extent of the chemical profiles throughout the geographical distribution of the species. The chemical compositions were submitted to canonical correlation analysis and canonical discriminant analysis that indicated a significant relationship between oil components and some environmental factors. According to their chemical composition and edapho-climatic characteristics, two major groups of populations were differentiated. The first group was composed of samples growing in regions with humid climate, clayey, sandy, and alkaline soils. These samples showed high thymol, α-terpineol, linalool, and carvacryl methyl oxide content. The second group consisted of plants belonging to semi-arid climate, and growing at high altitudes and silty soils. These samples were characterized by high carvacrol, α-thujene, α-terpinene, and myrcene content. However, populations exposed to sub-humid climate, appeared less homogeneous and belong mainly either to the first or second group. A significant correlation between some edaphic factors (pH, K 2 O content, soil texture) and the EOs yield of O. compactum plants was evidenced. In spite of the correlation obtained for the oil composition with edapho-climatic factors and the variance explained by the environmental data set, the observed EO diversity might be also genetically determined. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

Recovery of Areas Degraded by Mining Within the Amazon Forest: Interaction of the Physical Condition of Soil and Biological Activity

NASA Astrophysics Data System (ADS)

Ribeiro, A. I.; Mello, G. F.; Longo, R. M.; Fengler, F. H.; Peche Filho, A., Sr.

2017-12-01

One of the greatest natural riches of Brazil is the Amazon rainforest. The Amazon region is known for its abundance of mineral resources, and may include topaz, oil, and especially cassiterite. In this scope, the mining sector in Brazil has great strategic importance because it accounts for approximately 30% of the country's exports with a mineral production of 40 billion dollars (Brazilian Mining Institute, 2015). In this scenario, as a consequence of mining, the Amazonian ecosystem has been undergoing a constant process of degradation. An important artifice in the exploitation of mineral resources is the rehabilitation and/or recovery of degraded areas. This recovery requires the establishment of degradation indicators and also the quality of the soil associated with its biota, since the Amazonian environment is dynamic, heterogeneous and complex in its physical, chemical and biological characteristics. In this way, this work presupposes that it is possible to characterize the different stages of recovery of tillage floor areas in deactivated cassiterite mines, within the Amazonian forest, in order to evaluate the interactions between the level of biological activity (Serrapilheira Height, Coefficient Metabolic, Basal Breath) and physical soil characteristics (aggregate DMG, Porosity, Total Soil Density, Moisture Content), through canonical correlation analysis. The results present correlations between the groups of indicators. Thus, from the use of the groups defined by canonical correlations, it was possible to identify the response of the set of physical and biological variables to the areas at different stages of recovery.

Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis

NASA Astrophysics Data System (ADS)

Somers, Ben; Bertrand, Alexander

2016-12-01

Objective. Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. Approach. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. Main results. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Significance. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data

PubMed Central

Zhu, Yun; Fan, Ruzong; Xiong, Momiao

2017-01-01

Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the ten other statistics. PMID:29040274

Effect of intermodular connection on fast sparse synchronization in clustered small-world neural networks

NASA Astrophysics Data System (ADS)

Kim, Sang-Yoon; Lim, Woochang

2015-11-01

We consider a clustered network with small-world subnetworks of inhibitory fast spiking interneurons and investigate the effect of intermodular connection on the emergence of fast sparsely synchronized rhythms by varying both the intermodular coupling strength Jinter and the average number of intermodular links per interneuron Msyn(inter ). In contrast to the case of nonclustered networks, two kinds of sparsely synchronized states such as modular and global synchronization are found. For the case of modular sparse synchronization, the population behavior reveals the modular structure, because the intramodular dynamics of subnetworks make some mismatching. On the other hand, in the case of global sparse synchronization, the population behavior is globally identical, independently of the cluster structure, because the intramodular dynamics of subnetworks make perfect matching. We introduce a realistic cross-correlation modularity measure, representing the matching degree between the instantaneous subpopulation spike rates of the subnetworks, and examine whether the sparse synchronization is global or modular. Depending on its magnitude, the intermodular coupling strength Jinter seems to play "dual" roles for the pacing between spikes in each subnetwork. For large Jinter, due to strong inhibition it plays a destructive role to "spoil" the pacing between spikes, while for small Jinter it plays a constructive role to "favor" the pacing between spikes. Through competition between the constructive and the destructive roles of Jinter, there exists an intermediate optimal Jinter at which the pacing degree between spikes becomes maximal. In contrast, the average number of intermodular links per interneuron Msyn(inter ) seems to play a role just to favor the pacing between spikes. With increasing Msyn(inter ), the pacing degree between spikes increases monotonically thanks to the increase in the degree of effectiveness of global communication between spikes. Furthermore, we employ the realistic sub- and whole-population order parameters, based on the instantaneous sub- and whole-population spike rates, to determine the threshold values for the synchronization-unsynchronization transition in the sub- and whole populations, and the degrees of global and modular sparse synchronization are also measured in terms of the realistic sub- and whole-population statistical-mechanical spiking measures defined by considering both the occupation and the pacing degrees of spikes. It is expected that our results could have implications for the role of the brain plasticity in some functional behaviors associated with population synchronization.

A model of individualized canonical microcircuits supporting cognitive operations

PubMed Central

Peterson, Andre D. H.; Haueisen, Jens; Knösche, Thomas R.

2017-01-01

Major cognitive functions such as language, memory, and decision-making are thought to rely on distributed networks of a large number of basic elements, called canonical microcircuits. In this theoretical study we propose a novel canonical microcircuit model and find that it supports two basic computational operations: a gating mechanism and working memory. By means of bifurcation analysis we systematically investigate the dynamical behavior of the canonical microcircuit with respect to parameters that govern the local network balance, that is, the relationship between excitation and inhibition, and key intrinsic feedback architectures of canonical microcircuits. We relate the local behavior of the canonical microcircuit to cognitive processing and demonstrate how a network of interacting canonical microcircuits enables the establishment of spatiotemporal sequences in the context of syntax parsing during sentence comprehension. This study provides a framework for using individualized canonical microcircuits for the construction of biologically realistic networks supporting cognitive operations. PMID:29200435

Canonical Word Order of Japanese Ditransitive Sentences: A Preliminary Investigation through a Grammaticality Judgment Survey

ERIC Educational Resources Information Center

Shigenaga, Yasumasa

2014-01-01

There have been three competing analyses regarding the canonical word order of Japanese ditransitive sentences: a) "S-'ga' IO-'ni' DO-'o' V" is the canonical word order rather than "S-'ga' DO-'o' IO-'ni' V", b) both word orders are canonical, and c) the canonical word order depends on the type of the verb. The present study…

The effects of forest fragmentation on forest stand attributes

Treesearch

Ronald E. McRoberts; Greg C. Liknes

2002-01-01

For two study areas in Minnesota, USA, one heavily forested and one sparsely forested, maps of predicted proportion forest area were created using Landsat Thematic Mapper imagery, forest inventory plot data, and a logistic regression model. The maps were used to estimate quantitative indices of forest fragmentation. Correlations between the values of the indices and...

Consistency and similarity of MEG- and fMRI-signal time courses during movie viewing.

PubMed

Lankinen, Kaisu; Saari, Jukka; Hlushchuk, Yevhen; Tikka, Pia; Parkkonen, Lauri; Hari, Riitta; Koskinen, Miika

2018-06-01

Movie viewing allows human perception and cognition to be studied in complex, real-life-like situations in a brain-imaging laboratory. Previous studies with functional magnetic resonance imaging (fMRI) and with magneto- and electroencephalography (MEG and EEG) have demonstrated consistent temporal dynamics of brain activity across movie viewers. However, little is known about the similarities and differences of fMRI and MEG or EEG dynamics during such naturalistic situations. We thus compared MEG and fMRI responses to the same 15-min black-and-white movie in the same eight subjects who watched the movie twice during both MEG and fMRI recordings. We analyzed intra- and intersubject voxel-wise correlations within each imaging modality as well as the correlation of the MEG envelopes and fMRI signals. The fMRI signals showed voxel-wise within- and between-subjects correlations up to r = 0.66 and r = 0.37, respectively, whereas these correlations were clearly weaker for the envelopes of band-pass filtered (7 frequency bands below 100 Hz) MEG signals (within-subjects correlation r < 0.14 and between-subjects r < 0.05). Direct MEG-fMRI voxel-wise correlations were unreliable. Notably, applying a spatial-filtering approach to the MEG data uncovered consistent canonical variates that showed considerably stronger (up to r = 0.25) between-subjects correlations than the univariate voxel-wise analysis. Furthermore, the envelopes of the time courses of these variates up to about 10 Hz showed association with fMRI signals in a general linear model. Similarities between envelopes of MEG canonical variates and fMRI voxel time-courses were seen mostly in occipital, but also in temporal and frontal brain regions, whereas intra- and intersubject correlations for MEG and fMRI separately were strongest only in the occipital areas. In contrast to the conventional univariate analysis, the spatial-filtering approach was able to uncover associations between the MEG envelopes and fMRI time courses, shedding light on the similarities of hemodynamic and electromagnetic brain activities during movie viewing. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Non-canonical autophagy: an exception or an underestimated form of autophagy?

PubMed

Scarlatti, Francesca; Maffei, Roberta; Beau, Isabelle; Ghidoni, Riccardo; Codogno, Patrice

2008-11-01

Macroautophagy (hereafter called autophagy) is a dynamic and evolutionarily conserved process used to sequester and degrade cytoplasm and entire organelles in a sequestering vesicle with a double membrane, known as the autophagosome, which ultimately fuses with a lysosome to degrade its autophagic cargo. Recently, we have unraveled two distinct forms of autophagy in cancer cells, which we term canonical and non-canonical autophagy. In contrast to classical or canonical autophagy, non-canonical autophagy is a process that does not require the entire set of autophagy-related (Atg) proteins in particular Beclin 1, to form the autophagosome. Non-canonical autophagy is therefore not blocked by the knockdown of Beclin 1 or of its binding partner hVps34. Moreover overexpression of Bcl-2, which is known to block canonical starvation-induced autophagy by binding to Beclin 1, is unable to reverse the non-canonical autophagy triggered by the polyphenol resveratrol in the breast cancer MCF-7 cell line. In MCF-7 cells, at least, non-canonical autophagy is involved in the caspase-independent cell death induced by resveratrol.

Target Detection and Identification Using Canonical Correlations Analysis and Subspace Partitioning

DTIC Science & Technology

2008-04-01

Fig. 2. ROCs for DCC, DCC-P, NNLS, and NNLSP (Present chemical=t1, background= t56 , SNR= 5 dB) alarm, or 1−specificity, and PD is the probability of...discrimination values are given in each ROC plot. In Fig. 2, we use t56 as the background, and t1 as the target chemical. The SNR is 5 dB. For each

A Canonical Correlational Analysis of the Relationship between Observed Leader Behavior and Organizational Rewards.

DTIC Science & Technology

1982-02-01

behavior has on subordinate performance and satisfaction (Bass, 1981; Fleishman, 1973; Yukl, 1981). However, in the past several years, the traditional...organizational rewards has on employee performance and satisfaction has received considerable attention over the years and the role of leaders in administering...see: Bussom, Larson & Vicars, 1982). The singular theoretical perspective of viewing leadership as a causal determinant of subordinate performance

Mother’s IPV, Child Maltreatment Type and the Presence of PTSD in Children and Adolescents

PubMed Central

Castro, Maravillas; Alcántara-López, Mavi; Martínez, Antonia; Fernández, Visitación; Sánchez-Meca, Julio; López-Soler, Concepción

2017-01-01

This correlational cross-sectional study was designed to investigate whether the intimate partner violence (IPV) suffered by mothers (physical and psychological maltreatment), child eyewitness of psychological and physical maltreatment suffered by the mother, the neglect suffered by children, and the maltreatment (physical and psychological) directly suffered by children are statistically associated to post-traumatic stress disorder (PTSD) symptoms exhibited by the child. In addition, the prevalence of child PTSD was estimated, as well as the concordance between the PTSD symptoms assessed by the Child PTSD Symptom Scale (CPSS) and the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria for child PTSD. The sample consisted of 152 Spanish children aged 8 to 17 and their mothers, who were recruited from Centers of Specialized Assistance for Women Victims of IPV. PTSD prevalence was 20.4%. The results of a canonical correlation analysis showed that the two types of maltreatment with the largest contribution to the canonical variable were physical maltreatment directly suffered by the child, and child eyewitness of physical maltreatment suffered by the mother. The potential developmental pathway of PTSD when both children and mothers suffer severe maltreatment needs to be examined, and this will contribute to the choice of the most effective type of specialized intervention. PMID:28926979

Relating adaptive genetic traits to climate for Sandberg bluegrass from the intermountain western United States.

PubMed

Johnson, Richard C; Horning, Matthew E; Espeland, Erin K; Vance-Borland, Ken

2015-02-01

Genetic variation for potentially adaptive traits of the key restoration species Sandberg bluegrass (Poa secunda J. Presl) was assessed over the intermountain western United States in relation to source population climate. Common gardens were established at two intermountain west sites with progeny from two maternal parents from each of 130 wild populations. Data were collected over 2 years at each site on fifteen plant traits associated with production, phenology, and morphology. Analyses of variance revealed strong population differences for all plant traits (P < 0.0001), indicating genetic variation. Both the canonical correlation and linear correlation established associations between source populations and climate variability. Populations from warmer, more arid climates had generally lower dry weight, earlier phenology, and smaller, narrower leaves than those from cooler, moister climates. The first three canonical variates were regressed with climate variables resulting in significant models (P < 0.0001) used to map 12 seed zones. Of the 700 981 km(2) mapped, four seed zones represented 92% of the area in typically semi-arid and arid regions. The association of genetic variation with source climates in the intermountain west suggested climate driven natural selection and evolution. We recommend seed transfer zones and population movement guidelines to enhance adaptation and diversity for large-scale restoration projects.

Mother's IPV, Child Maltreatment Type and the Presence of PTSD in Children and Adolescents.

PubMed

Castro, Maravillas; Alcántara-López, Mavi; Martínez, Antonia; Fernández, Visitación; Sánchez-Meca, Julio; López-Soler, Concepción

2017-09-17

This correlational cross-sectional study was designed to investigate whether the intimate partner violence (IPV) suffered by mothers (physical and psychological maltreatment), child eyewitness of psychological and physical maltreatment suffered by the mother, the neglect suffered by children, and the maltreatment (physical and psychological) directly suffered by children are statistically associated to post-traumatic stress disorder (PTSD) symptoms exhibited by the child. In addition, the prevalence of child PTSD was estimated, as well as the concordance between the PTSD symptoms assessed by the Child PTSD Symptom Scale (CPSS) and the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria for child PTSD. The sample consisted of 152 Spanish children aged 8 to 17 and their mothers, who were recruited from Centers of Specialized Assistance for Women Victims of IPV. PTSD prevalence was 20.4%. The results of a canonical correlation analysis showed that the two types of maltreatment with the largest contribution to the canonical variable were physical maltreatment directly suffered by the child, and child eyewitness of physical maltreatment suffered by the mother. The potential developmental pathway of PTSD when both children and mothers suffer severe maltreatment needs to be examined, and this will contribute to the choice of the most effective type of specialized intervention.

High-temperature charge density wave correlations in La1.875Ba0.125CuO4 without spin–charge locking

PubMed Central

Lorenzana, J.; Seibold, G.; Peng, Y. Y.; Amorese, A.; Yakhou-Harris, F.; Kummer, K.; Brookes, N. B.; Konik, R. M.; Thampy, V.; Gu, G. D.; Ghiringhelli, G.; Braicovich, L.

2017-01-01

Although all superconducting cuprates display charge-ordering tendencies, their low-temperature properties are distinct, impeding efforts to understand the phenomena within a single conceptual framework. While some systems exhibit stripes of charge and spin, with a locked periodicity, others host charge density waves (CDWs) without any obviously related spin order. Here we use resonant inelastic X-ray scattering to follow the evolution of charge correlations in the canonical stripe-ordered cuprate La1.875Ba0.125CuO4 across its ordering transition. We find that high-temperature charge correlations are unlocked from the wavevector of the spin correlations, signaling analogies to CDW phases in various other cuprates. This indicates that stripe order at low temperatures is stabilized by the coupling of otherwise independent charge and spin density waves, with important implications for the relation between charge and spin correlations in the cuprates. PMID:29114049

Late-Notice HIE Investigation

NASA Technical Reports Server (NTRS)

Hejduk, M. D.

2016-01-01

Provide a response to MOWG action item 1410-01: Analyze close approaches which have required mission team action on short notice. Determine why the approaches were identified later in the process than most other events. Method: Performed an analysis to determine whether there is any correlation between late notice event identification and space weather, sparse tracking, or high drag objects, which would allow preventive action to be taken Examined specific late notice events identified by missions as problematic to try to identify root cause and attempt to relate them to the correlation analysis.

Large Covariance Estimation by Thresholding Principal Orthogonal Complements

PubMed Central

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented. PMID:24348088

Large Covariance Estimation by Thresholding Principal Orthogonal Complements.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2013-09-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.

Compressive hyperspectral and multispectral imaging fusion

NASA Astrophysics Data System (ADS)

Espitia, Óscar; Castillo, Sergio; Arguello, Henry

2016-05-01

Image fusion is a valuable framework which combines two or more images of the same scene from one or multiple sensors, allowing to improve the resolution of the images and increase the interpretable content. In remote sensing a common fusion problem consists of merging hyperspectral (HS) and multispectral (MS) images that involve large amount of redundant data, which ignores the highly correlated structure of the datacube along the spatial and spectral dimensions. Compressive HS and MS systems compress the spectral data in the acquisition step allowing to reduce the data redundancy by using different sampling patterns. This work presents a compressed HS and MS image fusion approach, which uses a high dimensional joint sparse model. The joint sparse model is formulated by combining HS and MS compressive acquisition models. The high spectral and spatial resolution image is reconstructed by using sparse optimization algorithms. Different fusion spectral image scenarios are used to explore the performance of the proposed scheme. Several simulations with synthetic and real datacubes show promising results as the reliable reconstruction of a high spectral and spatial resolution image can be achieved by using as few as just the 50% of the datacube.

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

PubMed

Lê Cao, Kim-Anh; Boitard, Simon; Besse, Philippe

2011-06-22

Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits. A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework. sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets.

[Population density, age distribution and urbanisation as factors influencing the frequency of home visits--an analysis for Mecklenburg-West Pomerania].

PubMed

Heymann, R; Weitmann, K; Weiss, S; Thierfelder, D; Flessa, S; Hoffmann, W

2009-07-01

This study examines and compares the frequency of home visits by general practitioners in regions with a lower population density and regions with a higher population density. The discussion centres on the hypothesis whether the number of home visits in rural and remote areas with a low population density is, in fact, higher than in urbanised areas with a higher population density. The average age of the population has been considered in both cases. The communities of Mecklenburg West-Pomerania were aggregated into postal code regions. The analysis is based on these postal code regions. The average frequency of home visits per 100 inhabitants/km2 has been calculated via a bivariate, linear regression model with the population density and the average age for the postal code region as independent variables. The results are based on billing data of the year 2006 as provided by the Association of Statutory Health Insurance Physicians of Mecklenburg-Western Pomerania. In a second step a variable which clustered the postal codes of urbanised areas was added to a multivariate model. The hypothesis of a negative correlation between the frequency of home visits and the population density of the areas examined cannot be confirmed for Mecklenburg-Western Pomerania. Following the dichotomisation of the postal code regions into sparsely and densely populated areas, only the very sparsely populated postal code regions (less than 100 inhabitants/km2) show a tendency towards a higher frequency of home visits. Overall, the frequency of home visits in sparsely populated postal code regions is 28.9% higher than in the densely populated postal code regions (more than 100 inhabitants/km2), although the number of general practitioners is approximately the same in both groups. In part this association seems to be confirmed by a positive correlation between the average age in the individual postal code regions and the number of home visits carried out in the area. As calculated on the basis of the data at hand, only the very sparsely populated areas with a still gradually decreasing population show a tendency towards a higher frequency of home visits. According to the data of 2006, the number of home visits remains high in sparsely populated areas. It may increase in the near future as the number of general practitioners in these areas will gradually decrease while the number of immobile and older inhabitants will increase.

A canonical state-space representation for SISO systems using multipoint Jordan CFE. [Continued-Fraction Expansion

NASA Technical Reports Server (NTRS)

Hwang, Chyi; Guo, Tong-Yi; Shieh, Leang-San

1991-01-01

A canonical state-space realization based on the multipoint Jordan continued-fraction expansion (CFE) is presented for single-input-single-output (SISO) systems. The similarity transformation matrix which relates the new canonical form to the phase-variable canonical form is also derived. The presented canonical state-space representation is particularly attractive for the application of SISO system theory in which a reduced-dimensional time-domain model is necessary.

Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method.

PubMed

Wu, Lin; Wang, Yang; Pan, Shirui

2017-12-01

It is now well established that sparse representation models are working effectively for many visual recognition tasks, and have pushed forward the success of dictionary learning therein. Recent studies over dictionary learning focus on learning discriminative atoms instead of purely reconstructive ones. However, the existence of intraclass diversities (i.e., data objects within the same category but exhibit large visual dissimilarities), and interclass similarities (i.e., data objects from distinct classes but share much visual similarities), makes it challenging to learn effective recognition models. To this end, a large number of labeled data objects are required to learn models which can effectively characterize these subtle differences. However, labeled data objects are always limited to access, committing it difficult to learn a monolithic dictionary that can be discriminative enough. To address the above limitations, in this paper, we propose a weakly-supervised dictionary learning method to automatically learn a discriminative dictionary by fully exploiting visual attribute correlations rather than label priors. In particular, the intrinsic attribute correlations are deployed as a critical cue to guide the process of object categorization, and then a set of subdictionaries are jointly learned with respect to each category. The resulting dictionary is highly discriminative and leads to intraclass diversity aware sparse representations. Extensive experiments on image classification and object recognition are conducted to show the effectiveness of our approach.

Microbial taxonomic and metabolic alterations during faecal microbiota transplantation to treat Clostridium difficile infection.

PubMed

Kellingray, Lee; Gall, Gwénaëlle Le; Defernez, Marianne; Beales, Ian L P; Franslem-Elumogo, Ngozi; Narbad, Arjan

2018-05-07

This study aimed to examine changes to the microbiota composition and metabolic profiles of seven patients with recurrent Clostridium difficile infection (rCDI), following treatment with faecal microbiota transplant (FMT). 16S rDNA sequencing and 1 H NMR were performed on faecal samples from the patients (pre-, post-FMT, and follow-up) and the associated donor samples. Sparse partial-least-square analysis was used to identify correlations between the two datasets. The patients' microbiota post-FMT tended to shift towards the donor microbiota, specifically through proportional increases of Bacteroides, Blautia, and Ruminococcus, and proportional decreases of Enterococcus, Escherichia, and Klebsiella. However, although cured of infection, one patient, who suffers from chronic alcohol abuse, retained the compositional characteristics of the pre-FMT microbiota. Following FMT, increased levels of short-chain fatty acids, particularly butyrate and acetate, were observed in all patients. Sparse partial-least-square analysis confirmed a positive correlation between butyrate and Bacteroides, Blautia, and Ruminococcus, with a negative correlation between butyrate and Klebsiella and Enterococcus. Clear differences were observed in the microbiota composition and metabolic profiles between donors and rCDI patients, which were largely resolved in patients following FMT. Increased levels of butyrate appear to be a factor associated with resolution of rCDI. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Canonical partition functions: ideal quantum gases, interacting classical gases, and interacting quantum gases

NASA Astrophysics Data System (ADS)

Zhou, Chi-Chun; Dai, Wu-Sheng

2018-02-01

In statistical mechanics, for a system with a fixed number of particles, e.g. a finite-size system, strictly speaking, the thermodynamic quantity needs to be calculated in the canonical ensemble. Nevertheless, the calculation of the canonical partition function is difficult. In this paper, based on the mathematical theory of the symmetric function, we suggest a method for the calculation of the canonical partition function of ideal quantum gases, including ideal Bose, Fermi, and Gentile gases. Moreover, we express the canonical partition functions of interacting classical and quantum gases given by the classical and quantum cluster expansion methods in terms of the Bell polynomial in mathematics. The virial coefficients of ideal Bose, Fermi, and Gentile gases are calculated from the exact canonical partition function. The virial coefficients of interacting classical and quantum gases are calculated from the canonical partition function by using the expansion of the Bell polynomial, rather than calculated from the grand canonical potential.

Origins of correlated spiking in the mammalian olfactory bulb

PubMed Central

Gerkin, Richard C.; Tripathy, Shreejoy J.; Urban, Nathaniel N.

2013-01-01

Mitral/tufted (M/T) cells of the main olfactory bulb transmit odorant information to higher brain structures. The relative timing of action potentials across M/T cells has been proposed to encode this information and to be critical for the activation of downstream neurons. Using ensemble recordings from the mouse olfactory bulb in vivo, we measured how correlations between cells are shaped by stimulus (odor) identity, common respiratory drive, and other cells’ activity. The shared respiration cycle is the largest source of correlated firing, but even after accounting for all observable factors a residual positive noise correlation was observed. Noise correlation was maximal on a ∼100-ms timescale and was seen only in cells separated by <200 µm. This correlation is explained primarily by common activity in groups of nearby cells. Thus, M/T-cell correlation principally reflects respiratory modulation and sparse, local network connectivity, with odor identity accounting for a minor component. PMID:24082089

Canonic FFT flow graphs for real-valued even/odd symmetric inputs

NASA Astrophysics Data System (ADS)

Lao, Yingjie; Parhi, Keshab K.

2017-12-01

Canonic real-valued fast Fourier transform (RFFT) has been proposed to reduce the arithmetic complexity by eliminating redundancies. In a canonic N-point RFFT, the number of signal values at each stage is canonic with respect to the number of signal values, i.e., N. The major advantage of the canonic RFFTs is that these require the least number of butterfly operations and only real datapaths when mapped to architectures. In this paper, we consider the FFT computation whose inputs are not only real but also even/odd symmetric, which indeed lead to the well-known discrete cosine and sine transforms (DCTs and DSTs). Novel algorithms for generating the flow graphs of canonic RFFTs with even/odd symmetric inputs are proposed. It is shown that the proposed algorithms lead to canonic structures with N/2 +1 signal values at each stage for an N-point real even symmetric FFT (REFFT) or N/2 -1 signal values at each stage for an N-point RFFT real odd symmetric FFT (ROFFT). In order to remove butterfly operations, several twiddle factor transformations are proposed in this paper. We also discuss the design of canonic REFFT for any composite length. Performances of the canonic REFFT/ROFFT are also discussed. It is shown that the flow graph of canonic REFFT/ROFFT has less number of interconnections, less butterfly operations, and less twiddle factor operations, compared to prior works.

Agonistic and Antagonistic Roles for TNIK and MINK in Non-Canonical and Canonical Wnt Signalling

PubMed Central

Mikryukov, Alexander; Moss, Tom

2012-01-01

Wnt signalling is a key regulatory factor in animal development and homeostasis and plays an important role in the establishment and progression of cancer. Wnt signals are predominantly transduced via the Frizzled family of serpentine receptors to two distinct pathways, the canonical ß-catenin pathway and a non-canonical pathway controlling planar cell polarity and convergent extension. Interference between these pathways is an important determinant of cellular and phenotypic responses, but is poorly understood. Here we show that TNIK (Traf2 and Nck-interacting kinase) and MINK (Misshapen/NIKs-related kinase) MAP4K signalling kinases are integral components of both canonical and non-canonical pathways in Xenopus. xTNIK and xMINK interact and are proteolytically cleaved in vivo to generate Kinase domain fragments that are active in signal transduction, and Citron-NIK-Homology (CNH) Domain fragments that are suppressive. The catalytic activity of the Kinase domain fragments of both xTNIK and xMINK mediate non-canonical signalling. However, while the Kinase domain fragments of xTNIK also mediate canonical signalling, the analogous fragments derived from xMINK strongly antagonize this signalling. Our data suggest that the proteolytic cleavage of xTNIK and xMINK determines their respective activities and is an important factor in controlling the balance between canonical and non-canonical Wnt signalling in vivo. PMID:22984420

Proactive Interference Does Not Meaningfully Distort Visual Working Memory Capacity Estimates in the Canonical Change Detection Task

PubMed Central

Lin, Po-Han; Luck, Steven J.

2012-01-01

The change detection task has become a standard method for estimating the storage capacity of visual working memory. Most researchers assume that this task isolates the properties of an active short-term storage system that can be dissociated from long-term memory systems. However, long-term memory storage may influence performance on this task. In particular, memory traces from previous trials may create proactive interference that sometimes leads to errors, thereby reducing estimated capacity. Consequently, the capacity of visual working memory may be higher than is usually thought, and correlations between capacity and other measures of cognition may reflect individual differences in proactive interference rather than individual differences in the capacity of the short-term storage system. Indeed, previous research has shown that change detection performance can be influenced by proactive interference under some conditions. The purpose of the present study was to determine whether the canonical version of the change detection task – in which the to-be-remembered information consists of simple, briefly presented features – is influenced by proactive interference. Two experiments were conducted using methods that ordinarily produce substantial evidence of proactive interference, but no proactive interference was observed. Thus, the canonical version of the change detection task can be used to assess visual working memory capacity with no meaningful influence of proactive interference. PMID:22403556

Proactive interference does not meaningfully distort visual working memory capacity estimates in the canonical change detection task.

PubMed

Lin, Po-Han; Luck, Steven J

2012-01-01

The change detection task has become a standard method for estimating the storage capacity of visual working memory. Most researchers assume that this task isolates the properties of an active short-term storage system that can be dissociated from long-term memory systems. However, long-term memory storage may influence performance on this task. In particular, memory traces from previous trials may create proactive interference that sometimes leads to errors, thereby reducing estimated capacity. Consequently, the capacity of visual working memory may be higher than is usually thought, and correlations between capacity and other measures of cognition may reflect individual differences in proactive interference rather than individual differences in the capacity of the short-term storage system. Indeed, previous research has shown that change detection performance can be influenced by proactive interference under some conditions. The purpose of the present study was to determine whether the canonical version of the change detection task - in which the to-be-remembered information consists of simple, briefly presented features - is influenced by proactive interference. Two experiments were conducted using methods that ordinarily produce substantial evidence of proactive interference, but no proactive interference was observed. Thus, the canonical version of the change detection task can be used to assess visual working memory capacity with no meaningful influence of proactive interference.

Functional Implications of Ubiquitous Semicircular Canal Non-Orthogonality in Mammals

PubMed Central

Berlin, Jeri C.; Kirk, E. Christopher; Rowe, Timothy B.

2013-01-01

The ‘canonical model’ of semicircular canal orientation in mammals assumes that 1) the three ipsilateral canals of an inner ear exist in orthogonal planes (i.e., orthogonality), 2) corresponding left and right canal pairs have equivalent angles (i.e., angle symmetry), and 3) contralateral synergistic canals occupy parallel planes (i.e., coplanarity). However, descriptions of vestibular anatomy that quantify semicircular canal orientation in single species often diverge substantially from this model. Data for primates further suggest that semicircular canal orthogonality varies predictably with the angular head velocities encountered in locomotion. These observations raise the possibility that orthogonality, symmetry, and coplanarity are misleading descriptors of semicircular canal orientation in mammals, and that deviations from these norms could have significant functional consequences. Here we critically assess the canonical model of semicircular canal orientation using high-resolution X-ray computed tomography scans of 39 mammal species. We find that substantial deviations from orthogonality, angle symmetry, and coplanarity are the rule for the mammals in our comparative sample. Furthermore, the degree to which the semicircular canals of a given species deviate from orthogonality is negatively correlated with estimated vestibular sensitivity. We conclude that the available comparative morphometric data do not support the canonical model and that its overemphasis as a heuristic generalization obscures a large amount of functionally relevant variation in semicircular canal orientation between species. PMID:24260256

Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data.

PubMed

Serra, Angela; Coretto, Pietro; Fratello, Michele; Tagliaferri, Roberto; Stegle, Oliver

2018-02-15

Microarray technology can be used to study the expression of thousands of genes across a number of different experimental conditions, usually hundreds. The underlying principle is that genes sharing similar expression patterns, across different samples, can be part of the same co-expression system, or they may share the same biological functions. Groups of genes are usually identified based on cluster analysis. Clustering methods rely on the similarity matrix between genes. A common choice to measure similarity is to compute the sample correlation matrix. Dimensionality reduction is another popular data analysis task which is also based on covariance/correlation matrix estimates. Unfortunately, covariance/correlation matrix estimation suffers from the intrinsic noise present in high-dimensional data. Sources of noise are: sampling variations, presents of outlying sample units, and the fact that in most cases the number of units is much larger than the number of genes. In this paper, we propose a robust correlation matrix estimator that is regularized based on adaptive thresholding. The resulting method jointly tames the effects of the high-dimensionality, and data contamination. Computations are easy to implement and do not require hand tunings. Both simulated and real data are analyzed. A Monte Carlo experiment shows that the proposed method is capable of remarkable performances. Our correlation metric is more robust to outliers compared with the existing alternatives in two gene expression datasets. It is also shown how the regularization allows to automatically detect and filter spurious correlations. The same regularization is also extended to other less robust correlation measures. Finally, we apply the ARACNE algorithm on the SyNTreN gene expression data. Sensitivity and specificity of the reconstructed network is compared with the gold standard. We show that ARACNE performs better when it takes the proposed correlation matrix estimator as input. The R software is available at https://github.com/angy89/RobustSparseCorrelation. aserra@unisa.it or robtag@unisa.it. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.

PubMed

Ziyatdinov, Andrey; Vázquez-Santiago, Miquel; Brunel, Helena; Martinez-Perez, Angel; Aschard, Hugues; Soria, Jose Manuel

2018-02-27

Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .

Canonical Transformations of Kepler Trajectories

ERIC Educational Resources Information Center

Mostowski, Jan

2010-01-01

In this paper, canonical transformations generated by constants of motion in the case of the Kepler problem are discussed. It is shown that canonical transformations generated by angular momentum are rotations of the trajectory. Particular attention is paid to canonical transformations generated by the Runge-Lenz vector. It is shown that these…

Patterns of metal composition and biological condition and their association in male common carp across an environmental contaminant gradient in Lake Mead National Recreation Area, Nevada and Arizona, USA

USGS Publications Warehouse

Patino, R.; Rosen, Michael R.; Orsak, E.L.; Goodbred, S.L.; May, T.W.; Alvarez, David; Echols, K.R.; Wieser, C.M.; Ruessler, S.; Torres, L.

2012-01-01

There is a contaminant gradient in Lake Mead National Recreation Area (LMNRA) that is partly driven by municipal and industrial runoff and wastewater inputs via Las Vegas Wash (LVW). Adult male common carp (Cyprinus carpio; 10 fish/site) were collected from LVW, Las Vegas Bay (receiving LVW flow), Overton Arm (OA, upstream reference), and Willow Beach (WB, downstream) in March 2008. Discriminant function analysis was used to describe differences in metal concentrations and biological condition of fish collected from the four study sites, and canonical correlation analysis was used to evaluate the association between metal and biological traits. Metal concentrations were determined in whole-body extracts. Of 63 metals screened, those initially used in the statistical analysis were Ag, As, Ba, Cd, Co, Fe, Hg, Pb, Se, Zn. Biological variables analyzed included total length (TL), Fulton's condition factor, gonadosomatic index (GSI), hematocrit (Hct), and plasma estradiol-17?? and 11-ketotestosterone (11kt) concentrations. Analysis of metal composition and biological condition both yielded strong discrimination of fish by site (respective canonical model, p< 0.0001). Compared to OA, pairwise Mahalanobis distances between group means were WB < LVB < LVW for metal concentrations and LVB < WB < LVW for biological traits. Respective primary drivers for these separations were Ag, As, Ba, Hg, Pb, Se and Zn; and TL, GSI, 11kt, and Hct. Canonical correlation analysis using the latter variable sets showed they are significantly associated (p<0.0003); with As, Ba, Hg, and Zn, and TL, 11kt, and Hct being the primary contributors to the association. In conclusion, male carp collected along a contaminant gradient in LMNRA have distinct, collection site-dependent metal and morpho-physiological profiles that are significantly associated with each other. These associations suggest that fish health and reproductive condition (as measured by the biological variables evaluated in this study) are influenced by levels of certain metals in the Lake Mead environment. ?? 2011.

Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests.

PubMed

Anastasiadou, Maria N; Christodoulakis, Manolis; Papathanasiou, Eleftherios S; Papacostas, Savvas S; Mitsis, Georgios D

2017-09-01

This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert neurophysiologists, EMG signal recording and user visual inspection. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Comparison between sparsely distributed memory and Hopfield-type neural network models

NASA Technical Reports Server (NTRS)

Keeler, James D.

1986-01-01

The Sparsely Distributed Memory (SDM) model (Kanerva, 1984) is compared to Hopfield-type neural-network models. A mathematical framework for comparing the two is developed, and the capacity of each model is investigated. The capacity of the SDM can be increased independently of the dimension of the stored vectors, whereas the Hopfield capacity is limited to a fraction of this dimension. However, the total number of stored bits per matrix element is the same in the two models, as well as for extended models with higher order interactions. The models are also compared in their ability to store sequences of patterns. The SDM is extended to include time delays so that contextual information can be used to cover sequences. Finally, it is shown how a generalization of the SDM allows storage of correlated input pattern vectors.

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2011-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.

Sensemaking: A Structure for an Intelligence Revolution

DTIC Science & Technology

2011-03-01

level of distraction among drivers who are using cell phones reveal an associated , diminished driver capacity.32 Non...relationships among sparse and ambiguous data .”87 Th is book accepts that perspective and develops the psychological, behavioral , and social levels of...correlations revealed . Benjamin Kleinmuntz obtained a similar result using the “Twenty Questions” game and a set of test cases to add structure

Rainfall prediction of Cimanuk watershed regions with canonical correlation analysis (CCA)

NASA Astrophysics Data System (ADS)

Rustiana, Shailla; Nurani Ruchjana, Budi; Setiawan Abdullah, Atje; Hermawan, Eddy; Berliana Sipayung, Sinta; Gede Nyoman Mindra Jaya, I.; Krismianto

2017-10-01

Rainfall prediction in Indonesia is very influential on various development sectors, such as agriculture, fisheries, water resources, industry, and other sectors. The inaccurate predictions can lead to negative effects. Cimanuk watershed is one of the main pillar of water resources in West Java. This watersheds divided into three parts, which is a headwater of Cimanuk sub-watershed, Middle of Cimanuk sub-watershed and downstream of Cimanuk sub- watershed. The flow of this watershed will flow through the Jatigede reservoir and will supply water to the north-coast area in the next few years. So, the reliable model of rainfall prediction is very needed in this watershed. Rainfall prediction conducted with Canonical Correlation Analysis (CCA) method using Climate Predictability Tool (CPT) software. The prediction is every 3months on 2016 (after January) based on Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over West Java. Predictors used in CPT were the monthly data index of Nino3.4, Dipole Mode (DMI), and Monsoon Index (AUSMI-ISMI-WNPMI-WYMI) with initial condition January. The initial condition is chosen by the last data update. While, the predictant were monthly rainfall data CHIRPS region of West Java. The results of prediction rainfall showed by skill map from Pearson Correlation. High correlation of skill map are on MAM (Mar-Apr-May), AMJ (Apr-May-Jun), and JJA (Jun-Jul-Aug) which means the model is reliable to forecast rainfall distribution over Cimanuk watersheds region (over West Java) on those seasons. CCA score over those season prediction mostly over 0.7. The accuracy of the model CPT also indicated by the Relative Operating Characteristic (ROC) curve of the results of Pearson correlation 3 representative point of sub-watershed (Sumedang, Majalengka, and Cirebon), were mostly located in the top line of non-skill, and evidenced by the same of rainfall patterns between observation and forecast. So, the model of CPT with CCA method is reliable to use.

Comparison and combination of "direct" and fragment based local correlation methods: Cluster in molecules and domain based local pair natural orbital perturbation and coupled cluster theories

NASA Astrophysics Data System (ADS)

Guo, Yang; Becker, Ute; Neese, Frank

2018-03-01

Local correlation theories have been developed in two main flavors: (1) "direct" local correlation methods apply local approximation to the canonical equations and (2) fragment based methods reconstruct the correlation energy from a series of smaller calculations on subsystems. The present work serves two purposes. First, we investigate the relative efficiencies of the two approaches using the domain-based local pair natural orbital (DLPNO) approach as the "direct" method and the cluster in molecule (CIM) approach as the fragment based approach. Both approaches are applied in conjunction with second-order many-body perturbation theory (MP2) as well as coupled-cluster theory with single-, double- and perturbative triple excitations [CCSD(T)]. Second, we have investigated the possible merits of combining the two approaches by performing CIM calculations with DLPNO methods serving as the method of choice for performing the subsystem calculations. Our cluster-in-molecule approach is closely related to but slightly deviates from approaches in the literature since we have avoided real space cutoffs. Moreover, the neglected distant pair correlations in the previous CIM approach are considered approximately. Six very large molecules (503-2380 atoms) were studied. At both MP2 and CCSD(T) levels of theory, the CIM and DLPNO methods show similar efficiency. However, DLPNO methods are more accurate for 3-dimensional systems. While we have found only little incentive for the combination of CIM with DLPNO-MP2, the situation is different for CIM-DLPNO-CCSD(T). This combination is attractive because (1) the better parallelization opportunities offered by CIM; (2) the methodology is less memory intensive than the genuine DLPNO-CCSD(T) method and, hence, allows for large calculations on more modest hardware; and (3) the methodology is applicable and efficient in the frequently met cases, where the largest subsystem calculation is too large for the canonical CCSD(T) method.

Snowmelt in a High Latitude Mountain Catchment: Effect of Vegetation Cover and Elevation

NASA Astrophysics Data System (ADS)

Pomeroy, J. W.; Essery, R. L.; Ellis, C. R.; Hedstrom, N. R.; Janowicz, R.; Granger, R. J.

2004-12-01

The energetics and mass balance of snowpacks in the premelt and melt period were compared from three elevation bands in a high latitude mountain catchment, Wolf Creek Research Basin, Yukon. Elevation is strongly correlated with vegetation cover and in this case the three elevation bands (low, middle, high) correspond to mature spruce forest, dense shrub tundra and sparse tundra (alpine). Measurements of radiation, ground heat flux, snow depth, snowfall, air temperature, wind speed were made on a half-hourly basis at the three elevations for a 10 year period. Sondes provided vertical gradients of air temperature, humidity, wind speed and air pressure. Snow depth and density surveys were conducted monthly. Comparisons of wind speed, air temperature and humidity at three elevations show that the expected elevational gradients in the free atmosphere were slightly enhanced just above the surface canopies, but that the climate at the snow surface was further influenced by complex canopy effects. Premelt snow accumulation was strongly affected by intercepted snow in the forest and blowing snow sublimation in the sparse tundra but not by the small elevational gradients in snowfall. As a result the maximum premelt SWE was found in the mid-elevation shrub tundra and was roughly double that of the sparse tundra or forest. Minimum variability of SWE was observed in the forest and shrub tundra (CV=0.25) while in the sparse tundra variability doubled (CV=0.5). Snowmelt was influenced by differences in premelt accumulation as well as differences in the net energy fluxes to snow. Elevation had a strong effect on the initiation of melt with the forest melt starting on average 16 days before the shrub tundra and 19 days before the sparse tundra. Mean melt rates showed a maximum in middle elevations and increased from 860 kJ/day in the forest to 1460 kJ/day in the sparse tundra and 2730 kJ/day in the shrub tundra. The forest canopy reduced melt while the shrub canopy enhanced it relative to the sparsely vegetated tundra. Duration of melt was similar in the forest and shrub tundra at 20 days while the sparse tundra was shorter at 13 days; the differences due to differing snow accumulation and melt rates. The greatest variability in the timing and rate of melt was found in the shrub tundra, where the effect of the shrub canopy over snow depends on snow depth and insolation and is reduced in years with high snow accumulation or extensive cloudy periods in spring. The results show that it is necessary to consider the combination of elevation and vegetation effects on snow microclimate and melt processes in high latitude mountain catchments, but that weather patterns induce substantial variability on the effect these factors.

Canonical methods in classical and quantum gravity: An invitation to canonical LQG

NASA Astrophysics Data System (ADS)

Reyes, Juan D.

2018-04-01

Loop Quantum Gravity (LQG) is a candidate quantum theory of gravity still under construction. LQG was originally conceived as a background independent canonical quantization of Einstein’s general relativity theory. This contribution provides some physical motivations and an overview of some mathematical tools employed in canonical Loop Quantum Gravity. First, Hamiltonian classical methods are reviewed from a geometric perspective. Canonical Dirac quantization of general gauge systems is sketched next. The Hamiltonian formultation of gravity in geometric ADM and connection-triad variables is then presented to finally lay down the canonical loop quantization program. The presentation is geared toward advanced undergradute or graduate students in physics and/or non-specialists curious about LQG.

The canonical Wnt signaling pathway in autism.

PubMed

Zhang, Yinghua; Yuan, Xiangshan; Wang, Zhongping; Li, Ruixi

2014-01-01

Mounting attention is being focused on the canonical Wnt signaling pathway which has been implicated in the pathogenesis of autism in some our and other recent studies. The canonical Wnt pathway is involved in cell proliferation, differentiation and migration, especially during nervous system development. Given its various functions, dysfunction of the canonical Wnt pathway may exert adverse effects on neurodevelopment and therefore leads to the pathogenesis of autism. Here, we review human and animal studies that implicate the canonical Wnt signal transduction pathway in the pathogenesis of autism. We also describe the crosstalk between the canonical Wnt pathway and the Notch signaling pathway in several types of autism spectrum disorders, including Asperger syndrome and Fragile X. Further research on the crosstalk between the canonical Wnt signaling pathway and other signaling cascades in autism may be an efficient avenue to understand the etiology of autism and ultimately lead to alternative medications for autism-like phenotypes.

Visual Search for Object Orientation Can Be Modulated by Canonical Orientation

ERIC Educational Resources Information Center

Ballaz, Cecile; Boutsen, Luc; Peyrin, Carole; Humphreys, Glyn W.; Marendaz, Christian

2005-01-01

The authors studied the influence of canonical orientation on visual search for object orientation. Displays consisted of pictures of animals whose axis of elongation was either vertical or tilted in their canonical orientation. Target orientation could be either congruent or incongruent with the object's canonical orientation. In Experiment 1,…

Generalized canonical ensembles and ensemble equivalence

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

Costeniuc, M.; Ellis, R.S.; Turkington, B.

2006-02-15

This paper is a companion piece to our previous work [J. Stat. Phys. 119, 1283 (2005)], which introduced a generalized canonical ensemble obtained by multiplying the usual Boltzmann weight factor e{sup -{beta}}{sup H} of the canonical ensemble with an exponential factor involving a continuous function g of the Hamiltonian H. We provide here a simplified introduction to our previous work, focusing now on a number of physical rather than mathematical aspects of the generalized canonical ensemble. The main result discussed is that, for suitable choices of g, the generalized canonical ensemble reproduces, in the thermodynamic limit, all the microcanonical equilibriummore » properties of the many-body system represented by H even if this system has a nonconcave microcanonical entropy function. This is something that in general the standard (g=0) canonical ensemble cannot achieve. Thus a virtue of the generalized canonical ensemble is that it can often be made equivalent to the microcanonical ensemble in cases in which the canonical ensemble cannot. The case of quadratic g functions is discussed in detail; it leads to the so-called Gaussian ensemble.« less

Streamflow Prediction in Ungauged, Irrigated Basins

NASA Astrophysics Data System (ADS)

Zhang, M.; Thompson, S. E.

2016-12-01

The international "predictions in ungauged basins" or "PUB" effort has broadened and improved the tools available to support water resources management in sparsely observed regions. These tools have, however, been primarily focused on regions with limited diversion of surface or shallow groundwater resources. Incorporating anthropogenic activity into PUB methods is essential given the high level of development of many basins. We extended an existing stochastic framework used to predict the flow duration curve to explore the effects of irrigation on streamflow dynamics. Four canonical scenarios were considered in which irrigation water was (i) primarily sourced from water imports, (ii) primarily sourced from direct in-channel diversions, (iii) sourced from shallow groundwater with direct connectivity to stream channels, or (iv) sourced from deep groundwater that is indirectly connected to surface flow via a shallow aquifer. By comparing the predicted flow duration curves to those predicted by accounting for climate and geomorphic factors in isolation, specific "fingerprints" of human water withdrawals could be identified for the different irrigation scenarios, and shown to be sensitive to irrigation volumes and scheduling. The results provide a first insight into PUB methodologies that could be employed in heavily managed basins.

Relationships between digestive efficiency and metabolomic profiles of serum and intestinal contents in chickens.

PubMed

Beauclercq, Stéphane; Nadal-Desbarats, Lydie; Hennequet-Antier, Christelle; Gabriel, Irène; Tesseraud, Sophie; Calenge, Fanny; Le Bihan-Duval, Elisabeth; Mignon-Grasteau, Sandrine

2018-04-27

The increasing cost of conventional feedstuffs has bolstered interest in genetic selection for digestive efficiency (DE), a component of feed efficiency, assessed by apparent metabolisable energy corrected to zero nitrogen retention (AMEn). However, its measurement is time-consuming and constraining, and its relationship with metabolic efficiency poorly understood. To simplify selection for this trait, we searched for indirect metabolic biomarkers through an analysis of the serum metabolome using nuclear magnetic resonance ( 1 H NMR). A partial least squares (PLS) model including six amino acids and two derivatives from butyrate predicted 59% of AMEn variability. Moreover, to increase our knowledge of the molecular mechanisms controlling DE, we investigated 1 H NMR metabolomes of ileal, caecal, and serum contents by fitting canonical sparse PLS. This analysis revealed strong associations between metabolites and DE. Models based on the ileal, caecal, and serum metabolome respectively explained 77%, 78%, and 74% of the variability of AMEn and its constitutive components (utilisation of starch, lipids, and nitrogen). In our conditions, the metabolites presenting the strongest associations with AMEn were proline in the serum, fumarate in the ileum and glucose in caeca. This study shows that serum metabolomics offers new opportunities to predict chicken DE.

Data-driven discovery of partial differential equations.

PubMed

Rudy, Samuel H; Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

2017-04-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg-de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.

Neoclassical canons of facial beauty: Do we see the deviations?

PubMed

Pavlic, Andrej; Trinajstic Zrinski, Magda; Katic, Visnja; Spalj, Stjepan

2017-05-01

To explore the presence of neoclassical canons of facial beauty among young people in Croatia and to question possible psychosocial repercussions occurring in those who demonstrate deviations in relation to canons. The study was cross-sectional and the sample included 249 subjects (60% female) aged 12-39 (median 20). Their en face and profile photographs were taken in Natural Head Position. Photogrammetry included analysis of nine neoclassical canons of facial beauty originating from the Renaissance. Psychosocial issues were assessed using the Self-Esteem Scale, Big Five Inventory and three domains of Orthognathic Quality of Life Questionnaire. Significant deviations from neoclassical facial beauty canons were observed in 55-65% of adolescents and young adults. Gender and age showed no relation to deviations. The deviations from canons that influenced the quality of life were mainly those related to vertical facial proportions and demonstrated increased facial aesthetics concern and social impact, and higher self-reported treatment need (p < 0.05). Deviations from canons were not related to self-esteem but a decrease in openness, agreeableness and neuroticism was observed. Neoclassical canons were not valid for the majority of adolescents and young adults in Croatia. Only deviations from some canons appear to provoke mild psychosocial repercussions. Copyright © 2017 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Controlled levels of canonical Wnt signaling are required for neural crest migration.

PubMed

Maj, Ewa; Künneke, Lutz; Loresch, Elisabeth; Grund, Anita; Melchert, Juliane; Pieler, Tomas; Aspelmeier, Timo; Borchers, Annette

2016-09-01

Canonical Wnt signaling plays a dominant role in the development of the neural crest (NC), a highly migratory cell population that generates a vast array of cell types. Canonical Wnt signaling is required for NC induction as well as differentiation, however its role in NC migration remains largely unknown. Analyzing nuclear localization of β-catenin as readout for canonical Wnt activity, we detect nuclear β-catenin in premigratory but not migratory Xenopus NC cells suggesting that canonical Wnt activity has to decrease to basal levels to enable NC migration. To define a possible function of canonical Wnt signaling in Xenopus NC migration, canonical Wnt signaling was modulated at different time points after NC induction. This was accomplished using either chemical modulators affecting β-catenin stability or inducible glucocorticoid fusion constructs of Lef/Tcf transcription factors. In vivo analysis of NC migration by whole mount in situ hybridization demonstrates that ectopic activation of canonical Wnt signaling inhibits cranial NC migration. Further, NC transplantation experiments confirm that this effect is tissue-autonomous. In addition, live-cell imaging in combination with biophysical data analysis of explanted NC cells confirms the in vivo findings and demonstrates that modulation of canonical Wnt signaling affects the ability of NC cells to perform single cell migration. Thus, our data support the hypothesis that canonical Wnt signaling needs to be tightly controlled to enable migration of NC cells. Copyright © 2016 Elsevier Inc. All rights reserved.

TCDD Inhibition of Canonical Wnt Signaling Disrupts Prostatic Bud Formation in Mouse Urogenital Sinus

PubMed Central

Peterson, Richard E.

2013-01-01

In mice, in utero exposure to 2,3,7,8-tetrachlorodibenzo-p- dioxin (TCDD) reduces the number of dorsolateral prostatic buds resulting in a smaller dorsolateral prostate and prevents formation of ventral buds culminating in ventral prostate agenesis. The genes and signaling pathways affected by TCDD that are responsible for disrupting prostate development are largely unknown. Here we show that treatment of urogenital sinus (UGS) organ cultures with known inhibitors of canonical Wnt signaling also inhibits prostatic bud formation. In support of the hypothesis that TCDD decreases canonical Wnt signaling, we identify inhibitory effects of TCDD on multiple components of the canonical Wnt signaling pathway in the UGS that temporally coincide with the inhibitory effect of TCDD on prostatic bud formation: (1) expression of R-spondins (Rspo2 and Rspo3) that promote canonical Wnt signaling is reduced; (2) expression of Lef1, Tcf1, and Wif1, established canonical Wnt target genes, is decreased; (3) expression of Lgr5, a RSPO receptor that activates canonical Wnt signaling, is reduced; and (4) expression of Dickkopfs (Dkks), inhibitors of canonical Wnt signaling, is not increased by TCDD. Thus, the TCDD-induced reduction in canonical Wnt signaling is associated with a decrease in activators (Rspo2 and Rspo3) rather than an increase in inhibitors (Dkk1 and Dkk2) of the pathway. This study focuses on determining whether treatment of TCDD-exposed UGS organ cultures with RSPO2 and/or RSPO3 is capable of rescuing the inhibitory effects of TCDD on canonical Wnt signaling and prostatic bud formation. We discovered that each RSPO alone or in combination partially rescues TCDD inhibition of both canonical Wnt signaling and prostatic bud formation. PMID:23429912

TCDD inhibition of canonical Wnt signaling disrupts prostatic bud formation in mouse urogenital sinus.

PubMed

Branam, Amanda M; Davis, Nicole M; Moore, Robert W; Schneider, Andrew J; Vezina, Chad M; Peterson, Richard E

2013-05-01

In mice, in utero exposure to 2,3,7,8-tetrachlorodibenzo-p- dioxin (TCDD) reduces the number of dorsolateral prostatic buds resulting in a smaller dorsolateral prostate and prevents formation of ventral buds culminating in ventral prostate agenesis. The genes and signaling pathways affected by TCDD that are responsible for disrupting prostate development are largely unknown. Here we show that treatment of urogenital sinus (UGS) organ cultures with known inhibitors of canonical Wnt signaling also inhibits prostatic bud formation. In support of the hypothesis that TCDD decreases canonical Wnt signaling, we identify inhibitory effects of TCDD on multiple components of the canonical Wnt signaling pathway in the UGS that temporally coincide with the inhibitory effect of TCDD on prostatic bud formation: (1) expression of R-spondins (Rspo2 and Rspo3) that promote canonical Wnt signaling is reduced; (2) expression of Lef1, Tcf1, and Wif1, established canonical Wnt target genes, is decreased; (3) expression of Lgr5, a RSPO receptor that activates canonical Wnt signaling, is reduced; and (4) expression of Dickkopfs (Dkks), inhibitors of canonical Wnt signaling, is not increased by TCDD. Thus, the TCDD-induced reduction in canonical Wnt signaling is associated with a decrease in activators (Rspo2 and Rspo3) rather than an increase in inhibitors (Dkk1 and Dkk2) of the pathway. This study focuses on determining whether treatment of TCDD-exposed UGS organ cultures with RSPO2 and/or RSPO3 is capable of rescuing the inhibitory effects of TCDD on canonical Wnt signaling and prostatic bud formation. We discovered that each RSPO alone or in combination partially rescues TCDD inhibition of both canonical Wnt signaling and prostatic bud formation.

Sparse graph regularization for robust crop mapping using hyperspectral remotely sensed imagery with very few in situ data

NASA Astrophysics Data System (ADS)

Xue, Zhaohui; Du, Peijun; Li, Jun; Su, Hongjun

2017-02-01

The generally limited availability of training data relative to the usually high data dimension pose a great challenge to accurate classification of hyperspectral imagery, especially for identifying crops characterized with highly correlated spectra. However, traditional parametric classification models are problematic due to the need of non-singular class-specific covariance matrices. In this research, a novel sparse graph regularization (SGR) method is presented, aiming at robust crop mapping using hyperspectral imagery with very few in situ data. The core of SGR lies in propagating labels from known data to unknown, which is triggered by: (1) the fraction matrix generated for the large unknown data by using an effective sparse representation algorithm with respect to the few training data serving as the dictionary; (2) the prediction function estimated for the few training data by formulating a regularization model based on sparse graph. Then, the labels of large unknown data can be obtained by maximizing the posterior probability distribution based on the two ingredients. SGR is more discriminative, data-adaptive, robust to noise, and efficient, which is unique with regard to previously proposed approaches and has high potentials in discriminating crops, especially when facing insufficient training data and high-dimensional spectral space. The study area is located at Zhangye basin in the middle reaches of Heihe watershed, Gansu, China, where eight crop types were mapped with Compact Airborne Spectrographic Imager (CASI) and Shortwave Infrared Airborne Spectrogrpahic Imager (SASI) hyperspectral data. Experimental results demonstrate that the proposed method significantly outperforms other traditional and state-of-the-art methods.

A multiple hold-out framework for Sparse Partial Least Squares.

PubMed

Monteiro, João M; Rao, Anil; Shawe-Taylor, John; Mourão-Miranda, Janaina

2016-09-15

Supervised classification machine learning algorithms may have limitations when studying brain diseases with heterogeneous populations, as the labels might be unreliable. More exploratory approaches, such as Sparse Partial Least Squares (SPLS), may provide insights into the brain's mechanisms by finding relationships between neuroimaging and clinical/demographic data. The identification of these relationships has the potential to improve the current understanding of disease mechanisms, refine clinical assessment tools, and stratify patients. SPLS finds multivariate associative effects in the data by computing pairs of sparse weight vectors, where each pair is used to remove its corresponding associative effect from the data by matrix deflation, before computing additional pairs. We propose a novel SPLS framework which selects the adequate number of voxels and clinical variables to describe each associative effect, and tests their reliability by fitting the model to different splits of the data. As a proof of concept, the approach was applied to find associations between grey matter probability maps and individual items of the Mini-Mental State Examination (MMSE) in a clinical sample with various degrees of dementia. The framework found two statistically significant associative effects between subsets of brain voxels and subsets of the questions/tasks. SPLS was compared with its non-sparse version (PLS). The use of projection deflation versus a classical PLS deflation was also tested in both PLS and SPLS. SPLS outperformed PLS, finding statistically significant effects and providing higher correlation values in hold-out data. Moreover, projection deflation provided better results. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Non-canonical microRNAs miR-320 and miR-702 promote proliferation in Dgcr8-deficient embryonic stem cells

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

Kim, Byeong-Moo; Department of Medicine, Harvard Medical School, Boston, MA 02115; Choi, Michael Y., E-mail: mchoi@partners.org

2012-09-21

Highlights: Black-Right-Pointing-Pointer Embryonic stem cells (ESCs) lacking non-canonical miRNAs proliferate slower. Black-Right-Pointing-Pointer miR-320 and miR-702 are two non-canonical miRNAs expressed in ESCs. Black-Right-Pointing-Pointer miR-320 and miR-702 promote proliferation of Dgcr8-deficient ESCs. Black-Right-Pointing-Pointer miR-320 targets p57 and helps to release Dgcr8-deficient ESCs from G1 arrest. Black-Right-Pointing-Pointer miR-702 targets p21 and helps to release Dgcr8-deficient ESCs from G1 arrest. -- Abstract: MicroRNAs are known to contribute significantly to stem cell phenotype by post-transcriptionally regulating gene expression. Most of our knowledge of microRNAs comes from the study of canonical microRNAs that require two sequential cleavages by the Drosha/Dgcr8 heterodimer and Dicer to generatemore » mature products. In contrast, non-canonical microRNAs bypass the cleavage by the Drosha/Dgcr8 heterodimer within the nucleus but still require cytoplasmic cleavage by Dicer. The function of non-canonical microRNAs in embryonic stem cells (ESCs) remains obscure. It has been hypothesized that non-canonical microRNAs have important roles in ESCs based upon the phenotypes of ESC lines that lack these specific classes of microRNAs; Dicer-deficient ESCs lacking both canonical and non-canonical microRNAs have much more severe proliferation defect than Dgcr8-deficient ESCs lacking only canonical microRNAs. Using these cell lines, we identified two non-canonical microRNAs, miR-320 and miR-702, that promote proliferation of Dgcr8-deficient ESCs by releasing them from G1 arrest. This is accomplished by targeting the 3 Prime -untranslated regions of the cell cycle inhibitors p57 and p21 and thereby inhibiting their expression. This is the first report of the crucial role of non-canonical microRNAs in ESCs.« less

Detection of periodicity based on independence tests - III. Phase distance correlation periodogram

NASA Astrophysics Data System (ADS)

Zucker, Shay

2018-02-01

I present the Phase Distance Correlation (PDC) periodogram - a new periodicity metric, based on the Distance Correlation concept of Gábor Székely. For each trial period, PDC calculates the distance correlation between the data samples and their phases. PDC requires adaptation of the Székely's distance correlation to circular variables (phases). The resulting periodicity metric is best suited to sparse data sets, and it performs better than other methods for sawtooth-like periodicities. These include Cepheid and RR-Lyrae light curves, as well as radial velocity curves of eccentric spectroscopic binaries. The performance of the PDC periodogram in other contexts is almost as good as that of the Generalized Lomb-Scargle periodogram. The concept of phase distance correlation can be adapted also to astrometric data, and it has the potential to be suitable also for large evenly spaced data sets, after some algorithmic perfection.

Quantum gravity in the Southern Cone Conference. Proceedings. Conference, Bariloche (Argentina), 7 - 10 Jan 1998.

NASA Astrophysics Data System (ADS)

1999-04-01

The following topics are discussed: Black hole formation by canonical dynamics of gravitating shells; canonical quantum gravity; Vassiliev invariants; midisuperspace models; quantum spacetime; large-N limit of superconformal field theories and supergravity; world-volume fields and background coupling of branes; gauge enhancement and chirality changes in nonperturbative orbifold models; chiral p-forms; formally renormalizable gravitationally self-interacting string models; gauge supergravities for all odd dimensions; black hole radiation and S-matrix; primordial black holes; fluctuations in a thermal field and dissipation of a black hole spacetime in far-field limit; adiabatic interpretation of particle creation in a de Sitter universe; nonequilibrium dynamics of quantum fields in inflationary cosmology; magnetic fields in the early Universe; classical regime of a quantum universe obtained through a functional method; decoherence and correlations in semiclassical cosmology; fluid of primordial fluctuations; causal statistical mechanics calculation of initial cosmic entropy and quantum gravity prospects and black hole-D-brane correspondence.

Pervasive, Coordinated Protein-Level Changes Driven by Transcript Isoform Switching during Meiosis.

PubMed

Cheng, Ze; Otto, George Maxwell; Powers, Emily Nicole; Keskin, Abdurrahman; Mertins, Philipp; Carr, Steven Alfred; Jovanovic, Marko; Brar, Gloria Ann

2018-02-22

To better understand the gene regulatory mechanisms that program developmental processes, we carried out simultaneous genome-wide measurements of mRNA, translation, and protein through meiotic differentiation in budding yeast. Surprisingly, we observed that the levels of several hundred mRNAs are anti-correlated with their corresponding protein products. We show that rather than arising from canonical forms of gene regulatory control, the regulation of at least 380 such cases, or over 8% of all measured genes, involves temporally regulated switching between production of a canonical, translatable transcript and a 5' extended isoform that is not efficiently translated into protein. By this pervasive mechanism for the modulation of protein levels through a natural developmental program, a single transcription factor can coordinately activate and repress protein synthesis for distinct sets of genes. The distinction is not based on whether or not an mRNA is induced but rather on the type of transcript produced. Copyright © 2018 Elsevier Inc. All rights reserved.

Conformal quantum mechanics and holography in noncommutative space-time

NASA Astrophysics Data System (ADS)

Gupta, Kumar S.; Harikumar, E.; Zuhair, N. S.

2017-09-01

We analyze the effects of noncommutativity in conformal quantum mechanics (CQM) using the κ-deformed space-time as a prototype. Up to the first order in the deformation parameter, the symmetry structure of the CQM algebra is preserved but the coupling in a canonical model of the CQM gets deformed. We show that the boundary conditions that ensure a unitary time evolution in the noncommutative CQM can break the scale invariance, leading to a quantum mechanical scaling anomaly. We calculate the scaling dimensions of the two and three point functions in the noncommutative CQM which are shown to be deformed. The AdS2 / CFT1 duality for the CQM suggests that the corresponding correlation functions in the holographic duals are modified. In addition, the Breitenlohner-Freedman bound also picks up a noncommutative correction. The strongly attractive regime of a canonical model of the CQM exhibit quantum instability. We show that the noncommutativity softens this singular behaviour and its implications for the corresponding holographic duals are discussed.

Memory, Literacy, and Invention: Reimagining the Canon of Memory for the Writing Classroom

ERIC Educational Resources Information Center

Ryan, Kathleen J.

2004-01-01

This article challenges the assumption that the canon of memory means memorization and transcription, and, as a result, has little relevance for the writing classroom. An examination of the canon's historical legacy and its relationships to literacy and invention open a space for redefining the canon of memory as "rememoried knowing." In brief,…

Minimally Informative Prior Distributions for PSA

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

Dana L. Kelly; Robert W. Youngblood; Kurt G. Vedros

2010-06-01

A salient feature of Bayesian inference is its ability to incorporate information from a variety of sources into the inference model, via the prior distribution (hereafter simply “the prior”). However, over-reliance on old information can lead to priors that dominate new data. Some analysts seek to avoid this by trying to work with a minimally informative prior distribution. Another reason for choosing a minimally informative prior is to avoid the often-voiced criticism of subjectivity in the choice of prior. Minimally informative priors fall into two broad classes: 1) so-called noninformative priors, which attempt to be completely objective, in that themore » posterior distribution is determined as completely as possible by the observed data, the most well known example in this class being the Jeffreys prior, and 2) priors that are diffuse over the region where the likelihood function is nonnegligible, but that incorporate some information about the parameters being estimated, such as a mean value. In this paper, we compare four approaches in the second class, with respect to their practical implications for Bayesian inference in Probabilistic Safety Assessment (PSA). The most commonly used such prior, the so-called constrained noninformative prior, is a special case of the maximum entropy prior. This is formulated as a conjugate distribution for the most commonly encountered aleatory models in PSA, and is correspondingly mathematically convenient; however, it has a relatively light tail and this can cause the posterior mean to be overly influenced by the prior in updates with sparse data. A more informative prior that is capable, in principle, of dealing more effectively with sparse data is a mixture of conjugate priors. A particular diffuse nonconjugate prior, the logistic-normal, is shown to behave similarly for some purposes. Finally, we review the so-called robust prior. Rather than relying on the mathematical abstraction of entropy, as does the constrained noninformative prior, the robust prior places a heavy-tailed Cauchy prior on the canonical parameter of the aleatory model.« less

Hazard-to-Risk: High-Performance Computing Simulations of Large Earthquake Ground Motions and Building Damage in the Near-Fault Region

NASA Astrophysics Data System (ADS)

Miah, M.; Rodgers, A. J.; McCallen, D.; Petersson, N. A.; Pitarka, A.

2017-12-01

We are running high-performance computing (HPC) simulations of ground motions for large (magnitude, M=6.5-7.0) earthquakes in the near-fault region (< 50 km) to 5 Hz and higher. Ground motions are then used as forcing functions for canonical steel moment frame buildings throughout the near-fault domain. For ground motions, we are using SW4, a fourth order summation-by-parts finite difference time-domain code running on 10,000-100,000's of cores. Earthquake ruptures are generated using the Graves and Pitarka (2017) method. We validated ground motion intensity measurements against Ground Motion Prediction Equations. We considered two events (M=6.5 and 7.0) for vertical strike-slip ruptures with three-dimensional (3D) basin structures, including stochastic heterogeneity. We have also considered M7.0 scenarios for a Hayward Fault rupture scenario which effects the San Francisco Bay Area and northern California using both 1D and 3D earth structure. Dynamic, inelastic response of canonical buildings is computed with the NEVADA, a nonlinear, finite-deformation finite element code. Canonical buildings include 3-, 9-, 20- and 40-story steel moment frame buildings. Damage potential is tracked by the peak inter-story drift (PID) ratio, which measures the maximum displacement between adjacent floors of the building and is strongly correlated with damage. PID ratios greater 1.0 generally indicate non-linear response and permanent deformation of the structure. We also track roof displacement to identify permanent deformation. PID (damage) for a given earthquake scenario (M, slip distribution, hypocenter) is spatially mapped throughout the SW4 domain with 1-2 km resolution. Results show that in the near fault region building damage is correlated with peak ground velocity (PGV), while farther away (> 20 km) it is better correlated with peak ground acceleration (PGA). We also show how simulated ground motions have peaks in the response spectra that shift to longer periods for larger magnitude events and for locations of forward directivity, as has been reported by Sommerville (2003). These advanced numerical simulation capabilities provide a detailed look at the regional distribution of ground motions and allow us to quantify how ground motion hazard translate to risk for specific structures on a regional scale.

A new approach for SSVEP detection using PARAFAC and canonical correlation analysis.

PubMed

Tello, Richard; Pouryazdian, Saeed; Ferreira, Andre; Beheshti, Soosan; Krishnan, Sridhar; Bastos, Teodiano

2015-01-01

This paper presents a new way for automatic detection of SSVEPs through correlation analysis between tensor models. 3-way EEG tensor of channel × frequency × time is decomposed into constituting factor matrices using PARAFAC model. PARAFAC analysis of EEG tensor enables us to decompose multichannel EEG into constituting temporal, spectral and spatial signatures. SSVEPs characterized with localized spectral and spatial signatures are then detected exploiting a correlation analysis between extracted signatures of the EEG tensor and the corresponding simulated signatures of all target SSVEP signals. The SSVEP that has the highest correlation is selected as the intended target. Two flickers blinking at 8 and 13 Hz were used as visual stimuli and the detection was performed based on data packets of 1 second without overlapping. Five subjects participated in the experiments and the highest classification rate of 83.34% was achieved, leading to the Information Transfer Rate (ITR) of 21.01 bits/min.

THE TOPOLOGY OF CANONICAL FLUX TUBES IN FLARED JET GEOMETRY

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

Lavine, Eric Sander; You, Setthivoine, E-mail: Slavine2@uw.edu, E-mail: syou@aa.washington.edu

2017-01-20

Magnetized plasma jets are generally modeled as magnetic flux tubes filled with flowing plasma governed by magnetohydrodynamics (MHD). We outline here a more fundamental approach based on flux tubes of canonical vorticity, where canonical vorticity is defined as the circulation of the species’ canonical momentum. This approach extends the concept of magnetic flux tube evolution to include the effects of finite particle momentum and enables visualization of the topology of plasma jets in regimes beyond MHD. A flared, current-carrying magnetic flux tube in an ion-electron plasma with finite ion momentum is thus equivalent to either a pair of electron andmore » ion flow flux tubes, a pair of electron and ion canonical momentum flux tubes, or a pair of electron and ion canonical vorticity flux tubes. We examine the morphology of all these flux tubes for increasing electrical currents, different radial current profiles, different electron Mach numbers, and a fixed, flared, axisymmetric magnetic geometry. Calculations of gauge-invariant relative canonical helicities track the evolution of magnetic, cross, and kinetic helicities in the system, and show that ion flow fields can unwind to compensate for an increasing magnetic twist. The results demonstrate that including a species’ finite momentum can result in a very long collimated canonical vorticity flux tube even if the magnetic flux tube is flared. With finite momentum, particle density gradients must be normal to canonical vorticities, not to magnetic fields, so observations of collimated astrophysical jets could be images of canonical vorticity flux tubes instead of magnetic flux tubes.« less

El Escritor y las Normas del Canon Literario (The Writer and the Norms of the Literary Canon).

ERIC Educational Resources Information Center

Policarpo, Alcibiades

This paper speculates about whether a literary canon exists in contemporary Latin American literature, particularly in the prose genre. The paper points to Carlos Fuentes, Gabriel Garcia Marquez, and Mario Vargas Llosa as the three authors who might form this traditional and liberal canon with their works "La Muerte de Artemio Cruz"…

Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs

PubMed Central

2017-01-01

Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package. PMID:29107980

International Conference on Chemical Kinetics: Program and Abstracts Held in Gaithersburg, Maryland on 17-19 June 1985.

DTIC Science & Technology

1985-06-01

relative to reactant and with the free energy of reaction. The correlation equations were derived from hyperbolic paraboloid models of the energy...obtained the differen- tial equations which define the best dividing surfaces of this type for both microcanonical and canonical cases. We have...is now generalized to non-uniform reaction systems. For kinetics-diffusion problems the equations of the generalized PAM were (similar in form to

Relations between basic and specific motor abilities and player quality of young basketball players.

PubMed

Marić, Kristijan; Katić, Ratko; Jelicić, Mario

2013-05-01

Subjects from 5 first league clubs from Herzegovina were tested with the purpose of determining the relations of basic and specific motor abilities, as well as the effect of specific abilities on player efficiency in young basketball players (cadets). A battery of 12 tests assessing basic motor abilities and 5 specific tests assessing basketball efficiency were used on a sample of 83 basketball players. Two significant canonical correlations, i.e. linear combinations explained the relation between the set of twelve variables of basic motor space and five variables of situational motor abilities. Underlying the first canonical linear combination is the positive effect of the general motor factor, predominantly defined by jumping explosive power, movement speed of the arms, static strength of the arms and coordination, on specific basketball abilities: movement efficiency, the power of the overarm throw, shooting and passing precision, and the skill of handling the ball. The impact of basic motor abilities of precision and balance on specific abilities of passing and shooting precision and ball handling is underlying the second linear combination. The results of regression correlation analysis between the variable set of specific motor abilities and game efficiency have shown that the ability of ball handling has the largest impact on player quality in basketball cadets, followed by shooting precision and passing precision, and the power of the overarm throw.

Vanishing of local non-Gaussianity in canonical single field inflation

NASA Astrophysics Data System (ADS)

Bravo, Rafael; Mooij, Sander; Palma, Gonzalo A.; Pradenas, Bastián

2018-05-01

We study the production of observable primordial local non-Gaussianity in two opposite regimes of canonical single field inflation: attractor (standard single field slow-roll inflation) and non attractor (ultra slow-roll inflation). In the attractor regime, the standard derivation of the bispectrum's squeezed limit using co-moving coordinates gives the well known Maldacena's consistency relation fNL = 5 (1‑ns) / 12. On the other hand, in the non-attractor regime, the squeezed limit offers a substantial violation of this relation given by fNL = 5/2. In this work we argue that, independently of whether inflation is attractor or non-attractor, the size of the observable primordial local non-Gaussianity is predicted to be fNLobs = 0 (a result that was already understood to hold in the case of attractor models). To show this, we follow the use of the so-called Conformal Fermi Coordinates (CFC), recently introduced in the literature. These coordinates parametrize the local environment of inertial observers in a perturbed FRW spacetime, allowing one to identify and compute gauge invariant quantities, such as n-point correlation functions. Concretely, we find that during inflation, after all the modes have exited the horizon, the squeezed limit of the 3-point correlation function of curvature perturbations vanishes in the CFC frame, regardless of the inflationary regime. We argue that such a cancellation should persist after inflation ends.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

PubMed

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-07-01

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

PubMed Central

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T.; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-01-01

Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153689

Analysis of ecosystem controls on soil carbon source-sink relationships in the northwest Great Plains

USGS Publications Warehouse

Tan, Z.; Liu, S.; Johnston, C.A.; Liu, J.; Tieszen, L.L.

2006-01-01

Our ability to forecast the role of ecosystem processes in mitigating global greenhouse effects relies on understanding the driving forces on terrestrial C dynamics. This study evaluated the controls on soil organic C (SOC) changes from 1973 to 2000 in the northwest Great Plains. SOC source-sink relationships were quantified using the General Ensemble Biogeochemical Modeling System (GEMS) based on 40 randomly located 10 × 10 km2 sample blocks. These sample blocks were aggregated into cropland, grassland, and forestland groups based on land cover composition within each sample block. Canonical correlation analysis indicated that SOC source-sink relationship from 1973 to 2000 was significantly related to the land cover type while the change rates mainly depended on the baseline SOC level and annual precipitation. Of all selected driving factors, the baseline SOC and nitrogen levels controlled the SOC change rates for the forestland and cropland groups, while annual precipitation determined the C source-sink relationship for the grassland group in which noticeable SOC sink strength was attributed to the conversion from cropped area to grass cover. Canonical correlation analysis also showed that grassland ecosystems are more complicated than others in the ecoregion, which may be difficult to identify on a field scale. Current model simulations need further adjustments to the model input variables for the grass cover-dominated ecosystems in the ecoregion.

An Optimal Bahadur-Efficient Method in Detection of Sparse Signals with Applications to Pathway Analysis in Sequencing Association Studies.

PubMed

Dai, Hongying; Wu, Guodong; Wu, Michael; Zhi, Degui

2016-01-01

Next-generation sequencing data pose a severe curse of dimensionality, complicating traditional "single marker-single trait" analysis. We propose a two-stage combined p-value method for pathway analysis. The first stage is at the gene level, where we integrate effects within a gene using the Sequence Kernel Association Test (SKAT). The second stage is at the pathway level, where we perform a correlated Lancaster procedure to detect joint effects from multiple genes within a pathway. We show that the Lancaster procedure is optimal in Bahadur efficiency among all combined p-value methods. The Bahadur efficiency,[Formula: see text], compares sample sizes among different statistical tests when signals become sparse in sequencing data, i.e. ε →0. The optimal Bahadur efficiency ensures that the Lancaster procedure asymptotically requires a minimal sample size to detect sparse signals ([Formula: see text]). The Lancaster procedure can also be applied to meta-analysis. Extensive empirical assessments of exome sequencing data show that the proposed method outperforms Gene Set Enrichment Analysis (GSEA). We applied the competitive Lancaster procedure to meta-analysis data generated by the Global Lipids Genetics Consortium to identify pathways significantly associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and total cholesterol.

Research on Hydrodynamic Interference Suppression of Bottom-Mounted Monitoring Platform with Fairing Structure

NASA Astrophysics Data System (ADS)

Wang, Zhen; Zheng, Yi; Mao, Yu-feng; Wang, Ya-zhou; Yu, Yan-ting; Liu, Hong-ning

2018-03-01

In the disturbance of unsteady flow field under the sea, the monitoring accuracy and precision of the bottom-mounted acoustic monitoring platform will decrease. In order to reduce the hydrodynamic interference, the platform wrapped with fairing structure and separated from the retrieval unit is described. The suppression effect evaluation based on the correlation theory of sound pressure and particle velocity for spherical wave in infinite homogeneous medium is proposed and the difference value between them is used to evaluate the hydrodynamic restraining performance of the bottom-mounted platform under far field condition. Through the sea test, it is indicated that the platform with sparse layers fairing structure (there are two layers for the fairing, in which the inside layer is 6-layers sparse metal net, and the outside layer is 1-layer polyester cloth, and then it takes sparse layers for short) has no attenuation in the sound pressure response to the sound source signal, but obvious suppression in the velocity response to the hydrodynamic noise. The effective frequency of the fairing structure is decreased below 10 Hz, and the noise magnitude is reduced by 10 dB. With the comparison of different fairing structures, it is concluded that the tighter fairing structure can enhance the performance of sound transmission and flow restraining.

Determining conduction patterns on a sparse electrode grid: Implications for the analysis of clinical arrhythmias

NASA Astrophysics Data System (ADS)

Vidmar, David; Narayan, Sanjiv M.; Krummen, David E.; Rappel, Wouter-Jan

2016-11-01

We present a general method of utilizing bioelectric recordings from a spatially sparse electrode grid to compute a dynamic vector field describing the underlying propagation of electrical activity. This vector field, termed the wave-front flow field, permits quantitative analysis of the magnitude of rotational activity (vorticity) and focal activity (divergence) at each spatial point. We apply this method to signals recorded during arrhythmias in human atria and ventricles using a multipolar contact catheter and show that the flow fields correlate with corresponding activation maps. Further, regions of elevated vorticity and divergence correspond to sites identified as clinically significant rotors and focal sources where therapeutic intervention can be effective. These flow fields can provide quantitative insights into the dynamics of normal and abnormal conduction in humans and could potentially be used to enhance therapies for cardiac arrhythmias.

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS

PubMed Central

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied. PMID:22661790

Sparse distributed memory and related models

NASA Technical Reports Server (NTRS)

Kanerva, Pentti

1992-01-01

Described here is sparse distributed memory (SDM) as a neural-net associative memory. It is characterized by two weight matrices and by a large internal dimension - the number of hidden units is much larger than the number of input or output units. The first matrix, A, is fixed and possibly random, and the second matrix, C, is modifiable. The SDM is compared and contrasted to (1) computer memory, (2) correlation-matrix memory, (3) feet-forward artificial neural network, (4) cortex of the cerebellum, (5) Marr and Albus models of the cerebellum, and (6) Albus' cerebellar model arithmetic computer (CMAC). Several variations of the basic SDM design are discussed: the selected-coordinate and hyperplane designs of Jaeckel, the pseudorandom associative neural memory of Hassoun, and SDM with real-valued input variables by Prager and Fallside. SDM research conducted mainly at the Research Institute for Advanced Computer Science (RIACS) in 1986-1991 is highlighted.

Sparse genetic tracing reveals regionally specific functional organization of mammalian nociceptors.

PubMed

Olson, William; Abdus-Saboor, Ishmail; Cui, Lian; Burdge, Justin; Raabe, Tobias; Ma, Minghong; Luo, Wenqin

2017-10-12

The human distal limbs have a high spatial acuity for noxious stimuli but a low density of pain-sensing neurites. To elucidate mechanisms underlying regional differences in processing nociception, we sparsely traced non-peptidergic nociceptors across the body using a newly generated Mrgprd CreERT2 mouse line. We found that mouse plantar paw skin is also innervated by a low density of Mrgprd + nociceptors, while individual arbors in different locations are comparable in size. Surprisingly, the central arbors of plantar paw and trunk innervating nociceptors have distinct morphologies in the spinal cord. This regional difference is well correlated with a heightened signal transmission for plantar paw circuits, as revealed by both spinal cord slice recordings and behavior assays. Taken together, our results elucidate a novel somatotopic functional organization of the mammalian pain system and suggest that regional central arbor structure could facilitate the "enlarged representation" of plantar paw regions in the CNS.

Compositions and methods for the expression of selenoproteins in eukaryotic cells

DOEpatents

Gladyshev, Vadim [Lincoln, NE; Novoselov, Sergey [Puschino, RU

2012-09-25

Recombinant nucleic acid constructs for the efficient expression of eukaryotic selenoproteins and related methods for production of recombinant selenoproteins are provided. The nucleic acid constructs comprise novel selenocysteine insertion sequence (SECIS) elements. Certain novel SECIS elements of the invention contain non-canonical quartet sequences. Other novel SECIS elements provided by the invention are chimeric SECIS elements comprising a canonical SECIS element that contains a non-canonical quartet sequence and chimeric SECIS elements comprising a non-canonical SECIS element that contains a canonical quartet sequence. The novel SECIS elements of the invention facilitate the insertion of selenocysteine residues into recombinant polypeptides.

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

PubMed

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

2010-04-15

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

Canonical correlation analysis of factors involved in the occurrence of peptic ulcers.

PubMed

Bayyurt, Nizamettin; Abasiyanik, M Fatih; Sander, Ersan; Salih, Barik A

2007-01-01

The impact of risk factors on the development of peptic ulcers has been shown to vary among different populations. We sought to establish a correlation between these factors and their involvement in the occurrence of peptic ulcers for which a canonical correlation analysis was applied. We included 7,014 patient records (48.6% women, 18.4% duodenal ulcer [DU], 4.6% gastric ulcer [GU]) of those underwent upper gastroendoscopy for the last 5 years. The variables measured are endoscopic findings (DU, GU, antral gastritis, erosive gastritis, pangastritis, pyloric deformity, bulbar deformity, bleeding, atrophy, Barret esophagus and gastric polyp) and risk factors (age, gender, Helicobacter pylori infection, smoking, alcohol, and nonsteroidal anti-inflammatory drugs [NSAIDs] and aspirin intake). We found that DU had significant positive correlation with bulbar deformity (P=2.6 x 10(-23)), pyloric deformity (P=2.6 x 10(-23)), gender (P=2.6 x 10(-23)), H. pylori (P=1.4 x 10(-15)), bleeding (P=6.9 x 10(-15)), smoking (P=1.4 x 10(-7)), aspirin use (P=1.1 x 10(-4)), alcohol intake (P=7.7 x 10(-4)), and NSAIDs (P=.01). GU had a significantly positive correlation with pyloric deformity (P=1,6 x 10(-15)), age (P=2.6 x 10(-14)), bleeding (P=3.7 x 10(-8)), gender (P=1.3 x 10(-7)), aspirin use (P=1.1 x 10(-6)), bulbar deformity (P=7.4 x 10(-4)), alcohol intake (P=.03), smoking (P=.04), and Barret esophagus (P=.03). The level of significance was much higher in some variables with DU than with GU and the correlations with GU in spite of being highly significant the majority, were small in magnitude. In conclusion, Turkish patients with the following endoscopic findings bulbar deformity and pyloric deformity are high-risk patients for peptic ulcers with the risk of the occurrence of DU being higher than that of GU. Factors such as H. pylori, smoking, alcohol use, and NSAIDs use (listed in a decreasing manner) are risk factors that have significant impact on the occurrence of DU; aspirin has a significant impact on both DU and GU.

Induction of Canonical Wnt Signaling by the Alarmins S100A8/A9 in Murine Knee Joints: Implications for Osteoarthritis.

PubMed

van den Bosch, Martijn H; Blom, Arjen B; Schelbergen, Rik F P; Vogl, Thomas; Roth, Johannes P; Slöetjes, Annet W; van den Berg, Wim B; van der Kraan, Peter M; van Lent, Peter L E M

2016-01-01

Both alarmins S100A8/A9 and canonical Wnt signaling have been found to play active roles in the development of experimental osteoarthritis (OA). However, what activates canonical Wnt signaling remains unknown. This study was undertaken to investigate whether S100A8 induces canonical Wnt signaling and whether S100 proteins exert their effects via activation of Wnt signaling. Expression of the genes for S100A8/A9 and Wnt signaling pathway members was measured in an experimental OA model. Selected Wnt signaling pathway members were overexpressed, and levels of S100A8/A9 were measured. Activation of canonical Wnt signaling was determined after injection of S100A8 into naive joints and induction of collagenase-induced OA in S100A9-deficient mice. Expression of Wnt signaling pathway members was tested in macrophages and fibroblasts after S100A8 stimulation. Canonical Wnt signaling was inhibited in vivo to determine if the effects of S100A8 injections were dependent on Wnt signaling. The alarmins S100A8/A9 and members of the Wnt signaling pathway showed coinciding expression in synovial tissue in an experimental OA model. Synovial overexpression of selected Wnt signaling pathway members did not result in increased expression of S100 proteins. In contrast, intraarticular injection of S100A8 increased canonical Wnt signaling, whereas canonical Wnt signaling was decreased after induction of experimental OA in S100A9-deficient mice. S100A8 stimulation of macrophages, but not fibroblasts, resulted in increased expression of canonical Wnt signaling members. Overexpression of Dkk-1 to inhibit canonical Wnt signaling decreased the induction of matrix metalloproteinase 3, interleukin-6, and macrophage inflammatory protein 1α after injection of S100A8. Our findings indicate that the alarmin S100A8 induces canonical Wnt signaling in macrophages and murine knee joints. The effects of S100A8 are partially dependent on activation of canonical Wnt signaling. © 2016, American College of Rheumatology.

Epithelial transformation by KLF4 requires Notch1 but not canonical Notch1 signaling

PubMed Central

Liu, Zhaoli; Teng, Lihong; Bailey, Sarah K.; Frost, Andra R.; Bland, Kirby I.; LoBuglio, Albert F.; Ruppert, J. Michael; Lobo-Ruppert, Susan M.

2009-01-01

The transcription factors Notch1 and KLF4 specify epithelial cell fates and confer stem cell properties. suggesting a functional relationship, each gene can act to promote or suppress tumorigenesis in a context-dependent manner, and alteration of KLF4 or Notch pathway genes in mice gives rise to similar phenotypes. Activation of a conditional allele of KLF4 in RK3E epithelial cells rapidly induces expression of Notch1 mRNA and the active, intracellular form of Notch1. KLF4-induced transformation was suppressed by knockdown of endogenous Notch1 using siRNA or an inhibitor of γ-secretase. Chromatin immunoprecipitation assay shows that KLF4 binds to the proximal Notch1 promoter in human mammary epithelial cells, and siRNA-mediated suppression of KLF4 in human mammary cancer cells results in reduced expression of Notch1. Furthermore, KLF4 and Notch1 expression are correlated in primary human breast tumors (N = 89; pearson analysis, r > 0.5, p < 0.0001). Like KLF4, Notch1 was previously shown to induce transformation of rat cells immortalized with adenovirus E1A, similar to RK3E cells. We therefore compared the signaling requirements for Notch1- or KLF4-induced malignant transformation of RK3E. As expected, transformation by Notch1 was suppressed by dominant-negative CSL or MaML1, inhibitors of canonical Notch1 signaling. However, these inhibitors did not suppress transformation by KLF4. Therefore, while KLF4-induced transformation requires Notch1, canonical Notch1 signaling is not required, and Notch1 may signal through a distinct pathway in cells with increased KLF4 activity. These results suggest that KLF4 could contribute to breast tumor progression by activating synthesis of Notch1 and by promoting signaling through a non-canonical Notch1 pathway. PMID:19717984

Mesenchymal stem cells promote the sustained expression of CD69 on activated T lymphocytes: roles of canonical and non-canonical NF-κB signalling

PubMed Central

Saldanha-Araujo, Felipe; Haddad, Rodrigo; de Farias, Kelen C R Malmegrim; Souza, Alessandra de Paula Alves; Palma, Patrícia V; Araujo, Amélia G; Orellana, Maristela D; Voltarelli, Julio C; Covas, Dimas T; Zago, Marco A; Panepucci, Rodrigo A

2012-01-01

Abstract Mesenchymal stem cells (MSCs) are known to induce the conversion of activated T cells into regulatory T cells in vitro. The marker CD69 is a target of canonical nuclear factor kappa-B (NF-κB) signalling and is transiently expressed upon activation; however, stable CD69 expression defines cells with immunoregulatory properties. Given its enormous therapeutic potential, we explored the molecular mechanisms underlying the induction of regulatory cells by MSCs. Peripheral blood CD3+ T cells were activated and cultured in the presence or absence of MSCs. CD4+ cell mRNA expression was then characterized by microarray analysis. The drug BAY11-7082 (BAY) and a siRNA against v-rel reticuloendotheliosis viral oncogene homolog B (RELB) were used to explore the differential roles of canonical and non-canonical NF-κB signalling, respectively. Flow cytometry and real-time PCR were used for analyses. Genes with immunoregulatory functions, CD69 and non-canonical NF-κB subunits (RELB and NFKB2) were all expressed at higher levels in lymphocytes co-cultured with MSCs. The frequency of CD69+ cells among lymphocytes cultured alone progressively decreased after activation. In contrast, the frequency of CD69+ cells increased significantly following activation in lymphocytes co-cultured with MSCs. Inhibition of canonical NF-κB signalling by BAY immediately following activation blocked the induction of CD69; however, inhibition of canonical NF-κB signalling on the third day further induced the expression of CD69. Furthermore, late expression of CD69 was inhibited by RELB siRNA. These results indicate that the canonical NF-κB pathway controls the early expression of CD69 after activation; however, in an immunoregulatory context, late and sustained CD69 expression is promoted by the non-canonical pathway and is inhibited by canonical NF-κB signalling. PMID:21777379

Distinct Effects of the mesenchymal dysplasia Gene Variant of Murine Patched-1 Protein on Canonical and Non-canonical Hedgehog Signaling Pathways*

PubMed Central

Harvey, Malcolm C.; Fleet, Andrew; Okolowsky, Nadia; Hamel, Paul A.

2014-01-01

Hedgehog (Hh) signaling requires regulation of the receptor Patched-1 (Ptch1), which, in turn, regulates Smoothened activity (canonical Hh signaling) as well as other non-canonical signaling pathways. The mutant Ptch1 allele mesenchymal dysplasia (mes), which truncates the Ptch1 C terminus, produces a limited spectrum of developmental defects in mice as well as deregulation of canonical Hh signaling in some, but not all, affected tissues. Paradoxically, mes suppresses canonical Hh signaling and binds to Hh ligands with an affinity similar to wild-type mouse Ptch1 (mPtch1). We characterized the distinct activities of the mes variant of mPtch1 mediating Hh signaling through both canonical and non-canonical pathways. We demonstrated that mPtch1 bound c-src in an Hh-regulated manner. Stimulation with Sonic Hedgehog (Shh) of primary mammary mesenchymal cells from wild-type and mes animals activated Erk1/2. Although Shh activated c-src in wild-type cells, c-src was constitutively activated in mes mesenchymal cells. Transient assays showed that wild-type mPtch1, mes, or mPtch1 lacking the C terminus repressed Hh signaling in Ptch1-deficient mouse embryo fibroblasts and that repression was reversed by Shh, revealing that the C terminus was dispensable for mPtch1-dependent regulation of canonical Hh signaling. In contrast to these transient assays, constitutively high levels of mGli1 but not mPtch1 were present in primary mammary mesenchymal cells from mes mice, whereas the expression of mPtch1 was similarly induced in both mes and wild-type cells. These data define a novel signal transduction pathway involving c-src that is activated by the Hh ligands and reveals the requirement for the C terminus of Ptch in regulation of canonical and non-canonical Hh signaling pathways. PMID:24570001

Gamma-ray emission from black holes

NASA Technical Reports Server (NTRS)

Ling, James C.

1991-01-01

Strong continuum gamma-ray emission at about 1 MeV possibly correlated with a narrow annihilation line at 511 keV has been observed from both Cygnus X-1 and the Galactic center. Such correlated emission has been interpreted as a unique gamma-ray signature for theoretically predicted relativistic, positron-electron pair-dominated plasma in regions surrounding the black holes. In this paper, the Cygnus X-1 results, which have provided important new insights about the source, are reviewed. Cygnus X-1 may be considered a canonical reference stellar black hole whose spectral and temporal characteristics can be used for comparison with those of other black-hole candidates including the Galactic center and AGN.

Far-red fluorescent probes for canonical and non-canonical nucleic acid structures: current progress and future implications.

PubMed

Suseela, Y V; Narayanaswamy, Nagarjun; Pratihar, Sumon; Govindaraju, Thimmaiah

2018-02-05

The structural diversity and functional relevance of nucleic acids (NAs), mainly deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), are indispensable for almost all living organisms, with minute aberrations in their structure and function becoming causative factors in numerous human diseases. The standard structures of NAs, termed canonical structures, are supported by Watson-Crick hydrogen bonding. Under special physiological conditions, NAs adopt distinct spatial organisations, giving rise to non-canonical conformations supported by hydrogen bonding other than the Watson-Crick type; such non-canonical structures have a definite function in controlling gene expression and are considered as novel diagnostic and therapeutic targets. Development of molecular probes for these canonical and non-canonical DNA/RNA structures has been an active field of research. Among the numerous probes studied, probes with turn-on fluorescence in the far-red (600-750 nm) region are highly sought-after due to minimal autofluorescence and cellular damage. Far-red fluorescent probes are vital for real-time imaging of NAs in live cells as they provide good resolution and minimal perturbation of the cell under investigation. In this review, we present recent advances in the area of far-red fluorescent probes of DNA/RNA and non-canonical G-quadruplex structures. For the sake of continuity and completeness, we provide a brief overview of visible fluorescent probes. Utmost importance is given to design criteria, characteristic properties and biological applications, including in cellulo imaging, apart from critical discussion on limitations of the far-red fluorescent probes. Finally, we offer current and future prospects in targeting canonical and non-canonical NAs specific to cellular organelles, through sequence- and conformation-specific far-red fluorescent probes. We also cover their implications in chemical and molecular biology, with particular focus on decoding various disease mechanisms involving NAs.

Liquid Water from First Principles: Validation of Different Sampling Approaches

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

Mundy, C J; Kuo, W; Siepmann, J

2004-05-20

A series of first principles molecular dynamics and Monte Carlo simulations were carried out for liquid water to assess the validity and reproducibility of different sampling approaches. These simulations include Car-Parrinello molecular dynamics simulations using the program CPMD with different values of the fictitious electron mass in the microcanonical and canonical ensembles, Born-Oppenheimer molecular dynamics using the programs CPMD and CP2K in the microcanonical ensemble, and Metropolis Monte Carlo using CP2K in the canonical ensemble. With the exception of one simulation for 128 water molecules, all other simulations were carried out for systems consisting of 64 molecules. It is foundmore » that the structural and thermodynamic properties of these simulations are in excellent agreement with each other as long as adiabatic sampling is maintained in the Car-Parrinello molecular dynamics simulations either by choosing a sufficiently small fictitious mass in the microcanonical ensemble or by Nos{acute e}-Hoover thermostats in the canonical ensemble. Using the Becke-Lee-Yang-Parr exchange and correlation energy functionals and norm-conserving Troullier-Martins or Goedecker-Teter-Hutter pseudopotentials, simulations at a fixed density of 1.0 g/cm{sup 3} and a temperature close to 315 K yield a height of the first peak in the oxygen-oxygen radial distribution function of about 3.0, a classical constant-volume heat capacity of about 70 J K{sup -1} mol{sup -1}, and a self-diffusion constant of about 0.1 Angstroms{sup 2}/ps.« less

High-temperature charge density wave correlations in La 1.875Ba 0.125CuO 4 without spin–charge locking

DOE PAGES

Miao, H.; Lorenzana, J.; Seibold, G.; ...

2017-11-07

Although all superconducting cuprates display charge-ordering tendencies, their low-temperature properties are distinct, impeding efforts to understand the phenomena within a single conceptual framework. While some systems exhibit stripes of charge and spin, with a locked periodicity, others host charge density waves (CDWs) without any obviously related spin order. Here we use resonant inelastic X-ray scattering to follow the evolution of charge correlations in the canonical stripe-ordered cuprate La 1.875Ba 0.125CuO 4 across its ordering transition. We find that high-temperature charge correlations are unlocked from the wavevector of the spin correlations, signaling analogies to CDW phases in various other cuprates. Thismore » indicates that stripe order at low temperatures is stabilized by the coupling of otherwise independent charge and spin density waves, with important implications for the relation between charge and spin correlations in the cuprates.« less

High-temperature charge density wave correlations in La 1.875Ba 0.125CuO 4 without spin–charge locking

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

Miao, H.; Lorenzana, J.; Seibold, G.

Although all superconducting cuprates display charge-ordering tendencies, their low-temperature properties are distinct, impeding efforts to understand the phenomena within a single conceptual framework. While some systems exhibit stripes of charge and spin, with a locked periodicity, others host charge density waves (CDWs) without any obviously related spin order. Here we use resonant inelastic X-ray scattering to follow the evolution of charge correlations in the canonical stripe-ordered cuprate La 1.875Ba 0.125CuO 4 across its ordering transition. We find that high-temperature charge correlations are unlocked from the wavevector of the spin correlations, signaling analogies to CDW phases in various other cuprates. Thismore » indicates that stripe order at low temperatures is stabilized by the coupling of otherwise independent charge and spin density waves, with important implications for the relation between charge and spin correlations in the cuprates.« less

Late onset canonical babbling: a possible early marker of abnormal development.

PubMed

Oller, D K; Eilers, R E; Neal, A R; Cobo-Lewis, A B

1998-11-01

By their 10th month of life, typically developing infants produce canonical babbling, which includes the well-formed syllables required for meaningful speech. Research suggests that emerging speech or language-related disorders might be associated with late onset of canonical babbling. Onset of canonical babbling was investigated for 1,536 high-risk infants, at about 10-months corrected age. Parental report by open-ended questionnaire was found to be an efficient method for ascertaining babbling status. Although delays were infrequent, they were often associated with genetic, neurological, anatomical, and/or physiological abnormalities. Over half the cases of late canonical babbling were not, at the time they were discovered associated with prior significant medical diagnoses. Late canonical-babbling onset may be a predictor of later developmental disabilities, including problems in speech, language, and reading.

Generalization between canonical and non-canonical views in object recognition

PubMed Central

Ghose, Tandra; Liu, Zili

2013-01-01

Viewpoint generalization in object recognition is the process that allows recognition of a given 3D object from many different viewpoints despite variations in its 2D projections. We used the canonical view effects as a foundation to empirically test the validity of a major theory in object recognition, the view-approximation model (Poggio & Edelman, 1990). This model predicts that generalization should be better when an object is first seen from a non-canonical view and then a canonical view than when seen in the reversed order. We also manipulated object similarity to study the degree to which this view generalization was constrained by shape details and task instructions (object vs. image recognition). Old-new recognition performance for basic and subordinate level objects was measured in separate blocks. We found that for object recognition, view generalization between canonical and non-canonical views was comparable for basic level objects. For subordinate level objects, recognition performance was more accurate from non-canonical to canonical views than the other way around. When the task was changed from object recognition to image recognition, the pattern of the results reversed. Interestingly, participants responded “old” to “new” images of “old” objects with a substantially higher rate than to “new” objects, despite instructions to the contrary, thereby indicating involuntary view generalization. Our empirical findings are incompatible with the prediction of the view-approximation theory, and argue against the hypothesis that views are stored independently. PMID:23283692

Intensity correlation imaging with sunlight-like source

NASA Astrophysics Data System (ADS)

Wang, Wentao; Tang, Zhiguo; Zheng, Huaibin; Chen, Hui; Yuan, Yuan; Liu, Jinbin; Liu, Yanyan; Xu, Zhuo

2018-05-01

We show a method of intensity correlation imaging of targets illuminated by a sunlight-like source both theoretically and experimentally. With a Faraday anomalous dispersion optical filter (FADOF), we have modulated the coherence time of a thermal source up to 0.167 ns. And we carried out measurements of temporal and spatial correlations, respectively, with an intensity interferometer setup. By skillfully using the even Fourier fitting on the very sparse sampling data, the images of targets are successfully reconstructed from the low signal-noise-ratio(SNR) interference pattern by applying an iterative phase retrieval algorithm. The resulting imaging quality is as well as the one obtained by the theoretical fitting. The realization of such a case will bring this technique closer to geostationary satellite imaging illuminated by sunlight.

Role of a non-canonical surface of Rad6 in ubiquitin conjugating activity

PubMed Central

Kumar, Pankaj; Magala, Pearl; Geiger-Schuller, Kathryn R.; Majumdar, Ananya; Tolman, Joel R.; Wolberger, Cynthia

2015-01-01

Rad6 is a yeast E2 ubiquitin conjugating enzyme that monoubiquitinates histone H2B in conjunction with the E3, Bre1, but can non-specifically modify histones on its own. We determined the crystal structure of a Rad6∼Ub thioester mimic, which revealed a network of interactions in the crystal in which the ubiquitin in one conjugate contacts Rad6 in another. The region of Rad6 contacted is located on the distal face of Rad6 opposite the active site, but differs from the canonical E2 backside that mediates free ubiquitin binding and polyubiquitination activity in other E2 enzymes. We find that free ubiquitin interacts weakly with both non-canonical and canonical backside residues of Rad6 and that mutations of non-canonical residues have deleterious effects on Rad6 activity comparable to those observed to mutations in the canonical E2 backside. The effect of non-canonical backside mutations is similar in the presence and absence of Bre1, indicating that contacts with non-canonical backside residues govern the intrinsic activity of Rad6. Our findings shed light on the determinants of intrinsic Rad6 activity and reveal new ways in which contacts with an E2 backside can regulate ubiquitin conjugating activity. PMID:26286193

Improving temporal resolution in fMRI using a 3D spiral acquisition and low rank plus sparse (L+S) reconstruction.

PubMed

Petrov, Andrii Y; Herbst, Michael; Andrew Stenger, V

2017-08-15

Rapid whole-brain dynamic Magnetic Resonance Imaging (MRI) is of particular interest in Blood Oxygen Level Dependent (BOLD) functional MRI (fMRI). Faster acquisitions with higher temporal sampling of the BOLD time-course provide several advantages including increased sensitivity in detecting functional activation, the possibility of filtering out physiological noise for improving temporal SNR, and freezing out head motion. Generally, faster acquisitions require undersampling of the data which results in aliasing artifacts in the object domain. A recently developed low-rank (L) plus sparse (S) matrix decomposition model (L+S) is one of the methods that has been introduced to reconstruct images from undersampled dynamic MRI data. The L+S approach assumes that the dynamic MRI data, represented as a space-time matrix M, is a linear superposition of L and S components, where L represents highly spatially and temporally correlated elements, such as the image background, while S captures dynamic information that is sparse in an appropriate transform domain. This suggests that L+S might be suited for undersampled task or slow event-related fMRI acquisitions because the periodic nature of the BOLD signal is sparse in the temporal Fourier transform domain and slowly varying low-rank brain background signals, such as physiological noise and drift, will be predominantly low-rank. In this work, as a proof of concept, we exploit the L+S method for accelerating block-design fMRI using a 3D stack of spirals (SoS) acquisition where undersampling is performed in the k z -t domain. We examined the feasibility of the L+S method to accurately separate temporally correlated brain background information in the L component while capturing periodic BOLD signals in the S component. We present results acquired in control human volunteers at 3T for both retrospective and prospectively acquired fMRI data for a visual activation block-design task. We show that a SoS fMRI acquisition with an acceleration of four and L+S reconstruction can achieve a brain coverage of 40 slices at 2mm isotropic resolution and 64 x 64 matrix size every 500ms. Copyright © 2017 Elsevier Inc. All rights reserved.

BMP regulates regional gene expression in the dorsal otocyst through canonical and non-canonical intracellular pathways

PubMed Central

2016-01-01

The inner ear consists of two otocyst-derived, structurally and functionally distinct components: the dorsal vestibular and ventral auditory compartments. BMP signaling is required to form the vestibular compartment, but how it complements other required signaling molecules and acts intracellularly is unknown. Using spatially and temporally controlled delivery of signaling pathway regulators to developing chick otocysts, we show that BMP signaling regulates the expression of Dlx5 and Hmx3, both of which encode transcription factors essential for vestibular formation. However, although BMP regulates Dlx5 through the canonical SMAD pathway, surprisingly, it regulates Hmx3 through a non-canonical pathway involving both an increase in cAMP-dependent protein kinase A activity and the GLI3R to GLI3A ratio. Thus, both canonical and non-canonical BMP signaling establish the precise spatiotemporal expression of Dlx5 and Hmx3 during dorsal vestibular development. The identification of the non-canonical pathway suggests an intersection point between BMP and SHH signaling, which is required for ventral auditory development. PMID:27151948

Restoring canonical partition functions from imaginary chemical potential

NASA Astrophysics Data System (ADS)

Bornyakov, V. G.; Boyda, D.; Goy, V.; Molochkov, A.; Nakamura, A.; Nikolaev, A.; Zakharov, V. I.

2018-03-01

Using GPGPU techniques and multi-precision calculation we developed the code to study QCD phase transition line in the canonical approach. The canonical approach is a powerful tool to investigate sign problem in Lattice QCD. The central part of the canonical approach is the fugacity expansion of the grand canonical partition functions. Canonical partition functions Zn(T) are coefficients of this expansion. Using various methods we study properties of Zn(T). At the last step we perform cubic spline for temperature dependence of Zn(T) at fixed n and compute baryon number susceptibility χB/T2 as function of temperature. After that we compute numerically ∂χ/∂T and restore crossover line in QCD phase diagram. We use improved Wilson fermions and Iwasaki gauge action on the 163 × 4 lattice with mπ/mρ = 0.8 as a sandbox to check the canonical approach. In this framework we obtain coefficient in parametrization of crossover line Tc(µ2B) = Tc(C-ĸµ2B/T2c) with ĸ = -0.0453 ± 0.0099.

Precursors to speech in infancy: the prediction of speech and language disorders.

PubMed

Oller, D K; Eilers, R E; Neal, A R; Schwartz, H K

1999-01-01

During the canonical stage of infant babbling, infants produce well-formed syllables, often in reduplicated sequences such as "bababa." Although nearly all infants with normal hearing begin the canonical stage by 10 months of age, a few are delayed, and these infants may be of special interest. Recent studies indicate that late onset of canonical babbling may be a predictor of disorders. A simple screening procedure that focuses on canonical babbling was used to evaluate over 3400 infants at risk who were about 10 months of age. Among infants who showed late onset of canonical babbling, fewer than half had been previously diagnosed as having a significant medical problem that might have accounted for the delay. A follow-up study indicated that infants with delayed canonical babbling had smaller production vocabularies at 18, 24, and 30 months than did infants in the control group. The results suggest that late onset of canonical babbling, a factor that can be monitored effectively through an interview with a parent, can predict delay in the onset of speech production.

Ultra-low dose quantitative CT myocardial perfusion imaging with sparse-view dynamic acquisition and image reconstruction: A feasibility study.

PubMed

Enjilela, Esmaeil; Lee, Ting-Yim; Hsieh, Jiang; Wisenberg, Gerald; Teefy, Patrick; Yadegari, Andrew; Bagur, Rodrigo; Islam, Ali; Branch, Kelley; So, Aaron

2018-03-01

We implemented and validated a compressed sensing (CS) based algorithm for reconstructing dynamic contrast-enhanced (DCE) CT images of the heart from sparsely sampled X-ray projections. DCE CT imaging of the heart was performed on five normal and ischemic pigs after contrast injection. DCE images were reconstructed with filtered backprojection (FBP) and CS from all projections (984-view) and 1/3 of all projections (328-view), and with CS from 1/4 of all projections (246-view). Myocardial perfusion (MP) measurements with each protocol were compared to those with the reference 984-view FBP protocol. Both the 984-view CS and 328-view CS protocols were in good agreements with the reference protocol. The Pearson correlation coefficients of 984-view CS and 328-view CS determined from linear regression analyses were 0.98 and 0.99 respectively. The corresponding mean biases of MP measurement determined from Bland-Altman analyses were 2.7 and 1.2ml/min/100g. When only 328 projections were used for image reconstruction, CS was more accurate than FBP for MP measurement with respect to 984-view FBP. However, CS failed to generate MP maps comparable to those with 984-view FBP when only 246 projections were used for image reconstruction. DCE heart images reconstructed from one-third of a full projection set with CS were minimally affected by aliasing artifacts, leading to accurate MP measurements with the effective dose reduced to just 33% of conventional full-view FBP method. The proposed CS sparse-view image reconstruction method could facilitate the implementation of sparse-view dynamic acquisition for ultra-low dose CT MP imaging. Copyright © 2017 Elsevier B.V. All rights reserved.

Efficient grid-based techniques for density functional theory

NASA Astrophysics Data System (ADS)

Rodriguez-Hernandez, Juan Ignacio

Understanding the chemical and physical properties of molecules and materials at a fundamental level often requires quantum-mechanical models for these substance's electronic structure. This type of many body quantum mechanics calculation is computationally demanding, hindering its application to substances with more than a few hundreds atoms. The supreme goal of many researches in quantum chemistry---and the topic of this dissertation---is to develop more efficient computational algorithms for electronic structure calculations. In particular, this dissertation develops two new numerical integration techniques for computing molecular and atomic properties within conventional Kohn-Sham-Density Functional Theory (KS-DFT) of molecular electronic structure. The first of these grid-based techniques is based on the transformed sparse grid construction. In this construction, a sparse grid is generated in the unit cube and then mapped to real space according to the pro-molecular density using the conditional distribution transformation. The transformed sparse grid was implemented in program deMon2k, where it is used as the numerical integrator for the exchange-correlation energy and potential in the KS-DFT procedure. We tested our grid by computing ground state energies, equilibrium geometries, and atomization energies. The accuracy on these test calculations shows that our grid is more efficient than some previous integration methods: our grids use fewer points to obtain the same accuracy. The transformed sparse grids were also tested for integrating, interpolating and differentiating in different dimensions (n = 1,2,3,6). The second technique is a grid-based method for computing atomic properties within QTAIM. It was also implemented in deMon2k. The performance of the method was tested by computing QTAIM atomic energies, charges, dipole moments, and quadrupole moments. For medium accuracy, our method is the fastest one we know of.

Galaxy redshift surveys with sparse sampling

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

Chiang, Chi-Ting; Wullstein, Philipp; Komatsu, Eiichiro

2013-12-01

Survey observations of the three-dimensional locations of galaxies are a powerful approach to measure the distribution of matter in the universe, which can be used to learn about the nature of dark energy, physics of inflation, neutrino masses, etc. A competitive survey, however, requires a large volume (e.g., V{sub survey} ∼ 10Gpc{sup 3}) to be covered, and thus tends to be expensive. A ''sparse sampling'' method offers a more affordable solution to this problem: within a survey footprint covering a given survey volume, V{sub survey}, we observe only a fraction of the volume. The distribution of observed regions should bemore » chosen such that their separation is smaller than the length scale corresponding to the wavenumber of interest. Then one can recover the power spectrum of galaxies with precision expected for a survey covering a volume of V{sub survey} (rather than the volume of the sum of observed regions) with the number density of galaxies given by the total number of observed galaxies divided by V{sub survey} (rather than the number density of galaxies within an observed region). We find that regularly-spaced sampling yields an unbiased power spectrum with no window function effect, and deviations from regularly-spaced sampling, which are unavoidable in realistic surveys, introduce calculable window function effects and increase the uncertainties of the recovered power spectrum. On the other hand, we show that the two-point correlation function (pair counting) is not affected by sparse sampling. While we discuss the sparse sampling method within the context of the forthcoming Hobby-Eberly Telescope Dark Energy Experiment, the method is general and can be applied to other galaxy surveys.« less

Integrated data analysis for genome-wide research.

PubMed

Steinfath, Matthias; Repsilber, Dirk; Scholz, Matthias; Walther, Dirk; Selbig, Joachim

2007-01-01

Integrated data analysis is introduced as the intermediate level of a systems biology approach to analyse different 'omics' datasets, i.e., genome-wide measurements of transcripts, protein levels or protein-protein interactions, and metabolite levels aiming at generating a coherent understanding of biological function. In this chapter we focus on different methods of correlation analyses ranging from simple pairwise correlation to kernel canonical correlation which were recently applied in molecular biology. Several examples are presented to illustrate their application. The input data for this analysis frequently originate from different experimental platforms. Therefore, preprocessing steps such as data normalisation and missing value estimation are inherent to this approach. The corresponding procedures, potential pitfalls and biases, and available software solutions are reviewed. The multiplicity of observations obtained in omics-profiling experiments necessitates the application of multiple testing correction techniques.

Thermodynamic properties Ar films on the surface of a bundle of carbon nanotubes

NASA Astrophysics Data System (ADS)

Cole, Milton; Gatica, Silvina

2005-03-01

We employ canonical Monte Carlo simulations to explore the properties of an Argon film adsorbed on the external surface of a bundle of carbon nanotubes. The study is concerned primarily with three properties: specific heat, differential heat of adsorption, and Ar-Ar correlation functions. These measurable functions exhibit information about the dependence of film structure on coverage and temperature. Our results are intended to stimulate further experimental studies of this system and analogous systems involving other gases on nanotube bundles. One of the more interesting general results is that the specific heat is typically larger than might have been expected. Particularly remarkable outcome from the correlation function studies include the reduced longitudinal correlations in the groove and striped phases as T rises above 60 K. These results would be amenable to testing by diffraction experiments.

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

Shen, Longxiang; Glowacki, Julie; Zhou, Shuanhu, E-mail: szhou@rics.bwh.harvard.edu

The WNT signaling pathway plays important roles in the self-renewal and differentiation of mesenchymal stem cells (MSCs). Little is known about WNT signaling in adipocyte differentiation of human MSCs. In this study, we tested the hypothesis that canonical and non-canonical WNTs differentially regulate in vitro adipocytogenesis in human MSCs. The expression of adipocyte gene PPAR{gamma}2, lipoprotein lipase, and adipsin increased during adipocytogenesis of hMSCs. Simultaneously, the expression of canonical WNT2, 10B, 13, and 14 decreased, whereas non-canonical WNT4 and 11 increased, and WNT5A was unchanged. A small molecule WNT mimetic, SB-216763, increased accumulation of {beta}-catenin protein, inhibited induction of WNT4more » and 11 and inhibited adipocytogenesis. In contrast, knockdown of {beta}-catenin with siRNA resulted in spontaneous adipocytogenesis. These findings support the view that canonical WNT signaling inhibits and non-canonical WNT signaling promotes adipocytogenesis in adult human marrow-derived mesenchymal stem cells.« less

Complete Hamiltonian analysis of cosmological perturbations at all orders II: non-canonical scalar field

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

Nandi, Debottam; Shankaranarayanan, S., E-mail: debottam@iisertvm.ac.in, E-mail: shanki@iisertvm.ac.in

2016-10-01

In this work, we present a consistent Hamiltonian analysis of cosmological perturbations for generalized non-canonical scalar fields. In order to do so, we introduce a new phase-space variable that is uniquely defined for different non-canonical scalar fields. We also show that this is the simplest and efficient way of expressing the Hamiltonian. We extend the Hamiltonian approach of [1] to non-canonical scalar field and obtain an unique expression of speed of sound in terms of phase-space variable. In order to invert generalized phase-space Hamilton's equations to Euler-Lagrange equations of motion, we prescribe a general inversion formulae and show that ourmore » approach for non-canonical scalar field is consistent. We also obtain the third and fourth order interaction Hamiltonian for generalized non-canonical scalar fields and briefly discuss the extension of our method to generalized Galilean scalar fields.« less

Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA.

PubMed

Rabiul Islam, Md; Khademul Islam Molla, Md; Nakanishi, Masaki; Tanaka, Toshihisa

2017-04-01

Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper develops a novel unsupervised method based on canonical correlation analysis (CCA) for accurate detection of stimulus frequency. A novel unsupervised technique termed as binary subband CCA (BsCCA) is implemented in a multiband approach to enhance the frequency recognition performance of SSVEP. In BsCCA, two subbands are used and a CCA-based correlation coefficient is computed for the individual subbands. In addition, a reduced set of artificial reference signals is used to calculate CCA for the second subband. The analyzing SSVEP is decomposed into multiple subband and the BsCCA is implemented for each one. Then, the overall recognition score is determined by a weighted sum of the canonical correlation coefficients obtained from each band. A 12-class SSVEP dataset (frequency range: 9.25-14.75 Hz with an interval of 0.5 Hz) for ten healthy subjects are used to evaluate the performance of the proposed method. The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods. The proposed method is an unsupervised approach with averaged information transfer rate (ITR) of 77.04 bits min -1 across 10 subjects. The maximum individual ITR is 107.55 bits min -1 for 12-class SSVEP dataset, whereas, the ITR of 69.29 and 69.44 bits min -1 are achieved with CCA and NCCA respectively. The statistical test shows that the proposed unsupervised method significantly improves the performance of the SSVEP-based BCI. It can be usable in real world applications.

In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia.

PubMed

Sui, Jing; Pearlson, Godfrey D; Du, Yuhui; Yu, Qingbao; Jones, Thomas R; Chen, Jiayu; Jiang, Tianzi; Bustillo, Juan; Calhoun, Vince D

2015-12-01

The cognitive deficits of schizophrenia are largely resistant to current treatments and thus are a lifelong illness burden. The Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) provides a reliable and valid assessment of cognition across major cognitive domains; however, the multimodal brain alterations specifically associated with MCCB in schizophrenia have not been examined. The interrelationships between MCCB and the abnormalities seen in three types of neuroimaging-derived maps-fractional amplitude of low-frequency fluctuations (fALFF) from resting-state functional magnetic resonance imaging (MRI), gray matter (GM) density from structural MRI, and fractional anisotropy from diffusion MRI-were investigated by using multiset canonical correlation analysis in data from 47 schizophrenia patients treated with antipsychotic medications and 50 age-matched healthy control subjects. One multimodal component (canonical variant 8) was identified as both group differentiating and significantly correlated with the MCCB composite. It demonstrated 1) increased cognitive performance associated with higher fALFF (intensity of regional spontaneous brain activity) and higher GM volumes in thalamus, striatum, hippocampus, and the mid-occipital region, with co-occurring fractional anisotropy changes in superior longitudinal fascicules, anterior thalamic radiation, and forceps major; 2) higher fALFF but lower GM volume in dorsolateral prefrontal cortex related to worse cognition in schizophrenia; and 3) distinct domains of MCCB might exhibit dissociable multimodal signatures, e.g., increased fALFF in inferior parietal lobule particularly correlated with decreased social cognition. Medication dose did not relate to these findings in schizophrenia. Our results suggest linked functional and structural deficits in distributed cortico-striato-thalamic circuits may be closely related to MCCB-measured cognitive impairments in schizophrenia. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Statistical correlations and risk analyses techniques for a diving dual phase bubble model and data bank using massively parallel supercomputers.

PubMed

Wienke, B R; O'Leary, T R

2008-05-01

Linking model and data, we detail the LANL diving reduced gradient bubble model (RGBM), dynamical principles, and correlation with data in the LANL Data Bank. Table, profile, and meter risks are obtained from likelihood analysis and quoted for air, nitrox, helitrox no-decompression time limits, repetitive dive tables, and selected mixed gas and repetitive profiles. Application analyses include the EXPLORER decompression meter algorithm, NAUI tables, University of Wisconsin Seafood Diver tables, comparative NAUI, PADI, Oceanic NDLs and repetitive dives, comparative nitrogen and helium mixed gas risks, USS Perry deep rebreather (RB) exploration dive,world record open circuit (OC) dive, and Woodville Karst Plain Project (WKPP) extreme cave exploration profiles. The algorithm has seen extensive and utilitarian application in mixed gas diving, both in recreational and technical sectors, and forms the bases forreleased tables and decompression meters used by scientific, commercial, and research divers. The LANL Data Bank is described, and the methods used to deduce risk are detailed. Risk functions for dissolved gas and bubbles are summarized. Parameters that can be used to estimate profile risk are tallied. To fit data, a modified Levenberg-Marquardt routine is employed with L2 error norm. Appendices sketch the numerical methods, and list reports from field testing for (real) mixed gas diving. A Monte Carlo-like sampling scheme for fast numerical analysis of the data is also detailed, as a coupled variance reduction technique and additional check on the canonical approach to estimating diving risk. The method suggests alternatives to the canonical approach. This work represents a first time correlation effort linking a dynamical bubble model with deep stop data. Supercomputing resources are requisite to connect model and data in application.

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.