Optimizing human activity patterns using global sensitivity analysis

PubMed Central

Hickmann, Kyle S.; Mniszewski, Susan M.; Del Valle, Sara Y.; Hyman, James M.

2014-01-01

Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations. PMID:25580080

Optimizing human activity patterns using global sensitivity analysis

DOE PAGES

Fairchild, Geoffrey; Hickmann, Kyle S.; Mniszewski, Susan M.; ...

2013-12-10

Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimizationmore » problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. Here we use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Finally, though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.« less

Fault detection in reciprocating compressor valves under varying load conditions

NASA Astrophysics Data System (ADS)

Pichler, Kurt; Lughofer, Edwin; Pichler, Markus; Buchegger, Thomas; Klement, Erich Peter; Huschenbett, Matthias

2016-03-01

This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method's performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions.

Object-oriented regression for building predictive models with high dimensional omics data from translational studies.

PubMed

Zhao, Lue Ping; Bolouri, Hamid

2016-04-01

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and has made the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient's similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient's HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (P-value=0.015). Copyright © 2016 Elsevier Inc. All rights reserved.

Object-Oriented Regression for Building Predictive Models with High Dimensional Omics Data from Translational Studies

PubMed Central

Zhao, Lue Ping; Bolouri, Hamid

2016-01-01

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and to make the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient’s similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient’s HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (p=0.015). PMID:26972839

Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI

PubMed Central

Davatzikos, C.; Resnick, S. M.; Wu, X.; Parmpi, P.; Clark, C. M.

2008-01-01

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting. PMID:18474436

Partitioned learning of deep Boltzmann machines for SNP data.

PubMed

Hess, Moritz; Lenz, Stefan; Blätte, Tamara J; Bullinger, Lars; Binder, Harald

2017-10-15

Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen the joint distribution of SNPs, followed by training several DBMs on SNP partitions that were identified by the screening. Aggregate features representing SNP patterns and the corresponding SNPs are extracted from the DBMs by a combination of statistical tests and sparse regression. In simulated case-control data, we show how this can uncover complex SNP patterns and augment results from univariate approaches, while maintaining type 1 error control. Time-to-event endpoints are considered in an application with acute myeloid leukemia patients, where SNP patterns are modeled after a pre-screening based on gene expression data. The proposed approach identified three SNPs that seem to jointly influence survival in a validation dataset. This indicates the added value of jointly investigating SNPs compared to standard univariate analyses and makes partitioned learning of DBMs an interesting complementary approach when analyzing SNP data. A Julia package is provided at 'http://github.com/binderh/BoltzmannMachines.jl'. binderh@imbi.uni-freiburg.de. 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

SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES

PubMed Central

Zhu, Liping; Huang, Mian; Li, Runze

2012-01-01

This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mild conditions, we show that the simple linear quantile regression offers a consistent estimate of the index parameter vector. This is a surprising and interesting result because the single-index model is possibly misspecified under the linear quantile regression. With a root-n consistent estimate of the index vector, one may employ a local polynomial regression technique to estimate the conditional quantile function. This procedure is computationally efficient, which is very appealing in high-dimensional data analysis. We show that the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of the index vector were known. The methodologies are demonstrated through comprehensive simulation studies and an application to a real dataset. PMID:24501536

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso.

PubMed

Kong, Shengchun; Nan, Bin

2014-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses.

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso

PubMed Central

Kong, Shengchun; Nan, Bin

2013-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses. PMID:24516328

Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns - an investigation using modelled scattering data

NASA Astrophysics Data System (ADS)

Salawu, Emmanuel Oluwatobi; Hesse, Evelyn; Stopford, Chris; Davey, Neil; Sun, Yi

2017-11-01

Better understanding and characterization of cloud particles, whose properties and distributions affect climate and weather, are essential for the understanding of present climate and climate change. Since imaging cloud probes have limitations of optical resolution, especially for small particles (with diameter < 25 μm), instruments like the Small Ice Detector (SID) probes, which capture high-resolution spatial light scattering patterns from individual particles down to 1 μm in size, have been developed. In this work, we have proposed a method using Machine Learning techniques to estimate simulated particles' orientation-averaged projected sizes (PAD) and aspect ratio from their 2D scattering patterns. The two-dimensional light scattering patterns (2DLSP) of hexagonal prisms are computed using the Ray Tracing with Diffraction on Facets (RTDF) model. The 2DLSP cover the same angular range as the SID probes. We generated 2DLSP for 162 hexagonal prisms at 133 orientations for each. In a first step, the 2DLSP were transformed into rotation-invariant Zernike moments (ZMs), which are particularly suitable for analyses of pattern symmetry. Then we used ZMs, summed intensities, and root mean square contrast as inputs to the advanced Machine Learning methods. We created one random forests classifier for predicting prism orientation, 133 orientation-specific (OS) support vector classification models for predicting the prism aspect-ratios, 133 OS support vector regression models for estimating prism sizes, and another 133 OS Support Vector Regression (SVR) models for estimating the size PADs. We have achieved a high accuracy of 0.99 in predicting prism aspect ratios, and a low value of normalized mean square error of 0.004 for estimating the particle's size and size PADs.

High dimensional linear regression models under long memory dependence and measurement error

NASA Astrophysics Data System (ADS)

Kaul, Abhishek

This dissertation consists of three chapters. The first chapter introduces the models under consideration and motivates problems of interest. A brief literature review is also provided in this chapter. The second chapter investigates the properties of Lasso under long range dependent model errors. Lasso is a computationally efficient approach to model selection and estimation, and its properties are well studied when the regression errors are independent and identically distributed. We study the case, where the regression errors form a long memory moving average process. We establish a finite sample oracle inequality for the Lasso solution. We then show the asymptotic sign consistency in this setup. These results are established in the high dimensional setup (p> n) where p can be increasing exponentially with n. Finally, we show the consistency, n½ --d-consistency of Lasso, along with the oracle property of adaptive Lasso, in the case where p is fixed. Here d is the memory parameter of the stationary error sequence. The performance of Lasso is also analysed in the present setup with a simulation study. The third chapter proposes and investigates the properties of a penalized quantile based estimator for measurement error models. Standard formulations of prediction problems in high dimension regression models assume the availability of fully observed covariates and sub-Gaussian and homogeneous model errors. This makes these methods inapplicable to measurement errors models where covariates are unobservable and observations are possibly non sub-Gaussian and heterogeneous. We propose weighted penalized corrected quantile estimators for the regression parameter vector in linear regression models with additive measurement errors, where unobservable covariates are nonrandom. The proposed estimators forgo the need for the above mentioned model assumptions. We study these estimators in both the fixed dimension and high dimensional sparse setups, in the latter setup, the dimensionality can grow exponentially with the sample size. In the fixed dimensional setting we provide the oracle properties associated with the proposed estimators. In the high dimensional setting, we provide bounds for the statistical error associated with the estimation, that hold with asymptotic probability 1, thereby providing the ℓ1-consistency of the proposed estimator. We also establish the model selection consistency in terms of the correctly estimated zero components of the parameter vector. A simulation study that investigates the finite sample accuracy of the proposed estimator is also included in this chapter.

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

PubMed

Jiang, Dingfeng; Huang, Jian; Zhang, Ying

2013-10-01

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.

Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.

PubMed

Heath, Anna; Manolopoulou, Ioanna; Baio, Gianluca

2016-10-15

The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the 'cost' of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision-theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via Monte Carlo simulations. However, recent developments have demonstrated that the EVPPI can be estimated by non-parametric regression methods, which have significantly decreased the computation time required to approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian Process (GP) regression is suggested, but this can still be prohibitively expensive. Applying fast computation methods developed in spatial statistics using Integrated Nested Laplace Approximations (INLA) and projecting from a high-dimensional into a low-dimensional input space allows us to decrease the computation time for fitting these high-dimensional GP, often substantially. We demonstrate that the EVPPI calculated using our method for GP regression is in line with the standard GP regression method and that despite the apparent methodological complexity of this new method, R functions are available in the package BCEA to implement it simply and efficiently. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models.

PubMed

Binder, Harald; Porzelius, Christine; Schumacher, Martin

2011-03-01

Analysis of molecular data promises identification of biomarkers for improving prognostic models, thus potentially enabling better patient management. For identifying such biomarkers, risk prediction models can be employed that link high-dimensional molecular covariate data to a clinical endpoint. In low-dimensional settings, a multitude of statistical techniques already exists for building such models, e.g. allowing for variable selection or for quantifying the added value of a new biomarker. We provide an overview of techniques for regularized estimation that transfer this toward high-dimensional settings, with a focus on models for time-to-event endpoints. Techniques for incorporating specific covariate structure are discussed, as well as techniques for dealing with more complex endpoints. Employing gene expression data from patients with diffuse large B-cell lymphoma, some typical modeling issues from low-dimensional settings are illustrated in a high-dimensional application. First, the performance of classical stepwise regression is compared to stage-wise regression, as implemented by a component-wise likelihood-based boosting approach. A second issues arises, when artificially transforming the response into a binary variable. The effects of the resulting loss of efficiency and potential bias in a high-dimensional setting are illustrated, and a link to competing risks models is provided. Finally, we discuss conditions for adequately quantifying the added value of high-dimensional gene expression measurements, both at the stage of model fitting and when performing evaluation. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bayesian propensity scores for high-dimensional causal inference: A comparison of drug-eluting to bare-metal coronary stents.

PubMed

Spertus, Jacob V; Normand, Sharon-Lise T

2018-04-23

High-dimensional data provide many potential confounders that may bolster the plausibility of the ignorability assumption in causal inference problems. Propensity score methods are powerful causal inference tools, which are popular in health care research and are particularly useful for high-dimensional data. Recent interest has surrounded a Bayesian treatment of propensity scores in order to flexibly model the treatment assignment mechanism and summarize posterior quantities while incorporating variance from the treatment model. We discuss methods for Bayesian propensity score analysis of binary treatments, focusing on modern methods for high-dimensional Bayesian regression and the propagation of uncertainty. We introduce a novel and simple estimator for the average treatment effect that capitalizes on conjugacy of the beta and binomial distributions. Through simulations, we show the utility of horseshoe priors and Bayesian additive regression trees paired with our new estimator, while demonstrating the importance of including variance from the treatment regression model. An application to cardiac stent data with almost 500 confounders and 9000 patients illustrates approaches and facilitates comparison with existing alternatives. As measured by a falsifiability endpoint, we improved confounder adjustment compared with past observational research of the same problem. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mining High-Dimensional Data

NASA Astrophysics Data System (ADS)

Wang, Wei; Yang, Jiong

With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the meaningfulness of the similarity measure in the high dimension space. In this chapter, we present several state-of-art techniques for analyzing high-dimensional data, e.g., frequent pattern mining, clustering, and classification. We will discuss how these methods deal with the challenges of high dimensionality.

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

PubMed

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

Clifford support vector machines for classification, regression, and recurrence.

PubMed

Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

2010-11-01

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

Model-free inference of direct network interactions from nonlinear collective dynamics.

PubMed

Casadiego, Jose; Nitzan, Mor; Hallerberg, Sarah; Timme, Marc

2017-12-19

The topology of interactions in network dynamical systems fundamentally underlies their function. Accelerating technological progress creates massively available data about collective nonlinear dynamics in physical, biological, and technological systems. Detecting direct interaction patterns from those dynamics still constitutes a major open problem. In particular, current nonlinear dynamics approaches mostly require to know a priori a model of the (often high dimensional) system dynamics. Here we develop a model-independent framework for inferring direct interactions solely from recording the nonlinear collective dynamics generated. Introducing an explicit dependency matrix in combination with a block-orthogonal regression algorithm, the approach works reliably across many dynamical regimes, including transient dynamics toward steady states, periodic and non-periodic dynamics, and chaos. Together with its capabilities to reveal network (two point) as well as hypernetwork (e.g., three point) interactions, this framework may thus open up nonlinear dynamics options of inferring direct interaction patterns across systems where no model is known.

High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis

PubMed Central

Daye, Z. John; Chen, Jinbo; Li, Hongzhe

2011-01-01

Summary We consider the problem of high-dimensional regression under non-constant error variances. Despite being a common phenomenon in biological applications, heteroscedasticity has, so far, been largely ignored in high-dimensional analysis of genomic data sets. We propose a new methodology that allows non-constant error variances for high-dimensional estimation and model selection. Our method incorporates heteroscedasticity by simultaneously modeling both the mean and variance components via a novel doubly regularized approach. Extensive Monte Carlo simulations indicate that our proposed procedure can result in better estimation and variable selection than existing methods when heteroscedasticity arises from the presence of predictors explaining error variances and outliers. Further, we demonstrate the presence of heteroscedasticity in and apply our method to an expression quantitative trait loci (eQTLs) study of 112 yeast segregants. The new procedure can automatically account for heteroscedasticity in identifying the eQTLs that are associated with gene expression variations and lead to smaller prediction errors. These results demonstrate the importance of considering heteroscedasticity in eQTL data analysis. PMID:22547833

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

PubMed

Liu, Han; Wang, Lie; Zhao, Tuo

2015-08-01

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O (1/ ϵ ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.

Homogeneity Pursuit

PubMed Central

Ke, Tracy; Fan, Jianqing; Wu, Yichao

2014-01-01

This paper explores the homogeneity of coefficients in high-dimensional regression, which extends the sparsity concept and is more general and suitable for many applications. Homogeneity arises when regression coefficients corresponding to neighboring geographical regions or a similar cluster of covariates are expected to be approximately the same. Sparsity corresponds to a special case of homogeneity with a large cluster of known atom zero. In this article, we propose a new method called clustering algorithm in regression via data-driven segmentation (CARDS) to explore homogeneity. New mathematics are provided on the gain that can be achieved by exploring homogeneity. Statistical properties of two versions of CARDS are analyzed. In particular, the asymptotic normality of our proposed CARDS estimator is established, which reveals better estimation accuracy for homogeneous parameters than that without homogeneity exploration. When our methods are combined with sparsity exploration, further efficiency can be achieved beyond the exploration of sparsity alone. This provides additional insights into the power of exploring low-dimensional structures in high-dimensional regression: homogeneity and sparsity. Our results also shed lights on the properties of the fussed Lasso. The newly developed method is further illustrated by simulation studies and applications to real data. Supplementary materials for this article are available online. PMID:26085701

Can a rapid measure of self-exposure to drugs of abuse provide dimensional information on depression comorbidity?

PubMed

Butelman, Eduardo Roque; Bacciardi, Silvia; Maremmani, Angelo Giovanni Icro; Darst-Campbell, Maya; Correa da Rosa, Joel; Kreek, Mary Jeanne

2017-09-01

Addictions to heroin or to cocaine are associated with substantial psychiatric comorbidity, including depression. Poly-drug self-exposure (eg, to heroin, cocaine, cannabis, or alcohol) is also common, and may further affect depression comorbidity. This case-control study examined the relationship of exposure to the above drugs and depression comorbidity. Participants were recruited from methadone maintenance clinics, and from the community. Adult male and female participants (n = 1,201) were ascertained consecutively by experienced licensed clinicians. The instruments used were the SCID-I, and Kreek-McHugh-Schluger-Kellogg (KMSK) scales, which provide a rapid dimensional measure of maximal lifetime self-exposure to each of the above drugs. This measure ranges from no exposure to high unit dose, high frequency, and long duration of exposure. A multiple logistic regression with stepwise variable selection revealed that increasing exposure to heroin or to cocaine was associated greater odds of depression, with all cases and controls combined. In cases with an opioid dependence diagnosis, increasing cocaine exposure was associated with a further increase in odds of depression. However, in cases with a cocaine dependence diagnosis, increasing exposure to either cannabis or alcohol, as well as heroin, was associated with a further increase in odds of depression. This dimensional analysis of exposure to specific drugs provides insights on depression comorbidity with addictive diseases, and the impact of poly-drug exposure. A rapid analysis of exposure to drugs of abuse reveals how specific patterns of drug and poly-drug exposure are associated with increasing odds of depression. This approach detected quantitatively how different patterns of poly-drug exposure can result in increased odds of depression comorbidity, in cases diagnosed with opioid versus cocaine dependence. (Am J Addict 2017;26:632-639). © 2017 American Academy of Addiction Psychiatry.

Reduced rank regression via adaptive nuclear norm penalization

PubMed Central

Chen, Kun; Dong, Hongbo; Chan, Kung-Sik

2014-01-01

Summary We propose an adaptive nuclear norm penalization approach for low-rank matrix approximation, and use it to develop a new reduced rank estimation method for high-dimensional multivariate regression. The adaptive nuclear norm is defined as the weighted sum of the singular values of the matrix, and it is generally non-convex under the natural restriction that the weight decreases with the singular value. However, we show that the proposed non-convex penalized regression method has a global optimal solution obtained from an adaptively soft-thresholded singular value decomposition. The method is computationally efficient, and the resulting solution path is continuous. The rank consistency of and prediction/estimation performance bounds for the estimator are established for a high-dimensional asymptotic regime. Simulation studies and an application in genetics demonstrate its efficacy. PMID:25045172

Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.

PubMed

Taslimitehrani, Vahid; Dong, Guozhu; Pereira, Naveen L; Panahiazar, Maryam; Pathak, Jyotishman

2016-04-01

Computerized survival prediction in healthcare identifying the risk of disease mortality, helps healthcare providers to effectively manage their patients by providing appropriate treatment options. In this study, we propose to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5year survival in heart failure (HF) using data from electronic health records (EHRs) at Mayo Clinic. The CPXR(Log) constructs a pattern aided logistic regression model defined by several patterns and corresponding local logistic regression models. One of the models generated by CPXR(Log) achieved an AUC and accuracy of 0.94 and 0.91, respectively, and significantly outperformed prognostic models reported in prior studies. Data extracted from EHRs allowed incorporation of patient co-morbidities into our models which helped improve the performance of the CPXR(Log) models (15.9% AUC improvement), although did not improve the accuracy of the models built by other classifiers. We also propose a probabilistic loss function to determine the large error and small error instances. The new loss function used in the algorithm outperforms other functions used in the previous studies by 1% improvement in the AUC. This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of EHR data. The risk models developed by CPXR(Log) also reveal that HF is a highly heterogeneous disease, i.e., different subgroups of HF patients require different types of considerations with their diagnosis and treatment. Our risk models provided two valuable insights for application of predictive modeling techniques in biomedicine: Logistic risk models often make systematic prediction errors, and it is prudent to use subgroup based prediction models such as those given by CPXR(Log) when investigating heterogeneous diseases. Copyright © 2016 Elsevier Inc. All rights reserved.

Patterned arrays of lateral heterojunctions within monolayer two-dimensional semiconductors

PubMed Central

Mahjouri-Samani, Masoud; Lin, Ming-Wei; Wang, Kai; Lupini, Andrew R.; Lee, Jaekwang; Basile, Leonardo; Boulesbaa, Abdelaziz; Rouleau, Christopher M.; Puretzky, Alexander A.; Ivanov, Ilia N.; Xiao, Kai; Yoon, Mina; Geohegan, David B.

2015-01-01

The formation of semiconductor heterojunctions and their high-density integration are foundations of modern electronics and optoelectronics. To enable two-dimensional crystalline semiconductors as building blocks in next-generation electronics, developing methods to deterministically form lateral heterojunctions is crucial. Here we demonstrate an approach for the formation of lithographically patterned arrays of lateral semiconducting heterojunctions within a single two-dimensional crystal. Electron beam lithography is used to pattern MoSe2 monolayer crystals with SiO2, and the exposed locations are selectively and totally converted to MoS2 using pulsed laser vaporization of sulfur to form MoSe2/MoS2 heterojunctions in predefined patterns. The junctions and conversion process are studied by Raman and photoluminescence spectroscopy, atomically resolved scanning transmission electron microscopy and device characterization. This demonstration of lateral heterojunction arrays within a monolayer crystal is an essential step for the integration of two-dimensional semiconductor building blocks with different electronic and optoelectronic properties for high-density, ultrathin devices. PMID:26198727

High-speed high-accuracy three-dimensional shape measurement using digital binary defocusing method versus sinusoidal method

NASA Astrophysics Data System (ADS)

Hyun, Jae-Sang; Li, Beiwen; Zhang, Song

2017-07-01

This paper presents our research findings on high-speed high-accuracy three-dimensional shape measurement using digital light processing (DLP) technologies. In particular, we compare two different sinusoidal fringe generation techniques using the DLP projection devices: direct projection of computer-generated 8-bit sinusoidal patterns (a.k.a., the sinusoidal method), and the creation of sinusoidal patterns by defocusing binary patterns (a.k.a., the binary defocusing method). This paper mainly examines their performance on high-accuracy measurement applications under precisely controlled settings. Two different projection systems were tested in this study: a commercially available inexpensive projector and the DLP development kit. Experimental results demonstrated that the binary defocusing method always outperforms the sinusoidal method if a sufficient number of phase-shifted fringe patterns can be used.

Optical Metrology for Directed Self-assembly Patterning Using Mueller Matrix Spectroscopic Ellipsometry Based Scatterometry

NASA Astrophysics Data System (ADS)

Dixit, Dhairya J.

The semiconductor industry continues to drive patterning solutions that enable devices with higher memory storage capacity, faster computing performance, lower cost per transistors, and higher transistor density. These developments in the field of semiconductor manufacturing along with the overall minimization of the size of transistors require cutting-edge metrology tools for characterization. Directed self-assembly (DSA) patterning process can be used to fabricate nanoscale line-space patterns and contact holes via thermodynamically driven micro-phase separation of block copolymer (BCP) films with boundary constraints from guiding templates. Its main advantages are high pattern resolution (~10 nm), high throughput, no requirement of a high-resolution mask, and compatibility with standard fab-equipment and processes. Although research into DSA patterning has demonstrated a high potential as a nanoscale patterning process, there are critical challenges that must be overcome before transferring DSA into high volume manufacturing, including achievement of low defect density and high process stability. For this, advances in critical dimension (CD) and overlay measurement as well as rapid defect characterization are required. Both scatterometry and critical dimension-scanning electron microscopy (CD-SEM) are routinely used for inline dimensional metrology. CD-SEM inspection is limited, as it does not easily provide detailed line-shape information, whereas scatterometry has the capability of measuring important feature dimensions including: line-width, line-shape, sidewall-angle, and thickness of the patterned samples quickly and non-destructively. The present work describes the application of Mueller matrix spectroscopic ellipsometry (MMSE) based scatterometry to optically characterize DSA patterned line- space grating and contact hole structures fabricated with phase-separated polystyrene-b-polymethylmethacrylate (PS-b-PMMA) at various integration steps of BCP DSA based patterning process. This work focuses on understanding the efficacy of MMSE base scatterometry for characterizing complex DSA structures. For example, the use of symmetry-antisymmetry properties associated with Mueller matrix (MM) elements to understand the topography of the periodic nanostructures and measure defectivity. Simulations (the forward problem approach of scatterometry) are used to investigate MM elements' sensitivity to changes in DSA structure such as one vs. two contact hole patterns and predict sensitivity to dimensional changes. A regression-based approach is used to extract feature shape parameters of the DSA structures by fitting simulated optical spectra to experimental optical spectra. Detection of the DSA defects is a key to reducing defect density for eventual manufacturability and production use of DSA process. Simulations of optical models of structures containing defects are used to evaluate the sensitivity of MM elements to DSA defects. This study describes the application of MMSE to determine the DSA pattern defectivity via spectral comparisons based on optical anisotropy and depolarization. The use of depolarization and optical anisotropy for characterization of experimental MMSE data is a very recent development in scatterometry. In addition, reconstructed scatterometry models are used to calculate line edge roughness in 28 nm pitch Si fins fabricated using DSA patterning process.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

Confounder Detection in High-Dimensional Linear Models Using First Moments of Spectral Measures.

PubMed

Liu, Furui; Chan, Laiwan

2018-06-12

In this letter, we study the confounder detection problem in the linear model, where the target variable [Formula: see text] is predicted using its [Formula: see text] potential causes [Formula: see text]. Based on an assumption of a rotation-invariant generating process of the model, recent study shows that the spectral measure induced by the regression coefficient vector with respect to the covariance matrix of [Formula: see text] is close to a uniform measure in purely causal cases, but it differs from a uniform measure characteristically in the presence of a scalar confounder. Analyzing spectral measure patterns could help to detect confounding. In this letter, we propose to use the first moment of the spectral measure for confounder detection. We calculate the first moment of the regression vector-induced spectral measure and compare it with the first moment of a uniform spectral measure, both defined with respect to the covariance matrix of [Formula: see text]. The two moments coincide in nonconfounding cases and differ from each other in the presence of confounding. This statistical causal-confounding asymmetry can be used for confounder detection. Without the need to analyze the spectral measure pattern, our method avoids the difficulty of metric choice and multiple parameter optimization. Experiments on synthetic and real data show the performance of this method.

Testing a single regression coefficient in high dimensional linear models

PubMed Central

Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2017-01-01

In linear regression models with high dimensional data, the classical z-test (or t-test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z-test to assess the significance of each covariate. Based on the p-value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively. PMID:28663668

Testing a single regression coefficient in high dimensional linear models.

PubMed

Lan, Wei; Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2016-11-01

In linear regression models with high dimensional data, the classical z -test (or t -test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z -test to assess the significance of each covariate. Based on the p -value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively.

Dimensionality of brain networks linked to life-long individual differences in self-control.

PubMed

Berman, Marc G; Yourganov, Grigori; Askren, Mary K; Ayduk, Ozlem; Casey, B J; Gotlib, Ian H; Kross, Ethan; McIntosh, Anthony R; Strother, Stephen; Wilson, Nicole L; Zayas, Vivian; Mischel, Walter; Shoda, Yuichi; Jonides, John

2013-01-01

The ability to delay gratification in childhood has been linked to positive outcomes in adolescence and adulthood. Here we examine a subsample of participants from a seminal longitudinal study of self-control throughout a subject's life span. Self-control, first studied in children at age 4 years, is now re-examined 40 years later, on a task that required control over the contents of working memory. We examine whether patterns of brain activation on this task can reliably distinguish participants with consistently low and high self-control abilities (low versus high delayers). We find that low delayers recruit significantly higher-dimensional neural networks when performing the task compared with high delayers. High delayers are also more homogeneous as a group in their neural patterns compared with low delayers. From these brain patterns, we can predict with 71% accuracy, whether a participant is a high or low delayer. The present results suggest that dimensionality of neural networks is a biological predictor of self-control abilities.

Laser Direct Writing of Tree-Shaped Hierarchical Cones on a Superhydrophobic Film for High-Efficiency Water Collection.

PubMed

Wang, Meng; Liu, Qian; Zhang, Haoran; Wang, Chuang; Wang, Lei; Xiang, Bingxi; Fan, Yongtao; Guo, Chuan Fei; Ruan, Shuangchen

2017-08-30

Directional water collection has stimulated a great deal of interest because of its potential applications in the field of microfluidics, liquid transportation, fog harvesting, and so forth. There have been some bio or bioinspired structures for directional water collection, from one-dimensional spider silk to two-dimensional star-like patterns to three-dimensional Nepenthes alata. Here we present a simple way for the accurate design and highly controllable driving of tiny droplets: by laser direct writing of hierarchical patterns with modified wettability and desired geometry on a superhydrophobic film, the patterned film can precisely and directionally drive tiny water droplets and dramatically improve the efficiency of water collection with a factor of ∼36 compared with the original superhydrophobic film. Such a patterned film might be an ideal platform for water collection from humid air and for planar microfluidics without tunnels.

Hyper-Spectral Image Analysis With Partially Latent Regression and Spatial Markov Dependencies

NASA Astrophysics Data System (ADS)

Deleforge, Antoine; Forbes, Florence; Ba, Sileye; Horaud, Radu

2015-09-01

Hyper-spectral data can be analyzed to recover physical properties at large planetary scales. This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations. Within this framework, we propose a spatially-constrained and partially-latent regression method which maps high-dimensional inputs (hyper-spectral images) onto low-dimensional responses (physical parameters such as the local chemical composition of the soil). The proposed regression model comprises two key features. Firstly, it combines a Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent response model. While the former makes high-dimensional regression tractable, the latter enables to deal with physical parameters that cannot be observed or, more generally, with data contaminated by experimental artifacts that cannot be explained with noise models. Secondly, spatial constraints are introduced in the model through a Markov random field (MRF) prior which provides a spatial structure to the Gaussian-mixture hidden variables. Experiments conducted on a database composed of remotely sensed observations collected from the Mars planet by the Mars Express orbiter demonstrate the effectiveness of the proposed model.

Prediction of epigenetically regulated genes in breast cancer cell lines.

PubMed

Loss, Leandro A; Sadanandam, Anguraj; Durinck, Steffen; Nautiyal, Shivani; Flaucher, Diane; Carlton, Victoria E H; Moorhead, Martin; Lu, Yontao; Gray, Joe W; Faham, Malek; Spellman, Paul; Parvin, Bahram

2010-06-04

Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profiles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profiles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fixed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically significant negative correlation between methylation profiles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identified 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.

An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology

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

Lynch, Cary D.; Hartin, Corinne A.; Bond-Lamberty, Benjamin

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. We exploremore » the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90°N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. As a result, this paper describes our library of least squared regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns.« less

An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology

DOE PAGES

Lynch, Cary D.; Hartin, Corinne A.; Bond-Lamberty, Benjamin; ...

2017-05-15

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. We exploremore » the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90°N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. As a result, this paper describes our library of least squared regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns.« less

Efficient High Order Central Schemes for Multi-Dimensional Hamilton-Jacobi Equations: Talk Slides

NASA Technical Reports Server (NTRS)

Bryson, Steve; Levy, Doron; Biegel, Brian R. (Technical Monitor)

2002-01-01

This viewgraph presentation presents information on the attempt to produce high-order, efficient, central methods that scale well to high dimension. The central philosophy is that the equations should evolve to the point where the data is smooth. This is accomplished by a cyclic pattern of reconstruction, evolution, and re-projection. One dimensional and two dimensional representational methods are detailed, as well.

Spatial regression analysis on 32 years of total column ozone data

NASA Astrophysics Data System (ADS)

Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.

2014-08-01

Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively between 30° N and 30° S, particularly over the Pacific. The contribution of explanatory variables describing seasonal ozone variation is generally large at mid- to high latitudes. We observe ozone increases with potential vorticity and day length and ozone decreases with geopotential height and variable ozone effects due to the polar vortex in regions to the north and south of the polar vortices. Recovery of ozone is identified globally. However, recovery rates and uncertainties strongly depend on choices that can be made in defining the explanatory variables. The application of several trend models, each with their own pros and cons, yields a large range of recovery rate estimates. Overall these results suggest that care has to be taken in determining ozone recovery rates, in particular for the Antarctic ozone hole.

Comparison of 3 Methods for Identifying Dietary Patterns Associated With Risk of Disease

PubMed Central

DiBello, Julia R.; Kraft, Peter; McGarvey, Stephen T.; Goldberg, Robert; Campos, Hannia

2008-01-01

Reduced rank regression and partial least-squares regression (PLS) are proposed alternatives to principal component analysis (PCA). Using all 3 methods, the authors derived dietary patterns in Costa Rican data collected on 3,574 cases and controls in 1994–2004 and related the resulting patterns to risk of first incident myocardial infarction. Four dietary patterns associated with myocardial infarction were identified. Factor 1, characterized by high intakes of lean chicken, vegetables, fruit, and polyunsaturated oil, was generated by all 3 dietary pattern methods and was associated with a significantly decreased adjusted risk of myocardial infarction (28%–46%, depending on the method used). PCA and PLS also each yielded a pattern associated with a significantly decreased risk of myocardial infarction (31% and 23%, respectively); this pattern was characterized by moderate intake of alcohol and polyunsaturated oil and low intake of high-fat dairy products. The fourth factor derived from PCA was significantly associated with a 38% increased risk of myocardial infarction and was characterized by high intakes of coffee and palm oil. Contrary to previous studies, the authors found PCA and PLS to produce more patterns associated with cardiovascular disease than reduced rank regression. The most effective method for deriving dietary patterns related to disease may vary depending on the study goals. PMID:18945692

Prognostic value of three-dimensional ultrasound for fetal hydronephrosis

PubMed Central

WANG, JUNMEI; YING, WEIWEN; TANG, DAXING; YANG, LIMING; LIU, DONGSHENG; LIU, YUANHUI; PAN, JIAOE; XIE, XING

2015-01-01

The present study evaluated the prognostic value of three-dimensional ultrasound for fetal hydronephrosis. Pregnant females with fetal hydronephrosis were enrolled and a novel three-dimensional ultrasound indicator, renal parenchymal volume/kidney volume, was introduced to predict the postnatal prognosis of fetal hydronephrosis in comparison with commonly used ultrasound indicators. All ultrasound indicators of fetal hydronephrosis could predict whether postnatal surgery was required for fetal hydronephrosis; however, the predictive performance of renal parenchymal volume/kidney volume measurements as an individual indicator was the highest. In conclusion, ultrasound is important in predicting whether postnatal surgery is required for fetal hydronephrosis, and the three-dimensional ultrasound indicator renal parenchymal volume/kidney volume has a high predictive performance. Furthermore, the majority of cases of fetal hydronephrosis spontaneously regress subsequent to birth, and the regression time is closely associated with ultrasound indicators. PMID:25667626

A repeatable geometric morphometric approach to the analysis of hand entheseal three-dimensional form.

PubMed

Karakostis, Fotios Alexandros; Hotz, Gerhard; Scherf, Heike; Wahl, Joachim; Harvati, Katerina

2018-05-01

The purpose of this study was to put forth a precise landmark-based technique for reconstructing the three-dimensional shape of human entheseal surfaces, to investigate whether the shape of human entheses is related to their size. The effects of age-at-death and bone length on entheseal shapes were also assessed. The sample comprised high-definition three-dimensional models of three right hand entheseal surfaces, which correspond to 45 male adult individuals of known age. For each enthesis, a particular landmark configuration was introduced, whose precision was tested both within and between observers. The effect of three-dimensional size, age-at-death, and bone length on shape was investigated through shape regression. The method presented high intra-observer and inter-observer repeatability. All entheses showed significant allometry, with the area of opponens pollicis demonstrating the most substantial relationship. This was particularly due to variation related to its proximal elongated ridge. The effect of age-at-death and bone length on entheses was limited. The introduced methodology can set a reliable basis for further research on the factors affecting entheseal shape. Using both size and shape, variables can provide further information on entheseal variation and its biomechanical implications. The low entheseal variation by age verifies that specimens under 50 years of age are not substantially affected by age-related changes. The lack of correlation between entheseal shape and bone length or age implies that other factors may regulate entheseal surfaces. Future research should focus on multivariate shape patterns among entheses and their association with occupation. © 2018 Wiley Periodicals, Inc.

Similarity-dissimilarity plot for visualization of high dimensional data in biomedical pattern classification.

PubMed

Arif, Muhammad

2012-06-01

In pattern classification problems, feature extraction is an important step. Quality of features in discriminating different classes plays an important role in pattern classification problems. In real life, pattern classification may require high dimensional feature space and it is impossible to visualize the feature space if the dimension of feature space is greater than four. In this paper, we have proposed a Similarity-Dissimilarity plot which can project high dimensional space to a two dimensional space while retaining important characteristics required to assess the discrimination quality of the features. Similarity-dissimilarity plot can reveal information about the amount of overlap of features of different classes. Separable data points of different classes will also be visible on the plot which can be classified correctly using appropriate classifier. Hence, approximate classification accuracy can be predicted. Moreover, it is possible to know about whom class the misclassified data points will be confused by the classifier. Outlier data points can also be located on the similarity-dissimilarity plot. Various examples of synthetic data are used to highlight important characteristics of the proposed plot. Some real life examples from biomedical data are also used for the analysis. The proposed plot is independent of number of dimensions of the feature space.

[Simulation of three-dimensional green biomass of urban forests in Shenyang City and the factors affecting the biomass].

PubMed

Liu, Chang-Fu; He, Xing-Yuan; Chen, Wei; Zhao, Gui-Ling; Xue, Wen-Duo

2008-06-01

Based on the fractal theory of forest growth, stepwise regression was employed to pursue a convenient and efficient method of measuring the three-dimensional green biomass (TGB) of urban forests in small area. A total of thirteen simulation equations of TGB of urban forests in Shenyang City were derived, with the factors affecting the TGB analyzed. The results showed that the coefficients of determination (R2) of the 13 simulation equations ranged from 0.612 to 0.842. No evident pattern was shown in residual analysis, and the precisions were all higher than 87% (alpha = 0.05) and 83% (alpha = 0.01). The most convenient simulation equation was ln Y = 7.468 + 0.926 lnx1, where Y was the simulated TGB and x1 was basal area at breast height per hectare (SDB). The correlations between the standard regression coefficients of the simulation equations and 16 tree characteristics suggested that SDB was the main factor affecting the TGB of urban forests in Shenyang.

An open-access CMIP5 pattern library for temperature and precipitation: description and methodology

NASA Astrophysics Data System (ADS)

Lynch, Cary; Hartin, Corinne; Bond-Lamberty, Ben; Kravitz, Ben

2017-05-01

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squares regression methods. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90° N/S). Bias and mean errors between modeled and pattern-predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5 °C, but the choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. This paper describes our library of least squares regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns. The dataset and netCDF data generation code are available at doi:10.5281/zenodo.495632.

A combinatorial code for pattern formation in Drosophila oogenesis.

PubMed

Yakoby, Nir; Bristow, Christopher A; Gong, Danielle; Schafer, Xenia; Lembong, Jessica; Zartman, Jeremiah J; Halfon, Marc S; Schüpbach, Trudi; Shvartsman, Stanislav Y

2008-11-01

Two-dimensional patterning of the follicular epithelium in Drosophila oogenesis is required for the formation of three-dimensional eggshell structures. Our analysis of a large number of published gene expression patterns in the follicle cells suggests that they follow a simple combinatorial code based on six spatial building blocks and the operations of union, difference, intersection, and addition. The building blocks are related to the distribution of inductive signals, provided by the highly conserved epidermal growth factor receptor and bone morphogenetic protein signaling pathways. We demonstrate the validity of the code by testing it against a set of patterns obtained in a large-scale transcriptional profiling experiment. Using the proposed code, we distinguish 36 distinct patterns for 81 genes expressed in the follicular epithelium and characterize their joint dynamics over four stages of oogenesis. The proposed combinatorial framework allows systematic analysis of the diversity and dynamics of two-dimensional transcriptional patterns and guides future studies of gene regulation.

Theoretical study for aerial image intensity in resist in high numerical aperture projection optics and experimental verification with one-dimensional patterns

NASA Astrophysics Data System (ADS)

Shibuya, Masato; Takada, Akira; Nakashima, Toshiharu

2016-04-01

In optical lithography, high-performance exposure tools are indispensable to obtain not only fine patterns but also preciseness in pattern width. Since an accurate theoretical method is necessary to predict these values, some pioneer and valuable studies have been proposed. However, there might be some ambiguity or lack of consensus regarding the treatment of diffraction by object, incoming inclination factor onto image plane in scalar imaging theory, and paradoxical phenomenon of the inclined entrance plane wave onto image in vector imaging theory. We have reconsidered imaging theory in detail and also phenomenologically resolved the paradox. By comparing theoretical aerial image intensity with experimental pattern width for one-dimensional pattern, we have validated our theoretical consideration.

Effects of three-dimensional and color patterns on nest location and progeny mortality in alfalfa leafcutting bee (Hymenoptera: Megachilidae).

PubMed

Guédot, Christelle; Bosch, Jordi; James, Rosalind R; Kemp, William P

2006-06-01

ABSTRACT In alfalfa, Medicago sativa L., seed production where high bee densities are released, alfalfa leafcutting bee, Megachile rotundata (F.) (Hymenoptera: Megachilidae), females may enter several nesting holes before locating their nests. Such levels of "wrong hole" visits lead to an increase in the time spent by females locating their own nests, thereby decreasing alfalfa pollination efficiency and possibly healthy brood production. The objectives of this study were to determine the effect of different nesting board configurations in commercial alfalfa leafcutting bee shelters (separating nesting boards, applying a three-dimensional pattern to the boards, applying a color contrast pattern, or applying a combination of three-dimensional and color contrast patterns) on nest location performance, on the incidence of chalkbrood disease, and on the incidence of broodless provisions. Separating the nesting boards inside shelters improved the ability of females to locate their nests. An increase in nest location performance also occurred in boards with the three-dimensional pattern and the combined three-dimensional and color contrast pattern, compared with the uniform board (a standard configuration currently used commercially). The percentage of provisioned cells that were broodless was not statistically different between treatments, but the percentage of larvae infected with chalkbrood decreased by half in the three-dimensional board design, compared with the uniform board.

The comparison of robust partial least squares regression with robust principal component regression on a real

NASA Astrophysics Data System (ADS)

Polat, Esra; Gunay, Suleyman

2013-10-01

One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

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

Direct Prototyping of Patterned Nanoporous Carbon: A Route from Materials to On-chip Devices

PubMed Central

Shen, Caiwei; Wang, Xiaohong; Zhang, Wenfeng; Kang, Feiyu

2013-01-01

Prototyping of nanoporous carbon membranes with three-dimensional microscale patterns is significant for integration of such multifunctional materials into various miniaturized systems. Incorporating nano material synthesis into microelectronics technology, we present a novel approach to direct prototyping of carbon membranes with highly nanoporous structures inside. Membranes with significant thicknesses (1 ~ 40 μm) are rapidly prototyped at wafer level by combining nano templating method with readily available microfabrication techniques, which include photolithography, high-temperature annealing and etching. In particular, the high-surface-area membranes are specified as three-dimensional electrodes for micro supercapacitors and show high performance compared to reported ones. Improvements in scalability, compatibility and cost make the general strategy promising for batch fabrication of operational on-chip devices or full integration of three-dimensional nanoporous membranes with existing micro systems. PMID:23887486

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

PubMed Central

Tao, Chenyang; Nichols, Thomas E.; Hua, Xue; Ching, Christopher R.K.; Rolls, Edmund T.; Thompson, Paul M.; Feng, Jianfeng

2017-01-01

We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches. PMID:27666385

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

PubMed Central

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-01-01

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

High-resolution proxies for wood density variations in Terminalia superba

PubMed Central

De Ridder, Maaike; Van den Bulcke, Jan; Vansteenkiste, Dries; Van Loo, Denis; Dierick, Manuel; Masschaele, Bert; De Witte, Yoni; Mannes, David; Lehmann, Eberhard; Beeckman, Hans; Van Hoorebeke, Luc; Van Acker, Joris

2011-01-01

Background and Aims Density is a crucial variable in forest and wood science and is evaluated by a multitude of methods. Direct gravimetric methods are mostly destructive and time-consuming. Therefore, faster and semi- to non-destructive indirect methods have been developed. Methods Profiles of wood density variations with a resolution of approx. 50 µm were derived from one-dimensional resistance drillings, two-dimensional neutron scans, and three-dimensional neutron and X-ray scans. All methods were applied on Terminalia superba Engl. & Diels, an African pioneer species which sometimes exhibits a brown heart (limba noir). Key Results The use of X-ray tomography combined with a reference material permitted direct estimates of wood density. These X-ray-derived densities overestimated gravimetrically determined densities non-significantly and showed high correlation (linear regression, R2 = 0·995). When comparing X-ray densities with the attenuation coefficients of neutron scans and the amplitude of drilling resistance, a significant linear relation was found with the neutron attenuation coefficient (R2 = 0·986) yet a weak relation with drilling resistance (R2 = 0·243). When density patterns are compared, all three methods are capable of revealing the same trends. Differences are mainly due to the orientation of tree rings and the different characteristics of the indirect methods. Conclusions High-resolution X-ray computed tomography is a promising technique for research on wood cores and will be explored further on other temperate and tropical species. Further study on limba noir is necessary to reveal the causes of density variations and to determine how resistance drillings can be further refined. PMID:21131386

Discovering biclusters in gene expression data based on high-dimensional linear geometries

PubMed Central

Gan, Xiangchao; Liew, Alan Wee-Chung; Yan, Hong

2008-01-01

Background In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits consistent pattern over a subset of conditions. Conventional clustering algorithms that deal with the entire row or column in an expression matrix would therefore fail to detect these useful patterns in the data. Recently, biclustering has been proposed to detect a subset of genes exhibiting consistent pattern over a subset of conditions. However, most existing biclustering algorithms are based on searching for sub-matrices within a data matrix by optimizing certain heuristically defined merit functions. Moreover, most of these algorithms can only detect a restricted set of bicluster patterns. Results In this paper, we present a novel geometric perspective for the biclustering problem. The biclustering process is interpreted as the detection of linear geometries in a high dimensional data space. Such a new perspective views biclusters with different patterns as hyperplanes in a high dimensional space, and allows us to handle different types of linear patterns simultaneously by matching a specific set of linear geometries. This geometric viewpoint also inspires us to propose a generic bicluster pattern, i.e. the linear coherent model that unifies the seemingly incompatible additive and multiplicative bicluster models. As a particular realization of our framework, we have implemented a Hough transform-based hyperplane detection algorithm. The experimental results on human lymphoma gene expression dataset show that our algorithm can find biologically significant subsets of genes. Conclusion We have proposed a novel geometric interpretation of the biclustering problem. We have shown that many common types of bicluster are just different spatial arrangements of hyperplanes in a high dimensional data space. An implementation of the geometric framework using the Fast Hough transform for hyperplane detection can be used to discover biologically significant subsets of genes under subsets of conditions for microarray data analysis. PMID:18433477

Entangled singularity patterns of photons in Ince-Gauss modes

NASA Astrophysics Data System (ADS)

Krenn, Mario; Fickler, Robert; Huber, Marcus; Lapkiewicz, Radek; Plick, William; Ramelow, Sven; Zeilinger, Anton

2013-01-01

Photons with complex spatial mode structures open up possibilities for new fundamental high-dimensional quantum experiments and for novel quantum information tasks. Here we show entanglement of photons with complex vortex and singularity patterns called Ince-Gauss modes. In these modes, the position and number of singularities vary depending on the mode parameters. We verify two-dimensional and three-dimensional entanglement of Ince-Gauss modes. By measuring one photon and thereby defining its singularity pattern, we nonlocally steer the singularity structure of its entangled partner, while the initial singularity structure of the photons is undefined. In addition we measure an Ince-Gauss specific quantum-correlation function with possible use in future quantum communication protocols.

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

PubMed

Gong, Xiajing; Hu, Meng; Zhao, Liang

2018-05-01

Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Generative Topographic Mapping of Conformational Space.

PubMed

Horvath, Dragos; Baskin, Igor; Marcou, Gilles; Varnek, Alexandre

2017-10-01

Herein, Generative Topographic Mapping (GTM) was challenged to produce planar projections of the high-dimensional conformational space of complex molecules (the 1LE1 peptide). GTM is a probability-based mapping strategy, and its capacity to support property prediction models serves to objectively assess map quality (in terms of regression statistics). The properties to predict were total, non-bonded and contact energies, surface area and fingerprint darkness. Map building and selection was controlled by a previously introduced evolutionary strategy allowed to choose the best-suited conformational descriptors, options including classical terms and novel atom-centric autocorrellograms. The latter condensate interatomic distance patterns into descriptors of rather low dimensionality, yet precise enough to differentiate between close favorable contacts and atom clashes. A subset of 20 K conformers of the 1LE1 peptide, randomly selected from a pool of 2 M geometries (generated by the S4MPLE tool) was employed for map building and cross-validation of property regression models. The GTM build-up challenge reached robust three-fold cross-validated determination coefficients of Q 2 =0.7…0.8, for all modeled properties. Mapping of the full 2 M conformer set produced intuitive and information-rich property landscapes. Functional and folding subspaces appear as well-separated zones, even though RMSD with respect to the PDB structure was never used as a selection criterion of the maps. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fuzzy Regression Prediction and Application Based on Multi-Dimensional Factors of Freight Volume

NASA Astrophysics Data System (ADS)

Xiao, Mengting; Li, Cheng

2018-01-01

Based on the reality of the development of air cargo, the multi-dimensional fuzzy regression method is used to determine the influencing factors, and the three most important influencing factors of GDP, total fixed assets investment and regular flight route mileage are determined. The system’s viewpoints and analogy methods, the use of fuzzy numbers and multiple regression methods to predict the civil aviation cargo volume. In comparison with the 13th Five-Year Plan for China’s Civil Aviation Development (2016-2020), it is proved that this method can effectively improve the accuracy of forecasting and reduce the risk of forecasting. It is proved that this model predicts civil aviation freight volume of the feasibility, has a high practical significance and practical operation.

Dietary patterns in the Avon Longitudinal Study of Parents and Children

PubMed Central

Jones, Louise R.; Northstone, Kate

2015-01-01

Publications from the Avon Longitudinal Study of Parents and Children that used empirically derived dietary patterns were reviewed. The relationships of dietary patterns with socioeconomic background and childhood development were examined. Diet was assessed using food frequency questionnaires and food records. Three statistical methods were used: principal components analysis, cluster analysis, and reduced rank regression. Throughout childhood, children and parents have similar dietary patterns. The “health-conscious” and “traditional” patterns were associated with high intakes of fruits and/or vegetables and better nutrient profiles than the “processed” patterns. There was evidence of tracking in childhood diet, with the “health-conscious” patterns tracking most strongly, followed by the “processed” pattern. An “energy-dense, low-fiber, high-fat” dietary pattern was extracted using reduced rank regression; high scores on this pattern were associated with increasing adiposity. Maternal education was a strong determinant of pattern score or cluster membership; low educational attainment was associated with higher scores on processed, energy-dense patterns in both parents and children. The Avon Longitudinal Study of Parents and Children has provided unique insights into the value of empirically derived dietary patterns and has demonstrated that they are a useful tool in nutritional epidemiology. PMID:26395343

Consideration of correlativity between litho and etching shape

NASA Astrophysics Data System (ADS)

Matsuoka, Ryoichi; Mito, Hiroaki; Shinoda, Shinichi; Toyoda, Yasutaka

2012-03-01

We developed an effective method for evaluating the correlation of shape of Litho and Etching pattern. The purpose of this method, makes the relations of the shape after that is the etching pattern an index in wafer same as a pattern shape on wafer made by a lithography process. Therefore, this method measures the characteristic of the shape of the wafer pattern by the lithography process and can predict the hotspot pattern shape by the etching process. The method adopts a metrology management system based on DBM (Design Based Metrology). This is the high accurate contouring created by an edge detection algorithm used wafer CD-SEM. Currently, as semiconductor manufacture moves towards even smaller feature size, this necessitates more aggressive optical proximity correction (OPC) to drive the super-resolution technology (RET). In other words, there is a trade-off between highly precise RET and lithography management, and this has a big impact on the semiconductor market that centers on the semiconductor business. 2-dimensional shape of wafer quantification is important as optimal solution over these problems. Although 1-dimensional shape measurement has been performed by the conventional technique, 2-dimensional shape management is needed in the mass production line under the influence of RET. We developed the technique of analyzing distribution of shape edge performance as the shape management technique. In this study, we conducted experiments for correlation method of the pattern (Measurement Based Contouring) as two-dimensional litho and etch evaluation technique. That is, observation of the identical position of a litho and etch was considered. It is possible to analyze variability of the edge of the same position with high precision.

Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing.

PubMed

Starck, Tuomo; Nikkinen, Juha; Rahko, Jukka; Remes, Jukka; Hurtig, Tuula; Haapsamo, Helena; Jussila, Katja; Kuusikko-Gauffin, Sanna; Mattila, Marja-Leena; Jansson-Verkasalo, Eira; Pauls, David L; Ebeling, Hanna; Moilanen, Irma; Tervonen, Osmo; Kiviniemi, Vesa J

2013-01-01

In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.

Three-dimensional nanostructure determination from a large diffraction data set recorded using scanning electron nanodiffraction.

PubMed

Meng, Yifei; Zuo, Jian-Min

2016-09-01

A diffraction-based technique is developed for the determination of three-dimensional nanostructures. The technique employs high-resolution and low-dose scanning electron nanodiffraction (SEND) to acquire three-dimensional diffraction patterns, with the help of a special sample holder for large-angle rotation. Grains are identified in three-dimensional space based on crystal orientation and on reconstructed dark-field images from the recorded diffraction patterns. Application to a nanocrystalline TiN thin film shows that the three-dimensional morphology of columnar TiN grains of tens of nanometres in diameter can be reconstructed using an algebraic iterative algorithm under specified prior conditions, together with their crystallographic orientations. The principles can be extended to multiphase nanocrystalline materials as well. Thus, the tomographic SEND technique provides an effective and adaptive way of determining three-dimensional nanostructures.

Bayesian semi-parametric analysis of Poisson change-point regression models: application to policy making in Cali, Colombia.

PubMed

Park, Taeyoung; Krafty, Robert T; Sánchez, Alvaro I

2012-07-27

A Poisson regression model with an offset assumes a constant baseline rate after accounting for measured covariates, which may lead to biased estimates of coefficients in an inhomogeneous Poisson process. To correctly estimate the effect of time-dependent covariates, we propose a Poisson change-point regression model with an offset that allows a time-varying baseline rate. When the nonconstant pattern of a log baseline rate is modeled with a nonparametric step function, the resulting semi-parametric model involves a model component of varying dimension and thus requires a sophisticated varying-dimensional inference to obtain correct estimates of model parameters of fixed dimension. To fit the proposed varying-dimensional model, we devise a state-of-the-art MCMC-type algorithm based on partial collapse. The proposed model and methods are used to investigate an association between daily homicide rates in Cali, Colombia and policies that restrict the hours during which the legal sale of alcoholic beverages is permitted. While simultaneously identifying the latent changes in the baseline homicide rate which correspond to the incidence of sociopolitical events, we explore the effect of policies governing the sale of alcohol on homicide rates and seek a policy that balances the economic and cultural dependencies on alcohol sales to the health of the public.

An algorithm for generating modular hierarchical neural network classifiers: a step toward larger scale applications

NASA Astrophysics Data System (ADS)

Roverso, Davide

2003-08-01

Many-class learning is the problem of training a classifier to discriminate among a large number of target classes. Together with the problem of dealing with high-dimensional patterns (i.e. a high-dimensional input space), the many class problem (i.e. a high-dimensional output space) is a major obstacle to be faced when scaling-up classifier systems and algorithms from small pilot applications to large full-scale applications. The Autonomous Recursive Task Decomposition (ARTD) algorithm is here proposed as a solution to the problem of many-class learning. Example applications of ARTD to neural classifier training are also presented. In these examples, improvements in training time are shown to range from 4-fold to more than 30-fold in pattern classification tasks of both static and dynamic character.

Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

PubMed Central

Fan, Yong; Batmanghelich, Nematollah; Clark, Chris M.; Davatzikos, Christos

2010-01-01

Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses. PMID:18053747

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.

Querying Patterns in High-Dimensional Heterogenous Datasets

ERIC Educational Resources Information Center

Singh, Vishwakarma

2012-01-01

The recent technological advancements have led to the availability of a plethora of heterogenous datasets, e.g., images tagged with geo-location and descriptive keywords. An object in these datasets is described by a set of high-dimensional feature vectors. For example, a keyword-tagged image is represented by a color-histogram and a…

Three-dimensional electron diffraction of plant light-harvesting complex

PubMed Central

Wang, Da Neng; Kühlbrandt, Werner

1992-01-01

Electron diffraction patterns of two-dimensional crystals of light-harvesting chlorophyll a/b-protein complex (LHC-II) from photosynthetic membranes of pea chloroplasts, tilted at different angles up to 60°, were collected to 3.2 Å resolution at -125°C. The reflection intensities were merged into a three-dimensional data set. The Friedel R-factor and the merging R-factor were 21.8 and 27.6%, respectively. Specimen flatness and crystal size were critical for recording electron diffraction patterns from crystals at high tilts. The principal sources of experimental error were attributed to limitations of the number of unit cells contributing to an electron diffraction pattern, and to the critical electron dose. The distribution of strong diffraction spots indicated that the three-dimensional structure of LHC-II is less regular than that of other known membrane proteins and is not dominated by a particular feature of secondary structure. ImagesFIGURE 1FIGURE 2 PMID:19431817

Prediction of epigenetically regulated genes in breast cancer cell lines

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

Loss, Leandro A; Sadanandam, Anguraj; Durinck, Steffen

Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines,more » which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fxed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically signifcant negative correlation between methylation profles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.« less

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

Meng, Yifei; Zuo, Jian -Min

A diffraction-based technique is developed for the determination of three-dimensional nanostructures. The technique employs high-resolution and low-dose scanning electron nanodiffraction (SEND) to acquire three-dimensional diffraction patterns, with the help of a special sample holder for large-angle rotation. Grains are identified in three-dimensional space based on crystal orientation and on reconstructed dark-field images from the recorded diffraction patterns. Application to a nanocrystalline TiN thin film shows that the three-dimensional morphology of columnar TiN grains of tens of nanometres in diameter can be reconstructed using an algebraic iterative algorithm under specified prior conditions, together with their crystallographic orientations. The principles can bemore » extended to multiphase nanocrystalline materials as well. Furthermore, the tomographic SEND technique provides an effective and adaptive way of determining three-dimensional nanostructures.« less

Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Kong, Shish-Jeng; Chang, Chih-Hua

2018-03-01

The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space-time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis.

Three-Dimensional Gene Map of Cancer Cell Types: Structural Entropy Minimisation Principle for Defining Tumour Subtypes

PubMed Central

Li, Angsheng; Yin, Xianchen; Pan, Yicheng

2016-01-01

In this study, we propose a method for constructing cell sample networks from gene expression profiles, and a structural entropy minimisation principle for detecting natural structure of networks and for identifying cancer cell subtypes. Our method establishes a three-dimensional gene map of cancer cell types and subtypes. The identified subtypes are defined by a unique gene expression pattern, and a three-dimensional gene map is established by defining the unique gene expression pattern for each identified subtype for cancers, including acute leukaemia, lymphoma, multi-tissue, lung cancer and healthy tissue. Our three-dimensional gene map demonstrates that a true tumour type may be divided into subtypes, each defined by a unique gene expression pattern. Clinical data analyses demonstrate that most cell samples of an identified subtype share similar survival times, survival indicators and International Prognostic Index (IPI) scores and indicate that distinct subtypes identified by our algorithms exhibit different overall survival times, survival ratios and IPI scores. Our three-dimensional gene map establishes a high-definition, one-to-one map between the biologically and medically meaningful tumour subtypes and the gene expression patterns, and identifies remarkable cells that form singleton submodules. PMID:26842724

Postural tasks are associated with center of pressure spatial patterns of three-dimensional statokinesigrams in young and elderly healthy subjects.

PubMed

Baracat, Patrícia Junqueira Ferraz; de Sá Ferreira, Arthur

2013-12-01

The present study investigated the association between postural tasks and center of pressure spatial patterns of three-dimensional statokinesigrams. Young (n=35; 27.0±7.7years) and elderly (n=38; 67.3±8.7years) healthy volunteers maintained an undisturbed standing position during postural tasks characterized by combined sensory (vision/no vision) and biomechanical challenges (feet apart/together). A method for the analysis of three-dimensional statokinesigrams based on nonparametric statistics and image-processing analysis was employed. Four patterns of spatial distribution were derived from ankle and hip strategies according to the quantity (single; double; multi) and location (anteroposterior; mediolateral) of high-density regions on three-dimensional statokinesigrams. Significant associations between postural task and spatial pattern were observed (young: gamma=0.548, p<.001; elderly: gamma=0.582, p<.001). Robustness analysis revealed small changes related to parameter choices for histogram processing. MANOVA revealed multivariate main effects for postural task [Wilks' Lambda=0.245, p<.001] and age [Wilks' Lambda=0.308, p<.001], with interaction [Wilks' Lambda=0.732, p<.001]. The quantity of high-density regions was positively correlated to stabilogram and statokinesigram variables (p<.05 or lower). In conclusion, postural tasks are associated with center of pressure spatial patterns and are similar in young and elderly healthy volunteers. Single-centered patterns reflected more stable postural conditions and were more frequent with complete visual input and a wide base of support. Copyright © 2013 Elsevier B.V. All rights reserved.

Three-dimensional nanostructure determination from a large diffraction data set recorded using scanning electron nanodiffraction

DOE PAGES

Meng, Yifei; Zuo, Jian -Min

2016-07-04

A diffraction-based technique is developed for the determination of three-dimensional nanostructures. The technique employs high-resolution and low-dose scanning electron nanodiffraction (SEND) to acquire three-dimensional diffraction patterns, with the help of a special sample holder for large-angle rotation. Grains are identified in three-dimensional space based on crystal orientation and on reconstructed dark-field images from the recorded diffraction patterns. Application to a nanocrystalline TiN thin film shows that the three-dimensional morphology of columnar TiN grains of tens of nanometres in diameter can be reconstructed using an algebraic iterative algorithm under specified prior conditions, together with their crystallographic orientations. The principles can bemore » extended to multiphase nanocrystalline materials as well. Furthermore, the tomographic SEND technique provides an effective and adaptive way of determining three-dimensional nanostructures.« less

Computer-aided diagnosis for phase-contrast X-ray computed tomography: quantitative characterization of human patellar cartilage with high-dimensional geometric features.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismüller, Axel

2014-02-01

Phase-contrast computed tomography (PCI-CT) has shown tremendous potential as an imaging modality for visualizing human cartilage with high spatial resolution. Previous studies have demonstrated the ability of PCI-CT to visualize (1) structural details of the human patellar cartilage matrix and (2) changes to chondrocyte organization induced by osteoarthritis. This study investigates the use of high-dimensional geometric features in characterizing such chondrocyte patterns in the presence or absence of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and statistical features derived from gray-level co-occurrence matrices were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic curve (AUC). SIM-derived geometrical features exhibited the best classification performance (AUC, 0.95 ± 0.06) and were most robust to changes in ROI size. These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated and non-subjective manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.

Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players?

PubMed

Dutt-Mazumder, Aviroop; Button, Chris; Robins, Anthony; Bartlett, Roger

2011-12-01

Recent studies have explored the organization of player movements in team sports using a range of statistical tools. However, the factors that best explain the performance of association football teams remain elusive. Arguably, this is due to the high-dimensional behavioural outputs that illustrate the complex, evolving configurations typical of team games. According to dynamical system analysts, movement patterns in team sports exhibit nonlinear self-organizing features. Nonlinear processing tools (i.e. Artificial Neural Networks; ANNs) are becoming increasingly popular to investigate the coordination of participants in sports competitions. ANNs are well suited to describing high-dimensional data sets with nonlinear attributes, however, limited information concerning the processes required to apply ANNs exists. This review investigates the relative value of various ANN learning approaches used in sports performance analysis of team sports focusing on potential applications for association football. Sixty-two research sources were summarized and reviewed from electronic literature search engines such as SPORTDiscus, Google Scholar, IEEE Xplore, Scirus, ScienceDirect and Elsevier. Typical ANN learning algorithms can be adapted to perform pattern recognition and pattern classification. Particularly, dimensionality reduction by a Kohonen feature map (KFM) can compress chaotic high-dimensional datasets into low-dimensional relevant information. Such information would be useful for developing effective training drills that should enhance self-organizing coordination among players. We conclude that ANN-based qualitative analysis is a promising approach to understand the dynamical attributes of association football players.

High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection

NASA Astrophysics Data System (ADS)

Zuo, Chao; Chen, Qian; Gu, Guohua; Feng, Shijie; Feng, Fangxiaoyu; Li, Rubin; Shen, Guochen

2013-08-01

This paper introduces a high-speed three-dimensional (3-D) shape measurement technique for dynamic scenes by using bi-frequency tripolar pulse-width-modulation (TPWM) fringe projection. Two wrapped phase maps with different wavelengths can be obtained simultaneously by our bi-frequency phase-shifting algorithm. Then the two phase maps are unwrapped using a simple look-up-table based number-theoretical approach. To guarantee the robustness of phase unwrapping as well as the high sinusoidality of projected patterns, TPWM technique is employed to generate ideal fringe patterns with slight defocus. We detailed our technique, including its principle, pattern design, and system setup. Several experiments on dynamic scenes were performed, verifying that our method can achieve a speed of 1250 frames per second for fast, dense, and accurate 3-D measurements.

Compound Identification Using Penalized Linear Regression on Metabolomics

PubMed Central

Liu, Ruiqi; Wu, Dongfeng; Zhang, Xiang; Kim, Seongho

2014-01-01

Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson’s correlation along with the penalized linear regression are proposed in this study. PMID:27212894

Ammonite paleobiogeography during the Pliensbachian-Toarcian crisis (Early Jurassic) reflecting paleoclimate, eustasy, and extinctions

NASA Astrophysics Data System (ADS)

Dera, Guillaume; Neige, Pascal; Dommergues, Jean-Louis; Brayard, Arnaud

2011-08-01

The Pliensbachian-Toarcian crisis (Early Jurassic) is one of the major Mesozoic paleoecological disturbances when ca. 20% of marine and continental families went extinct. Contemporaneously, profound paleobiogeographical changes occurred in most oceanic domains including a disruption of ammonite provincialism during the Early Toarcian. Here, we quantitatively reappraise the structure and evolution of paleobiogeographical patterns displayed by ammonite faunas before, during, and after the biological crisis, over a time-interval including 13 biochronozones. The high-resolution study presented here involves the use of hierarchical Cluster Analyses, non-metric Multi-Dimensional Scaling methods, and Bootstrap Spanning Network approaches that we apply to a large database including 772 ammonite species from 16 northwestern Tethyan and Arctic basins. Our results confirm a robust faunal dichotomy between Euro-Boreal and Mediterranean areas throughout the Pliensbachian, with the first emergence of an Arctic biome during the cooling regressive event of the Spinatum Zone. Whatever its complexity, Pliensbachian provincialism could be directly linked to paleogeographical barriers and to latitudinal paleoclimatic and paleoecological contrasts. During the Early Toarcian, this pattern was progressively lost, with northward expansions of Mediterranean ammonites during the Tenuicostatum Zone, followed by a strong interprovincial mixing during the Falciferum Zone. This faunal homogenization results from the combination of several parameters including a major sea-level rise facilitating basinal connections, a global warming event stretching the spatial range limits of southern taxa, and a mass extinction preferentially removing endemic species. Ammonite provincialism, although slightly different, was progressively re-established during the cooling regressive trend of the Middle Toarcian. These results therefore suggest a paramount influence of paleoclimatic, eustatic, and extinction constraints on the paleobiogeography of Early Jurassic ammonites, even if some threshold effects or independent biological factors may sporadically complicate the patterns.

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

PubMed

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

2017-07-11

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

EPS-LASSO: Test for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits.

PubMed

Xu, Chao; Fang, Jian; Shen, Hui; Wang, Yu-Ping; Deng, Hong-Wen

2018-01-25

Extreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in extreme phenotypic samples, EPS can boost the association power compared to random sampling. Most existing statistical methods for EPS examine the genetic factors individually, despite many quantitative traits have multiple genetic factors underlying their variation. It is desirable to model the joint effects of genetic factors, which may increase the power and identify novel quantitative trait loci under EPS. The joint analysis of genetic data in high-dimensional situations requires specialized techniques, e.g., the least absolute shrinkage and selection operator (LASSO). Although there are extensive research and application related to LASSO, the statistical inference and testing for the sparse model under EPS remain unknown. We propose a novel sparse model (EPS-LASSO) with hypothesis test for high-dimensional regression under EPS based on a decorrelated score function. The comprehensive simulation shows EPS-LASSO outperforms existing methods with stable type I error and FDR control. EPS-LASSO can provide a consistent power for both low- and high-dimensional situations compared with the other methods dealing with high-dimensional situations. The power of EPS-LASSO is close to other low-dimensional methods when the causal effect sizes are small and is superior when the effects are large. Applying EPS-LASSO to a transcriptome-wide gene expression study for obesity reveals 10 significant body mass index associated genes. Our results indicate that EPS-LASSO is an effective method for EPS data analysis, which can account for correlated predictors. The source code is available at https://github.com/xu1912/EPSLASSO. hdeng2@tulane.edu. Supplementary data are available at Bioinformatics online. © The Author (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Development for 2D pattern quantification method on mask and wafer

NASA Astrophysics Data System (ADS)

Matsuoka, Ryoichi; Mito, Hiroaki; Toyoda, Yasutaka; Wang, Zhigang

2010-03-01

We have developed the effective method of mask and silicon 2-dimensional metrology. The aim of this method is evaluating the performance of the silicon corresponding to Hotspot on a mask. The method adopts a metrology management system based on DBM (Design Based Metrology). This is the high accurate contouring created by an edge detection algorithm used in mask CD-SEM and silicon CD-SEM. Currently, as semiconductor manufacture moves towards even smaller feature size, this necessitates more aggressive optical proximity correction (OPC) to drive the super-resolution technology (RET). In other words, there is a trade-off between highly precise RET and mask manufacture, and this has a big impact on the semiconductor market that centers on the mask business. 2-dimensional Shape quantification is important as optimal solution over these problems. Although 1-dimensional shape measurement has been performed by the conventional technique, 2-dimensional shape management is needed in the mass production line under the influence of RET. We developed the technique of analyzing distribution of shape edge performance as the shape management technique. On the other hand, there is roughness in the silicon shape made from a mass-production line. Moreover, there is variation in the silicon shape. For this reason, quantification of silicon shape is important, in order to estimate the performance of a pattern. In order to quantify, the same shape is equalized in two dimensions. And the method of evaluating based on the shape is popular. In this study, we conducted experiments for averaging method of the pattern (Measurement Based Contouring) as two-dimensional mask and silicon evaluation technique. That is, observation of the identical position of a mask and a silicon was considered. It is possible to analyze variability of the edge of the same position with high precision. The result proved its detection accuracy and reliability of variability on two-dimensional pattern (mask and silicon) and is adaptable to following fields of mask quality management. - Estimate of the correlativity of shape variability and a process margin. - Determination of two-dimensional variability of pattern. - Verification of the performance of the pattern of various kinds of Hotspots. In this report, we introduce the experimental results and the application. We expect that the mask measurement and the shape control on mask production will make a huge contribution to mask yield-enhancement and that the DFM solution for mask quality control process will become much more important technology than ever. It is very important to observe the shape of the same location of Design, Mask, and Silicon in such a viewpoint.

Three-dimensionally patterned energy absorptive material and method of fabrication

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

Duoss, Eric; Frank, James M.; Kuntz, Joshua

A three-dimensionally patterned energy absorptive material and fabrication method having multiple layers of patterned filaments extrusion-formed from a curable pre-cursor material and stacked and cured in a three-dimensionally patterned architecture so that the energy absorptive material produced thereby has an engineered bulk property associated with the three-dimensionally patterned architecture.

Evaluation of 3D metrology potential using a multiple detector CDSEM

NASA Astrophysics Data System (ADS)

Hakii, Hidemitsu; Yonekura, Isao; Nishiyama, Yasushi; Tanaka, Keishi; Komoto, Kenji; Murakawa, Tsutomu; Hiroyama, Mitsuo; Shida, Soichi; Kuribara, Masayuki; Iwai, Toshimichi; Matsumoto, Jun; Nakamura, Takayuki

2012-06-01

As feature sizes of semiconductor device structures have continuously decreased, needs for metrology tools with high precision and excellent linearity over actual pattern sizes have been growing. And it has become important to measure not only two-dimensional (2D) but also three-dimensional (3D) shapes of patterns at 22 nm node and beyond. To meet requirements for 3D metrology capabilities, various pattern metrology tools have been developed. Among those, we assume that CDSEM metrology is the most qualified candidate in the light of its non-destructive, high throughput measurement capabilities that are expected to be extended to the much-awaited 3D metrology technology. On the basis of this supposition, we have developed the 3D metrology system, in which side wall angles and heights of photomask patterns can be measured with high accuracy through analyzing CDSEM images generated by multi-channel detectors. In this paper, we will discuss our attempts to measure 3D shapes of defect patterns on a photomask by using Advantest's "Multi Vision Metrology SEM" E3630 (MVM-SEM' E3630).

Three-dimensional metamaterials

DOEpatents

Burckel, David Bruce [Albuquerque, NM

2012-06-12

A fabrication method is capable of creating canonical metamaterial structures arrayed in a three-dimensional geometry. The method uses a membrane suspended over a cavity with predefined pattern as a directional evaporation mask. Metallic and/or dielectric material can be evaporated at high vacuum through the patterned membrane to deposit resonator structures on the interior walls of the cavity, thereby providing a unit cell of micron-scale dimension. The method can produce volumetric metamaterial structures comprising layers of such unit cells of resonator structures.

Three-dimensional patterning methods and related devices

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

Putnam, Morgan C.; Kelzenberg, Michael D.; Atwater, Harry A.

2016-12-27

Three-dimensional patterning methods of a three-dimensional microstructure, such as a semiconductor wire array, are described, in conjunction with etching and/or deposition steps to pattern the three-dimensional microstructure.

Zn-metalloprotease sequences in extremophiles

NASA Astrophysics Data System (ADS)

Holden, T.; Dehipawala, S.; Golebiewska, U.; Cheung, E.; Tremberger, G., Jr.; Williams, E.; Schneider, P.; Gadura, N.; Lieberman, D.; Cheung, T.

2010-09-01

The Zn-metalloprotease family contains conserved amino acid structures such that the nucleotide fluctuation at the DNA level would exhibit correlated randomness as described by fractal dimension. A nucleotide sequence fractal dimension can be calculated from a numerical series consisting of the atomic numbers of each nucleotide. The structure's vibration modes can also be studied using a Gaussian Network Model. The vibration measure and fractal dimension values form a two-dimensional plot with a standard vector metric that can be used for comparison of structures. The preference for amino acid usage in extremophiles may suppress nucleotide fluctuations that could be analyzed in terms of fractal dimension and Shannon entropy. A protein level cold adaptation study of the thermolysin Zn-metalloprotease family using molecular dynamics simulation was reported recently and our results show that the associated nucleotide fluctuation suppression is consistent with a regression pattern generated from the sequences's fractal dimension and entropy values (R-square { 0.98, N =5). It was observed that cold adaptation selected for high entropy and low fractal dimension values. Extension to the Archaemetzincin M54 family in extremophiles reveals a similar regression pattern (R-square = 0.98, N = 6). It was observed that the metalloprotease sequences of extremely halophilic organisms possess high fractal dimension and low entropy values as compared with non-halophiles. The zinc atom is usually bonded to the histidine residue, which shows limited levels of vibration in the Gaussian Network Model. The variability of the fractal dimension and entropy for a given protein structure suggests that extremophiles would have evolved after mesophiles, consistent with the bias usage of non-prebiotic amino acids by extremophiles. It may be argued that extremophiles have the capacity to offer extinction protection during drastic changes in astrobiological environments.

Adaptive projection intensity adjustment for avoiding saturation in three-dimensional shape measurement

NASA Astrophysics Data System (ADS)

Chen, Chao; Gao, Nan; Wang, Xiangjun; Zhang, Zonghua

2018-03-01

Phase-based fringe projection methods have been commonly used for three-dimensional (3D) measurements. However, image saturation results in incorrect intensities in captured fringe pattern images, leading to phase and measurement errors. Existing solutions are complex. This paper proposes an adaptive projection intensity adjustment method to avoid image saturation and maintain good fringe modulation in measuring objects with a high range of surface reflectivities. The adapted fringe patterns are created using only one prior step of fringe-pattern projection and image capture. First, a set of phase-shifted fringe patterns with maximum projection intensity value of 255 and a uniform gray level pattern are projected onto the surface of an object. The patterns are reflected from and deformed by the object surface and captured by a digital camera. The best projection intensities corresponding to each saturated-pixel clusters are determined by fitting a polynomial function to transform captured intensities to projected intensities. Subsequently, the adapted fringe patterns are constructed using the best projection intensities at projector pixel coordinate. Finally, the adapted fringe patterns are projected for phase recovery and 3D shape calculation. The experimental results demonstrate that the proposed method achieves high measurement accuracy even for objects with a high range of surface reflectivities.

Three-dimensional direct cell patterning in collagen hydrogels with near-infrared femtosecond laser

PubMed Central

Hribar, Kolin C.; Meggs, Kyle; Liu, Justin; Zhu, Wei; Qu, Xin; Chen, Shaochen

2015-01-01

We report a methodology for three-dimensional (3D) cell patterning in a hydrogel in situ. Gold nanorods within a cell-encapsulating collagen hydrogel absorb a focused near-infrared femtosecond laser beam, locally denaturing the collagen and forming channels, into which cells migrate, proliferate, and align in 3D. Importantly, pattern resolution is tunable based on writing speed and laser power, and high cell viability (>90%) is achieved using higher writing speeds and lower laser intensities. Overall, this patterning technique presents a flexible direct-write method that is applicable in tissue engineering systems where 3D alignment is critical (such as vascular, neural, cardiac, and muscle tissue). PMID:26603915

On the modeling of the bottom particles segregation with non-linear diffusion equations: application to the marine sand ripples

NASA Astrophysics Data System (ADS)

Tiguercha, Djlalli; Bennis, Anne-claire; Ezersky, Alexander

2015-04-01

The elliptical motion in surface waves causes an oscillating motion of the sand grains leading to the formation of ripple patterns on the bottom. Investigation how the grains with different properties are distributed inside the ripples is a difficult task because of the segration of particle. The work of Fernandez et al. (2003) was extended from one-dimensional to two-dimensional case. A new numerical model, based on these non-linear diffusion equations, was developed to simulate the grain distribution inside the marine sand ripples. The one and two-dimensional models are validated on several test cases where segregation appears. Starting from an homogeneous mixture of grains, the two-dimensional simulations demonstrate different segregation patterns: a) formation of zones with high concentration of light and heavy particles, b) formation of «cat's eye» patterns, c) appearance of inverse Brazil nut effect. Comparisons of numerical results with the new set of field data and wave flume experiments show that the two-dimensional non-linear diffusion equations allow us to reproduce qualitatively experimental results on particles segregation.

A generalized approach to automated NMR peak list editing: application to reduced dimensionality triple resonance spectra.

PubMed

Moseley, Hunter N B; Riaz, Nadeem; Aramini, James M; Szyperski, Thomas; Montelione, Gaetano T

2004-10-01

We present an algorithm and program called Pattern Picker that performs editing of raw peak lists derived from multidimensional NMR experiments with characteristic peak patterns. Pattern Picker detects groups of correlated peaks within peak lists from reduced dimensionality triple resonance (RD-TR) NMR spectra, with high fidelity and high yield. With typical quality RD-TR NMR data sets, Pattern Picker performs almost as well as human analysis, and is very robust in discriminating real peak sets from noise and other artifacts in unedited peak lists. The program uses a depth-first search algorithm with short-circuiting to efficiently explore a search tree representing every possible combination of peaks forming a group. The Pattern Picker program is particularly valuable for creating an automated peak picking/editing process. The Pattern Picker algorithm can be applied to a broad range of experiments with distinct peak patterns including RD, G-matrix Fourier transformation (GFT) NMR spectra, and experiments to measure scalar and residual dipolar coupling, thus promoting the use of experiments that are typically harder for a human to analyze. Since the complexity of peak patterns becomes a benefit rather than a drawback, Pattern Picker opens new opportunities in NMR experiment design.

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

PubMed Central

Shimizu, Yu; Yoshimoto, Junichiro; Takamura, Masahiro; Okada, Go; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area. PMID:28700672

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

PubMed Central

Huang, Jian; Zhang, Cun-Hui

2013-01-01

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results. PMID:24348100

[New method of mixed gas infrared spectrum analysis based on SVM].

PubMed

Bai, Peng; Xie, Wen-Jun; Liu, Jun-Hua

2007-07-01

A new method of infrared spectrum analysis based on support vector machine (SVM) for mixture gas was proposed. The kernel function in SVM was used to map the seriously overlapping absorption spectrum into high-dimensional space, and after transformation, the high-dimensional data could be processed in the original space, so the regression calibration model was established, then the regression calibration model with was applied to analyze the concentration of component gas. Meanwhile it was proved that the regression calibration model with SVM also could be used for component recognition of mixture gas. The method was applied to the analysis of different data samples. Some factors such as scan interval, range of the wavelength, kernel function and penalty coefficient C that affect the model were discussed. Experimental results show that the component concentration maximal Mean AE is 0.132%, and the component recognition accuracy is higher than 94%. The problems of overlapping absorption spectrum, using the same method for qualitative and quantitative analysis, and limit number of training sample, were solved. The method could be used in other mixture gas infrared spectrum analyses, promising theoretic and application values.

HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION

PubMed Central

Mukherjee, Rajarshi; Pillai, Natesh S.; Lin, Xihong

2015-01-01

In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing problem when the design matrix is sparse. We observe a new phenomenon in the behavior of detection boundary which does not occur in the case of Gaussian linear regression. We derive the detection boundary as a function of two components: a design matrix sparsity index and signal strength, each of which is a function of the sparsity of the alternative. For any alternative, if the design matrix sparsity index is too high, any test is asymptotically powerless irrespective of the magnitude of signal strength. For binary design matrices with the sparsity index that is not too high, our results are parallel to those in the Gaussian case. In this context, we derive detection boundaries for both dense and sparse regimes. For the dense regime, we show that the generalized likelihood ratio is rate optimal; for the sparse regime, we propose an extended Higher Criticism Test and show it is rate optimal and sharp. We illustrate the finite sample properties of the theoretical results using simulation studies. PMID:26246645

Personality factors and weight preoccupation: a continuum approach to the association between eating disorders and personality disorders.

PubMed

Davis, C; Claridge, G; Cerullo, D

1997-01-01

Evidence shows a high comorbidity of eating disorders and some forms of personality disorder. Adopting a dimensional approach to both, our study explored their connection among a non-clinical sample. 191 young women completed personality scales of general neuroticism, and of borderline, schizotypal, obsessive-compulsive, and narcissistic (both adjustive and maladaptive) traits. Weight preoccupation (WP), as a normal analogue of eating disorders, was assessed with scales from the Eating Disorder Inventory, and height and weight measured. The data were analysed with multiple regression techniques, with WP as the dependent variable. In low to normal weight subjects, after controlling for the significant influence of body mass, the specific predictors of WP in the regression model were borderline personality and maladaptive narcissism, in the positive direction, and adjustive narcissism and obsessive-compulsiveness in the negative direction. In heavier women, narcissism made no contribution--nor, more significantly, did body mass. Patterns of association between eating pathology and personality disorder, especially borderline and narcissism, can be clearly mapped across to personality traits in the currently non-clinical population. This finding has important implications for understanding dynamics of, and identifying individuals at risk for, eating disorders.

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study.

PubMed

Ogihara, Takeshi; Mita, Tomoya; Osonoi, Yusuke; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Gosho, Masahiko; Kanazawa, Akio; Watada, Hirotaka

2017-01-01

While individuals tend to show accumulation of certain lifestyle patterns, the effect of such patterns in real daily life on cardio-renal-metabolic parameters remains largely unknown. This study aimed to assess clustering of lifestyle patterns and investigate the relationships between such patterns and cardio-renal-metabolic parameters. The study participants were 726 Japanese type 2 diabetes mellitus (T2DM) outpatients free of history of cardiovascular diseases. The relationship between lifestyle patterns and cardio-renal-metabolic parameters was investigated by linear and logistic regression analyses. Factor analysis identified three lifestyle patterns. Subjects characterized by evening type, poor sleep quality and depressive status (type 1 pattern) had high levels of HbA1c, alanine aminotransferase and albuminuria. Subjects characterized by high consumption of food, alcohol and cigarettes (type 2 pattern) had high levels of γ-glutamyl transpeptidase, triglycerides, HDL-cholesterol, blood pressure, and brachial-ankle pulse wave velocity. Subjects characterized by high physical activity (type 3 pattern) had low uric acid and mild elevation of alanine aminotransferase and aspartate aminotransferase. In multivariate regression analysis adjusted by age, gender and BMI, type 1 pattern was associated with higher HbA1c levels, systolic BP and brachial-ankle pulse wave velocity. Type 2 pattern was associated with higher HDL-cholesterol levels, triglycerides, aspartate aminotransferase, ɤ- glutamyl transpeptidase levels, and diastolic BP. The study identified three lifestyle patterns that were associated with distinct cardio-metabolic-renal parameters in T2DM patients. UMIN000010932.

High-resolution liquid patterns via three-dimensional droplet shape control.

PubMed

Raj, Rishi; Adera, Solomon; Enright, Ryan; Wang, Evelyn N

2014-09-25

Understanding liquid dynamics on surfaces can provide insight into nature's design and enable fine manipulation capability in biological, manufacturing, microfluidic and thermal management applications. Of particular interest is the ability to control the shape of the droplet contact area on the surface, which is typically circular on a smooth homogeneous surface. Here, we show the ability to tailor various droplet contact area shapes ranging from squares, rectangles, hexagons, octagons, to dodecagons via the design of the structure or chemical heterogeneity on the surface. We simultaneously obtain the necessary physical insights to develop a universal model for the three-dimensional droplet shape by characterizing the droplet side and top profiles. Furthermore, arrays of droplets with controlled shapes and high spatial resolution can be achieved using this approach. This liquid-based patterning strategy promises low-cost fabrication of integrated circuits, conductive patterns and bio-microarrays for high-density information storage and miniaturized biochips and biosensors, among others.

The impact of gendered friendship patterns on the prevalence of homophobia among belgian late adolescents.

PubMed

Hooghe, Marc

2011-06-01

In order to assess the determinants of homophobia among Belgian adolescents, a shortened version of the Homophobia scale (Wright et al., 1999) was included in a representative survey among Belgian adolescents (n = 4,870). Principal component analysis demonstrated that the scale was one-dimensional and internally coherent. The results showed that homophobia is still widespread among Belgian adolescents, despite various legal reforms in the country aiming to combat discrimination of gay women and men. A multivariate regression analysis demonstrated that boys, ethnic minorities, individuals with high levels of ethnocentrism and an instrumental worldview, Muslim minorities, and those with low levels of associational involvement scored significantly higher on the scale. While among boys an extensive friendship network was associated with higher levels of homophobia, the opposite phenomenon was found among girls. We discuss the possible relation between notions of masculinity within predominantly male adolescent friendship networks and social support for homophobia.

Phase contrast imaging X-ray computed tomography: quantitative characterization of human patellar cartilage matrix with topological and geometrical features

NASA Astrophysics Data System (ADS)

Nagarajan, Mahesh B.; Coan, Paola; Huber, Markus B.; Diemoz, Paul C.; Wismüller, Axel

2014-03-01

Current assessment of cartilage is primarily based on identification of indirect markers such as joint space narrowing and increased subchondral bone density on x-ray images. In this context, phase contrast CT imaging (PCI-CT) has recently emerged as a novel imaging technique that allows a direct examination of chondrocyte patterns and their correlation to osteoarthritis through visualization of cartilage soft tissue. This study investigates the use of topological and geometrical approaches for characterizing chondrocyte patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage. For this purpose, topological features derived from Minkowski Functionals and geometric features derived from the Scaling Index Method (SIM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of healthy and osteoarthritic specimens of human patellar cartilage. The extracted features were then used in a machine learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with high-dimensional geometrical feature vectors derived from SIM (0.95 ± 0.06) which outperformed all Minkowski Functionals (p < 0.001). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving SIM-derived geometrical features can distinguish between healthy and osteoarthritic tissue with high accuracy.

High-speed autofocusing of a cell using diffraction pattern

NASA Astrophysics Data System (ADS)

Oku, Hiromasa; Ishikawa, Masatoshi; Theodorus; Hashimoto, Koichi

2006-05-01

This paper proposes a new autofocusing method for observing cells under a transmission illumination. The focusing method uses a quick and simple focus estimation technique termed “depth from diffraction,” which is based on a diffraction pattern in a defocused image of a biological specimen. Since this method can estimate the focal position of the specimen from only a single defocused image, it can easily realize high-speed autofocusing. To demonstrate the method, it was applied to continuous focus tracking of a swimming paramecium, in combination with two-dimensional position tracking. Three-dimensional tracking of the paramecium for 70 s was successfully demonstrated.

Thermally induced rarefied gas flow in a three-dimensional enclosure with square cross-section

NASA Astrophysics Data System (ADS)

Zhu, Lianhua; Yang, Xiaofan; Guo, Zhaoli

2017-12-01

Rarefied gas flow in a three-dimensional enclosure induced by nonuniform temperature distribution is numerically investigated. The enclosure has a square channel-like geometry with alternatively heated closed ends and lateral walls with a linear temperature distribution. A recently proposed implicit discrete velocity method with a memory reduction technique is used to numerically simulate the problem based on the nonlinear Shakhov kinetic equation. The Knudsen number dependencies of the vortices pattern, slip velocity at the planar walls and edges, and heat transfer are investigated. The influences of the temperature ratio imposed at the ends of the enclosure and the geometric aspect ratio are also evaluated. The overall flow pattern shows similarities with those observed in two-dimensional configurations in literature. However, features due to the three-dimensionality are observed with vortices that are not identified in previous studies on similar two-dimensional enclosures at high Knudsen and small aspect ratios.

Dimensionality reduction of collective motion by principal manifolds

NASA Astrophysics Data System (ADS)

Gajamannage, Kelum; Butail, Sachit; Porfiri, Maurizio; Bollt, Erik M.

2015-01-01

While the existence of low-dimensional embedding manifolds has been shown in patterns of collective motion, the current battery of nonlinear dimensionality reduction methods is not amenable to the analysis of such manifolds. This is mainly due to the necessary spectral decomposition step, which limits control over the mapping from the original high-dimensional space to the embedding space. Here, we propose an alternative approach that demands a two-dimensional embedding which topologically summarizes the high-dimensional data. In this sense, our approach is closely related to the construction of one-dimensional principal curves that minimize orthogonal error to data points subject to smoothness constraints. Specifically, we construct a two-dimensional principal manifold directly in the high-dimensional space using cubic smoothing splines, and define the embedding coordinates in terms of geodesic distances. Thus, the mapping from the high-dimensional data to the manifold is defined in terms of local coordinates. Through representative examples, we show that compared to existing nonlinear dimensionality reduction methods, the principal manifold retains the original structure even in noisy and sparse datasets. The principal manifold finding algorithm is applied to configurations obtained from a dynamical system of multiple agents simulating a complex maneuver called predator mobbing, and the resulting two-dimensional embedding is compared with that of a well-established nonlinear dimensionality reduction method.

Learning and recall of form discriminations during reversible cooling deactivation of ventral-posterior suprasylvian cortex in the cat.

PubMed Central

Lomber, S G; Payne, B R; Cornwell, P

1996-01-01

Extrastriate visual cortex of the ventral-posterior suprasylvian gyrus (vPS cortex) of freely behaving cats was reversibly deactivated with cooling to determine its role in performance on a battery of simple or masked two-dimensional pattern discriminations, and three-dimensional object discriminations. Deactivation of vPS cortex by cooling profoundly impaired the ability of the cats to recall the difference between all previously learned pattern and object discriminations. However, the cats' ability to learn or relearn pattern and object discriminations while vPS was deactivated depended upon the nature of the pattern or object and the cats' prior level of exposure to them. During cooling of vPS cortex, the cats could neither learn the novel object discriminations nor relearn a highly familiar masked or partially occluded pattern discrimination, although they could relearn both the highly familiar object and simple pattern discriminations. These cooling-induced deficits resemble those induced by cooling of the topologically equivalent inferotemporal cortex of monkeys and provides evidence that the equivalent regions contribute to visual processing in similar ways. Images Fig. 1 Fig. 3 PMID:8643686

Fast Fourier single-pixel imaging via binary illumination.

PubMed

Zhang, Zibang; Wang, Xueying; Zheng, Guoan; Zhong, Jingang

2017-09-20

Fourier single-pixel imaging (FSI) employs Fourier basis patterns for encoding spatial information and is capable of reconstructing high-quality two-dimensional and three-dimensional images. Fourier-domain sparsity in natural scenes allows FSI to recover sharp images from undersampled data. The original FSI demonstration, however, requires grayscale Fourier basis patterns for illumination. This requirement imposes a limitation on the imaging speed as digital micro-mirror devices (DMDs) generate grayscale patterns at a low refreshing rate. In this paper, we report a new strategy to increase the speed of FSI by two orders of magnitude. In this strategy, we binarize the Fourier basis patterns based on upsampling and error diffusion dithering. We demonstrate a 20,000 Hz projection rate using a DMD and capture 256-by-256-pixel dynamic scenes at a speed of 10 frames per second. The reported technique substantially accelerates image acquisition speed of FSI. It may find broad imaging applications at wavebands that are not accessible using conventional two-dimensional image sensors.

Striation pattern of target particle and heat fluxes in three dimensional simulations for DIII-D [On the striation pattern of target particle and heat fluxes in three dimensional simulations for DIII-D

DOE PAGES

Frerichs, H.; Schmitz, Oliver; Reiter, D.; ...

2014-02-04

The application of resonant magnetic perturbations (RMPs) results in a non-axisymmetric striation pattern of magnetic field lines from the plasma interior which intersect the divertor targets. The impact on related particle and heat fluxes is investigated by three dimensional computer simulations for two different recycling conditions (controlled via neutral gas pumping). It is demonstrated that a mismatch between the particle and heat flux striation pattern, as is repeatedly observed in ITER similar shape H-mode plasmas at DIII-D, can be reproduced by the simulations for high recycling conditions at the onset of partial detachment. Finally, these results indicate that a detailedmore » knowledge of the particle and energy balance is at least as important for realistic simulations as the consideration of a change in the magnetic field structure by plasma response effects.« less

Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics

PubMed Central

Lin, Wei; Feng, Rui; Li, Hongzhe

2014-01-01

In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expressions and genetic variants can both be much larger than the sample size. Motivated by such modern applications, we consider the problem of variable selection and estimation in high-dimensional sparse instrumental variables models. To overcome the difficulty of high dimensionality and unknown optimal instruments, we propose a two-stage regularization framework for identifying and estimating important covariate effects while selecting and estimating optimal instruments. The methodology extends the classical two-stage least squares estimator to high dimensions by exploiting sparsity using sparsity-inducing penalty functions in both stages. The resulting procedure is efficiently implemented by coordinate descent optimization. For the representative L1 regularization and a class of concave regularization methods, we establish estimation, prediction, and model selection properties of the two-stage regularized estimators in the high-dimensional setting where the dimensionality of co-variates and instruments are both allowed to grow exponentially with the sample size. The practical performance of the proposed method is evaluated by simulation studies and its usefulness is illustrated by an analysis of mouse obesity data. Supplementary materials for this article are available online. PMID:26392642

Heat Transport upon River-Water Infiltration investigated by Fiber-Optic High-Resolution Temperature Profiling

NASA Astrophysics Data System (ADS)

Vogt, T.; Schirmer, M.; Cirpka, O. A.

2010-12-01

Infiltrating river water is of high relevance for drinking water supply by river bank filtration as well as for riparian groundwater ecology. Quantifying flow patterns and velocities, however, is hampered by temporal and spatial variations of exchange fluxes. In recent years, heat has become a popular natural tracer to estimate exchange rates between rivers and groundwater. Nevertheless, field investigations are often limited by insufficient sensors spacing or simplifying assumptions such as one-dimensional flow. Our interest lies in a detailed local survey of river water infiltration at a restored river section at the losing river Thur in northeast Switzerland. Here, we measured three high-resolution temperature profiles along an assumed flow path by means of distributed temperature sensing (DTS) using fiber optic cables wrapped around poles. Moreover, piezometers were equipped with standard temperature sensors for a comparison to the DTS data. Diurnal temperature oscillations were tracked in the river bed and the riparian groundwater and analyzed by means of dynamic harmonic regression and subsequent modeling of heat transport with sinusoidal boundary conditions to quantify seepage velocities and thermal diffusivities. Compared to the standard temperature sensors, the DTS data give a higher vertical resolution, facilitating the detection of process- and structure-dependent patterns of the spatiotemporal temperature field. This advantage overcompensates the scatter in the data due to instrument noise. In particular, we could demonstrate the impact of heat conduction through the unsaturated zone on the riparian groundwater by the high resolution temperature profiles.

Sufficient Forecasting Using Factor Models

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Yao, Jiawei

2017-01-01

We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric (approximate) factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function. The proposed method extends the sufficient dimension reduction to high-dimensional regimes by condensing the cross-sectional information through factor models. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables. PMID:29731537

Functional inks and printing of two-dimensional materials.

PubMed

Hu, Guohua; Kang, Joohoon; Ng, Leonard W T; Zhu, Xiaoxi; Howe, Richard C T; Jones, Christopher G; Hersam, Mark C; Hasan, Tawfique

2018-05-08

Graphene and related two-dimensional materials provide an ideal platform for next generation disruptive technologies and applications. Exploiting these solution-processed two-dimensional materials in printing can accelerate this development by allowing additive patterning on both rigid and conformable substrates for flexible device design and large-scale, high-speed, cost-effective manufacturing. In this review, we summarise the current progress on ink formulation of two-dimensional materials and the printable applications enabled by them. We also present our perspectives on their research and technological future prospects.

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study

PubMed Central

Ogihara, Takeshi; Osonoi, Yusuke; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Gosho, Masahiko; Kanazawa, Akio; Watada, Hirotaka

2017-01-01

Introduction While individuals tend to show accumulation of certain lifestyle patterns, the effect of such patterns in real daily life on cardio-renal—metabolic parameters remains largely unknown. This study aimed to assess clustering of lifestyle patterns and investigate the relationships between such patterns and cardio-renal-metabolic parameters. Participants and methods The study participants were 726 Japanese type 2 diabetes mellitus (T2DM) outpatients free of history of cardiovascular diseases. The relationship between lifestyle patterns and cardio-renal-metabolic parameters was investigated by linear and logistic regression analyses. Results Factor analysis identified three lifestyle patterns. Subjects characterized by evening type, poor sleep quality and depressive status (type 1 pattern) had high levels of HbA1c, alanine aminotransferase and albuminuria. Subjects characterized by high consumption of food, alcohol and cigarettes (type 2 pattern) had high levels of γ-glutamyl transpeptidase, triglycerides, HDL-cholesterol, blood pressure, and brachial-ankle pulse wave velocity. Subjects characterized by high physical activity (type 3 pattern) had low uric acid and mild elevation of alanine aminotransferase and aspartate aminotransferase. In multivariate regression analysis adjusted by age, gender and BMI, type 1 pattern was associated with higher HbA1c levels, systolic BP and brachial-ankle pulse wave velocity. Type 2 pattern was associated with higher HDL-cholesterol levels, triglycerides, aspartate aminotransferase, ɤ- glutamyl transpeptidase levels, and diastolic BP. Conclusions The study identified three lifestyle patterns that were associated with distinct cardio-metabolic-renal parameters in T2DM patients. Trial registration UMIN000010932 PMID:28273173

High-speed three-dimensional shape measurement using GOBO projection

NASA Astrophysics Data System (ADS)

Heist, Stefan; Lutzke, Peter; Schmidt, Ingo; Dietrich, Patrick; Kühmstedt, Peter; Tünnermann, Andreas; Notni, Gunther

2016-12-01

A projector which uses a rotating slide structure to project aperiodic sinusoidal fringe patterns at high frame rates and with high radiant flux is introduced. It is used in an optical three-dimensional (3D) sensor based on coded-light projection, thus allowing the analysis of fast processes. Measurements of an inflating airbag, a rope skipper, and a soccer ball kick at a 3D frame rate of more than 1300 independent point clouds per second are presented.

Study of high speed complex number algorithms. [for determining antenna for field radiation patterns

NASA Technical Reports Server (NTRS)

Heisler, R.

1981-01-01

A method of evaluating the radiation integral on the curved surface of a reflecting antenna is presented. A three dimensional Fourier transform approach is used to generate a two dimensional radiation cross-section along a planer cut at any angle phi through the far field pattern. Salient to the method is an algorithm for evaluating a subset of the total three dimensional discrete Fourier transform results. The subset elements are selectively evaluated to yield data along a geometric plane of constant. The algorithm is extremely efficient so that computation of the induced surface currents via the physical optics approximation dominates the computer time required to compute a radiation pattern. Application to paraboloid reflectors with off-focus feeds in presented, but the method is easily extended to offset antenna systems and reflectors of arbitrary shapes. Numerical results were computed for both gain and phase and are compared with other published work.

Accelerated High-Dimensional MR Imaging with Sparse Sampling Using Low-Rank Tensors

PubMed Central

He, Jingfei; Liu, Qiegen; Christodoulou, Anthony G.; Ma, Chao; Lam, Fan

2017-01-01

High-dimensional MR imaging often requires long data acquisition time, thereby limiting its practical applications. This paper presents a low-rank tensor based method for accelerated high-dimensional MR imaging using sparse sampling. This method represents high-dimensional images as low-rank tensors (or partially separable functions) and uses this mathematical structure for sparse sampling of the data space and for image reconstruction from highly undersampled data. More specifically, the proposed method acquires two datasets with complementary sampling patterns, one for subspace estimation and the other for image reconstruction; image reconstruction from highly undersampled data is accomplished by fitting the measured data with a sparsity constraint on the core tensor and a group sparsity constraint on the spatial coefficients jointly using the alternating direction method of multipliers. The usefulness of the proposed method is demonstrated in MRI applications; it may also have applications beyond MRI. PMID:27093543

Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces.

PubMed

Gentry, Amanda Elswick; Jackson-Cook, Colleen K; Lyon, Debra E; Archer, Kellie J

2015-01-01

The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, breast cancer subtype is a nonpenalized predictor, and CpG site methylation values from the Illumina Human Methylation 450K assay are penalized predictors. The method has been made available in the ordinalgmifs package in the R programming environment.

Drought Patterns Forecasting using an Auto-Regressive Logistic Model

NASA Astrophysics Data System (ADS)

del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.

2014-12-01

Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.

Silicone elastomers capable of large isotropic dimensional change

DOEpatents

Lewicki, James; Worsley, Marcus A.

2017-07-18

Described herein is a highly effective route towards the controlled and isotropic reduction in size-scale, of complex 3D structures using silicone network polymer chemistry. In particular, a class of silicone structures were developed that once patterned and cured can `shrink` micron scale additive manufactured and lithographically patterned structures by as much as 1 order of magnitude while preserving the dimensions and integrity of these parts. This class of silicone materials is compatible with existing additive manufacture and soft lithographic fabrication processes and will allow access to a hitherto unobtainable dimensionality of fabrication.

Patterned arrays of lateral heterojunctions within monolayer two-dimensional semiconductors

DOE PAGES

Mahjouri-Samani, Masoud; Lin, Ming-Wei; Wang, Kai; ...

2015-07-22

The formation of semiconductor heterojunctions and their high density integration are foundations of modern electronics and optoelectronics. To enable two-dimensional (2D) crystalline semiconductors as building blocks in next generation electronics, developing methods to deterministically form lateral heterojunctions is crucial. Here we demonstrate a process strategy for the formation of lithographically-patterned lateral semiconducting heterojunctions within a single 2D crystal. E-beam lithography is used to pattern MoSe 2 monolayer crystals with SiO 2, and the exposed locations are selectively and totally converted to MoS 2 using pulsed laser deposition (PLD) of sulfur in order to form MoSe 2/MoS 2 heterojunctions in predefinedmore » patterns. The junctions and conversion process are characterized by atomically resolved scanning transmission electron microscopy, photoluminescence, and Raman spectroscopy. This demonstration of lateral semiconductor heterojunction arrays within a single 2D crystal is an essential step for the lateral integration of 2D semiconductor building blocks with different electronic and optoelectronic properties for high-density, ultrathin circuitry.« less

Retention modelling of polychlorinated biphenyls in comprehensive two-dimensional gas chromatography.

PubMed

D'Archivio, Angelo Antonio; Incani, Angela; Ruggieri, Fabrizio

2011-01-01

In this paper, we use a quantitative structure-retention relationship (QSRR) method to predict the retention times of polychlorinated biphenyls (PCBs) in comprehensive two-dimensional gas chromatography (GC×GC). We analyse the GC×GC retention data taken from the literature by comparing predictive capability of different regression methods. The various models are generated using 70 out of 209 PCB congeners in the calibration stage, while their predictive performance is evaluated on the remaining 139 compounds. The two-dimensional chromatogram is initially estimated by separately modelling retention times of PCBs in the first and in the second column ((1) t (R) and (2) t (R), respectively). In particular, multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is performed to extract two small subsets of predictors for (1) t (R) and (2) t (R) from a large set of theoretical molecular descriptors provided by the popular software Dragon, which after removal of highly correlated or almost constant variables consists of 237 structure-related quantities. Based on GA-MLR analysis, a four-dimensional and a five-dimensional relationship modelling (1) t (R) and (2) t (R), respectively, are identified. Single-response partial least square (PLS-1) regression is alternatively applied to independently model (1) t (R) and (2) t (R) without the need for preliminary GA variable selection. Further, we explore the possibility of predicting the two-dimensional chromatogram of PCBs in a single calibration procedure by using a two-response PLS (PLS-2) model or a feed-forward artificial neural network (ANN) with two output neurons. In the first case, regression is carried out on the full set of 237 descriptors, while the variables previously selected by GA-MLR are initially considered as ANN inputs and subjected to a sensitivity analysis to remove the redundant ones. Results show PLS-1 regression exhibits a noticeably better descriptive and predictive performance than the other investigated approaches. The observed values of determination coefficients for (1) t (R) and (2) t (R) in calibration (0.9999 and 0.9993, respectively) and prediction (0.9987 and 0.9793, respectively) provided by PLS-1 demonstrate that GC×GC behaviour of PCBs is properly modelled. In particular, the predicted two-dimensional GC×GC chromatogram of 139 PCBs not involved in the calibration stage closely resembles the experimental one. Based on the above lines of evidence, the proposed approach ensures accurate simulation of the whole GC×GC chromatogram of PCBs using experimental determination of only 1/3 retention data of representative congeners.

Placental three-dimensional power Doppler indices in mid-pregnancy and late pregnancy complicated by gestational diabetes mellitus.

PubMed

Surányi, A; Kozinszky, Z; Molnár, A; Nyári, T; Bitó, T; Pál, A

2013-10-01

The aim of our study was to evaluate placental three-dimensional power Doppler indices in diabetic pregnancies in the second and third trimesters and to compare them with those of the normal controls. Placental vascularization of pregnant women was determined by three-dimensional power Doppler ultrasound technique. The calculated indices included vascularization index (VI), flow index (FI), and vascularization flow index (VFI). Uncomplicated pregnancies (n = 113) were compared with pregnancies complicated by gestational diabetes mellitus (n = 56) and diabetes mellitus (n = 43). The three-dimensional power Doppler indices were not significantly different between the two diabetic subgroups. All the indices in diabetic patients were significantly reduced compared with those in non-diabetic individuals (p < 0.001). Placental three-dimensional power Doppler indices are slightly diminished throughout diabetic pregnancy [regression coefficients: -0.23 (FI), -0.06 (VI), and -0.04 (VFI)] and normal pregnancy [regression coefficients: -0.13 (FI), -0.20 (VI), and -0.11 (VFI)]. The uteroplacental circulation (umbilical and uterine artery) was not correlated significantly to the three-dimensional power Doppler indices. If all placental indices are low during late pregnancy, then the odds of the diabetes are significantly high (adjusted odds ratio: 1.10). A decreased placental vascularization could be an adjunct sonographic marker in the diagnosis of diabetic pregnancy in mid-gestation and late gestation. © 2013 John Wiley & Sons, Ltd.

General aviation air traffic pattern safety analysis

NASA Technical Reports Server (NTRS)

Parker, L. C.

1973-01-01

A concept is described for evaluating the general aviation mid-air collision hazard in uncontrolled terminal airspace. Three-dimensional traffic pattern measurements were conducted at uncontrolled and controlled airports. Computer programs for data reduction, storage retrieval and statistical analysis have been developed. Initial general aviation air traffic pattern characteristics are presented. These preliminary results indicate that patterns are highly divergent from the expected standard pattern, and that pattern procedures observed can affect the ability of pilots to see and avoid each other.

A comparison of the accuracy of patterns processed from an inlay casting wax, an auto-polymerized resin and a light-cured resin pattern material.

PubMed

Rajagopal, Praveen; Chitre, Vidya; Aras, Meena A

2012-01-01

Traditionally, inlay casting waxes have been used to fabricate patterns for castings. Newer resin pattern materials offer greater rigidity and strength, allowing easier laboratory and intraoral adjustment without the fear of pattern damage. They also claim to possess a greater dimensional stability when compared to inlay wax. This study attempted to determine and compare the marginal accuracy of patterns fabricated from an inlay casting wax, an autopolymerized pattern resin and a light polymerized pattern resin on storage off the die for varying time intervals. Ten patterns each were fabricated from an inlay casting wax (GC Corp., Tokyo, Japan), an autopolymerized resin pattern material (Pattern resin, GC Corp, Tokyo, Japan) and a light-cured resin pattern material (Palavit GLC, Hereaus Kulzer GmbH, Germany). The completed patterns were stored off the die at room temperature. Marginal gaps were evaluated by reseating the patterns on their respective dies and observing it under a stereomicroscope at 1, 12, and 24 h intervals after pattern fabrication. The results revealed that the inlay wax showed a significantly greater marginal discrepancy at the 12 and 24 h intervals. The autopolymerized resin showed an initial (at 1 h) marginal discrepancy slightly greater than inlay wax, but showed a significantly less marginal gap (as compared to inlay wax) at the other two time intervals. The light-cured resin proved to be significantly more dimensionally stable, and showed minimal change during the storage period. The resin pattern materials studied, undergo a significantly less dimensional change than the inlay waxes on prolonged storage. They would possibly be a better alternative to inlay wax in situations requiring high precision or when delayed investment (more than 1 h) of patterns can be expected.

Low-rank separated representation surrogates of high-dimensional stochastic functions: Application in Bayesian inference

NASA Astrophysics Data System (ADS)

Validi, AbdoulAhad

2014-03-01

This study introduces a non-intrusive approach in the context of low-rank separated representation to construct a surrogate of high-dimensional stochastic functions, e.g., PDEs/ODEs, in order to decrease the computational cost of Markov Chain Monte Carlo simulations in Bayesian inference. The surrogate model is constructed via a regularized alternative least-square regression with Tikhonov regularization using a roughening matrix computing the gradient of the solution, in conjunction with a perturbation-based error indicator to detect optimal model complexities. The model approximates a vector of a continuous solution at discrete values of a physical variable. The required number of random realizations to achieve a successful approximation linearly depends on the function dimensionality. The computational cost of the model construction is quadratic in the number of random inputs, which potentially tackles the curse of dimensionality in high-dimensional stochastic functions. Furthermore, this vector-valued separated representation-based model, in comparison to the available scalar-valued case, leads to a significant reduction in the cost of approximation by an order of magnitude equal to the vector size. The performance of the method is studied through its application to three numerical examples including a 41-dimensional elliptic PDE and a 21-dimensional cavity flow.

Recent growth of conifer species of western North America: Assessing spatial patterns of radial growth trends

USGS Publications Warehouse

McKenzie, D.; Hessl, Amy E.; Peterson, D.L.

2001-01-01

We explored spatial patterns of low-frequency variability in radial tree growth among western North American conifer species and identified predictors of the variability in these patterns. Using 185 sites from the International Tree-Ring Data Bank, each of which contained 10a??60 raw ring-width series, we rebuilt two chronologies for each site, using two conservative methods designed to retain any low-frequency variability associated with recent environmental change. We used factor analysis to identify regional low-frequency patterns in site chronologies and estimated the slope of the growth trend since 1850 at each site from a combination of linear regression and time-series techniques. This slope was the response variable in a regression-tree model to predict the effects of environmental gradients and species-level differences on growth trends. Growth patterns at 27 sites from the American Southwest were consistent with quasi-periodic patterns of drought. Either 12 or 32 of the 185 sites demonstrated patterns of increasing growth between 1850 and 1980 A.D., depending on the standardization technique used. Pronounced growth increases were associated with high-elevation sites (above 3000 m) and high-latitude sites in maritime climates. Future research focused on these high-elevation and high-latitude sites should address the precise mechanisms responsible for increased 20th century growth.

Topological patterns of mesh textures in serpentinites

NASA Astrophysics Data System (ADS)

Miyazawa, M.; Suzuki, A.; Shimizu, H.; Okamoto, A.; Hiraoka, Y.; Obayashi, I.; Tsuji, T.; Ito, T.

2017-12-01

Serpentinization is a hydration process that forms serpentine minerals and magnetite within the oceanic lithosphere. Microfractures crosscut these minerals during the reactions, and the structures look like mesh textures. It has been known that the patterns of microfractures and the system evolutions are affected by the hydration reaction and fluid transport in fractures and within matrices. This study aims at quantifying the topological patterns of the mesh textures and understanding possible conditions of fluid transport and reaction during serpentinization in the oceanic lithosphere. Two-dimensional simulation by the distinct element method (DEM) generates fracture patterns due to serpentinization. The microfracture patterns are evaluated by persistent homology, which measures features of connected components of a topological space and encodes multi-scale topological features in the persistence diagrams. The persistence diagrams of the different mesh textures are evaluated by principal component analysis to bring out the strong patterns of persistence diagrams. This approach help extract feature values of fracture patterns from high-dimensional and complex datasets.

Diffraction Correlation to Reconstruct Highly Strained Particles

NASA Astrophysics Data System (ADS)

Brown, Douglas; Harder, Ross; Clark, Jesse; Kim, J. W.; Kiefer, Boris; Fullerton, Eric; Shpyrko, Oleg; Fohtung, Edwin

2015-03-01

Through the use of coherent x-ray diffraction a three-dimensional diffraction pattern of a highly strained nano-crystal can be recorded in reciprocal space by a detector. Only the intensities are recorded, resulting in a loss of the complex phase. The recorded diffraction pattern therefore requires computational processing to reconstruct the density and complex distribution of the diffracted nano-crystal. For highly strained crystals, standard methods using HIO and ER algorithms are no longer sufficient to reconstruct the diffraction pattern. Our solution is to correlate the symmetry in reciprocal space to generate an a priori shape constraint to guide the computational reconstruction of the diffraction pattern. This approach has improved the ability to accurately reconstruct highly strained nano-crystals.

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

PubMed Central

Ghanbari, Yasser; Smith, Alex R.; Schultz, Robert T.; Verma, Ragini

2014-01-01

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain’s traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations. PMID:25037933

Kaleidoscopic imaging patterns of complex structures fabricated by laser-induced deformation

PubMed Central

Zhang, Haoran; Yang, Fengyou; Dong, Jianjie; Du, Lena; Wang, Chuang; Zhang, Jianming; Guo, Chuan Fei; Liu, Qian

2016-01-01

Complex surface structures have stimulated a great deal of interests due to many potential applications in surface devices. However, in the fabrication of complex surface micro-/nanostructures, there are always great challenges in precise design, or good controllability, or low cost, or high throughput. Here, we present a route for the accurate design and highly controllable fabrication of surface quasi-three-dimensional (quasi-3D) structures based on a thermal deformation of simple two-dimensional laser-induced patterns. A complex quasi-3D structure, coaxially nested convex–concave microlens array, as an example, demonstrates our capability of design and fabrication of surface elements with this method. Moreover, by using only one relief mask with the convex–concave microlens structure, we have gotten hundreds of target patterns at different imaging planes, offering a cost-effective solution for mass production in lithography and imprinting, and portending a paradigm in quasi-3D manufacturing. PMID:27910852

Cooperative simulation of lithography and topography for three-dimensional high-aspect-ratio etching

NASA Astrophysics Data System (ADS)

Ichikawa, Takashi; Yagisawa, Takashi; Furukawa, Shinichi; Taguchi, Takafumi; Nojima, Shigeki; Murakami, Sadatoshi; Tamaoki, Naoki

2018-06-01

A topography simulation of high-aspect-ratio etching considering transports of ions and neutrals is performed, and the mechanism of reactive ion etching (RIE) residues in three-dimensional corner patterns is revealed. Limited ion flux and CF2 diffusion from the wide space of the corner is found to have an effect on the RIE residues. Cooperative simulation of lithography and topography is used to solve the RIE residue problem.

A Structure-Based Distance Metric for High-Dimensional Space Exploration with Multi-Dimensional Scaling

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

Lee, Hyun Jung; McDonnell, Kevin T.; Zelenyuk, Alla

2014-03-01

Although the Euclidean distance does well in measuring data distances within high-dimensional clusters, it does poorly when it comes to gauging inter-cluster distances. This significantly impacts the quality of global, low-dimensional space embedding procedures such as the popular multi-dimensional scaling (MDS) where one can often observe non-intuitive layouts. We were inspired by the perceptual processes evoked in the method of parallel coordinates which enables users to visually aggregate the data by the patterns the polylines exhibit across the dimension axes. We call the path of such a polyline its structure and suggest a metric that captures this structure directly inmore » high-dimensional space. This allows us to better gauge the distances of spatially distant data constellations and so achieve data aggregations in MDS plots that are more cognizant of existing high-dimensional structure similarities. Our MDS plots also exhibit similar visual relationships as the method of parallel coordinates which is often used alongside to visualize the high-dimensional data in raw form. We then cast our metric into a bi-scale framework which distinguishes far-distances from near-distances. The coarser scale uses the structural similarity metric to separate data aggregates obtained by prior classification or clustering, while the finer scale employs the appropriate Euclidean distance.« less

The application of a sparse, distributed memory to the detection, identification and manipulation of physical objects

NASA Technical Reports Server (NTRS)

Kanerva, P.

1986-01-01

To determine the relation of the sparse, distributed memory to other architectures, a broad review of the literature was made. The memory is called a pattern memory because they work with large patterns of features (high-dimensional vectors). A pattern is stored in a pattern memory by distributing it over a large number of storage elements and by superimposing it over other stored patterns. A pattern is retrieved by mathematical or statistical reconstruction from the distributed elements. Three pattern memories are discussed.

From Airborne EM to Geology, some examples

NASA Astrophysics Data System (ADS)

Gunnink, Jan

2014-05-01

Introduction Airborne Electro Magnetics (AEM) provide a model of the 3-dimensional distribution of resistivity of the subsurface. These resistivity models were used for delineating geological structures (e.g. Buried Valleys and salt domes) and for geohydrological modeling of aquifers (sandy sediments) and aquitards (clayey sediments). Most of the interpretation of the AEM has been carried out manually, by interpretation of 2 and 3-dimensional resistivity models into geological units by a skilled geologists / geophysicist. The manual interpretation is tiresome, takes a long time and is prone to subjective choices of the interpreter. Therefore, semi-automatic interpretation of AEM resistivity models into geological units is a recent research topic. Two examples are presented that show how resistivity, as obtained from AEM, can be "converted" to useful geological / geohydrolocal models. Statistical relation between borehole data and resistivity In the northeastern part of the Netherlands, the 3D distribution of clay deposits - formed in a glacio-lacustrine environment with buried glacial valleys - was modelled. Boreholes with description of lithology, were linked to AEM resistivity. First, 1D AEM resistivity models from each individual sounding were interpolated to cover the entire study area, resulting in a 3-dimensional model of resistivity. For each interval of clay and sand in the boreholes, the corresponding resistivity was extracted from the 3D resistivity model. Linear regression was used to link the clay and non-clay proportion in each borehole interval to the Ln(resistivity). This regression is then used to "convert" the 3D resistivity model into proportion of clay for the entire study area. This so-called "soft information" is combined with the "hard data" (boreholes) to model the proportion of clay for the entire study area using geostatistical simulation techniques (Sequential Indicator Simulation with collocated co-kriging). 100 realizations of the 3-dimensional distribution of clay and sand were calculated giving an appreciation of the variability of the 3-dimensional distribution of clay and sand. Each realization was input into a groundwatermodel to assess the protection the of the clay against pollution from the surface. Artificial Neural Networks AEM resistivity models in an area in Northern part of the Netherlands were interpreted by Artificial Neural Networks (ANN) to obtain a 3-dimensional model of a glacial till deposit that is important in geohydrological modeling. The groundwater in the study area was brackish to saline, causing the AEM resistivity model to be dominated by the low resistivity of the groundwater. After conducting Electrical Cone Penetration Tests (ECPTs) it became clear that the glacial till showed a distinct, non-linear, pattern of resistivity, that was discriminating it from the surrounding sediments. The patterns, found in the ECPTs were used to train an ANN and was consequently applied to the resistivity model that was derived from the AEM. The result was a 3-dimensional model of the probability of having the glacial till, which was checked against boreholes and proved to be quite reasonable. Conclusion Resistivity derived from AEM can be linked to geological features in a number of ways. Besides manual interpretation, statistical techniques are used, either in the form of regression or by means of Neural Networks, to extract geological and geohydrological meaningful interpretations from the resistivity model.

TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

PubMed

Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

2017-12-01

Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. sunkim.bioinfo@snu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Motion patterns in acupuncture needle manipulation.

PubMed

Seo, Yoonjeong; Lee, In-Seon; Jung, Won-Mo; Ryu, Ho-Sun; Lim, Jinwoong; Ryu, Yeon-Hee; Kang, Jung-Won; Chae, Younbyoung

2014-10-01

In clinical practice, acupuncture manipulation is highly individualised for each practitioner. Before we establish a standard for acupuncture manipulation, it is important to understand completely the manifestations of acupuncture manipulation in the actual clinic. To examine motion patterns during acupuncture manipulation, we generated a fitted model of practitioners' motion patterns and evaluated their consistencies in acupuncture manipulation. Using a motion sensor, we obtained real-time motion data from eight experienced practitioners while they conducted acupuncture manipulation using their own techniques. We calculated the average amplitude and duration of a sampled motion unit for each practitioner and, after normalisation, we generated a true regression curve of motion patterns for each practitioner using a generalised additive mixed modelling (GAMM). We observed significant differences in rotation amplitude and duration in motion samples among practitioners. GAMM showed marked variations in average regression curves of motion patterns among practitioners but there was strong consistency in motion parameters for individual practitioners. The fitted regression model showed that the true regression curve accounted for an average of 50.2% of variance in the motion pattern for each practitioner. Our findings suggest that there is great inter-individual variability between practitioners, but remarkable intra-individual consistency within each practitioner. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Are We All in the Same Boat? The Role of Perceptual Distance in Organizational Health Interventions.

PubMed

Hasson, Henna; von Thiele Schwarz, Ulrica; Nielsen, Karina; Tafvelin, Susanne

2016-10-01

The study investigates how agreement between leaders' and their team's perceptions influence intervention outcomes in a leadership-training intervention aimed at improving organizational learning. Agreement, i.e. perceptual distance was calculated for the organizational learning dimensions at baseline. Changes in the dimensions from pre-intervention to post-intervention were evaluated using polynomial regression analysis with response surface analysis. The general pattern of the results indicated that the organizational learning improved when leaders and their teams agreed on the level of organizational learning prior to the intervention. The improvement was greatest when the leader's and the team's perceptions at baseline were aligned and high rather than aligned and low. The least beneficial scenario was when the leader's perceptions were higher than the team's perceptions. These results give insights into the importance of comparing leaders' and their team's perceptions in intervention research. Polynomial regression analyses with response surface methodology allow three-dimensional examination of relationship between two predictor variables and an outcome. This contributes with knowledge on how combination of predictor variables may affect outcome and allows studies of potential non-linearity relating to the outcome. Future studies could use these methods in process evaluation of interventions. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

High-Resolution Gamma-Ray Imaging Measurements Using Externally Segmented Germanium Detectors

NASA Technical Reports Server (NTRS)

Callas, J.; Mahoney, W.; Skelton, R.; Varnell, L.; Wheaton, W.

1994-01-01

Fully two-dimensional gamma-ray imaging with simultaneous high-resolution spectroscopy has been demonstrated using an externally segmented germanium sensor. The system employs a single high-purity coaxial detector with its outer electrode segmented into 5 distinct charge collection regions and a lead coded aperture with a uniformly redundant array (URA) pattern. A series of one-dimensional responses was collected around 511 keV while the system was rotated in steps through 180 degrees. A non-negative, linear least-squares algorithm was then employed to reconstruct a 2-dimensional image. Corrections for multiple scattering in the detector, and the finite distance of source and detector are made in the reconstruction process.

A comprehensive analysis of earthquake damage patterns using high dimensional model representation feature selection

NASA Astrophysics Data System (ADS)

Taşkin Kaya, Gülşen

2013-10-01

Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input-output relationships in high-dimensional systems for many problems in science and engineering. The HDMR method is developed to improve the efficiency of the deducing high dimensional behaviors. The method is formed by a particular organization of low dimensional component functions, in which each function is the contribution of one or more input variables to the output variables.

The impact of perceived intensity and frequency of police work occupational stressors on the cortisol awakening response (CAR): Findings from the BCOPS study.

PubMed

Violanti, John M; Fekedulegn, Desta; Andrew, Michael E; Hartley, Tara A; Charles, Luenda E; Miller, Diane B; Burchfiel, Cecil M

2017-01-01

Police officers encounter unpredictable, evolving, and escalating stressful demands in their work. Utilizing the Spielberger Police Stress Survey (60-item instrument for assessing specific conditions or events considered to be stressors in police work), the present study examined the association of the top five highly rated and bottom five least rated work stressors among police officers with their awakening cortisol pattern. Participants were police officers enrolled in the Buffalo Cardio-Metabolic Occupational Police Stress (BCOPS) study (n=338). For each group, the total stress index (product of rating and frequency of the stressor) was calculated. Participants collected saliva by means of Salivettes at four time points: on awakening, 15, 30 and 45min after waking to examine the cortisol awakening response (CAR). Saliva samples were analyzed for free cortisol concentrations. A slope reflecting the awakening pattern of cortisol over time was estimated by fitting a linear regression model relating cortisol in log-scale to time of collection. The slope served as the outcome variable. Analysis of covariance, regression, and repeated measures models were used to determine if there was an association of the stress index with the waking cortisol pattern. There was a significant negative linear association between total stress index of the five highest stressful events and slope of the awakening cortisol regression line (trend p-value=0.0024). As the stress index increased, the pattern of the awakening cortisol regression line tended to flatten. Officers with a zero stress index showed a steep and steady increase in cortisol from baseline (which is often observed) while officers with a moderate or high stress index showed a dampened or flatter response over time. Conversely, the total stress index of the five least rated events was not significantly associated with the awakening cortisol pattern. The study suggests that police events or conditions considered highly stressful by the officers may be associated with disturbances of the typical awakening cortisol pattern. The results are consistent with previous research where chronic exposure to stressors is associated with a diminished awakening cortisol response pattern. Copyright © 2016 Elsevier Ltd. All rights reserved.

The impact of perceived intensity and frequency of police work occupational stressors on the cortisol awakening response (CAR): Findings from the BCOPS study

PubMed Central

Violanti, John M.; Fekedulegn, Desta; Andrew, Michael E.; Hartley, Tara A.; Charles, Luenda E.; Miller, Diane B.; Burchfiel, Cecil M.

2016-01-01

Police officers encounter unpredictable, evolving, and escalating stressful demands in their work. Utilizing the Spielberger Police Stress Survey (60-item instrument for assessing specific conditions or events considered to be stressors in police work), the present study examined the association of the top five highly rated and bottom five least rated work stressors among police officers with their awakening cortisol pattern. Participants were police officers enrolled in the Buffalo Cardio-Metabolic Occupational Police Stress (BCOPS) study (n = 338). For each group, the total stress index (product of rating and frequency of the stressor) was calculated. Participants collected saliva by means of Salivettes at four time points: on awakening, 15, 30 and 45 min after waking to examine the cortisol awakening response (CAR). Saliva samples were analyzed for free cortisol concentrations. A slope reflecting the awakening pattern of cortisol over time was estimated by fitting a linear regression model relating cortisol in log-scale to time of collection. The slope served as the outcome variable. Analysis of covariance, regression, and repeated measures models were used to determine if there was an association of the stress index with the waking cortisol pattern. There was a significant negative linear association between total stress index of the five highest stressful events and slope of the awakening cortisol regression line (trend p-value = 0.0024). As the stress index increased, the pattern of the awakening cortisol regression line tended to flatten. Officers with a zero stress index showed a steep and steady increase in cortisol from baseline (which is often observed) while officers with a moderate or high stress index showed a dampened or flatter response over time. Conversely, the total stress index of the five least rated events was not significantly associated with the awakening cortisol pattern. The study suggests that police events or conditions considered highly stressful by the officers may be associated with disturbances of the typical awakening cortisol pattern. The results are consistent with previous research where chronic exposure to stressors is associated with a diminished awakening cortisol response pattern. PMID:27816820

Skin microrelief profiles as a cutaneous aging index.

PubMed

Kim, Dai Hyun; Rhyu, Yeon Seung; Ahn, Hyo Hyun; Hwang, Eenjun; Uhm, Chang Sub

2016-10-01

An objective measurement of cutaneous topographical information is important for quantifying the degree of skin aging. Our aim was to improve methods for measuring microrelief patterns using a three-dimensional analysis based on silicone replicas and scanning electron microscope (SEM). Another objective was to compare the results with those obtained using a two-dimensional analysis method based on dermoscopy. Silicone replicas were obtained from forearms, dorsum of the hands and fingers of 51 volunteers. Cutaneous profiles obtained by SEM with silicone replicas showed more consistent correlations with age than data obtained by dermoscopy. This indicates the advantage of three-dimensional topography analysis using silicone replicas and SEM over the widely used dermoscopic assessment. The cutaneous age was calculated using stepwise linear regression, and the result was 57.40-9.47 × (number of furrows on dorsum of the hand) × (width of furrows on dorsum of the hand). © The Author 2016. Published by Oxford University Press on behalf of The Japanese Society of Microscopy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Directed Block Copolymer Assembly versus Electron Beam Lithography for Bit-Patterned Media with Areal Density of 1 Terabit/inch(2) and Beyond.

PubMed

Yang, Xiaomin; Wan, Lei; Xiao, Shuaigang; Xu, Yuan; Weller, Dieter K

2009-07-28

The directed self-assembly of block copolymer (BCP) offers a new route to perfect nanolithographic patterning at sub-50 nm length scale with molecular scale precision. We have explored the feasibility of using the BCP approach versus the conventional electron beam (e-beam) lithography to create highly dense dot patterns for bit-patterned media (BPM) applications. Cylinder-forming poly(styrene-b-methyl methacrylate) (PS-b-PMMA) directly self-assembled on a chemically prepatterned substrate. The nearly perfect hexagonal arrays of perpendicularly oriented cylindrical pores at a density of approximately 1 Terabit per square inch (Tb/in.(2)) are achieved over an arbitrarily large area. Considerable gains in the BCP process are observed relative to the conventional e-beam lithography in terms of the dot size variation, the placement accuracy, the pattern uniformity, and the exposure latitude. The maximum dimensional latitude in the cylinder-forming BCP patterns and the maximum skew angle that the BCP can tolerate have been investigated for the first time. The dimensional latitude restricts the formation of more than one lattice configuration in certain ranges. More defects in BCP patterns are observed when using low molecular weight BCP materials or on non-hexagonal prepatterns due to the dimensional latitude restriction. Finally, the limitations and challenges in the BCP approach that are associated with BPM applications will be briefly discussed.

Bayesian feature selection for high-dimensional linear regression via the Ising approximation with applications to genomics.

PubMed

Fisher, Charles K; Mehta, Pankaj

2015-06-01

Feature selection, identifying a subset of variables that are relevant for predicting a response, is an important and challenging component of many methods in statistics and machine learning. Feature selection is especially difficult and computationally intensive when the number of variables approaches or exceeds the number of samples, as is often the case for many genomic datasets. Here, we introduce a new approach--the Bayesian Ising Approximation (BIA)-to rapidly calculate posterior probabilities for feature relevance in L2 penalized linear regression. In the regime where the regression problem is strongly regularized by the prior, we show that computing the marginal posterior probabilities for features is equivalent to computing the magnetizations of an Ising model with weak couplings. Using a mean field approximation, we show it is possible to rapidly compute the feature selection path described by the posterior probabilities as a function of the L2 penalty. We present simulations and analytical results illustrating the accuracy of the BIA on some simple regression problems. Finally, we demonstrate the applicability of the BIA to high-dimensional regression by analyzing a gene expression dataset with nearly 30 000 features. These results also highlight the impact of correlations between features on Bayesian feature selection. An implementation of the BIA in C++, along with data for reproducing our gene expression analyses, are freely available at http://physics.bu.edu/∼pankajm/BIACode. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Manifold learning in machine vision and robotics

NASA Astrophysics Data System (ADS)

Bernstein, Alexander

2017-02-01

Smart algorithms are used in Machine vision and Robotics to organize or extract high-level information from the available data. Nowadays, Machine learning is an essential and ubiquitous tool to automate extraction patterns or regularities from data (images in Machine vision; camera, laser, and sonar sensors data in Robotics) in order to solve various subject-oriented tasks such as understanding and classification of images content, navigation of mobile autonomous robot in uncertain environments, robot manipulation in medical robotics and computer-assisted surgery, and other. Usually such data have high dimensionality, however, due to various dependencies between their components and constraints caused by physical reasons, all "feasible and usable data" occupy only a very small part in high dimensional "observation space" with smaller intrinsic dimensionality. Generally accepted model of such data is manifold model in accordance with which the data lie on or near an unknown manifold (surface) of lower dimensionality embedded in an ambient high dimensional observation space; real-world high-dimensional data obtained from "natural" sources meet, as a rule, this model. The use of Manifold learning technique in Machine vision and Robotics, which discovers a low-dimensional structure of high dimensional data and results in effective algorithms for solving of a large number of various subject-oriented tasks, is the content of the conference plenary speech some topics of which are in the paper.

Two-dimensional patterning of colloidal crystals by means of lateral autocloning in edge-patterned cells

NASA Astrophysics Data System (ADS)

Emoto, Akira; Kamei, Tadayoshi; Shioda, Tatsutoshi; Kawatsuki, Nobuhiro; Ono, Hiroshi

2009-06-01

We report the experimental results of two-dimensional patterning of colloidal crystals using edge-patterned cells. Solvent evaporation of a colloidal suspension from the edge of the cell induces self-organized crystallization of spherical colloidal particles. From a reservoir of colloidal suspension in the cell, different colloidal suspensions are injected repetitively. An edge-patterned substrate is introduced into the cell as an upper substrate. As a result, different colloidal crystals are alternately stacked in the lateral direction according to the edge pattern. The characteristics of cloning formation are specifically showed including deformations from the original pattern. This two-dimensional patterning of three-dimensional colloidal crystals by means of lateral autocloning is promising for the development of photonic crystal arrays for use in optic and photonic devices.

Clustering high dimensional data using RIA

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

Aziz, Nazrina

2015-05-15

Clustering may simply represent a convenient method for organizing a large data set so that it can easily be understood and information can efficiently be retrieved. However, identifying cluster in high dimensionality data sets is a difficult task because of the curse of dimensionality. Another challenge in clustering is some traditional functions cannot capture the pattern dissimilarity among objects. In this article, we used an alternative dissimilarity measurement called Robust Influence Angle (RIA) in the partitioning method. RIA is developed using eigenstructure of the covariance matrix and robust principal component score. We notice that, it can obtain cluster easily andmore » hence avoid the curse of dimensionality. It is also manage to cluster large data sets with mixed numeric and categorical value.« less

Simulation of range imaging-based estimation of respiratory lung motion. Influence of noise, signal dimensionality and sampling patterns.

PubMed

Wilms, M; Werner, R; Blendowski, M; Ortmüller, J; Handels, H

2014-01-01

A major problem associated with the irradiation of thoracic and abdominal tumors is respiratory motion. In clinical practice, motion compensation approaches are frequently steered by low-dimensional breathing signals (e.g., spirometry) and patient-specific correspondence models, which are used to estimate the sought internal motion given a signal measurement. Recently, the use of multidimensional signals derived from range images of the moving skin surface has been proposed to better account for complex motion patterns. In this work, a simulation study is carried out to investigate the motion estimation accuracy of such multidimensional signals and the influence of noise, the signal dimensionality, and different sampling patterns (points, lines, regions). A diffeomorphic correspondence modeling framework is employed to relate multidimensional breathing signals derived from simulated range images to internal motion patterns represented by diffeomorphic non-linear transformations. Furthermore, an automatic approach for the selection of optimal signal combinations/patterns within this framework is presented. This simulation study focuses on lung motion estimation and is based on 28 4D CT data sets. The results show that the use of multidimensional signals instead of one-dimensional signals significantly improves the motion estimation accuracy, which is, however, highly affected by noise. Only small differences exist between different multidimensional sampling patterns (lines and regions). Automatically determined optimal combinations of points and lines do not lead to accuracy improvements compared to results obtained by using all points or lines. Our results show the potential of multidimensional breathing signals derived from range images for the model-based estimation of respiratory motion in radiation therapy.

Ensemble learning with trees and rules: supervised, semi-supervised, unsupervised

USDA-ARS?s Scientific Manuscript database

In this article, we propose several new approaches for post processing a large ensemble of conjunctive rules for supervised and semi-supervised learning problems. We show with various examples that for high dimensional regression problems the models constructed by the post processing the rules with ...

Association Between Dietary-related Risk Factors and Ischemic Stroke Using Reduced Rank Regression: The Multi-Ethnic Study of Atherosclerosis (MESA), USA.

PubMed

Nazari, Seyed Saeed Hashemi; Mokhayeri, Yaser; Mansournia, Mohammad Ali; Khodakarim, Soheila; Soori, Hamid

2018-05-21

Some studies shed light on the association between dietary patterns and stroke, though, none of them applied reduced rank regression (RRR). Therefore, we sought to extract dietary patterns using RRR, and showed how well the extracted scores by RRR predict stroke in comparison to those scores produced by partial least squares (PLS) and principal components regression (PCR). Diet data at baseline with four response variables including body mass index (BMI), fibrinogen, IL-6, low-density lipoprotein (LDL) cholesterol were used to extract dietary patterns. Analyses were based on 5468 men and women aged 45-84 y who had no clinical cardiovascular diseases (CVD) from Multi-Ethnic Study of Atherosclerosis (MESA). Dietary patterns were created by three methods RRR, PLS, and PCR. The RRR1 was positively associated with stroke incidence in both models (for model 1 hazard ratio (HR): 7.49; 95% CI: 1.66, 33.69 P for trend = 0.01 and for model 2 HR: 6.83; 95% CI: 1.51, 30.87 for quintile 5 compared with the reference category P for trend = 0.02). The RRR1, PLS1, and PCR1 were high in fats and oils, poultry, tomatoes, fried potato and processed meat. Additionally, RRR1 and PLS1 were high in dark-yellow and cruciferous vegetables which negatively were correlated with the first dietary pattern. Mainly according to the RRR, we identified that a dietary pattern high in fats and oil, poultry, non-diet soda, processed meat, tomatoes, legumes, chicken, tuna and egg salad, fried potato and low in dark-yellow and cruciferous vegetables may increase the incidence of stroke.

Influence of urban pattern on inundation flow in floodplains of lowland rivers.

PubMed

Bruwier, M; Mustafa, A; Aliaga, D G; Archambeau, P; Erpicum, S; Nishida, G; Zhang, X; Pirotton, M; Teller, J; Dewals, B

2018-05-01

The objective of this paper is to investigate the respective influence of various urban pattern characteristics on inundation flow. A set of 2000 synthetic urban patterns were generated using an urban procedural model providing locations and shapes of streets and buildings over a square domain of 1×1km 2 . Steady two-dimensional hydraulic computations were performed over the 2000 urban patterns with identical hydraulic boundary conditions. To run such a large amount of simulations, the computational efficiency of the hydraulic model was improved by using an anisotropic porosity model. This model computes on relatively coarse computational cells, but preserves information from the detailed topographic data through porosity parameters. Relationships between urban characteristics and the computed inundation water depths have been based on multiple linear regressions. Finally, a simple mechanistic model based on two district-scale porosity parameters, combining several urban characteristics, is shown to capture satisfactorily the influence of urban characteristics on inundation water depths. The findings of this study give guidelines for more flood-resilient urban planning. Copyright © 2017 Elsevier B.V. All rights reserved.

Suppressed carrier density for the patterned high mobility two-dimensional electron gas at γ-Al2O3/SrTiO3 heterointerfaces

NASA Astrophysics Data System (ADS)

Niu, Wei; Gan, Yulin; Zhang, Yu; Valbjørn Christensen, Dennis; von Soosten, Merlin; Wang, Xuefeng; Xu, Yongbing; Zhang, Rong; Pryds, Nini; Chen, Yunzhong

2017-07-01

The two-dimensional electron gas (2DEG) at the non-isostructural interface between spinel γ-Al2O3 and perovskite SrTiO3 is featured by a record electron mobility among complex oxide interfaces in addition to a high carrier density up to the order of 1015 cm-2. Herein, we report on the patterning of 2DEG at the γ-Al2O3/SrTiO3 interface grown at 650 °C by pulsed laser deposition using a hard mask of LaMnO3. The patterned 2DEG exhibits a critical thickness of 2 unit cells of γ-Al2O3 for the occurrence of interface conductivity, similar to the unpatterned sample. However, its maximum carrier density is found to be approximately 3 × 1013 cm-2, much lower than that of the unpatterned sample (˜1015 cm-2). Remarkably, a high electron mobility of approximately 3600 cm2 V-1 s-1 was obtained at low temperatures for the patterned 2DEG at a carrier density of ˜7 × 1012 cm-2, which exhibits clear Shubnikov-de Haas quantum oscillations. The patterned high-mobility 2DEG at the γ-Al2O3/SrTiO3 interface paves the way for the design and application of spinel/perovskite interfaces for high-mobility all-oxide electronic devices.

Support vector methods for survival analysis: a comparison between ranking and regression approaches.

PubMed

Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

2011-10-01

To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods including only regression or both regression and ranking constraints on clinical data. On high dimensional data, the former model performs better. However, this approach does not have a theoretical link with standard statistical models for survival data. This link can be made by means of transformation models when ranking constraints are included. Copyright © 2011 Elsevier B.V. All rights reserved.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

Exploration of graphene oxide as an intelligent platform for cancer vaccines

NASA Astrophysics Data System (ADS)

Yue, Hua; Wei, Wei; Gu, Zonglin; Ni, Dezhi; Luo, Nana; Yang, Zaixing; Zhao, Lin; Garate, Jose Antonio; Zhou, Ruhong; Su, Zhiguo; Ma, Guanghui

2015-11-01

We explored an intelligent vaccine system via facile approaches using both experimental and theoretical techniques based on the two-dimensional graphene oxide (GO). Without extra addition of bio/chemical stimulators, the microsized GO imparted various immune activation tactics to improve the antigen immunogenicity. A high antigen adsorption was acquired, and the mechanism was revealed to be a combination of electrostatic, hydrophobic, and π-π stacking interactions. The ``folding GO'' acted as a cytokine self-producer and antigen reservoir and showed a particular autophagy, which efficiently promoted the activation of antigen presenting cells (APCs) and subsequent antigen cross-presentation. Such a ``One but All'' modality thus induced a high level of anti-tumor responses in a programmable way and resulted in efficient tumor regression in vivo. This work may shed light on the potential use of a new dimensional nano-platform in the development of high-performance cancer vaccines.We explored an intelligent vaccine system via facile approaches using both experimental and theoretical techniques based on the two-dimensional graphene oxide (GO). Without extra addition of bio/chemical stimulators, the microsized GO imparted various immune activation tactics to improve the antigen immunogenicity. A high antigen adsorption was acquired, and the mechanism was revealed to be a combination of electrostatic, hydrophobic, and π-π stacking interactions. The ``folding GO'' acted as a cytokine self-producer and antigen reservoir and showed a particular autophagy, which efficiently promoted the activation of antigen presenting cells (APCs) and subsequent antigen cross-presentation. Such a ``One but All'' modality thus induced a high level of anti-tumor responses in a programmable way and resulted in efficient tumor regression in vivo. This work may shed light on the potential use of a new dimensional nano-platform in the development of high-performance cancer vaccines. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr04986e

Estimating the relationship between urban 3D morphology and land surface temperature using airborne LiDAR and Landsat-8 Thermal Infrared Sensor data

NASA Astrophysics Data System (ADS)

Lee, J. H.

2015-12-01

Urban forests are known for mitigating the urban heat island effect and heat-related health issues by reducing air and surface temperature. Beyond the amount of the canopy area, however, little is known what kind of spatial patterns and structures of urban forests best contributes to reducing temperatures and mitigating the urban heat effects. Previous studies attempted to find the relationship between the land surface temperature and various indicators of vegetation abundance using remote sensed data but the majority of those studies relied on two dimensional area based metrics, such as tree canopy cover, impervious surface area, and Normalized Differential Vegetation Index, etc. This study investigates the relationship between the three-dimensional spatial structure of urban forests and urban surface temperature focusing on vertical variance. We use a Landsat-8 Thermal Infrared Sensor image (acquired on July 24, 2014) to estimate the land surface temperature of the City of Sacramento, CA. We extract the height and volume of urban features (both vegetation and non-vegetation) using airborne LiDAR (Light Detection and Ranging) and high spatial resolution aerial imagery. Using regression analysis, we apply empirical approach to find the relationship between the land surface temperature and different sets of variables, which describe spatial patterns and structures of various urban features including trees. Our analysis demonstrates that incorporating vertical variance parameters improve the accuracy of the model. The results of the study suggest urban tree planting is an effective and viable solution to mitigate urban heat by increasing the variance of urban surface as well as evaporative cooling effect.

Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults.

PubMed

Batis, Carolina; Mendez, Michelle A; Gordon-Larsen, Penny; Sotres-Alvarez, Daniela; Adair, Linda; Popkin, Barry

2016-02-01

We examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in glycated Hb (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and fasting glucose. We measured diet over a 3 d period with 24 h recalls and a household food inventory in 2006 and used it to derive PCA and RRR dietary patterns. The outcomes were measured in 2009. Adults (n 4316) from the China Health and Nutrition Survey. The adjusted odds ratio for diabetes prevalence (HbA1c≥6·5 %), comparing the highest dietary pattern score quartile with the lowest, was 1·26 (95 % CI 0·76, 2·08) for a modern high-wheat pattern (PCA; wheat products, fruits, eggs, milk, instant noodles and frozen dumplings), 0·76 (95 % CI 0·49, 1·17) for a traditional southern pattern (PCA; rice, meat, poultry and fish) and 2·37 (95 % CI 1·56, 3·60) for the pattern derived with RRR. By comparing the dietary pattern structures of RRR and PCA, we found that the RRR pattern was also behaviourally meaningful. It combined the deleterious effects of the modern high-wheat pattern (high intakes of wheat buns and breads, deep-fried wheat and soya milk) with the deleterious effects of consuming the opposite of the traditional southern pattern (low intakes of rice, poultry and game, fish and seafood). Our findings suggest that using both PCA and RRR provided useful insights when studying the association of dietary patterns with diabetes.

Land use regression modelling of air pollution in high density high rise cities: A case study in Hong Kong.

PubMed

Lee, Martha; Brauer, Michael; Wong, Paulina; Tang, Robert; Tsui, Tsz Him; Choi, Crystal; Cheng, Wei; Lai, Poh-Chin; Tian, Linwei; Thach, Thuan-Quoc; Allen, Ryan; Barratt, Benjamin

2017-08-15

Land use regression (LUR) is a common method of predicting spatial variability of air pollution to estimate exposure. Nitrogen dioxide (NO 2 ), nitric oxide (NO), fine particulate matter (PM 2.5 ), and black carbon (BC) concentrations were measured during two sampling campaigns (April-May and November-January) in Hong Kong (a prototypical high-density high-rise city). Along with 365 potential geospatial predictor variables, these concentrations were used to build two-dimensional land use regression (LUR) models for the territory. Summary statistics for combined measurements over both campaigns were: a) NO 2 (Mean=106μg/m 3 , SD=38.5, N=95), b) NO (M=147μg/m 3 , SD=88.9, N=40), c) PM 2.5 (M=35μg/m 3 , SD=6.3, N=64), and BC (M=10.6μg/m 3 , SD=5.3, N=76). Final LUR models had the following statistics: a) NO 2 (R 2 =0.46, RMSE=28μg/m 3 ) b) NO (R 2 =0.50, RMSE=62μg/m 3 ), c) PM 2.5 (R 2 =0.59; RMSE=4μg/m 3 ), and d) BC (R 2 =0.50, RMSE=4μg/m 3 ). Traditional LUR predictors such as road length, car park density, and land use types were included in most models. The NO 2 prediction surface values were highest in Kowloon and the northern region of Hong Kong Island (downtown Hong Kong). NO showed a similar pattern in the built-up region. Both PM 2.5 and BC predictions exhibited a northwest-southeast gradient, with higher concentrations in the north (close to mainland China). For BC, the port was also an area of elevated predicted concentrations. The results matched with existing literature on spatial variation in concentrations of air pollutants and in relation to important emission sources in Hong Kong. The success of these models suggests LUR is appropriate in high-density, high-rise cities. Copyright © 2017 Elsevier B.V. All rights reserved.

Eye-fixation patterns of high- and low-span young and older adults: down the garden path and back again.

PubMed

Kemper, Susan; Crow, Angela; Kemtes, Karen

2004-03-01

Young and older adults' eye fixations were monitored as they read sentences with temporary ambiguities such as "The experienced soldiers warned about the dangers conducted the midnight raid." Their fixation patterns were similar except that older adults made many regressions. In a 2nd experiment, high- and low-span older adults were compared with high- and low-span young adults. Pint-pass fixations were similar, except low-span readers made many regressions and their total fixation times were longer. High-span readers also used the focus operator "only" (e.g., "Only experienced soldiers warned about the dangers.") to immediately resolve the temporary ambiguities. No age group differences were observed. These results are discussed with reference to theories of the role of working memory in sentence processing.

Comparison of three-dimensional multi-segmental foot models used in clinical gait laboratories.

PubMed

Nicholson, Kristen; Church, Chris; Takata, Colton; Niiler, Tim; Chen, Brian Po-Jung; Lennon, Nancy; Sees, Julie P; Henley, John; Miller, Freeman

2018-05-16

Many skin-mounted three-dimensional multi-segmented foot models are currently in use for gait analysis. Evidence regarding the repeatability of models, including between trial and between assessors, is mixed, and there are no between model comparisons of kinematic results. This study explores differences in kinematics and repeatability between five three-dimensional multi-segmented foot models. The five models include duPont, Heidelberg, Oxford Child, Leardini, and Utah. Hind foot, forefoot, and hallux angles were calculated with each model for ten individuals. Two physical therapists applied markers three times to each individual to assess within and between therapist variability. Standard deviations were used to evaluate marker placement variability. Locally weighted regression smoothing with alpha-adjusted serial T tests analysis was used to assess kinematic similarities. All five models had similar variability, however, the Leardini model showed high standard deviations in plantarflexion/dorsiflexion angles. P-value curves for the gait cycle were used to assess kinematic similarities. The duPont and Oxford models had the most similar kinematics. All models demonstrated similar marker placement variability. Lower variability was noted in the sagittal and coronal planes compared to rotation in the transverse plane, suggesting a higher minimal detectable change when clinically considering rotation and a need for additional research. Between the five models, the duPont and Oxford shared the most kinematic similarities. While patterns of movement were very similar between all models, offsets were often present and need to be considered when evaluating published data. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimal Wavelength Selection on Hyperspectral Data with Fused Lasso for Biomass Estimation of Tropical Rain Forest

NASA Astrophysics Data System (ADS)

Takayama, T.; Iwasaki, A.

2016-06-01

Above-ground biomass prediction of tropical rain forest using remote sensing data is of paramount importance to continuous large-area forest monitoring. Hyperspectral data can provide rich spectral information for the biomass prediction; however, the prediction accuracy is affected by a small-sample-size problem, which widely exists as overfitting in using high dimensional data where the number of training samples is smaller than the dimensionality of the samples due to limitation of require time, cost, and human resources for field surveys. A common approach to addressing this problem is reducing the dimensionality of dataset. Also, acquired hyperspectral data usually have low signal-to-noise ratio due to a narrow bandwidth and local or global shifts of peaks due to instrumental instability or small differences in considering practical measurement conditions. In this work, we propose a methodology based on fused lasso regression that select optimal bands for the biomass prediction model with encouraging sparsity and grouping, which solves the small-sample-size problem by the dimensionality reduction from the sparsity and the noise and peak shift problem by the grouping. The prediction model provided higher accuracy with root-mean-square error (RMSE) of 66.16 t/ha in the cross-validation than other methods; multiple linear analysis, partial least squares regression, and lasso regression. Furthermore, fusion of spectral and spatial information derived from texture index increased the prediction accuracy with RMSE of 62.62 t/ha. This analysis proves efficiency of fused lasso and image texture in biomass estimation of tropical forests.

Viscoelastic Parameters for Quantifying Liver Fibrosis: Three-Dimensional Multifrequency MR Elastography Study on Thin Liver Rat Slices

PubMed Central

Ronot, Maxime; Lambert, Simon A.; Wagner, Mathilde; Garteiser, Philippe; Doblas, Sabrina; Albuquerque, Miguel; Paradis, Valérie; Vilgrain, Valérie; Sinkus, Ralph; Van Beers, Bernard E.

2014-01-01

Objective To assess in a high-resolution model of thin liver rat slices which viscoelastic parameter at three-dimensional multifrequency MR elastography has the best diagnostic performance for quantifying liver fibrosis. Materials and Methods The study was approved by the ethics committee for animal care of our institution. Eight normal rats and 42 rats with carbon tetrachloride induced liver fibrosis were used in the study. The rats were sacrificed, their livers were resected and three-dimensional MR elastography of 5±2 mm liver slices was performed at 7T with mechanical frequencies of 500, 600 and 700 Hz. The complex shear, storage and loss moduli, and the coefficient of the frequency power law were calculated. At histopathology, fibrosis and inflammation were assessed with METAVIR score, fibrosis was further quantified with morphometry. The diagnostic value of the viscoelastic parameters for assessing fibrosis severity was evaluated with simple and multiple linear regressions, receiver operating characteristic analysis and Obuchowski measures. Results At simple regression, the shear, storage and loss moduli were associated with the severity of fibrosis. At multiple regression, the storage modulus at 600 Hz was the only parameter associated with fibrosis severity (r = 0.86, p<0.0001). This parameter had an Obuchowski measure of 0.89+/−0.03. This measure was significantly larger than that of the loss modulus (0.78+/−0.04, p = 0.028), but not than that of the complex shear modulus (0.88+/−0.03, p = 0.84). Conclusion Our high resolution, three-dimensional multifrequency MR elastography study of thin liver slices shows that the storage modulus is the viscoelastic parameter that has the best association with the severity of liver fibrosis. However, its diagnostic performance does not differ significantly from that of the complex shear modulus. PMID:24722733

Fringe pattern demodulation with a two-dimensional digital phase-locked loop algorithm.

PubMed

Gdeisat, Munther A; Burton, David R; Lalor, Michael J

2002-09-10

A novel technique called a two-dimensional digital phase-locked loop (DPLL) for fringe pattern demodulation is presented. This algorithm is more suitable for demodulation of fringe patterns with varying phase in two directions than the existing DPLL techniques that assume that the phase of the fringe patterns varies only in one direction. The two-dimensional DPLL technique assumes that the phase of a fringe pattern is continuous in both directions and takes advantage of the phase continuity; consequently, the algorithm has better noise performance than the existing DPLL schemes. The two-dimensional DPLL algorithm is also suitable for demodulation of fringe patterns with low sampling rates, and it outperforms the Fourier fringe analysis technique in this aspect.

Stronger tests of mechanisms underlying geographic gradients of biodiversity: insights from the dimensionality of biodiversity.

PubMed

Stevens, Richard D; Tello, J Sebastián; Gavilanez, María Mercedes

2013-01-01

Inference involving diversity gradients typically is gathered by mechanistic tests involving single dimensions of biodiversity such as species richness. Nonetheless, because traits such as geographic range size, trophic status or phenotypic characteristics are tied to a particular species, mechanistic effects driving broad diversity patterns should manifest across numerous dimensions of biodiversity. We develop an approach of stronger inference based on numerous dimensions of biodiversity and apply it to evaluate one such putative mechanism: the mid-domain effect (MDE). Species composition of 10,000-km(2) grid cells was determined by overlaying geographic range maps of 133 noctilionoid bat taxa. We determined empirical diversity gradients in the Neotropics by calculating species richness and three indices each of phylogenetic, functional and phenetic diversity for each grid cell. We also created 1,000 simulated gradients of each examined metric of biodiversity based on a MDE model to estimate patterns expected if species distributions were randomly placed within the Neotropics. For each simulation run, we regressed the observed gradient onto the MDE-expected gradient. If a MDE drives empirical gradients, then coefficients of determination from such an analysis should be high, the intercept no different from zero and the slope no different than unity. Species richness gradients predicted by the MDE fit empirical patterns. The MDE produced strong spatially structured gradients of taxonomic, phylogenetic, functional and phenetic diversity. Nonetheless, expected values generated from the MDE for most dimensions of biodiversity exhibited poor fit to most empirical patterns. The MDE cannot account for most empirical patterns of biodiversity. Fuller understanding of latitudinal gradients will come from simultaneous examination of relative effects of random, environmental and historical mechanisms to better understand distribution and abundance of the current biota.

Stronger Tests of Mechanisms Underlying Geographic Gradients of Biodiversity: Insights from the Dimensionality of Biodiversity

PubMed Central

Stevens, Richard D.; Tello, J. Sebastián; Gavilanez, María Mercedes

2013-01-01

Inference involving diversity gradients typically is gathered by mechanistic tests involving single dimensions of biodiversity such as species richness. Nonetheless, because traits such as geographic range size, trophic status or phenotypic characteristics are tied to a particular species, mechanistic effects driving broad diversity patterns should manifest across numerous dimensions of biodiversity. We develop an approach of stronger inference based on numerous dimensions of biodiversity and apply it to evaluate one such putative mechanism: the mid-domain effect (MDE). Species composition of 10,000-km2 grid cells was determined by overlaying geographic range maps of 133 noctilionoid bat taxa. We determined empirical diversity gradients in the Neotropics by calculating species richness and three indices each of phylogenetic, functional and phenetic diversity for each grid cell. We also created 1,000 simulated gradients of each examined metric of biodiversity based on a MDE model to estimate patterns expected if species distributions were randomly placed within the Neotropics. For each simulation run, we regressed the observed gradient onto the MDE-expected gradient. If a MDE drives empirical gradients, then coefficients of determination from such an analysis should be high, the intercept no different from zero and the slope no different than unity. Species richness gradients predicted by the MDE fit empirical patterns. The MDE produced strong spatially structured gradients of taxonomic, phylogenetic, functional and phenetic diversity. Nonetheless, expected values generated from the MDE for most dimensions of biodiversity exhibited poor fit to most empirical patterns. The MDE cannot account for most empirical patterns of biodiversity. Fuller understanding of latitudinal gradients will come from simultaneous examination of relative effects of random, environmental and historical mechanisms to better understand distribution and abundance of the current biota. PMID:23451099

Interactions among genetic variants in apoptosis pathway genes, reflux symptoms, body mass index, and smoking indicate two distinct etiologic patterns of esophageal adenocarcinoma.

PubMed

Zhai, Rihong; Chen, Feng; Liu, Geoffrey; Su, Li; Kulke, Matthew H; Asomaning, Kofi; Lin, Xihong; Heist, Rebecca S; Nishioka, Norman S; Sheu, Chau-Chyun; Wain, John C; Christiani, David C

2010-05-10

Apoptosis pathway, gastroesophageal reflux symptoms (reflux), higher body mass index (BMI), and tobacco smoking have been individually associated with esophageal adenocarcinoma (EA) development. However, how multiple factors jointly affect EA risk remains unclear. In total, 305 patients with EA and 339 age- and sex-matched controls were studied. High-order interactions among reflux, BMI, smoking, and functional polymorphisms in five apoptotic genes (FAS, FASL, IL1B, TP53BP, and BAT3) were investigated by entropy-based multifactor dimensionality reduction (MDR), classification and regression tree (CART), and traditional logistic regression (LR) models. In LR analysis, reflux, BMI, and smoking were significantly associated with EA risk, with reflux as the strongest individual factor. No individual single nucleotide polymorphism was associated with EA susceptibility. However, there was a two-way interaction between IL1B + 3954C>T and reflux (P = .008). In both CART and MDR analyses, reflux was also the strongest individual factor for EA risk. In individuals with reflux symptoms, CART analysis indicated that strongest interaction was among variant genotypes of IL1B + 3954C>T and BAT3S625P, higher BMI, and smoking (odds ratio [OR], 5.76; 95% CI, 2.48 to 13.38), a finding independently found using MDR analysis. In contrast, for participants without reflux symptoms, the strongest interaction was found between higher BMI and smoking (OR, 3.27; 95% CI, 1.88 to 5.68), also echoed by entropy-based MDR analysis. Although a history of reflux is an important risk for EA, multifactor interactions also play important roles in EA risk. Gene-environment interaction patterns differ between patients with and without reflux symptoms.

Prediction of brain maturity in infants using machine-learning algorithms.

PubMed

Smyser, Christopher D; Dosenbach, Nico U F; Smyser, Tara A; Snyder, Abraham Z; Rogers, Cynthia E; Inder, Terrie E; Schlaggar, Bradley L; Neil, Jeffrey J

2016-08-01

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p<0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants. Copyright © 2016 Elsevier Inc. All rights reserved.

Prediction of brain maturity in infants using machine-learning algorithms

PubMed Central

Smyser, Christopher D.; Dosenbach, Nico U.F.; Smyser, Tara A.; Snyder, Abraham Z.; Rogers, Cynthia E.; Inder, Terrie E.; Schlaggar, Bradley L.; Neil, Jeffrey J.

2016-01-01

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23–29 weeks of gestation and without moderate–severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p < 0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants. PMID:27179605

Ionospheric hot spot at high latitudes

NASA Technical Reports Server (NTRS)

Schunk, R. W.; Sojka, J. J.

1982-01-01

Schunk and Raitt (1980) and Sojka et al. (1981) have developed a model of the convecting high-latitude ionosphere in order to determine the extent to which various chemical and transport processes affect the ion composition and electron density at F-region altitudes. The numerical model produces time-dependent, three-dimensional ion density distributions for the ions NO(+), O2(+), N2(+), O(+), N(+), and He(+). Recently, the high-latitude ionospheric model has been improved by including thermal conduction and diffusion-thermal heat flow terms. Schunk and Sojka (1982) have studied the ion temperature variations in the daytime high-latitude F-region. In the present study, a time-dependent three-dimensional ion temperature distribution is obtained for the high-latitude ionosphere for an asymmetric convection electric field pattern with enhanced flow in the dusk sector of the polar region. It is shown that such a convection pattern produces a hot spot in the ion temperature distribution which coincides with the location of the strong convection cell.

Revisiting the Scale-Invariant, Two-Dimensional Linear Regression Method

ERIC Educational Resources Information Center

Patzer, A. Beate C.; Bauer, Hans; Chang, Christian; Bolte, Jan; Su¨lzle, Detlev

2018-01-01

The scale-invariant way to analyze two-dimensional experimental and theoretical data with statistical errors in both the independent and dependent variables is revisited by using what we call the triangular linear regression method. This is compared to the standard least-squares fit approach by applying it to typical simple sets of example data…

The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data.

PubMed

Cheng, Qiang; Zhou, Hongbo; Cheng, Jie

2011-06-01

Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle high-dimensional data efficiently or scalably, or can only obtain local optimum instead of global optimum. Toward the selection of the globally optimal subset of features efficiently, we introduce a new selector--which we call the Fisher-Markov selector--to identify those features that are the most useful in describing essential differences among the possible groups. In particular, in this paper we present a way to represent essential discriminating characteristics together with the sparsity as an optimization objective. With properly identified measures for the sparseness and discriminativeness in possibly high-dimensional settings, we take a systematic approach for optimizing the measures to choose the best feature subset. We use Markov random field optimization techniques to solve the formulated objective functions for simultaneous feature selection. Our results are noncombinatorial, and they can achieve the exact global optimum of the objective function for some special kernels. The method is fast; in particular, it can be linear in the number of features and quadratic in the number of observations. We apply our procedure to a variety of real-world data, including mid--dimensional optical handwritten digit data set and high-dimensional microarray gene expression data sets. The effectiveness of our method is confirmed by experimental results. In pattern recognition and from a model selection viewpoint, our procedure says that it is possible to select the most discriminating subset of variables by solving a very simple unconstrained objective function which in fact can be obtained with an explicit expression.

Three-dimensional volume containing multiple two-dimensional information patterns

NASA Astrophysics Data System (ADS)

Nakayama, Hirotaka; Shiraki, Atsushi; Hirayama, Ryuji; Masuda, Nobuyuki; Shimobaba, Tomoyoshi; Ito, Tomoyoshi

2013-06-01

We have developed an algorithm for recording multiple gradated two-dimensional projection patterns in a single three-dimensional object. When a single pattern is observed, information from the other patterns can be treated as background noise. The proposed algorithm has two important features: the number of patterns that can be recorded is theoretically infinite and no meaningful information can be seen outside of the projection directions. We confirmed the effectiveness of the proposed algorithm by performing numerical simulations of two laser crystals: an octagonal prism that contained four patterns in four projection directions and a dodecahedron that contained six patterns in six directions. We also fabricated and demonstrated an actual prototype laser crystal from a glass cube engraved by a laser beam. This algorithm has applications in various fields, including media art, digital signage, and encryption technology.

Using both Principal Component Analysis and Reduced Rank Regression to Study Dietary Patterns and Diabetes in Chinese Adults

PubMed Central

Batis, Carolina; Mendez, Michelle A.; Gordon-Larsen, Penny; Sotres-Alvarez, Daniela; Adair, Linda; Popkin, Barry

2014-01-01

Objective We examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in hemoglobin A1c (HbA1c), homeostasis model of insulin resistance (HOMA-IR), and fasting glucose. Design We measured diet over a 3-day period with 24-hour recalls and a household food inventory in 2006 and used it to derive PCA and RRR dietary patterns. The outcomes were measured in 2009. Setting Adults (n = 4,316) from the China Health and Nutrition Survey. Results The adjusted odds ratio for diabetes prevalence (HbA1c ≥ 6.5%), comparing the highest dietary pattern score quartile to the lowest, was 1.26 (0.76, 2.08) for a modern high-wheat pattern (PCA; wheat products, fruits, eggs, milk, instant noodles and frozen dumplings), 0.76 (0.49, 1.17) for a traditional southern pattern (PCA; rice, meat, poultry, and fish), and 2.37 (1.56, 3.60) for the pattern derived with RRR. By comparing the dietary pattern structures of RRR and PCA, we found that the RRR pattern was also behaviorally meaningful. It combined the deleterious effects of the modern high-wheat (high intake of wheat buns and breads, deep-fried wheat, and soy milk) with the deleterious effects of consuming the opposite of the traditional southern (low intake of rice, poultry and game, fish and seafood). Conclusions Our findings suggest that using both PCA and RRR provided useful insights when studying the association of dietary patterns with diabetes. PMID:26784586

An Assessment of Stream Confluence Flow Dynamics using Large Scale Particle Image Velocimetry Captured from Unmanned Aerial Systems

NASA Astrophysics Data System (ADS)

Lewis, Q. W.; Rhoads, B. L.

2017-12-01

The merging of rivers at confluences results in complex three-dimensional flow patterns that influence sediment transport, bed morphology, downstream mixing, and physical habitat conditions. The capacity to characterize comprehensively flow at confluences using traditional sensors, such as acoustic Doppler velocimeters and profiles, is limited by the restricted spatial resolution of these sensors and difficulties in measuring velocities simultaneously at many locations within a confluence. This study assesses two-dimensional surficial patterns of flow structure at a small stream confluence in Illinois, USA, using large scale particle image velocimetry (LSPIV) derived from videos captured by unmanned aerial systems (UAS). The method captures surface velocity patterns at high spatial and temporal resolution over multiple scales, ranging from the entire confluence to details of flow within the confluence mixing interface. Flow patterns at high momentum ratio are compared to flow patterns when the two incoming flows have nearly equal momentum flux. Mean surface flow patterns during the two types of events provide details on mean patterns of surface flow in different hydrodynamic regions of the confluence and on changes in these patterns with changing momentum flux ratio. LSPIV data derived from the highest resolution imagery also reveal general characteristics of large-scale vortices that form along the shear layer between the flows during the high-momentum ratio event. The results indicate that the use of LSPIV and UAS is well-suited for capturing in detail mean surface patterns of flow at small confluences, but that characterization of evolving turbulent structures is limited by scale considerations related to structure size, image resolution, and camera instability. Complementary methods, including camera platforms mounted at fixed positions close to the water surface, provide opportunities to accurately characterize evolving turbulent flow structures in confluences.

Robust learning for optimal treatment decision with NP-dimensionality

PubMed Central

Shi, Chengchun; Song, Rui; Lu, Wenbin

2016-01-01

In order to identify important variables that are involved in making optimal treatment decision, Lu, Zhang and Zeng (2013) proposed a penalized least squared regression framework for a fixed number of predictors, which is robust against the misspecification of the conditional mean model. Two problems arise: (i) in a world of explosively big data, effective methods are needed to handle ultra-high dimensional data set, for example, with the dimension of predictors is of the non-polynomial (NP) order of the sample size; (ii) both the propensity score and conditional mean models need to be estimated from data under NP dimensionality. In this paper, we propose a robust procedure for estimating the optimal treatment regime under NP dimensionality. In both steps, penalized regressions are employed with the non-concave penalty function, where the conditional mean model of the response given predictors may be misspecified. The asymptotic properties, such as weak oracle properties, selection consistency and oracle distributions, of the proposed estimators are investigated. In addition, we study the limiting distribution of the estimated value function for the obtained optimal treatment regime. The empirical performance of the proposed estimation method is evaluated by simulations and an application to a depression dataset from the STAR*D study. PMID:28781717

Signatures of a globally optimal searching strategy in the three-dimensional foraging flights of bumblebees

NASA Astrophysics Data System (ADS)

Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.

2016-07-01

Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.

Three Dimensional Sector Design with Optimal Number of Sectors

NASA Technical Reports Server (NTRS)

Xue, Min

2010-01-01

In the national airspace system, sectors get overloaded due to high traffic demand and inefficient airspace designs. Overloads can be eliminated in some cases by redesigning sector boundaries. This paper extends the Voronoi-based sector design method by automatically selecting the number of sectors, allowing three-dimensional partitions, and enforcing traffic pattern conformance. The method was used to design sectors at Fort-Worth and Indianapolis centers for current traffic scenarios. Results show that new designs can eliminate overloaded sectors, although not in all cases, reduce the number of necessary sectors, and conform to major traffic patterns. Overall, the new methodology produces enhanced and efficient sector designs.

Vertical visual features have a strong influence on cuttlefish camouflage.

PubMed

Ulmer, K M; Buresch, K C; Kossodo, M M; Mäthger, L M; Siemann, L A; Hanlon, R T

2013-04-01

Cuttlefish and other cephalopods use visual cues from their surroundings to adaptively change their body pattern for camouflage. Numerous previous experiments have demonstrated the influence of two-dimensional (2D) substrates (e.g., sand and gravel habitats) on camouflage, yet many marine habitats have varied three-dimensional (3D) structures among which cuttlefish camouflage from predators, including benthic predators that view cuttlefish horizontally against such 3D backgrounds. We conducted laboratory experiments, using Sepia officinalis, to test the relative influence of horizontal versus vertical visual cues on cuttlefish camouflage: 2D patterns on benthic substrates were tested versus 2D wall patterns and 3D objects with patterns. Specifically, we investigated the influence of (i) quantity and (ii) placement of high-contrast elements on a 3D object or a 2D wall, as well as (iii) the diameter and (iv) number of 3D objects with high-contrast elements on cuttlefish body pattern expression. Additionally, we tested the influence of high-contrast visual stimuli covering the entire 2D benthic substrate versus the entire 2D wall. In all experiments, visual cues presented in the vertical plane evoked the strongest body pattern response in cuttlefish. These experiments support field observations that, in some marine habitats, cuttlefish will respond to vertically oriented background features even when the preponderance of visual information in their field of view seems to be from the 2D surrounding substrate. Such choices highlight the selective decision-making that occurs in cephalopods with their adaptive camouflage capability.

Associations of dietary intake patterns identified using reduced rank regression with markers of arterial stiffness among youth with type 1 diabetes.

PubMed

Lamichhane, A P; Liese, A D; Urbina, E M; Crandell, J L; Jaacks, L M; Dabelea, D; Black, M H; Merchant, A T; Mayer-Davis, E J

2014-12-01

Youth with type 1 diabetes (T1DM) are at substantially increased risk for adverse vascular outcomes, but little is known about the influence of dietary behavior on cardiovascular disease (CVD) risk profile. We aimed to identify dietary intake patterns associated with CVD risk factors and evaluate their impact on arterial stiffness (AS) measures collected thereafter in a cohort of youth with T1DM. Baseline diet data from a food frequency questionnaire and CVD risk factors (triglycerides, low density lipoprotein-cholesterol, systolic blood pressure, hemoglobin A1c, C-reactive protein and waist circumference) were available for 1153 youth aged ⩾10 years with T1DM from the SEARCH for Diabetes in Youth Study. A dietary intake pattern was identified using 33 food groups as predictors and six CVD risk factors as responses in reduced rank regression (RRR) analysis. Associations of this RRR-derived dietary pattern with AS measures (augmentation index (AIx75), n=229; pulse wave velocity, n=237; and brachial distensibility, n=228) were then assessed using linear regression. The RRR-derived pattern was characterized by high intakes of sugar-sweetened beverages (SSB) and diet soda, eggs, potatoes and high-fat meats and low intakes of sweets/desserts and low-fat dairy; major contributors were SSB and diet soda. This pattern captured the largest variability in adverse CVD risk profile and was subsequently associated with AIx75 (β=0.47; P<0.01). The mean difference in AIx75 concentration between the highest and the lowest dietary pattern quartiles was 4.3% in fully adjusted model. Intervention strategies to reduce consumption of unhealthy foods and beverages among youth with T1DM may significantly improve CVD risk profile and ultimately reduce the risk for AS.

Fuel decomposition and boundary-layer combustion processes of hybrid rocket motors

NASA Technical Reports Server (NTRS)

Chiaverini, Martin J.; Harting, George C.; Lu, Yeu-Cherng; Kuo, Kenneth K.; Serin, Nadir; Johnson, David K.

1995-01-01

Using a high-pressure, two-dimensional hybrid motor, an experimental investigation was conducted on fundamental processes involved in hybrid rocket combustion. HTPB (Hydroxyl-terminated Polybutadiene) fuel cross-linked with diisocyanate was burned with GOX under various operating conditions. Large-amplitude pressure oscillations were encountered in earlier test runs. After identifying the source of instability and decoupling the GOX feed-line system and combustion chamber, the pressure oscillations were drastically reduced from +/-20% of the localized mean pressure to an acceptable range of +/-1.5% Embedded fine-wire thermocouples indicated that the surface temperature of the burning fuel was around 1000 K depending upon axial locations and operating conditions. Also, except near the leading-edge region, the subsurface thermal wave profiles in the upstream locations are thicker than those in the downstream locations since the solid-fuel regression rate, in general, increases with distance along the fuel slab. The recovered solid fuel slabs in the laminar portion of the boundary layer exhibited smooth surfaces, indicating the existence of a liquid melt layer on the burning fuel surface in the upstream region. After the transition section, which displayed distinct transverse striations, the surface roughness pattern became quite random and very pronounced in the downstream turbulent boundary-layer region. Both real-time X-ray radiography and ultrasonic pulse-echo techniques were used to determine the instantaneous web thickness burned and instantaneous solid-fuel regression rates over certain portions of the fuel slabs. Globally averaged and axially dependent but time-averaged regression rates were also obtained and presented.

Estimating the functional dimensionality of neural representations.

PubMed

Ahlheim, Christiane; Love, Bradley C

2018-06-07

Recent advances in multivariate fMRI analysis stress the importance of information inherent to voxel patterns. Key to interpreting these patterns is estimating the underlying dimensionality of neural representations. Dimensions may correspond to psychological dimensions, such as length and orientation, or involve other coding schemes. Unfortunately, the noise structure of fMRI data inflates dimensionality estimates and thus makes it difficult to assess the true underlying dimensionality of a pattern. To address this challenge, we developed a novel approach to identify brain regions that carry reliable task-modulated signal and to derive an estimate of the signal's functional dimensionality. We combined singular value decomposition with cross-validation to find the best low-dimensional projection of a pattern of voxel-responses at a single-subject level. Goodness of the low-dimensional reconstruction is measured as Pearson correlation with a test set, which allows to test for significance of the low-dimensional reconstruction across participants. Using hierarchical Bayesian modeling, we derive the best estimate and associated uncertainty of underlying dimensionality across participants. We validated our method on simulated data of varying underlying dimensionality, showing that recovered dimensionalities match closely true dimensionalities. We then applied our method to three published fMRI data sets all involving processing of visual stimuli. The results highlight three possible applications of estimating the functional dimensionality of neural data. Firstly, it can aid evaluation of model-based analyses by revealing which areas express reliable, task-modulated signal that could be missed by specific models. Secondly, it can reveal functional differences across brain regions. Thirdly, knowing the functional dimensionality allows assessing task-related differences in the complexity of neural patterns. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.

PubMed

Azevedo, C F; Nascimento, M; Silva, F F; Resende, M D V; Lopes, P S; Guimarães, S E F; Glória, L S

2015-10-09

A significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.

Three-Dimensional Effects in Multi-Element High Lift Computations

NASA Technical Reports Server (NTRS)

Rumsey, Christopher L.; LeeReusch, Elizabeth M.; Watson, Ralph D.

2003-01-01

In an effort to discover the causes for disagreement between previous two-dimensional (2-D) computations and nominally 2-D experiment for flow over the three-element McDonnell Douglas 30P-30N airfoil configuration at high lift, a combined experimental/CFD investigation is described. The experiment explores several different side-wall boundary layer control venting patterns, documents venting mass flow rates, and looks at corner surface flow patterns. The experimental angle of attack at maximum lift is found to be sensitive to the side-wall venting pattern: a particular pattern increases the angle of attack at maximum lift by at least 2 deg. A significant amount of spanwise pressure variation is present at angles of attack near maximum lift. A CFD study using three-dimensional (3-D) structured-grid computations, which includes the modeling of side-wall venting, is employed to investigate 3-D effects on the flow. Side-wall suction strength is found to affect the angle at which maximum lift is predicted. Maximum lift in the CFD is shown to be limited by the growth of an off-body corner flow vortex and consequent increase in spanwise pressure variation and decrease in circulation. The 3-D computations with and without wall venting predict similar trends to experiment at low angles of attack, but either stall too early or else overpredict lift levels near maximum lift by as much as 5%. Unstructured-grid computations demonstrate that mounting brackets lower the lift levels near maximum lift conditions.

Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Huang, Bo; Lin, Chuan-Yao

2015-02-01

This paper explores the spatio-temporal patterns of particulate matter (PM) in Taiwan based on a series of methods. Using fuzzy c-means clustering first, the spatial heterogeneity (six clusters) in the PM data collected between 2005 and 2009 in Taiwan are identified and the industrial and urban areas of Taiwan (southwestern, west central, northwestern, and northern Taiwan) are found to have high PM concentrations. The PM10-PM2.5 relationship is then modeled with global ordinary least squares regression, geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR). The GTWR and GWR produce consistent results; however, GTWR provides more detailed information of spatio-temporal variations of the PM10-PM2.5 relationship. The results also show that GTWR provides a relatively high goodness of fit and sufficient space-time explanatory power. In particular, the PM2.5 or PM10 varies with time and space, depending on weather conditions and the spatial distribution of land use and emission patterns in local areas. Such information can be used to determine patterns of spatio-temporal heterogeneity in PM that will allow the control of pollutants and the reduction of public exposure.

Fuzzy usage pattern in customizing public transport fleet and its maintenance options

NASA Astrophysics Data System (ADS)

Husniah, H.; Herdiani, L.; Kusmaya; Supriatna, A. K.

2018-05-01

In this paper we study a two-dimensional maintenance contract for a fleet of public transport, such as buses, shuttle etc. The buses are sold with a two-dimensional warranty. The warranty and the maintenance contract are characterized by two parameters – age and usage – which define a two-dimensional region. However, we use one dimensional approach to model these age and usage of the buses. The under-laying maintenance service contracts is the one which offers policy limit cost to protect a service provider (an agent) from over claim and to pursue the owner to do maintenance under specified cost in house. This in turn gives benefit for both the owner of the buses and the agent of service contract. The decision problem for an agent is to determine the optimal price for each option offered, and for the owner is to select the best contract option. We use a Nash game theory formulation in order to obtain a win-win solution – i.e. the optimal price for the agent and the optimal option for the owner. We further assume that there will be three different usage pattern of the buses, i.e. low, medium, and high pattern of the usage rate. In many situations it is often that we face a blur boundary between the adjacent patterns. In this paper we look for the optimal price for the agent and the optimal option for the owner, which minimizes the expected total cost while considering the fuzziness of the usage rate pattern.

Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards: Smoke Detection Using an Autologistic Regression Classifier.

PubMed

Wolters, Mark A; Dean, C B

2017-01-01

Remote sensing images from Earth-orbiting satellites are a potentially rich data source for monitoring and cataloguing atmospheric health hazards that cover large geographic regions. A method is proposed for classifying such images into hazard and nonhazard regions using the autologistic regression model, which may be viewed as a spatial extension of logistic regression. The method includes a novel and simple approach to parameter estimation that makes it well suited to handling the large and high-dimensional datasets arising from satellite-borne instruments. The methodology is demonstrated on both simulated images and a real application to the identification of forest fire smoke.

HiSPoD: a program for high-speed polychromatic X-ray diffraction experiments and data analysis on polycrystalline samples

DOE PAGES

Sun, Tao; Fezzaa, Kamel

2016-06-17

Here, a high-speed X-ray diffraction technique was recently developed at the 32-ID-B beamline of the Advanced Photon Source for studying highly dynamic, yet non-repeatable and irreversible, materials processes. In experiments, the microstructure evolution in a single material event is probed by recording a series of diffraction patterns with extremely short exposure time and high frame rate. Owing to the limited flux in a short pulse and the polychromatic nature of the incident X-rays, analysis of the diffraction data is challenging. Here, HiSPoD, a stand-alone Matlab-based software for analyzing the polychromatic X-ray diffraction data from polycrystalline samples, is described. With HiSPoD,more » researchers are able to perform diffraction peak indexing, extraction of one-dimensional intensity profiles by integrating a two-dimensional diffraction pattern, and, more importantly, quantitative numerical simulations to obtain precise sample structure information.« less

1-Dimensional AgVO3 nanowires hybrid with 2-dimensional graphene nanosheets to create 3-dimensional composite aerogels and their improved electrochemical properties

NASA Astrophysics Data System (ADS)

Liang, Liying; Xu, Yimeng; Lei, Yong; Liu, Haimei

2014-03-01

Three-dimensional (3D) porous composite aerogels have been synthesized via an innovative in situ hydrothermal method assisted by a freeze-drying process. In this hybrid structure, one-dimensional (1D) AgVO3 nanowires are uniformly dispersed on two-dimensional (2D) graphene nanosheet surfaces and/or are penetrated through the graphene sheets, forming 3D porous composite aerogels. As cathode materials for lithium-ion batteries, the composite aerogels exhibit high discharge capacity, excellent rate capability, and good cycling stability.Three-dimensional (3D) porous composite aerogels have been synthesized via an innovative in situ hydrothermal method assisted by a freeze-drying process. In this hybrid structure, one-dimensional (1D) AgVO3 nanowires are uniformly dispersed on two-dimensional (2D) graphene nanosheet surfaces and/or are penetrated through the graphene sheets, forming 3D porous composite aerogels. As cathode materials for lithium-ion batteries, the composite aerogels exhibit high discharge capacity, excellent rate capability, and good cycling stability. Electronic supplementary information (ESI) available: Preparation, characterization, SEM images, XRD patterns, and XPS of AgVO3/GAs. See DOI: 10.1039/c3nr06899d

Integrated analysis of DNA-methylation and gene expression using high-dimensional penalized regression: a cohort study on bone mineral density in postmenopausal women.

PubMed

Lien, Tonje G; Borgan, Ørnulf; Reppe, Sjur; Gautvik, Kaare; Glad, Ingrid Kristine

2018-03-07

Using high-dimensional penalized regression we studied genome-wide DNA-methylation in bone biopsies of 80 postmenopausal women in relation to their bone mineral density (BMD). The women showed BMD varying from severely osteoporotic to normal. Global gene expression data from the same individuals was available, and since DNA-methylation often affects gene expression, the overall aim of this paper was to include both of these omics data sets into an integrated analysis. The classical penalized regression uses one penalty, but we incorporated individual penalties for each of the DNA-methylation sites. These individual penalties were guided by the strength of association between DNA-methylations and gene transcript levels. DNA-methylations that were highly associated to one or more transcripts got lower penalties and were therefore favored compared to DNA-methylations showing less association to expression. Because of the complex pathways and interactions among genes, we investigated both the association between DNA-methylations and their corresponding cis gene, as well as the association between DNA-methylations and trans-located genes. Two integrating penalized methods were used: first, an adaptive group-regularized ridge regression, and secondly, variable selection was performed through a modified version of the weighted lasso. When information from gene expressions was integrated, predictive performance was considerably improved, in terms of predictive mean square error, compared to classical penalized regression without data integration. We found a 14.7% improvement in the ridge regression case and a 17% improvement for the lasso case. Our version of the weighted lasso with data integration found a list of 22 interesting methylation sites. Several corresponded to genes that are known to be important in bone formation. Using BMD as response and these 22 methylation sites as covariates, least square regression analyses resulted in R 2 =0.726, comparable to an average R 2 =0.438 for 10000 randomly selected groups of DNA-methylations with group size 22. Two recent types of penalized regression methods were adapted to integrate DNA-methylation and their association to gene expression in the analysis of bone mineral density. In both cases predictions clearly benefit from including the additional information on gene expressions.

Self-supervised ARTMAP.

PubMed

Amis, Gregory P; Carpenter, Gail A

2010-03-01

Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semi-supervised learning). In each case input patterns have a fixed number of features throughout training and testing. Human and machine learning contexts present additional opportunities for expanding incomplete knowledge from formal training, via self-directed learning that incorporates features not previously experienced. This article defines a new self-supervised learning paradigm to address these richer learning contexts, introducing a neural network called self-supervised ARTMAP. Self-supervised learning integrates knowledge from a teacher (labeled patterns with some features), knowledge from the environment (unlabeled patterns with more features), and knowledge from internal model activation (self-labeled patterns). Self-supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled patterns. A category selection function bases system predictions on known features, and distributed network activation scales unlabeled learning to prediction confidence. Slow distributed learning on unlabeled patterns focuses on novel features and confident predictions, defining classification boundaries that were ambiguous in the labeled patterns. Self-supervised ARTMAP improves test accuracy on illustrative low-dimensional problems and on high-dimensional benchmarks. Model code and benchmark data are available from: http://techlab.eu.edu/SSART/. Copyright 2009 Elsevier Ltd. All rights reserved.

How many stakes are required to measure the mass balance of a glacier?

USGS Publications Warehouse

Fountain, A.G.; Vecchia, A.

1999-01-01

Glacier mass balance is estimated for South Cascade Glacier and Maclure Glacier using a one-dimensional regression of mass balance with altitude as an alternative to the traditional approach of contouring mass balance values. One attractive feature of regression is that it can be applied to sparse data sets where contouring is not possible and can provide an objective error of the resulting estimate. Regression methods yielded mass balance values equivalent to contouring methods. The effect of the number of mass balance measurements on the final value for the glacier showed that sample sizes as small as five stakes provided reasonable estimates, although the error estimates were greater than for larger sample sizes. Different spatial patterns of measurement locations showed no appreciable influence on the final value as long as different surface altitudes were intermittently sampled over the altitude range of the glacier. Two different regression equations were examined, a quadratic, and a piecewise linear spline, and comparison of results showed little sensitivity to the type of equation. These results point to the dominant effect of the gradient of mass balance with altitude of alpine glaciers compared to transverse variations. The number of mass balance measurements required to determine the glacier balance appears to be scale invariant for small glaciers and five to ten stakes are sufficient.

Using Gamma and Quantile Regressions to Explore the Association between Job Strain and Adiposity in the ELSA-Brasil Study: Does Gender Matter?

PubMed

Fonseca, Maria de Jesus Mendes da; Juvanhol, Leidjaira Lopes; Rotenberg, Lúcia; Nobre, Aline Araújo; Griep, Rosane Härter; Alves, Márcia Guimarães de Mello; Cardoso, Letícia de Oliveira; Giatti, Luana; Nunes, Maria Angélica; Aquino, Estela M L; Chor, Dóra

2017-11-17

This paper explores the association between job strain and adiposity, using two statistical analysis approaches and considering the role of gender. The research evaluated 11,960 active baseline participants (2008-2010) in the ELSA-Brasil study. Job strain was evaluated through a demand-control questionnaire, while body mass index (BMI) and waist circumference (WC) were evaluated in continuous form. The associations were estimated using gamma regression models with an identity link function. Quantile regression models were also estimated from the final set of co-variables established by gamma regression. The relationship that was found varied by analytical approach and gender. Among the women, no association was observed between job strain and adiposity in the fitted gamma models. In the quantile models, a pattern of increasing effects of high strain was observed at higher BMI and WC distribution quantiles. Among the men, high strain was associated with adiposity in the gamma regression models. However, when quantile regression was used, that association was found not to be homogeneous across outcome distributions. In addition, in the quantile models an association was observed between active jobs and BMI. Our results point to an association between job strain and adiposity, which follows a heterogeneous pattern. Modelling strategies can produce different results and should, accordingly, be used to complement one another.

Energy intake and dietary patterns in childhood and throughout adulthood and mammographic density: results from a British prospective cohort.

PubMed

Mishra, Gita D; dos Santos Silva, Isabel; McNaughton, Sarah A; Stephen, Alison; Kuh, Diana

2011-02-01

To examine the role of energy intake and dietary patterns in childhood and throughout adulthood on subsequent mammographic density. Prospective data were available from a cohort of 1161 British women followed up since their birth in 1946. Dietary intakes at age 4 years were determined by 24-hour recalls and during adulthood, average food consumed at ages 36 and 43 years by 5-day food records. Dietary patterns were determined by factor analysis. Associations between energy intake, dietary patterns, and percent breast density were investigated using regression analysis. During adulthood, energy intake was positively associated with percent breast density (adjusted regression coefficient [per SD) (95% CI): 0.12 (0.01, 0.23)]. The effect of the high fat and sugar dietary pattern remained similar when adjusted for total energy intake [0.06 (-0.01, 0.13)]. There was no evidence of an associations for the patterns low fat, high fiber pattern 0.03 (-0.04, 0.11); the alcohol and fish -0.02 (-0.13, 0.17); meat, potatoes, and vegetables -0.03 (-0.10, 0.04). No association was found for dietary pattern at age 4 and percent breast density. This study supports the hypothesis that overall energy intake during middle life is a determinant of subsequent mammographic breast density measured 15 years later.

Decimated Input Ensembles for Improved Generalization

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Oza, Nikunj C.; Norvig, Peter (Technical Monitor)

1999-01-01

Recently, many researchers have demonstrated that using classifier ensembles (e.g., averaging the outputs of multiple classifiers before reaching a classification decision) leads to improved performance for many difficult generalization problems. However, in many domains there are serious impediments to such "turnkey" classification accuracy improvements. Most notable among these is the deleterious effect of highly correlated classifiers on the ensemble performance. One particular solution to this problem is generating "new" training sets by sampling the original one. However, with finite number of patterns, this causes a reduction in the training patterns each classifier sees, often resulting in considerably worsened generalization performance (particularly for high dimensional data domains) for each individual classifier. Generally, this drop in the accuracy of the individual classifier performance more than offsets any potential gains due to combining, unless diversity among classifiers is actively promoted. In this work, we introduce a method that: (1) reduces the correlation among the classifiers; (2) reduces the dimensionality of the data, thus lessening the impact of the 'curse of dimensionality'; and (3) improves the classification performance of the ensemble.

Predictive value of hippocampal MR imaging-based high-dimensional mapping in mesial temporal epilepsy: preliminary findings.

PubMed

Hogan, R E; Wang, L; Bertrand, M E; Willmore, L J; Bucholz, R D; Nassif, A S; Csernansky, J G

2006-01-01

We objectively assessed surface structural changes of the hippocampus in mesial temporal sclerosis (MTS) and assessed the ability of large-deformation high-dimensional mapping (HDM-LD) to demonstrate hippocampal surface symmetry and predict group classification of MTS in right and left MTS groups compared with control subjects. Using eigenvector field analysis of HDM-LD segmentations of the hippocampus, we compared the symmetry of changes in the right and left MTS groups with a group of 15 matched controls. To assess the ability of HDM-LD to predict group classification, eigenvectors were selected by a logistic regression procedure when comparing the MTS group with control subjects. Multivariate analysis of variance on the coefficients from the first 9 eigenvectors accounted for 75% of the total variance between groups. The first 3 eigenvectors showed the largest differences between the control group and each of the MTS groups, but with eigenvector 2 showing the greatest difference in the MTS groups. Reconstruction of the hippocampal deformation vector fields due solely to eigenvector 2 shows symmetrical patterns in the right and left MTS groups. A "leave-one-out" (jackknife) procedure correctly predicted group classification in 14 of 15 (93.3%) left MTS subjects and all 15 right MTS subjects. Analysis of principal dimensions of hippocampal shape change suggests that MTS, after accounting for normal right-left asymmetries, affects the right and left hippocampal surface structure very symmetrically. Preliminary analysis using HDM-LD shows it can predict group classification of MTS and control hippocampi in this well-defined population of patients with MTS and mesial temporal lobe epilepsy (MTLE).

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

PubMed

Ortega, Julio; Asensio-Cubero, Javier; Gan, John Q; Ortiz, Andrés

2016-07-15

Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

Fluorine-Based DRIE of Fused Silica

NASA Technical Reports Server (NTRS)

Yee, Karl; Shcheglov, Kirill; Li, Jian; Choi, Daniel

2007-01-01

A process of deep reactive-ion etching (DRIE) using a fluorine-based gas mixture enhanced by induction-coupled plasma (ICP) has been demonstrated to be effective in forming high-aspect-ratio three-dimensional patterns in fused silica. The patterns are defined in part by an etch mask in the form of a thick, high-quality aluminum film. The process was developed to satisfy a need to fabricate high-aspect-ratio fused-silica resonators for vibratory microgyroscopes, and could be used to satisfy similar requirements for fabricating other fused-silica components.

Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.

PubMed

Strappini, Francesca; Gilboa, Elad; Pitzalis, Sabrina; Kay, Kendrick; McAvoy, Mark; Nehorai, Arye; Snyder, Abraham Z

2017-03-01

Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 38:1438-1459, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Biosignature Discovery for Substance Use Disorders Using Statistical Learning.

PubMed

Baurley, James W; McMahan, Christopher S; Ervin, Carolyn M; Pardamean, Bens; Bergen, Andrew W

2018-02-01

There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Three-dimensional patterning in polymer optical waveguides using focused ion beam milling

NASA Astrophysics Data System (ADS)

Kruse, Kevin; Burrell, Derek; Middlebrook, Christopher

2016-07-01

Waveguide (WG) photonic-bridge taper modules are designed for symmetric planar coupling between silicon WGs and single-mode fibers (SMFs) to minimize photonic chip and packaging footprint requirements with improving broadband functionality. Micromachined fabrication and evaluation of polymer WG tapers utilizing high-resolution focused ion beam (FIB) milling is performed and presented. Polymer etch rates utilizing the FIB and optimal methods for milling polymer tapers are identified for three-dimensional patterning. Polymer WG tapers with low sidewall roughness are manufactured utilizing FIB milling and optically tested for fabrication loss. FIB platforms utilize a focused beam of ions (Ga+) to etch submicron patterns into substrates. Fabricating low-loss polymer WG taper prototypes with the FIB before moving on to mass-production techniques provides theoretical understanding of the polymer taper and its feasibility for connectorization devices between silicon WGs and SMFs.

Friendship networks of inner-city adults: a latent class analysis and multi-level regression of supporter types and the association of supporter latent class membership with supporter and recipient drug use.

PubMed

Bohnert, Amy S B; German, Danielle; Knowlton, Amy R; Latkin, Carl A

2010-03-01

Social support is a multi-dimensional construct that is important to drug use cessation. The present study identified types of supportive friends among the social network members in a community-based sample and examined the relationship of supporter-type classes with supporter, recipient, and supporter-recipient relationship characteristics. We hypothesized that the most supportive network members and their support recipients would be less likely to be current heroin/cocaine users. Participants (n=1453) were recruited from low-income neighborhoods with a high prevalence of drug use. Participants identified their friends via a network inventory, and all nominated friends were included in a latent class analysis and grouped based on their probability of providing seven types of support. These latent classes were included as the dependent variable in a multi-level regression of supporter drug use, recipient drug use, and other characteristics. The best-fitting latent class model identified five support patterns: friends who provided Little/No Support, Low/Moderate Support, High Support, Socialization Support, and Financial Support. In bivariate models, friends in the High, Low/Moderate, and Financial Support were less likely to use heroin or cocaine and had less conflict with and were more trusted by the support recipient than friends in the Low/No Support class. Individuals with supporters in those same support classes compared to the Low/No Support class were less likely to use heroin or cocaine, or to be homeless or female. Multivariable models suggested similar trends. Those with current heroin/cocaine use were less likely to provide or receive comprehensive support from friends. Published by Elsevier Ireland Ltd.

Differentiating Fragmentation Pathways of Cholesterol by Two-Dimensional Fourier Transform Ion Cyclotron Resonance Mass Spectrometry.

PubMed

van Agthoven, Maria A; Barrow, Mark P; Chiron, Lionel; Coutouly, Marie-Aude; Kilgour, David; Wootton, Christopher A; Wei, Juan; Soulby, Andrew; Delsuc, Marc-André; Rolando, Christian; O'Connor, Peter B

2015-12-01

Two-dimensional Fourier transform ion cyclotron resonance mass spectrometry is a data-independent analytical method that records the fragmentation patterns of all the compounds in a sample. This study shows the implementation of atmospheric pressure photoionization with two-dimensional (2D) Fourier transform ion cyclotron resonance mass spectrometry. In the resulting 2D mass spectrum, the fragmentation patterns of the radical and protonated species from cholesterol are differentiated. This study shows the use of fragment ion lines, precursor ion lines, and neutral loss lines in the 2D mass spectrum to determine fragmentation mechanisms of known compounds and to gain information on unknown ion species in the spectrum. In concert with high resolution mass spectrometry, 2D Fourier transform ion cyclotron resonance mass spectrometry can be a useful tool for the structural analysis of small molecules. Graphical Abstract ᅟ.

Photoluminescence enhancement of monolayer tungsten disulfide in complicated plasmonic microstructures

NASA Astrophysics Data System (ADS)

Zhou, Yi; Hu, Xiaoyong; Gao, Wei; Song, Hanfa; Chu, Saisai; Yang, Hong; Gong, Qihuang

2018-06-01

Two-dimensional van der Waals materials are interesting for fundamental physics exploration and device applications because of their attractive physical properties. Here, we report a strategy to realize photoluminescence (PL) enhancement of two-dimensional transition-metal dichalcogenides (TMDCs) in the visible range using a plasmonic microstructure with patterned gold nanoantennas and a metal-insulator-semiconductor-insulator-metal structure. The PL intensity was enhanced by a factor of two under Y-polarization due to the increased radiative decay rate by the surface plasmon radiation channel in the gold nanoantennas and the decreased nonradiative decay rate by suppressing exciton quenching in the SiO2 isolation layer. The fluorescence lifetime of monolayer tungsten disulfide in this structure was shorter than that of a sample without patterned gold nanoantennas. Tailoring the light-matter interactions between two-dimensional TMDCs and plasmonic nanostructures may provide highly efficient optoelectronic devices such as TMDC-based light emitters.

Toward a Periodic Table of Niches, or Exploring the Lizard Niche Hypervolume.

PubMed

Pianka, Eric R; Vitt, Laurie J; Pelegrin, Nicolás; Fitzgerald, Daniel B; Winemiller, Kirk O

2017-11-01

Widespread niche convergence suggests that species can be organized according to functional trait combinations to create a framework analogous to a periodic table. We compiled ecological data for lizards to examine patterns of global and regional niche diversification, and we used multivariate statistical approaches to develop the beginnings for a periodic table of niches. Data (50+ variables) for five major niche dimensions (habitat, diet, life history, metabolism, defense) were compiled for 134 species of lizards representing 24 of the 38 extant families. Principal coordinates analyses were performed on niche dimensional data sets, and species scores for the first three axes were used as input for a principal components analysis to ordinate species in continuous niche space and for a regression tree analysis to separate species into discrete niche categories. Three-dimensional models facilitate exploration of species positions in relation to major gradients within the niche hypervolume. The first gradient loads on body size, foraging mode, and clutch size. The second was influenced by metabolism and terrestrial versus arboreal microhabitat. The third was influenced by activity time, life history, and diet. Natural dichotomies are activity time, foraging mode, parity mode, and habitat. Regression tree analysis identified 103 cases of extreme niche conservatism within clades and 100 convergences between clades. Extending this approach to other taxa should lead to a wider understanding of niche evolution.

Statistical Exploration of Electronic Structure of Molecules from Quantum Monte-Carlo Simulations

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

Prabhat, Mr; Zubarev, Dmitry; Lester, Jr., William A.

In this report, we present results from analysis of Quantum Monte Carlo (QMC) simulation data with the goal of determining internal structure of a 3N-dimensional phase space of an N-electron molecule. We are interested in mining the simulation data for patterns that might be indicative of the bond rearrangement as molecules change electronic states. We examined simulation output that tracks the positions of two coupled electrons in the singlet and triplet states of an H2 molecule. The electrons trace out a trajectory, which was analyzed with a number of statistical techniques. This project was intended to address the following scientificmore » questions: (1) Do high-dimensional phase spaces characterizing electronic structure of molecules tend to cluster in any natural way? Do we see a change in clustering patterns as we explore different electronic states of the same molecule? (2) Since it is hard to understand the high-dimensional space of trajectories, can we project these trajectories to a lower dimensional subspace to gain a better understanding of patterns? (3) Do trajectories inherently lie in a lower-dimensional manifold? Can we recover that manifold? After extensive statistical analysis, we are now in a better position to respond to these questions. (1) We definitely see clustering patterns, and differences between the H2 and H2tri datasets. These are revealed by the pamk method in a fairly reliable manner and can potentially be used to distinguish bonded and non-bonded systems and get insight into the nature of bonding. (2) Projecting to a lower dimensional subspace ({approx}4-5) using PCA or Kernel PCA reveals interesting patterns in the distribution of scalar values, which can be related to the existing descriptors of electronic structure of molecules. Also, these results can be immediately used to develop robust tools for analysis of noisy data obtained during QMC simulations (3) All dimensionality reduction and estimation techniques that we tried seem to indicate that one needs 4 or 5 components to account for most of the variance in the data, hence this 5D dataset does not necessarily lie on a well-defined, low dimensional manifold. In terms of specific clustering techniques, K-means was generally useful in exploring the dataset. The partition around medoids (pam) technique produced the most definitive results for our data showing distinctive patterns for both a sample of the complete data and time-series. The gap statistic with tibshirani criteria did not provide any distinction across the 2 dataset. The gap statistic w/DandF criteria, Model based clustering and hierarchical modeling simply failed to run on our datasets. Thankfully, the vanilla PCA technique was successful in handling our entire dataset. PCA revealed some interesting patterns for the scalar value distribution. Kernel PCA techniques (vanilladot, RBF, Polynomial) and MDS failed to run on the entire dataset, or even a significant fraction of the dataset, and we resorted to creating an explicit feature map followed by conventional PCA. Clustering using K-means and PAM in the new basis set seems to produce promising results. Understanding the new basis set in the scientific context of the problem is challenging, and we are currently working to further examine and interpret the results.« less

Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes

PubMed Central

2013-01-01

Motivation Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive. Results We have developed a parallel version of the RF algorithm for regression and genetic similarity learning tasks in large-scale population genetic association studies involving multivariate traits, called PaRFR (Parallel Random Forest Regression). Our implementation takes advantage of the MapReduce programming model and is deployed on Hadoop, an open-source software framework that supports data-intensive distributed applications. Notable speed-ups are obtained by introducing a distance-based criterion for node splitting in the tree estimation process. PaRFR has been applied to a genome-wide association study on Alzheimer's disease (AD) in which the quantitative trait consists of a high-dimensional neuroimaging phenotype describing longitudinal changes in the human brain structure. PaRFR provides a ranking of SNPs associated to this trait, and produces pair-wise measures of genetic proximity that can be directly compared to pair-wise measures of phenotypic proximity. Several known AD-related variants have been identified, including APOE4 and TOMM40. We also present experimental evidence supporting the hypothesis of a linear relationship between the number of top-ranked mutated states, or frequent mutation patterns, and an indicator of disease severity. Availability The Java codes are freely available at http://www2.imperial.ac.uk/~gmontana. PMID:24564704

Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.

PubMed

Wang, Yue; Goh, Wilson; Wong, Limsoon; Montana, Giovanni

2013-01-01

Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive. We have developed a parallel version of the RF algorithm for regression and genetic similarity learning tasks in large-scale population genetic association studies involving multivariate traits, called PaRFR (Parallel Random Forest Regression). Our implementation takes advantage of the MapReduce programming model and is deployed on Hadoop, an open-source software framework that supports data-intensive distributed applications. Notable speed-ups are obtained by introducing a distance-based criterion for node splitting in the tree estimation process. PaRFR has been applied to a genome-wide association study on Alzheimer's disease (AD) in which the quantitative trait consists of a high-dimensional neuroimaging phenotype describing longitudinal changes in the human brain structure. PaRFR provides a ranking of SNPs associated to this trait, and produces pair-wise measures of genetic proximity that can be directly compared to pair-wise measures of phenotypic proximity. Several known AD-related variants have been identified, including APOE4 and TOMM40. We also present experimental evidence supporting the hypothesis of a linear relationship between the number of top-ranked mutated states, or frequent mutation patterns, and an indicator of disease severity. The Java codes are freely available at http://www2.imperial.ac.uk/~gmontana.

Three-Dimensional Model of Heat and Mass Transfer in Fractured Rocks to Estimate Environmental Conditions Along Heated Drifts

NASA Astrophysics Data System (ADS)

Fedors, R. W.; Painter, S. L.

2004-12-01

Temperature gradients along the thermally-perturbed drifts of the potential high-level waste repository at Yucca Mountain, Nevada, will drive natural convection and associated heat and mass transfer along drifts. A three-dimensional, dual-permeability, thermohydrological model of heat and mass transfer was used to estimate the magnitude of temperature gradients along a drift. Temperature conditions along heated drifts are needed to support estimates of repository-edge cooling and as input to computational fluid dynamics modeling of in-drift axial convection and the cold-trap process. Assumptions associated with abstracted heat transfer models and two-dimensional thermohydrological models weakly coupled to mountain-scale thermal models can readily be tested using the three-dimensional thermohydrological model. Although computationally expensive, the fully coupled three-dimensional thermohydrological model is able to incorporate lateral heat transfer, including host rock processes of conduction, convection in gas phase, advection in liquid phase, and latent-heat transfer. Results from the three-dimensional thermohydrological model showed that weakly coupling three-dimensional thermal and two-dimensional thermohydrological models lead to underestimates of temperatures and underestimates of temperature gradients over large portions of the drift. The representative host rock thermal conductivity needed for abstracted heat transfer models are overestimated using the weakly coupled models. If axial flow patterns over large portions of drifts are not impeded by the strong cross-sectional flow patterns imparted by the heat rising directly off the waste package, condensation from the cold-trap process will not be limited to the extreme ends of each drift. Based on the three-dimensional thermohydrological model, axial temperature gradients occur sooner over a larger portion of the drift, though high gradients nearest the edge of the potential repository are dampened. This abstract is an independent product of CNWRA and does not necessarily reflect the view or regulatory position of the Nuclear Regulatory Commission.

Production and perception rules underlying visual patterns: effects of symmetry and hierarchy.

PubMed

Westphal-Fitch, Gesche; Huber, Ludwig; Gómez, Juan Carlos; Fitch, W Tecumseh

2012-07-19

Formal language theory has been extended to two-dimensional patterns, but little is known about two-dimensional pattern perception. We first examined spontaneous two-dimensional visual pattern production by humans, gathered using a novel touch screen approach. Both spontaneous creative production and subsequent aesthetic ratings show that humans prefer ordered, symmetrical patterns over random patterns. We then further explored pattern-parsing abilities in different human groups, and compared them with pigeons. We generated visual plane patterns based on rules varying in complexity. All human groups tested, including children and individuals diagnosed with autism spectrum disorder (ASD), were able to detect violations of all production rules tested. Our ASD participants detected pattern violations with the same speed and accuracy as matched controls. Children's ability to detect violations of a relatively complex rotational rule correlated with age, whereas their ability to detect violations of a simple translational rule did not. By contrast, even with extensive training, pigeons were unable to detect orientation-based structural violations, suggesting that, unlike humans, they did not learn the underlying structural rules. Visual two-dimensional patterns offer a promising new formally-grounded way to investigate pattern production and perception in general, widely applicable across species and age groups.

Production and perception rules underlying visual patterns: effects of symmetry and hierarchy

PubMed Central

Westphal-Fitch, Gesche; Huber, Ludwig; Gómez, Juan Carlos; Fitch, W. Tecumseh

2012-01-01

Formal language theory has been extended to two-dimensional patterns, but little is known about two-dimensional pattern perception. We first examined spontaneous two-dimensional visual pattern production by humans, gathered using a novel touch screen approach. Both spontaneous creative production and subsequent aesthetic ratings show that humans prefer ordered, symmetrical patterns over random patterns. We then further explored pattern-parsing abilities in different human groups, and compared them with pigeons. We generated visual plane patterns based on rules varying in complexity. All human groups tested, including children and individuals diagnosed with autism spectrum disorder (ASD), were able to detect violations of all production rules tested. Our ASD participants detected pattern violations with the same speed and accuracy as matched controls. Children's ability to detect violations of a relatively complex rotational rule correlated with age, whereas their ability to detect violations of a simple translational rule did not. By contrast, even with extensive training, pigeons were unable to detect orientation-based structural violations, suggesting that, unlike humans, they did not learn the underlying structural rules. Visual two-dimensional patterns offer a promising new formally-grounded way to investigate pattern production and perception in general, widely applicable across species and age groups. PMID:22688636

Measurement of two-dimensional thickness of micro-patterned thin film based on image restoration in a spectroscopic imaging reflectometer.

PubMed

Kim, Min-Gab; Kim, Jin-Yong

2018-05-01

In this paper, we introduce a method to overcome the limitation of thickness measurement of a micro-patterned thin film. A spectroscopic imaging reflectometer system that consists of an acousto-optic tunable filter, a charge-coupled-device camera, and a high-magnitude objective lens was proposed, and a stack of multispectral images was generated. To secure improved accuracy and lateral resolution in the reconstruction of a two-dimensional thin film thickness, prior to the analysis of spectral reflectance profiles from each pixel of multispectral images, the image restoration based on an iterative deconvolution algorithm was applied to compensate for image degradation caused by blurring.

Alignment of Ge nanoislands on Si(111) by Ga-induced substrate self-patterning.

PubMed

Schmidt, Th; Flege, J I; Gangopadhyay, S; Clausen, T; Locatelli, A; Heun, S; Falta, J

2007-02-09

A novel mechanism is described which enables the selective formation of three-dimensional Ge islands. Submonolayer adsorption of Ga on Si(111) at high temperature leads to a self-organized two-dimensional pattern formation by separation of the 7 x 7 substrate and Ga/Si(111)-(square root[3] x square root[3])-R30 degrees domains. The latter evolve at step edges and domain boundaries of the initial substrate reconstruction. Subsequent Ge deposition results in the growth of 3D islands which are aligned at the boundaries between bare and Ga-covered domains. This result is explained in terms of preferential nucleation conditions due to a modulation of the surface chemical potential.

Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering.

PubMed

Ji, Shuiwang

2013-07-11

The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development. In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space. Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship.

Local Context Finder (LCF) reveals multidimensional relationships among mRNA expression profiles of Arabidopsis responding to pathogen infection

PubMed Central

Katagiri, Fumiaki; Glazebrook, Jane

2003-01-01

A major task in computational analysis of mRNA expression profiles is definition of relationships among profiles on the basis of similarities among them. This is generally achieved by pattern recognition in the distribution of data points representing each profile in a high-dimensional space. Some drawbacks of commonly used pattern recognition algorithms stem from their use of a globally linear space and/or limited degrees of freedom. A pattern recognition method called Local Context Finder (LCF) is described here. LCF uses nonlinear dimensionality reduction for pattern recognition. Then it builds a network of profiles based on the nonlinear dimensionality reduction results. LCF was used to analyze mRNA expression profiles of the plant host Arabidopsis interacting with the bacterial pathogen Pseudomonas syringae. In one case, LCF revealed two dimensions essential to explain the effects of the NahG transgene and the ndr1 mutation on resistant and susceptible responses. In another case, plant mutants deficient in responses to pathogen infection were classified on the basis of LCF analysis of their profiles. The classification by LCF was consistent with the results of biological characterization of the mutants. Thus, LCF is a powerful method for extracting information from expression profile data. PMID:12960373

Spectral Regression Discriminant Analysis for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Pan, Y.; Wu, J.; Huang, H.; Liu, J.

2012-08-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

PubMed Central

Nowicka, Malgorzata; Krieg, Carsten; Weber, Lukas M.; Hartmann, Felix J.; Guglietta, Silvia; Becher, Burkhard; Levesque, Mitchell P.; Robinson, Mark D.

2017-01-01

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals). PMID:28663787

Dietary patterns derived by reduced rank regression (RRR) and depressive symptoms in Japanese employees: The Furukawa nutrition and health study.

PubMed

Miki, Takako; Kochi, Takeshi; Kuwahara, Keisuke; Eguchi, Masafumi; Kurotani, Kayo; Tsuruoka, Hiroko; Ito, Rie; Kabe, Isamu; Kawakami, Norito; Mizoue, Tetsuya; Nanri, Akiko

2015-09-30

Depression has been linked to the overall diet using both exploratory and pre-defined methods. However, neither of these methods incorporates specific knowledge on nutrient-disease associations. The aim of the present study was to empirically identify dietary patterns using reduced rank regression and to examine their relations to depressive symptoms. Participants were 2006 Japanese employees aged 19-69 years. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale. Diet was assessed using a validated, self-administered diet history questionnaire. Dietary patterns were extracted by reduced rank regression with 6 depression-related nutrients as response variables. Logistic regression was used to estimate odds ratios of depressive symptoms adjusted for potential confounders. A dietary pattern characterized by a high intake of vegetables, mushrooms, seaweeds, soybean products, green tea, potatoes, fruits, and small fish with bones and a low intake of rice was associated with fewer depressive symptoms. The multivariable-adjusted odds ratios of having depressive symptoms were 0.62 (95% confidence interval, 0.48-0.81) in the highest versus lowest tertiles of dietary score. Results suggest that adherence to a diet rich in vegetables, fruits, and typical Japanese foods, including mushrooms, seaweeds, soybean products, and green tea, is associated with a lower probability of having depressive symptoms. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Visual exploration of high-dimensional data through subspace analysis and dynamic projections

DOE PAGES

Liu, S.; Wang, B.; Thiagarajan, J. J.; ...

2015-06-01

Here, we introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that createmore » smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.« less

Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections

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

Liu, S.; Wang, B.; Thiagarajan, Jayaraman J.

2015-06-01

We introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smoothmore » animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.« less

Comparative study of Poincaré plot analysis using short electroencephalogram signals during anaesthesia with spectral edge frequency 95 and bispectral index.

PubMed

Hayashi, K; Yamada, T; Sawa, T

2015-03-01

The return or Poincaré plot is a non-linear analytical approach in a two-dimensional plane, where a timed signal is plotted against itself after a time delay. Its scatter pattern reflects the randomness and variability in the signals. Quantification of a Poincaré plot of the electroencephalogram has potential to determine anaesthesia depth. We quantified the degree of dispersion (i.e. standard deviation, SD) along the diagonal line of the electroencephalogram-Poincaré plot (named as SD1/SD2), and compared SD1/SD2 values with spectral edge frequency 95 (SEF95) and bispectral index values. The regression analysis showed a tight linear regression equation with a coefficient of determination (R(2) ) value of 0.904 (p < 0.0001) between the Poincaré index (SD1/SD2) and SEF95, and a moderate linear regression equation between SD1/SD2 and bispectral index (R(2)  = 0.346, p < 0.0001). Quantification of the Poincaré plot tightly correlates with SEF95, reflecting anaesthesia-dependent changes in electroencephalogram oscillation. © 2014 The Association of Anaesthetists of Great Britain and Ireland.

Influence plots for LASSO

DOE PAGES

Jang, Dae -Heung; Anderson-Cook, Christine Michaela

2016-11-22

With many predictors in regression, fitting the full model can induce multicollinearity problems. Least Absolute Shrinkage and Selection Operation (LASSO) is useful when the effects of many explanatory variables are sparse in a high-dimensional dataset. Influential points can have a disproportionate impact on the estimated values of model parameters. Here, this paper describes a new influence plot that can be used to increase understanding of the contributions of individual observations and the robustness of results. This can serve as a complement to other regression diagnostics techniques in the LASSO regression setting. Using this influence plot, we can find influential pointsmore » and their impact on shrinkage of model parameters and model selection. Lastly, we provide two examples to illustrate the methods.« less

Influence plots for LASSO

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

Jang, Dae -Heung; Anderson-Cook, Christine Michaela

With many predictors in regression, fitting the full model can induce multicollinearity problems. Least Absolute Shrinkage and Selection Operation (LASSO) is useful when the effects of many explanatory variables are sparse in a high-dimensional dataset. Influential points can have a disproportionate impact on the estimated values of model parameters. Here, this paper describes a new influence plot that can be used to increase understanding of the contributions of individual observations and the robustness of results. This can serve as a complement to other regression diagnostics techniques in the LASSO regression setting. Using this influence plot, we can find influential pointsmore » and their impact on shrinkage of model parameters and model selection. Lastly, we provide two examples to illustrate the methods.« less

Multi-Dimensional Pattern Discovery of Trajectories Using Contextual Information

NASA Astrophysics Data System (ADS)

Sharif, M.; Alesheikh, A. A.

2017-10-01

Movement of point objects are highly sensitive to the underlying situations and conditions during the movement, which are known as contexts. Analyzing movement patterns, while accounting the contextual information, helps to better understand how point objects behave in various contexts and how contexts affect their trajectories. One potential solution for discovering moving objects patterns is analyzing the similarities of their trajectories. This article, therefore, contextualizes the similarity measure of trajectories by not only their spatial footprints but also a notion of internal and external contexts. The dynamic time warping (DTW) method is employed to assess the multi-dimensional similarities of trajectories. Then, the results of similarity searches are utilized in discovering the relative movement patterns of the moving point objects. Several experiments are conducted on real datasets that were obtained from commercial airplanes and the weather information during the flights. The results yielded the robustness of DTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80 % accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.

Regularized matrix regression

PubMed Central

Zhou, Hua; Li, Lexin

2014-01-01

Summary Modern technologies are producing a wealth of data with complex structures. For instance, in two-dimensional digital imaging, flow cytometry and electroencephalography, matrix-type covariates frequently arise when measurements are obtained for each combination of two underlying variables. To address scientific questions arising from those data, new regression methods that take matrices as covariates are needed, and sparsity or other forms of regularization are crucial owing to the ultrahigh dimensionality and complex structure of the matrix data. The popular lasso and related regularization methods hinge on the sparsity of the true signal in terms of the number of its non-zero coefficients. However, for the matrix data, the true signal is often of, or can be well approximated by, a low rank structure. As such, the sparsity is frequently in the form of low rank of the matrix parameters, which may seriously violate the assumption of the classical lasso. We propose a class of regularized matrix regression methods based on spectral regularization. A highly efficient and scalable estimation algorithm is developed, and a degrees-of-freedom formula is derived to facilitate model selection along the regularization path. Superior performance of the method proposed is demonstrated on both synthetic and real examples. PMID:24648830

Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data

PubMed Central

Király, András; Abonyi, János

2014-01-01

During the last decade various algorithms have been developed and proposed for discovering overlapping clusters in high-dimensional data. The two most prominent application fields in this research, proposed independently, are frequent itemset mining (developed for market basket data) and biclustering (applied to gene expression data analysis). The common limitation of both methodologies is the limited applicability for very large binary data sets. In this paper we propose a novel and efficient method to find both frequent closed itemsets and biclusters in high-dimensional binary data. The method is based on simple but very powerful matrix and vector multiplication approaches that ensure that all patterns can be discovered in a fast manner. The proposed algorithm has been implemented in the commonly used MATLAB environment and freely available for researchers. PMID:24616651

Logistic regression trees for initial selection of interesting loci in case-control studies

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability. PMID:18466557

Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach

NASA Astrophysics Data System (ADS)

Liu, Wenyang; Sawant, Amit; Ruan, Dan

2016-07-01

The development of high-dimensional imaging systems in image-guided radiotherapy provides important pathways to the ultimate goal of real-time full volumetric motion monitoring. Effective motion management during radiation treatment usually requires prediction to account for system latency and extra signal/image processing time. It is challenging to predict high-dimensional respiratory motion due to the complexity of the motion pattern combined with the curse of dimensionality. Linear dimension reduction methods such as PCA have been used to construct a linear subspace from the high-dimensional data, followed by efficient predictions on the lower-dimensional subspace. In this study, we extend such rationale to a more general manifold and propose a framework for high-dimensional motion prediction with manifold learning, which allows one to learn more descriptive features compared to linear methods with comparable dimensions. Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where accurate and efficient prediction can be performed. A fixed-point iterative pre-image estimation method is used to recover the predicted value in the original state space. We evaluated and compared the proposed method with a PCA-based approach on level-set surfaces reconstructed from point clouds captured by a 3D photogrammetry system. The prediction accuracy was evaluated in terms of root-mean-squared-error. Our proposed method achieved consistent higher prediction accuracy (sub-millimeter) for both 200 ms and 600 ms lookahead lengths compared to the PCA-based approach, and the performance gain was statistically significant.

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Analyzing musculoskeletal neck pain, measured as present pain and periods of pain, with three different regression models: a cohort study.

PubMed

Grimby-Ekman, Anna; Andersson, Eva M; Hagberg, Mats

2009-06-19

In the literature there are discussions on the choice of outcome and the need for more longitudinal studies of musculoskeletal disorders. The general aim of this longitudinal study was to analyze musculoskeletal neck pain, in a group of young adults. Specific aims were to determine whether psychosocial factors, computer use, high work/study demands, and lifestyle are long-term or short-term factors for musculoskeletal neck pain, and whether these factors are important for developing or ongoing musculoskeletal neck pain. Three regression models were used to analyze the different outcomes. Pain at present was analyzed with a marginal logistic model, for number of years with pain a Poisson regression model was used and for developing and ongoing pain a logistic model was used. Presented results are odds ratios and proportion ratios (logistic models) and rate ratios (Poisson model). The material consisted of web-based questionnaires answered by 1204 Swedish university students from a prospective cohort recruited in 2002. Perceived stress was a risk factor for pain at present (PR = 1.6), for developing pain (PR = 1.7) and for number of years with pain (RR = 1.3). High work/study demands was associated with pain at present (PR = 1.6); and with number of years with pain when the demands negatively affect home life (RR = 1.3). Computer use pattern (number of times/week with a computer session > or = 4 h, without break) was a risk factor for developing pain (PR = 1.7), but also associated with pain at present (PR = 1.4) and number of years with pain (RR = 1.2). Among life style factors smoking (PR = 1.8) was found to be associated to pain at present. The difference between men and women in prevalence of musculoskeletal pain was confirmed in this study. It was smallest for the outcome ongoing pain (PR = 1.4) compared to pain at present (PR = 2.4) and developing pain (PR = 2.5). By using different regression models different aspects of neck pain pattern could be addressed and the risk factors impact on pain pattern was identified. Short-term risk factors were perceived stress, high work/study demands and computer use pattern (break pattern). Those were also long-term risk factors. For developing pain perceived stress and computer use pattern were risk factors.

Analyzing musculoskeletal neck pain, measured as present pain and periods of pain, with three different regression models: a cohort study

PubMed Central

Grimby-Ekman, Anna; Andersson, Eva M; Hagberg, Mats

2009-01-01

Background In the literature there are discussions on the choice of outcome and the need for more longitudinal studies of musculoskeletal disorders. The general aim of this longitudinal study was to analyze musculoskeletal neck pain, in a group of young adults. Specific aims were to determine whether psychosocial factors, computer use, high work/study demands, and lifestyle are long-term or short-term factors for musculoskeletal neck pain, and whether these factors are important for developing or ongoing musculoskeletal neck pain. Methods Three regression models were used to analyze the different outcomes. Pain at present was analyzed with a marginal logistic model, for number of years with pain a Poisson regression model was used and for developing and ongoing pain a logistic model was used. Presented results are odds ratios and proportion ratios (logistic models) and rate ratios (Poisson model). The material consisted of web-based questionnaires answered by 1204 Swedish university students from a prospective cohort recruited in 2002. Results Perceived stress was a risk factor for pain at present (PR = 1.6), for developing pain (PR = 1.7) and for number of years with pain (RR = 1.3). High work/study demands was associated with pain at present (PR = 1.6); and with number of years with pain when the demands negatively affect home life (RR = 1.3). Computer use pattern (number of times/week with a computer session ≥ 4 h, without break) was a risk factor for developing pain (PR = 1.7), but also associated with pain at present (PR = 1.4) and number of years with pain (RR = 1.2). Among life style factors smoking (PR = 1.8) was found to be associated to pain at present. The difference between men and women in prevalence of musculoskeletal pain was confirmed in this study. It was smallest for the outcome ongoing pain (PR = 1.4) compared to pain at present (PR = 2.4) and developing pain (PR = 2.5). Conclusion By using different regression models different aspects of neck pain pattern could be addressed and the risk factors impact on pain pattern was identified. Short-term risk factors were perceived stress, high work/study demands and computer use pattern (break pattern). Those were also long-term risk factors. For developing pain perceived stress and computer use pattern were risk factors. PMID:19545386

Modeling Joint Exposures and Health Outcomes for Cumulative Risk Assessment: The Case of Radon and Smoking

PubMed Central

Chahine, Teresa; Schultz, Bradley D.; Zartarian, Valerie G.; Xue, Jianping; Subramanian, SV; Levy, Jonathan I.

2011-01-01

Community-based cumulative risk assessment requires characterization of exposures to multiple chemical and non-chemical stressors, with consideration of how the non-chemical stressors may influence risks from chemical stressors. Residential radon provides an interesting case example, given its large attributable risk, effect modification due to smoking, and significant variability in radon concentrations and smoking patterns. In spite of this fact, no study to date has estimated geographic and sociodemographic patterns of both radon and smoking in a manner that would allow for inclusion of radon in community-based cumulative risk assessment. In this study, we apply multi-level regression models to explain variability in radon based on housing characteristics and geological variables, and construct a regression model predicting housing characteristics using U.S. Census data. Multi-level regression models of smoking based on predictors common to the housing model allow us to link the exposures. We estimate county-average lifetime lung cancer risks from radon ranging from 0.15 to 1.8 in 100, with high-risk clusters in areas and for subpopulations with high predicted radon and smoking rates. Our findings demonstrate the viability of screening-level assessment to characterize patterns of lung cancer risk from radon, with an approach that can be generalized to multiple chemical and non-chemical stressors. PMID:22016710

Colony patterning and collective hyphal growth of filamentous fungi

NASA Astrophysics Data System (ADS)

Matsuura, Shu

2002-11-01

Colony morphology of wild and mutant strains of Aspergillus nidulans at various nutrient and agar levels was investigated. Two types of colony patterning were found for these strains. One type produced uniform colonies at all nutrient and agar levels tested, and the other exhibited morphological change into disordered ramified colonies at low nutrient levels. Both types showed highly condensed compact colonies at high nutrient levels on low agar media that was highly diffusive. Disordered colonies were found to develop with low hyphal extension rates at low nutrient levels. To understand basic pattern selection rules, a colony model with three parameters, i.e., the initial nutrient level and the step length of nutrient random walk as the external parameters, and the frequency of nutrient uptake as an internal parameter, was constructed. At low nutrient levels, with decreasing nutrient uptake frequency under diffusive conditions, the model colony exhibited onsets of disordered ramification. Further, in the growth process of A. nidulans, reduction of hyphal extension rate due to a population effect of hyphae was found when hyphae form three-dimensional dense colonies, as compared to the case in which hyphal growth was restricted into two-dimensional space. A hyphal population effect was introduced in the colony model. Thickening of colony periphery due to the population effect became distinctive as the nutrient diffusion effect was raised at high nutrient levels with low hyphal growth rate. It was considered that colony patterning and onset of disorder were strongly governed by the combination of nutrient diffusion and hyphal growth rate.

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

PubMed

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

2018-05-30

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

Three-dimensional vortex patterns in a starting flow

NASA Astrophysics Data System (ADS)

Freymuth, P.; Finaish, F.; Bank, W.

1985-12-01

Freymuth et al. (1983, 1984, 1985) have conducted investigations involving chordwise vortical-pattern visualizations in a starting flow of constant acceleration around an airfoil. Detailed resolution of vortical shapes in two dimensions could be obtained. No visualization in the third spanwise dimension is needed as long as the flow remains two-dimensional. However, some time after flow startup, chordwise vortical patterns become blurred, indicating the onset of turbulence. The present investigation is concerned with an extension of the flow visualization from a chordwise cross section to the spanwise dimension. The investigation has the objective to look into the two-dimensionality of the initial vortical developments and to resolve three-dimensional effects during the transition to turbulence. Attention is given to the visualization method, the chordwise vs spanwise visualization in the two-dimensional regime, the spanwise visualization of transition, and the visualization of vortical patterns behind the trailing edge.

Crystal Growth of Graphene Films and Graphene Nanoribbons via Chemical Vapor Deposition

NASA Astrophysics Data System (ADS)

Jacobberger, Robert Michael

Graphene is a two-dimensional carbon allotrope that has exceptional properties, including high charge carrier mobility, thermal conductivity, mechanical strength, and flexibility. Graphene is a semimetal, prohibiting its use in semiconductor applications in which a bandgap is required. However, graphene can be transformed from a semimetal into a semiconductor if it is confined into one-dimensional nanoribbons narrower than 10 nm with well-defined armchair edges. In this work, we study the crystal growth of graphene via chemical vapor deposition (CVD), which is the most promising method to produce graphene films on the industrial scale. We explore the growth of isolated graphene crystals, continuous graphene films, and narrow graphene nanoribbons with armchair edges. We gain key insight into the critical growth parameters and mechanisms that influence the crystal morphology, orientation, defect density, and evolution, providing an empirical understanding of the diverse growth behaviors observed in literature. Using this knowledge, we synthesize graphene with remarkably low pinhole density and achieve high-quality graphene at 750 °C on Cu(111), which is over 250 °C lower than the temperature typically used to grow graphene on copper from methane. We also describe our breakthrough in graphene nanoribbon synthesis. Highly anisotropic nanoribbons are formed on Ge(001) if an exceptionally slow growth rate is used. The nanoribbons are self-defining with predominantly smooth armchair edges, are self-aligning, and have tunable width to < 10 nm. High-performance field-effect transistors incorporating these nanoribbons as channels display high conductance modulation > 10,000 and high conductance > 5 muS. This directional and anisotropic growth enables the fabrication of semiconducting nanoribbons directly on conventional semiconductor wafers and, thus, promises to allow the integration of nanoribbons into future hybrid integrated circuits. We additionally report our discovery that chemical patterns consisting of alternating stripes of graphene and germanium can direct the self-assembly of block copolymers into rationally-designed patterns with nanoscale features. Density multiplication of 10 is achieved and faster assembly kinetics are observed on graphene/germanium templates than on conventional chemical patterns based on polymer mats and brushes. This work opens the door for extensive assembly studies on chemical patterns based on two-dimensional materials.

Study of shape evaluation for mask and silicon using large field of view

NASA Astrophysics Data System (ADS)

Matsuoka, Ryoichi; Mito, Hiroaki; Shinoda, Shinichi; Toyoda, Yasutaka

2010-09-01

We have developed a highly integrated method of mask and silicon metrology. The aim of this integration is evaluating the performance of the silicon corresponding to Hotspot on a mask. It can use the mask shape of a large field, besides. The method adopts a metrology management system based on DBM (Design Based Metrology). This is the high accurate contouring created by an edge detection algorithm used in mask CD-SEM and silicon CD-SEM. Currently, as semiconductor manufacture moves towards even smaller feature size, this necessitates more aggressive optical proximity correction (OPC) to drive the super-resolution technology (RET). In other words, there is a trade-off between highly precise RET and mask manufacture, and this has a big impact on the semiconductor market that centers on the mask business. As an optimal solution to these issues, we provide a DFM solution that extracts 2-dimensional data for a more realistic and error-free simulation by reproducing accurately the contour of the actual mask, in addition to the simulation results from the mask data. On the other hand, there is roughness in the silicon form made from a mass-production line. Moreover, there is variation in the silicon form. For this reason, quantification of silicon form is important, in order to estimate the performance of a pattern. In order to quantify, the same form is equalized in two dimensions. And the method of evaluating based on the form is popular. In this study, we conducted experiments for averaging method of the pattern (Measurement Based Contouring) as two-dimensional mask and silicon evaluation technique. That is, observation of the identical position of a mask and a silicon was considered. The result proved its detection accuracy and reliability of variability on two-dimensional pattern (mask and silicon) and is adaptable to following fields of mask quality management. •Discrimination of nuisance defects for fine pattern. •Determination of two-dimensional variability of pattern. •Verification of the performance of the pattern of various kinds of Hotspots. In this report, we introduce the experimental results and the application. We expect that the mask measurement and the shape control on mask production will make a huge contribution to mask yield-enhancement and that the DFM solution for mask quality control process will become much more important technology than ever. It is very important to observe the form of the same location of Design, Mask, and Silicon in such a viewpoint. And we report it about algorithm of the image composition in Large Field.

New method of 2-dimensional metrology using mask contouring

NASA Astrophysics Data System (ADS)

Matsuoka, Ryoichi; Yamagata, Yoshikazu; Sugiyama, Akiyuki; Toyoda, Yasutaka

2008-10-01

We have developed a new method of accurately profiling and measuring of a mask shape by utilizing a Mask CD-SEM. The method is intended to realize high accuracy, stability and reproducibility of the Mask CD-SEM adopting an edge detection algorithm as the key technology used in CD-SEM for high accuracy CD measurement. In comparison with a conventional image processing method for contour profiling, this edge detection method is possible to create the profiles with much higher accuracy which is comparable with CD-SEM for semiconductor device CD measurement. This method realizes two-dimensional metrology for refined pattern that had been difficult to measure conventionally by utilizing high precision contour profile. In this report, we will introduce the algorithm in general, the experimental results and the application in practice. As shrinkage of design rule for semiconductor device has further advanced, an aggressive OPC (Optical Proximity Correction) is indispensable in RET (Resolution Enhancement Technology). From the view point of DFM (Design for Manufacturability), a dramatic increase of data processing cost for advanced MDP (Mask Data Preparation) for instance and surge of mask making cost have become a big concern to the device manufacturers. This is to say, demands for quality is becoming strenuous because of enormous quantity of data growth with increasing of refined pattern on photo mask manufacture. In the result, massive amount of simulated error occurs on mask inspection that causes lengthening of mask production and inspection period, cost increasing, and long delivery time. In a sense, it is a trade-off between the high accuracy RET and the mask production cost, while it gives a significant impact on the semiconductor market centered around the mask business. To cope with the problem, we propose the best method of a DFM solution using two-dimensional metrology for refined pattern.

A nonlinear dynamics approach for incorporating wind-speed patterns into wind-power project evaluation.

PubMed

Huffaker, Ray; Bittelli, Marco

2015-01-01

Wind-energy production may be expanded beyond regions with high-average wind speeds (such as the Midwest U.S.A.) to sites with lower-average speeds (such as the Southeast U.S.A.) by locating favorable regional matches between natural wind-speed and energy-demand patterns. A critical component of wind-power evaluation is to incorporate wind-speed dynamics reflecting documented diurnal and seasonal behavioral patterns. Conventional probabilistic approaches remove patterns from wind-speed data. These patterns must be restored synthetically before they can be matched with energy-demand patterns. How to accurately restore wind-speed patterns is a vexing problem spurring an expanding line of papers. We propose a paradigm shift in wind power evaluation that employs signal-detection and nonlinear-dynamics techniques to empirically diagnose whether synthetic pattern restoration can be avoided altogether. If the complex behavior of observed wind-speed records is due to nonlinear, low-dimensional, and deterministic system dynamics, then nonlinear dynamics techniques can reconstruct wind-speed dynamics from observed wind-speed data without recourse to conventional probabilistic approaches. In the first study of its kind, we test a nonlinear dynamics approach in an application to Sugarland Wind-the first utility-scale wind project proposed in Florida, USA. We find empirical evidence of a low-dimensional and nonlinear wind-speed attractor characterized by strong temporal patterns that match up well with regular daily and seasonal electricity demand patterns.

Thermal motion of a nonlinear localized pattern in a quasi-one-dimensional system.

PubMed

Dessup, Tommy; Coste, Christophe; Saint Jean, Michel

2016-07-01

We study the dynamics of localized nonlinear patterns in a quasi-one-dimensional many-particle system near a subcritical pitchfork bifurcation. The normal form at the bifurcation is given and we show that these patterns can be described as solitary-wave envelopes. They are stable in a large temperature range and can diffuse along the chain of interacting particles. During their displacements the particles are continually redistributed on the envelope. This change of particle location induces a small modulation of the potential energy of the system, with an amplitude that depends on the transverse confinement. At high temperature, this modulation is irrelevant and the thermal motion of the localized patterns displays all the characteristics of a free quasiparticle diffusion with a diffusion coefficient that may be deduced from the normal form. At low temperature, significant physical effects are induced by the modulated potential. In particular, the localized pattern may be trapped at very low temperature. We also exhibit a series of confinement values for which the modulation amplitudes vanishes. For these peculiar confinements, the mean-square displacement of the localized patterns also evidences free-diffusion behavior at low temperature.

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression

NASA Astrophysics Data System (ADS)

Ndiaye, Eugene; Fercoq, Olivier; Gramfort, Alexandre; Leclère, Vincent; Salmon, Joseph

2017-10-01

In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider ℓ 1 penalty to enforce sparsity in such scenarios. Sparsity enforcing methods, the Lasso being a canonical example, are popular candidates to address high dimension. For efficiency, they rely on tuning a parameter trading data fitting versus sparsity. For the Lasso theory to hold this tuning parameter should be proportional to the noise level, yet the latter is often unknown in practice. A possible remedy is to jointly optimize over the regression parameter as well as over the noise level. This has been considered under several names in the literature: Scaled-Lasso, Square-root Lasso, Concomitant Lasso estimation for instance, and could be of interest for uncertainty quantification. In this work, after illustrating numerical difficulties for the Concomitant Lasso formulation, we propose a modification we coined Smoothed Concomitant Lasso, aimed at increasing numerical stability. We propose an efficient and accurate solver leading to a computational cost no more expensive than the one for the Lasso. We leverage on standard ingredients behind the success of fast Lasso solvers: a coordinate descent algorithm, combined with safe screening rules to achieve speed efficiency, by eliminating early irrelevant features.

Regression Simulation of Turbine Engine Performance - Accuracy Improvement (TASK IV)

DTIC Science & Technology

1978-09-30

33 21 Generalized Form of the Regression Equation for the Optimized Polynomial Exponent M ethod...altitude, Mach number and power setting combinations were generated during the ARES evaluation. The orthogonal Latin Square selection procedure...pattern. In data generation , the low (L), mid (M), and high (H) values of a variable are not always the same. At some of the corner points where

Estimation of Phase in Fringe Projection Technique Using High-order Instantaneous Moments Based Method

NASA Astrophysics Data System (ADS)

Gorthi, Sai Siva; Rajshekhar, G.; Rastogi, Pramod

2010-04-01

For three-dimensional (3D) shape measurement using fringe projection techniques, the information about the 3D shape of an object is encoded in the phase of a recorded fringe pattern. The paper proposes a high-order instantaneous moments based method to estimate phase from a single fringe pattern in fringe projection. The proposed method works by approximating the phase as a piece-wise polynomial and subsequently determining the polynomial coefficients using high-order instantaneous moments to construct the polynomial phase. Simulation results are presented to show the method's potential.

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors

PubMed Central

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-01-01

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms. PMID:26198233

An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors.

PubMed

Luo, Liyan; Xu, Luping; Zhang, Hua

2015-07-07

In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.

Scalable patterning using laser-induced shock waves

NASA Astrophysics Data System (ADS)

Ilhom, Saidjafarzoda; Kholikov, Khomidkhodza; Li, Peizhen; Ottman, Claire; Sanford, Dylan; Thomas, Zachary; San, Omer; Karaca, Haluk E.; Er, Ali O.

2018-04-01

An advanced direct imprinting method with low cost, quick, and minimal environmental impact to create a thermally controllable surface pattern using the laser pulses is reported. Patterned microindents were generated on Ni50Ti50 shape memory alloys and aluminum using an Nd: YAG laser operating at 1064 nm combined with a suitable transparent overlay, a sacrificial layer of graphite, and copper grid. Laser pulses at different energy densities, which generate pressure pulses up to a few GPa on the surface, were focused through the confinement medium, ablating the copper grid to create plasma and transferring the grid pattern onto the surface. Scanning electron microscope and optical microscope images show that various patterns were obtained on the surface with high fidelity. One-dimensional profile analysis indicates that the depth of the patterned sample initially increases with the laser energy and later levels off. Our simulations of laser irradiation process also confirm that high temperature and high pressure could be generated when the laser energy density of 2 J/cm2 is used.

Fabrication of three dimensional patterns of wide dimensional range using microbes and their applications

NASA Astrophysics Data System (ADS)

Mehta, Sunita; Murugeson, Saravanan; Prakash, Balaji; Deepak

2015-10-01

Inspired by the wound healing property of certain trees, we report a novel microbes based additive process for producing three dimensional patterns, which has a potential of engineering applications in a variety of fields. Imposing a two dimensional pattern of microbes on a gel media and allowing them to grow in the third dimension is known from its use in biological studies. Instead, we have introduced an intermediate porous substrate between the gel media and the microbial growth, which enables three dimensional patterns in specific forms that can be lifted off and used in engineering applications. In order to demonstrate the applicability of this idea in a diverse set of areas, two applications are selected. In one, using this method of microbial growth, we have fabricated microlenses for enhanced light extraction in organic light emitting diodes, where densely packed microlenses of the diameters of hundreds of microns lead to luminance increase by a factor of 1.24X. In another entirely different type of application, braille text patterns are prepared on a normal office paper where the grown microbial colonies serve as braille tactile dots. Braille dot patterns thus prepared meet the standard specifications (size and spacing) for braille books.

Structural health monitoring feature design by genetic programming

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2014-09-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

Use of forecasting signatures to help distinguish periodicity, randomness, and chaos in ripples and other spatial patterns

USGS Publications Warehouse

Rubin, D.M.

1992-01-01

Forecasting of one-dimensional time series previously has been used to help distinguish periodicity, chaos, and noise. This paper presents two-dimensional generalizations for making such distinctions for spatial patterns. The techniques are evaluated using synthetic spatial patterns and then are applied to a natural example: ripples formed in sand by blowing wind. Tests with the synthetic patterns demonstrate that the forecasting techniques can be applied to two-dimensional spatial patterns, with the same utility and limitations as when applied to one-dimensional time series. One limitation is that some combinations of periodicity and randomness exhibit forecasting signatures that mimic those of chaos. For example, sine waves distorted with correlated phase noise have forecasting errors that increase with forecasting distance, errors that, are minimized using nonlinear models at moderate embedding dimensions, and forecasting properties that differ significantly between the original and surrogates. Ripples formed in sand by flowing air or water typically vary in geometry from one to another, even when formed in a flow that is uniform on a large scale; each ripple modifies the local flow or sand-transport field, thereby influencing the geometry of the next ripple downcurrent. Spatial forecasting was used to evaluate the hypothesis that such a deterministic process - rather than randomness or quasiperiodicity - is responsible for the variation between successive ripples. This hypothesis is supported by a forecasting error that increases with forecasting distance, a greater accuracy of nonlinear relative to linear models, and significant differences between forecasts made with the original ripples and those made with surrogate patterns. Forecasting signatures cannot be used to distinguish ripple geometry from sine waves with correlated phase noise, but this kind of structure can be ruled out by two geometric properties of the ripples: Successive ripples are highly correlated in wavelength, and ripple crests display dislocations such as branchings and mergers. ?? 1992 American Institute of Physics.

Chimera patterns in two-dimensional networks of coupled neurons.

PubMed

Schmidt, Alexander; Kasimatis, Theodoros; Hizanidis, Johanne; Provata, Astero; Hövel, Philipp

2017-03-01

We discuss synchronization patterns in networks of FitzHugh-Nagumo and leaky integrate-and-fire oscillators coupled in a two-dimensional toroidal geometry. A common feature between the two models is the presence of fast and slow dynamics, a typical characteristic of neurons. Earlier studies have demonstrated that both models when coupled nonlocally in one-dimensional ring networks produce chimera states for a large range of parameter values. In this study, we give evidence of a plethora of two-dimensional chimera patterns of various shapes, including spots, rings, stripes, and grids, observed in both models, as well as additional patterns found mainly in the FitzHugh-Nagumo system. Both systems exhibit multistability: For the same parameter values, different initial conditions give rise to different dynamical states. Transitions occur between various patterns when the parameters (coupling range, coupling strength, refractory period, and coupling phase) are varied. Many patterns observed in the two models follow similar rules. For example, the diameter of the rings grows linearly with the coupling radius.

Chimera patterns in two-dimensional networks of coupled neurons

NASA Astrophysics Data System (ADS)

Schmidt, Alexander; Kasimatis, Theodoros; Hizanidis, Johanne; Provata, Astero; Hövel, Philipp

2017-03-01

We discuss synchronization patterns in networks of FitzHugh-Nagumo and leaky integrate-and-fire oscillators coupled in a two-dimensional toroidal geometry. A common feature between the two models is the presence of fast and slow dynamics, a typical characteristic of neurons. Earlier studies have demonstrated that both models when coupled nonlocally in one-dimensional ring networks produce chimera states for a large range of parameter values. In this study, we give evidence of a plethora of two-dimensional chimera patterns of various shapes, including spots, rings, stripes, and grids, observed in both models, as well as additional patterns found mainly in the FitzHugh-Nagumo system. Both systems exhibit multistability: For the same parameter values, different initial conditions give rise to different dynamical states. Transitions occur between various patterns when the parameters (coupling range, coupling strength, refractory period, and coupling phase) are varied. Many patterns observed in the two models follow similar rules. For example, the diameter of the rings grows linearly with the coupling radius.

Spin flip statistics and spin wave interference patterns in Ising ferromagnetic films: A Monte Carlo study.

PubMed

Acharyya, Muktish

2017-07-01

The spin wave interference is studied in two dimensional Ising ferromagnet driven by two coherent spherical magnetic field waves by Monte Carlo simulation. The spin waves are found to propagate and interfere according to the classic rule of interference pattern generated by two point sources. The interference pattern of spin wave is observed in one boundary of the lattice. The interference pattern is detected and studied by spin flip statistics at high and low temperatures. The destructive interference is manifested as the large number of spin flips and vice versa.

On the convergence between PSY-5 domains and PID-5 domains and facets: implications for assessment of DSM-5 personality traits.

PubMed

Anderson, Jaime L; Sellbom, Martin; Bagby, R Michael; Quilty, Lena C; Veltri, Carlo O C; Markon, Kristian E; Krueger, Robert F

2013-06-01

The DSM-5 Personality and Personality Disorders workgroup and their consultants have developed the 220-item, self-report Personality Inventory for the DSM-5 (PID-5) for direct assessment of the proposed personality trait system for DSM-5; however, most practicing clinical psychologists will likely continue to rely on separate omnibus measures to index symptoms and traits associated with psychopathology. The Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) is one such measure and assesses the Personality Psychopathology Five (PSY-5) domains, which are conceptual cognates of the DSM-5 trait domains. The current study examined the associations between the MMPI-2-RF PSY-5 scales and the DSM-5 trait domains and facets indexed by the PID-5. A clear pattern of convergence was found indicating that each of the PSY-5 scales was most highly correlated with its conceptually expected PID-5 counterpart (rs = .44-.67; Mdn r = .53) and facet correlations generally showed the same pattern. Similarly, when each of the PSY-5 scales was regressed onto the PID-5 domains, the conceptually expected pattern of associations emerged even more clearly. Finally, a joint exploratory factor analysis with the PSY-5 and PID-5 trait facet scales indicated a five-factor solution that clearly resembled both of the PSY-5/DSM-5 trait domains. These results show clear evidence that the MMPI-2-RF has utility in the assessment of dimensional personality traits proposed for the upcoming DSM-5.

Statistical discrimination of footwear: a method for the comparison of accidentals on shoe outsoles inspired by facial recognition techniques.

PubMed

Petraco, Nicholas D K; Gambino, Carol; Kubic, Thomas A; Olivio, Dayhana; Petraco, Nicholas

2010-01-01

In the field of forensic footwear examination, it is a widely held belief that patterns of accidental marks found on footwear and footwear impressions possess a high degree of "uniqueness." This belief, however, has not been thoroughly studied in a numerical way using controlled experiments. As a result, this form of valuable physical evidence has been the subject of admissibility challenges. In this study, we apply statistical techniques used in facial pattern recognition, to a minimal set of information gleaned from accidental patterns. That is, in order to maximize the amount of potential similarity between patterns, we only use the coordinate locations of accidental marks (on the top portion of a footwear impression) to characterize the entire pattern. This allows us to numerically gauge how similar two patterns are to one another in a worst-case scenario, i.e., in the absence of a tremendous amount of information normally available to the footwear examiner such as accidental mark size and shape. The patterns were recorded from the top portion of the shoe soles (i.e., not the heel) of five shoe pairs. All shoes were the same make and model and all were worn by the same person for a period of 30 days. We found that in 20-30 dimensional principal component (PC) space (99.5% variance retained), patterns from the same shoe, even at different points in time, tended to cluster closer to each other than patterns from different shoes. Correct shoe identification rates using maximum likelihood linear classification analysis and the hold-one-out procedure ranged from 81% to 100%. Although low in variance, three-dimensional PC plots were made and generally corroborated the findings in the much higher dimensional PC-space. This study is intended to be a starting point for future research to build statistical models on the formation and evolution of accidental patterns.

Optimized stereo matching in binocular three-dimensional measurement system using structured light.

PubMed

Liu, Kun; Zhou, Changhe; Wei, Shengbin; Wang, Shaoqing; Fan, Xin; Ma, Jianyong

2014-09-10

In this paper, we develop an optimized stereo-matching method used in an active binocular three-dimensional measurement system. A traditional dense stereo-matching algorithm is time consuming due to a long search range and the high complexity of a similarity evaluation. We project a binary fringe pattern in combination with a series of N binary band limited patterns. In order to prune the search range, we execute an initial matching before exhaustive matching and evaluate a similarity measure using logical comparison instead of a complicated floating-point operation. Finally, an accurate point cloud can be obtained by triangulation methods and subpixel interpolation. The experiment results verify the computational efficiency and matching accuracy of the method.

High-resolution three-dimensional partially coherent diffraction imaging.

PubMed

Clark, J N; Huang, X; Harder, R; Robinson, I K

2012-01-01

The wave properties of light, particularly its coherence, are responsible for interference effects, which can be exploited in powerful imaging applications. Coherent diffractive imaging relies heavily on coherence and has recently experienced rapid growth. Coherent diffractive imaging recovers an object from its diffraction pattern by computational phasing with the potential of wavelength-limited resolution. Diminished coherence results in reconstructions that suffer from artefacts or fail completely. Here we demonstrate ab initio phasing of partially coherent diffraction patterns in three dimensions, while simultaneously determining the coherence properties of the illuminating wavefield. Both the dramatic improvements in image interpretability and the three-dimensional evaluation of the coherence will have broad implications for quantitative imaging of nanostructures and wavefield characterization with X-rays and electrons.

Combustion of solid fuel slabs with gaseous oxygen in a hybrid motor analog

NASA Technical Reports Server (NTRS)

Chiaverini, Martin J.; Harting, George C.; Lu, Yeu-Cherng; Kuo, Kenneth K.; Serin, Nadir; Johnson, David K.

1995-01-01

Using a high-pressure, two-dimensional hybrid motor, an experimental investigation was conducted on fundamental processes involved in hybrid rocket combustion. HTPB (Hydroxyl-terminated Polybutadiene) fuel cross-linked with diisocyanate was burned with gaseous oxygen (GOX) under various operating conditions. Large-amplitude pressure oscillations were encountered in earlier test runs. After identifying the source of instability and decoupling the GOX feed-line system and combustion chamber, the pressure oscillations were drastically reduced from plus or minus 20% of the localized mean pressure to an acceptable range of plus or minus 1.5%. Embedded fine--wire thermocouples indicated that the surface temperature of the burning fuel was around 1000 K depending upon axial locations and operating conditions. Also, except near the leading edge region, the subsurface thermal wave profiles in the upstream locations are thicker than those in the downstream locations since the solid-fuel regression rate, in general, increases with distance along the fuel slab. The recovered solid fuel slabs in the laminar portion of the boundary layer exhibited smooth surfaces, indicating the existence of a liquid melt layer on the burning fuel surface in the upstream region. After the transition section, which displayed distinct transverse striations, the surface roughness pattern became quite random and very pronounced in the downstream turbulent boundary-layer region. Both real-time X-ray radiography and ultrasonic pulse echo techniques were used to determine the instantaneous web thicknesses and instantaneous solid-fuel regression rates over certain portions of the fuel slabs. Globally averaged and axially dependent but time-averaged regression rates were also obtained and presented. Several tests were conducted using, simultaneously, one translucent fuel slab and one fuel slab processed with carbon black powder. The addition of carbon black did not affect the measured regression rates or surface temperatures in comparison to the translucent fuel slabs.

Classification of 2-dimensional array patterns: assembling many small neural networks is better than using a large one.

PubMed

Chen, Liang; Xue, Wei; Tokuda, Naoyuki

2010-08-01

In many pattern classification/recognition applications of artificial neural networks, an object to be classified is represented by a fixed sized 2-dimensional array of uniform type, which corresponds to the cells of a 2-dimensional grid of the same size. A general neural network structure, called an undistricted neural network, which takes all the elements in the array as inputs could be used for problems such as these. However, a districted neural network can be used to reduce the training complexity. A districted neural network usually consists of two levels of sub-neural networks. Each of the lower level neural networks, called a regional sub-neural network, takes the elements in a region of the array as its inputs and is expected to output a temporary class label, called an individual opinion, based on the partial information of the entire array. The higher level neural network, called an assembling sub-neural network, uses the outputs (opinions) of regional sub-neural networks as inputs, and by consensus derives the label decision for the object. Each of the sub-neural networks can be trained separately and thus the training is less expensive. The regional sub-neural networks can be trained and performed in parallel and independently, therefore a high speed can be achieved. We prove theoretically in this paper, using a simple model, that a districted neural network is actually more stable than an undistricted neural network in noisy environments. We conjecture that the result is valid for all neural networks. This theory is verified by experiments involving gender classification and human face recognition. We conclude that a districted neural network is highly recommended for neural network applications in recognition or classification of 2-dimensional array patterns in highly noisy environments. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

A High-Dimensional, Multivariate Copula Approach to Modeling Multivariate Agricultural Price Relationships and Tail Dependencies

Treesearch

Xuan Chi; Barry Goodwin

2012-01-01

Spatial and temporal relationships among agricultural prices have been an important topic of applied research for many years. Such research is used to investigate the performance of markets and to examine linkages up and down the marketing chain. This research has empirically evaluated price linkages by using correlation and regression models and, later, linear and...

Burning rate for steel-cased, pressed binderless HMX

NASA Technical Reports Server (NTRS)

Fifer, R. A.; Cole, J. E.

1980-01-01

The burning behavior of pressed binderless HMX laterally confined in 6.4 mm i.d. steel cases was measured over the pressure range 1.45 to 338 MPa in a constant pressure strand burner. The measured regression rates are compared to those reported previously for unconfined samples. It is shown that lateral confinement results in a several-fold decrease in the regression rate for the coarse particle size HMX above the transition to super fast regression. For class E samples, confinement shifts the transition to super fast regression from low pressure to high pressure. These results are interpreted in terms of the previously proposed progressive deconsolidation mechanism. Preliminary holographic photography and closed bomb tests are also described. Theoretical one dimensional modeling calculations were carried out to predict the expected flame height (particle burn out distance) as a function of particle size and pressure for binderless HMX burning by a progressive deconsolidation mechanism.

Majorization Minimization by Coordinate Descent for Concave Penalized Generalized Linear Models

PubMed Central

Jiang, Dingfeng; Huang, Jian

2013-01-01

Recent studies have demonstrated theoretical attractiveness of a class of concave penalties in variable selection, including the smoothly clipped absolute deviation and minimax concave penalties. The computation of the concave penalized solutions in high-dimensional models, however, is a difficult task. We propose a majorization minimization by coordinate descent (MMCD) algorithm for computing the concave penalized solutions in generalized linear models. In contrast to the existing algorithms that use local quadratic or local linear approximation to the penalty function, the MMCD seeks to majorize the negative log-likelihood by a quadratic loss, but does not use any approximation to the penalty. This strategy makes it possible to avoid the computation of a scaling factor in each update of the solutions, which improves the efficiency of coordinate descent. Under certain regularity conditions, we establish theoretical convergence property of the MMCD. We implement this algorithm for a penalized logistic regression model using the SCAD and MCP penalties. Simulation studies and a data example demonstrate that the MMCD works sufficiently fast for the penalized logistic regression in high-dimensional settings where the number of covariates is much larger than the sample size. PMID:25309048

Subjective figure reversal in two- and three-dimensional perceptual space.

PubMed

Radilová, J; Radil-Weiss, T

1984-08-01

A permanently illuminated pattern of Mach's truncated pyramid can be perceived according to the experimental instruction given, either as a three-dimensional reversible figure with spontaneously changing convex and concave interpretation (in one experiment), or as a two-dimensional reversible figure-ground pattern (in another experiment). The reversal rate was about twice as slow, without the subjects being aware of it, if it was perceived as a three-dimensional figure compared to the situation when it was perceived as two-dimensional. It may be hypothetized that in the three-dimensional case, the process of perception requires more sequential steps than in the two-dimensional one.

Three-dimensional plant architecture and sunlit-shaded patterns: a stochastic model of light dynamics in canopies.

PubMed

Retkute, Renata; Townsend, Alexandra J; Murchie, Erik H; Jensen, Oliver E; Preston, Simon P

2018-05-25

Diurnal changes in solar position and intensity combined with the structural complexity of plant architecture result in highly variable and dynamic light patterns within the plant canopy. This affects productivity through the complex ways that photosynthesis responds to changes in light intensity. Current methods to characterize light dynamics, such as ray-tracing, are able to produce data with excellent spatio-temporal resolution but are computationally intensive and the resulting data are complex and high-dimensional. This necessitates development of more economical models for summarizing the data and for simulating realistic light patterns over the course of a day. High-resolution reconstructions of field-grown plants are assembled in various configurations to form canopies, and a forward ray-tracing algorithm is applied to the canopies to compute light dynamics at high (1 min) temporal resolution. From the ray-tracer output, the sunlit or shaded state for each patch on the plants is determined, and these data are used to develop a novel stochastic model for the sunlit-shaded patterns. The model is designed to be straightforward to fit to data using maximum likelihood estimation, and fast to simulate from. For a wide range of contrasting 3-D canopies, the stochastic model is able to summarize, and replicate in simulations, key features of the light dynamics. When light patterns simulated from the stochastic model are used as input to a model of photoinhibition, the predicted reduction in carbon gain is similar to that from calculations based on the (extremely costly) ray-tracer data. The model provides a way to summarize highly complex data in a small number of parameters, and a cost-effective way to simulate realistic light patterns. Simulations from the model will be particularly useful for feeding into larger-scale photosynthesis models for calculating how light dynamics affects the photosynthetic productivity of canopies.

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

PubMed

Estelles-Lopez, Lucia; Ropodi, Athina; Pavlidis, Dimitris; Fotopoulou, Jenny; Gkousari, Christina; Peyrodie, Audrey; Panagou, Efstathios; Nychas, George-John; Mohareb, Fady

2017-09-01

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. Copyright © 2017 Elsevier Ltd. All rights reserved.

Control of the ZnO nanowires nucleation site using microfluidic channels.

PubMed

Lee, Sang Hyun; Lee, Hyun Jung; Oh, Dongcheol; Lee, Seog Woo; Goto, Hiroki; Buckmaster, Ryan; Yasukawa, Tomoyuki; Matsue, Tomokazu; Hong, Soon-Ku; Ko, HyunChul; Cho, Meoung-Whan; Yao, Takafumi

2006-03-09

We report on the growth of uniquely shaped ZnO nanowires with high surface area and patterned over large areas by using a poly(dimethylsiloxane) (PDMS) microfluidic channel technique. The synthesis uses first a patterned seed template fabricated by zinc acetate solution flowing though a microfluidic channel and then growth of ZnO nanowire at the seed using thermal chemical vapor deposition on a silicon substrate. Variations the ZnO nanowire by seed pattern formed within the microfluidic channel were also observed for different substrates and concentrations of the zinc acetate solution. The photocurrent properties of the patterned ZnO nanowires with high surface area, due to their unique shape, were also investigated. These specialized shapes and patterning technique increase the possibility of realizing one-dimensional nanostructure devices such as sensors and optoelectric devices.

Enhancement of partial robust M-regression (PRM) performance using Bisquare weight function

NASA Astrophysics Data System (ADS)

Mohamad, Mazni; Ramli, Norazan Mohamed; Ghani@Mamat, Nor Azura Md; Ahmad, Sanizah

2014-09-01

Partial Least Squares (PLS) regression is a popular regression technique for handling multicollinearity in low and high dimensional data which fits a linear relationship between sets of explanatory and response variables. Several robust PLS methods are proposed to accommodate the classical PLS algorithms which are easily affected with the presence of outliers. The recent one was called partial robust M-regression (PRM). Unfortunately, the use of monotonous weighting function in the PRM algorithm fails to assign appropriate and proper weights to large outliers according to their severity. Thus, in this paper, a modified partial robust M-regression is introduced to enhance the performance of the original PRM. A re-descending weight function, known as Bisquare weight function is recommended to replace the fair function in the PRM. A simulation study is done to assess the performance of the modified PRM and its efficiency is also tested in both contaminated and uncontaminated simulated data under various percentages of outliers, sample sizes and number of predictors.

Sparse partial least squares regression for simultaneous dimension reduction and variable selection

PubMed Central

Chun, Hyonho; Keleş, Sündüz

2010-01-01

Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data. PMID:20107611

Coherent diffraction imaging: consistency of the assembled three-dimensional distribution.

PubMed

Tegze, Miklós; Bortel, Gábor

2016-07-01

The short pulses of X-ray free-electron lasers can produce diffraction patterns with structural information before radiation damage destroys the particle. From the recorded diffraction patterns the structure of particles or molecules can be determined on the nano- or even atomic scale. In a coherent diffraction imaging experiment thousands of diffraction patterns of identical particles are recorded and assembled into a three-dimensional distribution which is subsequently used to solve the structure of the particle. It is essential to know, but not always obvious, that the assembled three-dimensional reciprocal-space intensity distribution is really consistent with the measured diffraction patterns. This paper shows that, with the use of correlation maps and a single parameter calculated from them, the consistency of the three-dimensional distribution can be reliably validated.

Reflection high energy electron diffraction observation of surface mass transport at the two- to three-dimensional growth transition of InAs on GaAs(001)

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

Patella, F.; Arciprete, F.; Fanfoni, M.

2005-12-19

We have followed by reflection high-energy electron diffraction the nucleation of InAs quantum dots on GaAs(001), grown by molecular-beam epitaxy with growth interruptions. Surface mass transport gives rise, at the critical InAs thickness, to a huge nucleation of three-dimensional islands within 0.2 monolayers (ML). Such surface mass diffusion has been evidenced by observing the transition of the reflection high-energy electron diffraction pattern from two- to three-dimensional during the growth interruption after the deposition of 1.59 ML of InAs. It is suggested that the process is driven by the As{sub 2} adsorption-desorption process and by the lowering of the In bindingmore » energy due to compressive strain. The last condition is met first in the region surrounding dots at step edges where nucleation predominantly occurs.« less

Three-dimensional fine structure of the organization of microtubules in neurite varicosities by ultra-high voltage electron microscope tomography.

PubMed

Nishida, Tomoki; Yoshimura, Ryoichi; Endo, Yasuhisa

2017-09-01

Neurite varicosities are highly specialized compartments that are involved in neurotransmitter/ neuromodulator release and provide a physiological platform for neural functions. However, it remains unclear how microtubule organization contributes to the form of varicosity. Here, we examine the three-dimensional structure of microtubules in varicosities of a differentiated PC12 neural cell line using ultra-high voltage electron microscope tomography. Three-dimensional imaging showed that a part of the varicosities contained an accumulation of organelles that were separated from parallel microtubule arrays. Further detailed analysis using serial sections and whole-mount tomography revealed microtubules running in a spindle shape of swelling in some other types of varicosities. These electron tomographic results showed that the structural diversity and heterogeneity of microtubule organization supported the form of varicosities, suggesting that a different distribution pattern of microtubules in varicosities is crucial to the regulation of varicosities development.

Using Betweenness Centrality to Identify Manifold Shortcuts

PubMed Central

Cukierski, William J.; Foran, David J.

2010-01-01

High-dimensional data presents a challenge to tasks of pattern recognition and machine learning. Dimensionality reduction (DR) methods remove the unwanted variance and make these tasks tractable. Several nonlinear DR methods, such as the well known ISOMAP algorithm, rely on a neighborhood graph to compute geodesic distances between data points. These graphs can contain unwanted edges which connect disparate regions of one or more manifolds. This topological sensitivity is well known [1], [2], [3], yet handling high-dimensional, noisy data in the absence of a priori manifold knowledge, remains an open and difficult problem. This work introduces a divisive, edge-removal method based on graph betweenness centrality which can robustly identify manifold-shorting edges. The problem of graph construction in high dimension is discussed and the proposed algorithm is fit into the ISOMAP workflow. ROC analysis is performed and the performance is tested on synthetic and real datasets. PMID:20607142

Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

PubMed Central

Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

2016-01-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering

PubMed Central

2013-01-01

Background The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development. Results In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space. Conclusions Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship. PMID:23845024

Hot Electrons Regain Coherence in Semiconducting Nanowires

NASA Astrophysics Data System (ADS)

Reiner, Jonathan; Nayak, Abhay Kumar; Avraham, Nurit; Norris, Andrew; Yan, Binghai; Fulga, Ion Cosma; Kang, Jung-Hyun; Karzig, Toesten; Shtrikman, Hadas; Beidenkopf, Haim

2017-04-01

The higher the energy of a particle is above equilibrium, the faster it relaxes because of the growing phase space of available electronic states it can interact with. In the relaxation process, phase coherence is lost, thus limiting high-energy quantum control and manipulation. In one-dimensional systems, high relaxation rates are expected to destabilize electronic quasiparticles. Here, we show that the decoherence induced by relaxation of hot electrons in one-dimensional semiconducting nanowires evolves nonmonotonically with energy such that above a certain threshold hot electrons regain stability with increasing energy. We directly observe this phenomenon by visualizing, for the first time, the interference patterns of the quasi-one-dimensional electrons using scanning tunneling microscopy. We visualize the phase coherence length of the one-dimensional electrons, as well as their phase coherence time, captured by crystallographic Fabry-Pèrot resonators. A remarkable agreement with a theoretical model reveals that the nonmonotonic behavior is driven by the unique manner in which one-dimensional hot electrons interact with the cold electrons occupying the Fermi sea. This newly discovered relaxation profile suggests a high-energy regime for operating quantum applications that necessitate extended coherence or long thermalization times, and may stabilize electronic quasiparticles in one dimension.

Surface-Sensitive Microwear Texture Analysis of Attrition and Erosion.

PubMed

Ranjitkar, S; Turan, A; Mann, C; Gully, G A; Marsman, M; Edwards, S; Kaidonis, J A; Hall, C; Lekkas, D; Wetselaar, P; Brook, A H; Lobbezoo, F; Townsend, G C

2017-03-01

Scale-sensitive fractal analysis of high-resolution 3-dimensional surface reconstructions of wear patterns has advanced our knowledge in evolutionary biology, and has opened up opportunities for translatory applications in clinical practice. To elucidate the microwear characteristics of attrition and erosion in worn natural teeth, we scanned 50 extracted human teeth using a confocal profiler at a high optical resolution (X-Y, 0.17 µm; Z < 3 nm). Our hypothesis was that microwear complexity would be greater in erosion and that anisotropy would be greater in attrition. The teeth were divided into 4 groups, including 2 wear types (attrition and erosion) and 2 locations (anterior and posterior teeth; n = 12 for each anterior group, n = 13 for each posterior group) for 2 tissue types (enamel and dentine). The raw 3-dimensional data cloud was subjected to a newly developed rigorous standardization technique to reduce interscanner variability as well as to filter anomalous scanning data. Linear mixed effects (regression) analyses conducted separately for the dependent variables, complexity and anisotropy, showed the following effects of the independent variables: significant interactions between wear type and tissue type ( P = 0.0157 and P = 0.0003, respectively) and significant effects of location ( P < 0.0001 and P = 0.0035, respectively). There were significant associations between complexity and anisotropy when the dependent variable was either complexity ( P = 0.0003) or anisotropy ( P = 0.0014). Our findings of greater complexity in erosion and greater anisotropy in attrition confirm our hypothesis. The greatest geometric means were noted in dentine erosion for complexity and dentine attrition for anisotropy. Dentine also exhibited microwear characteristics that were more consistent with wear types than enamel. Overall, our findings could complement macrowear assessment in dental clinical practice and research and could assist in the early detection and management of pathologic tooth wear.

Interpret with caution: multicollinearity in multiple regression of cognitive data.

PubMed

Morrison, Catriona M

2003-08-01

Shibihara and Kondo in 2002 reported a reanalysis of the 1997 Kanji picture-naming data of Yamazaki, Ellis, Morrison, and Lambon-Ralph in which independent variables were highly correlated. Their addition of the variable visual familiarity altered the previously reported pattern of results, indicating that visual familiarity, but not age of acquisition, was important in predicting Kanji naming speed. The present paper argues that caution should be taken when drawing conclusions from multiple regression analyses in which the independent variables are so highly correlated, as such multicollinearity can lead to unreliable output.

Implementation of pattern generation algorithm in forming Gilmore and Gomory model for two dimensional cutting stock problem

NASA Astrophysics Data System (ADS)

Octarina, Sisca; Radiana, Mutia; Bangun, Putra B. J.

2018-01-01

Two dimensional cutting stock problem (CSP) is a problem in determining the cutting pattern from a set of stock with standard length and width to fulfill the demand of items. Cutting patterns were determined in order to minimize the usage of stock. This research implemented pattern generation algorithm to formulate Gilmore and Gomory model of two dimensional CSP. The constraints of Gilmore and Gomory model was performed to assure the strips which cut in the first stage will be used in the second stage. Branch and Cut method was used to obtain the optimal solution. Based on the results, it found many patterns combination, if the optimal cutting patterns which correspond to the first stage were combined with the second stage.

Reduction of shock induced noise in imperfectly expanded supersonic jets using convex optimization

NASA Astrophysics Data System (ADS)

Adhikari, Sam

2007-11-01

Imperfectly expanded jets generate screech noise. The imbalance between the backpressure and the exit pressure of the imperfectly expanded jets produce shock cells and expansion or compression waves from the nozzle. The instability waves and the shock cells interact to generate the screech sound. The mathematical model consists of cylindrical coordinate based full Navier-Stokes equations and large-eddy-simulation turbulence modeling. Analytical and computational analysis of the three-dimensional helical effects provide a model that relates several parameters with shock cell patterns, screech frequency and distribution of shock generation locations. Convex optimization techniques minimize the shock cell patterns and the instability waves. The objective functions are (convex) quadratic and the constraint functions are affine. In the quadratic optimization programs, minimization of the quadratic functions over a set of polyhedrons provides the optimal result. Various industry standard methods like regression analysis, distance between polyhedra, bounding variance, Markowitz optimization, and second order cone programming is used for Quadratic Optimization.

High adherence to the Western, Prudent, and Mediterranean dietary patterns and risk of gastric adenocarcinoma: MCC-Spain study.

PubMed

Castelló, Adela; Fernández de Larrea, Nerea; Martín, Vicente; Dávila-Batista, Verónica; Boldo, Elena; Guevara, Marcela; Moreno, Víctor; Castaño-Vinyals, Gemma; Gómez-Acebo, Inés; Fernández-Tardón, Guillermo; Peiró, Rosana; Olmedo-Requena, Rocío; Capelo, Rocio; Navarro, Carmen; Pacho-Valbuena, Silvino; Pérez-Gómez, Beatriz; Kogevinas, Manolis; Pollán, Marina; Aragonés, Nuria

2018-05-01

The influence of dietary habits on the development of gastric adenocarcinoma is not clear. The objective of the present study was to explore the association of three previously identified dietary patterns with gastric adenocarcinoma by sex, age, cancer site, and morphology. MCC-Spain is a multicase-control study that included 295 incident cases of gastric adenocarcinoma and 3040 controls. The association of the Western, Prudent, and Mediterranean dietary patterns-derived in another Spanish case-control study-with gastric adenocarcinoma was assessed using multivariable logistic regression models with random province-specific intercepts and considering a possible interaction with sex and age. Risk according to tumor site (cardia, non-cardia) and morphology (intestinal/diffuse) was evaluated using multinomial regression models. A high adherence to the Western pattern increased gastric adenocarcinoma risk [odds ratio fourth_vs._first_quartile (95% confidence interval), 2.09 (1.31; 3.33)] even at low levels [odds ratio second_vs._first_quartile (95% confidence interval), 1.63 (1.05; 2.52)]. High adherence to the Mediterranean dietary pattern could prevent gastric adenocarcinoma [odds ratio fourth_vs._first_quartile (95% confidence interval), 0.53 (0.34; 0.82)]. Although no significant heterogeneity of effects was observed, the harmful effect of the Western pattern was stronger among older participants and for non-cardia adenocarcinomas, whereas the protective effect of the Mediterranean pattern was only observed among younger participants and for non-cardia tumors. Decreasing the consumption of fatty and sugary products and of red and processed meat in favor of an increase in the intake of fruits, vegetables, legumes, olive oil, nuts, and fish might prevent gastric adenocarcinoma.

Fabrication of 3D nano-structures using reverse imprint lithography

NASA Astrophysics Data System (ADS)

Han, Kang-Soo; Hong, Sung-Hoon; Kim, Kang-In; Cho, Joong-Yeon; Choi, Kyung-woo; Lee, Heon

2013-02-01

In spite of the fact that the fabrication process of three-dimensional nano-structures is complicated and expensive, it can be applied to a range of devices to increase their efficiency and sensitivity. Simple and inexpensive fabrication of three-dimensional nano-structures is necessary. In this study, reverse imprint lithography (RIL) with UV-curable benzylmethacrylate, methacryloxypropyl terminated poly-dimethylsiloxane (M-PDMS) resin and ZnO-nano-particle-dispersed resin was used to fabricate three-dimensional nano-structures. UV-curable resins were placed between a silicon stamp and a PVA transfer template, followed by a UV curing process. Then, the silicon stamp was detached and a 2D pattern layer was transferred to the substrate using diluted UV-curable glue. Consequently, three-dimensional nano-structures were formed by stacking the two-dimensional nano-patterned layers. RIL was applied to a light-emitting diode (LED) to evaluate the optical effects of a nano-patterned layer. As a result, the light extraction of the patterned LED was increased by about 12% compared to an unpatterned LED.

Fabrication of 3D nano-structures using reverse imprint lithography.

PubMed

Han, Kang-Soo; Hong, Sung-Hoon; Kim, Kang-In; Cho, Joong-Yeon; Choi, Kyung-Woo; Lee, Heon

2013-02-01

In spite of the fact that the fabrication process of three-dimensional nano-structures is complicated and expensive, it can be applied to a range of devices to increase their efficiency and sensitivity. Simple and inexpensive fabrication of three-dimensional nano-structures is necessary. In this study, reverse imprint lithography (RIL) with UV-curable benzylmethacrylate, methacryloxypropyl terminated poly-dimethylsiloxane (M-PDMS) resin and ZnO-nano-particle-dispersed resin was used to fabricate three-dimensional nano-structures.UV-curable resins were placed between a silicon stamp and a PVA transfer template, followed by a UV curing process. Then, the silicon stamp was detached and a 2D pattern layer was transferred to the substrate using diluted UV-curable glue. Consequently, three-dimensional nano-structures were formed by stacking the two-dimensional nano-patterned layers. RIL was applied to a light-emitting diode (LED) to evaluate the optical effects of a nano-patterned layer. As a result, the light extraction of the patterned LED was increased by about 12% compared to an unpatterned LED.

Dietary patterns derived with multiple methods from food diaries and breast cancer risk in the UK Dietary Cohort Consortium

PubMed Central

Pot, Gerda K; Stephen, Alison M; Dahm, Christina C; Key, Timothy J; Cairns, Benjamin J; Burley, Victoria J; Cade, Janet E; Greenwood, Darren C; Keogh, Ruth H; Bhaniani, Amit; McTaggart, Alison; Lentjes, Marleen AH; Mishra, Gita; Brunner, Eric J; Khaw, Kay Tee

2015-01-01

Background/ Objectives In spite of several studies relating dietary patterns to breast cancer risk, evidence so far remains inconsistent. This study aimed to investigate associations of dietary patterns derived with three different methods with breast cancer risk. Subjects/ Methods The Mediterranean Diet Score (MDS), principal components analyses (PCA) and reduced rank regression (RRR) were used to derive dietary patterns in a case-control study of 610 breast cancer cases and 1891 matched controls within 4 UK cohort studies. Dietary intakes were collected prospectively using 4-to 7-day food diaries and resulting food consumption data were grouped into 42 food groups. Conditional logistic regression models were used to estimate odds ratios (ORs) for associations between pattern scores and breast cancer risk adjusting for relevant covariates. A separate model was fitted for post-menopausal women only. Results The MDS was not associated with breast cancer risk (OR comparing 1st tertile with 3rd 1.20 (95% CI 0.92; 1.56)), nor the first PCA-derived dietary pattern, explaining 2.7% of variation of diet and characterized by cheese, crisps and savoury snacks, legumes, nuts and seeds (OR 1.18 (95% CI 0.91; 1.53)). The first RRR-derived pattern, a ‘high-alcohol’ pattern, was associated with a higher risk of breast cancer (OR 1.27; 95% CI 1.00; 1.62), which was most pronounced in post-menopausal women (OR 1.46 (95% CI 1.08; 1.98). Conclusions A ‘high-alcohol’ dietary pattern derived with RRR was associated with an increased breast cancer risk; no evidence of associations of other dietary patterns with breast cancer risk was observed in this study. PMID:25052230

Fabrication of three dimensional patterns of wide dimensional range using microbes and their applications

PubMed Central

Mehta, Sunita; Murugeson, Saravanan; Prakash, Balaji; Deepak

2015-01-01

Inspired by the wound healing property of certain trees, we report a novel microbes based additive process for producing three dimensional patterns, which has a potential of engineering applications in a variety of fields. Imposing a two dimensional pattern of microbes on a gel media and allowing them to grow in the third dimension is known from its use in biological studies. Instead, we have introduced an intermediate porous substrate between the gel media and the microbial growth, which enables three dimensional patterns in specific forms that can be lifted off and used in engineering applications. In order to demonstrate the applicability of this idea in a diverse set of areas, two applications are selected. In one, using this method of microbial growth, we have fabricated microlenses for enhanced light extraction in organic light emitting diodes, where densely packed microlenses of the diameters of hundreds of microns lead to luminance increase by a factor of 1.24X. In another entirely different type of application, braille text patterns are prepared on a normal office paper where the grown microbial colonies serve as braille tactile dots. Braille dot patterns thus prepared meet the standard specifications (size and spacing) for braille books. PMID:26486847

A Selective Review of Group Selection in High-Dimensional Models

PubMed Central

Huang, Jian; Breheny, Patrick; Ma, Shuangge

2013-01-01

Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study. PMID:24174707

Learning structure-property relationship in crystalline materials: A study of lanthanide-transition metal alloys

NASA Astrophysics Data System (ADS)

Pham, Tien-Lam; Nguyen, Nguyen-Duong; Nguyen, Van-Doan; Kino, Hiori; Miyake, Takashi; Dam, Hieu-Chi

2018-05-01

We have developed a descriptor named Orbital Field Matrix (OFM) for representing material structures in datasets of multi-element materials. The descriptor is based on the information regarding atomic valence shell electrons and their coordination. In this work, we develop an extension of OFM called OFM1. We have shown that these descriptors are highly applicable in predicting the physical properties of materials and in providing insights on the materials space by mapping into a low embedded dimensional space. Our experiments with transition metal/lanthanide metal alloys show that the local magnetic moments and formation energies can be accurately reproduced using simple nearest-neighbor regression, thus confirming the relevance of our descriptors. Using kernel ridge regressions, we could accurately reproduce formation energies and local magnetic moments calculated based on first-principles, with mean absolute errors of 0.03 μB and 0.10 eV/atom, respectively. We show that meaningful low-dimensional representations can be extracted from the original descriptor using descriptive learning algorithms. Intuitive prehension on the materials space, qualitative evaluation on the similarities in local structures or crystalline materials, and inference in the designing of new materials by element substitution can be performed effectively based on these low-dimensional representations.

Helium beam shadowing for high spatial resolution patterning of antibodies on microstructured diagnostic surfaces

PubMed Central

Cacao, Eliedonna; Sherlock, Tim; Nasrullah, Azeem; Kemper, Steven; Knoop, Jennifer; Kourentzi, Katerina; Ruchhoeft, Paul; Stein, Gila E; Atmar, Robert L; Willson, Richard C

2013-01-01

Abstract We have developed a technique for the high-resolution, self-aligning, and high-throughput patterning of antibody binding functionality on surfaces by selectively changing the reactivity of protein-coated surfaces in specific regions of a workpiece with a beam of energetic helium particles. The exposed areas are passivated with bovine serum albumin (BSA) and no longer bind the antigen. We demonstrate that patterns can be formed (1) by using a stencil mask with etched openings that forms a patterned exposure, or (2) by using angled exposure to cast shadows of existing raised microstructures on the surface to form self-aligned patterns. We demonstrate the efficacy of this process through the patterning of anti-lysozyme, anti-Norwalk virus, and anti-Escherichia coli antibodies and the subsequent detection of each of their targets by the enzyme-mediated formation of colored or silver deposits, and also by binding of gold nanoparticles. The process allows for the patterning of three-dimensional structures by inclining the sample relative to the beam so that the shadowed regions remain unaltered. We demonstrate that the resolution of the patterning process is of the order of hundreds of nanometers, and that the approach is well-suited for high throughput patterning. PMID:24706125

Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data.

PubMed

Sun, Hokeun; Wang, Shuang

2013-05-30

The matched case-control designs are commonly used to control for potential confounding factors in genetic epidemiology studies especially epigenetic studies with DNA methylation. Compared with unmatched case-control studies with high-dimensional genomic or epigenetic data, there have been few variable selection methods for matched sets. In an earlier paper, we proposed the penalized logistic regression model for the analysis of unmatched DNA methylation data using a network-based penalty. However, for popularly applied matched designs in epigenetic studies that compare DNA methylation between tumor and adjacent non-tumor tissues or between pre-treatment and post-treatment conditions, applying ordinary logistic regression ignoring matching is known to bring serious bias in estimation. In this paper, we developed a penalized conditional logistic model using the network-based penalty that encourages a grouping effect of (1) linked Cytosine-phosphate-Guanine (CpG) sites within a gene or (2) linked genes within a genetic pathway for analysis of matched DNA methylation data. In our simulation studies, we demonstrated the superiority of using conditional logistic model over unconditional logistic model in high-dimensional variable selection problems for matched case-control data. We further investigated the benefits of utilizing biological group or graph information for matched case-control data. We applied the proposed method to a genome-wide DNA methylation study on hepatocellular carcinoma (HCC) where we investigated the DNA methylation levels of tumor and adjacent non-tumor tissues from HCC patients by using the Illumina Infinium HumanMethylation27 Beadchip. Several new CpG sites and genes known to be related to HCC were identified but were missed by the standard method in the original paper. Copyright © 2012 John Wiley & Sons, Ltd.

Topologically Micropatterned Collagen and Poly(ε-caprolactone) Struts Fabricated Using the Poly(vinyl alcohol) Fibrillation/Leaching Process To Develop Efficiently Engineered Skeletal Muscle Tissue.

PubMed

Kim, Minseong; Kim, WonJin; Kim, GeunHyung

2017-12-20

Optimally designed three-dimensional (3D) biomedical scaffolds for skeletal muscle tissue regeneration pose significant research challenges. Currently, most studies on scaffolds focus on the two-dimensional (2D) surface structures that are patterned in the micro-/nanoscales with various repeating sizes and shapes to induce the alignment of myoblasts and myotube formation. The 2D patterned surface clearly provides effective analytical results of pattern size and shape of the myoblast alignment and differentiation. However, it is inconvenient in terms of the direct application for clinical usage due to the limited thickness and 3D shapeability. Hence, the present study suggests an innovative hydrogel or synthetic structure that consists of uniaxially surface-patterned cylindrical struts for skeleton muscle regeneration. The alignment of the pattern on the hydrogel (collagen) and poly(ε-caprolactone) struts was attained with the fibrillation of poly(vinyl alcohol) and the leaching process. Various cell culture results indicate that the C2C12 cells on the micropatterned collagen structure were fully aligned, and that a significantly high level of myotube formation was achieved when compared to the collagen structures that were not treated with the micropatterning process.

A Nonlinear Dynamics Approach for Incorporating Wind-Speed Patterns into Wind-Power Project Evaluation

PubMed Central

Huffaker, Ray; Bittelli, Marco

2015-01-01

Wind-energy production may be expanded beyond regions with high-average wind speeds (such as the Midwest U.S.A.) to sites with lower-average speeds (such as the Southeast U.S.A.) by locating favorable regional matches between natural wind-speed and energy-demand patterns. A critical component of wind-power evaluation is to incorporate wind-speed dynamics reflecting documented diurnal and seasonal behavioral patterns. Conventional probabilistic approaches remove patterns from wind-speed data. These patterns must be restored synthetically before they can be matched with energy-demand patterns. How to accurately restore wind-speed patterns is a vexing problem spurring an expanding line of papers. We propose a paradigm shift in wind power evaluation that employs signal-detection and nonlinear-dynamics techniques to empirically diagnose whether synthetic pattern restoration can be avoided altogether. If the complex behavior of observed wind-speed records is due to nonlinear, low-dimensional, and deterministic system dynamics, then nonlinear dynamics techniques can reconstruct wind-speed dynamics from observed wind-speed data without recourse to conventional probabilistic approaches. In the first study of its kind, we test a nonlinear dynamics approach in an application to Sugarland Wind—the first utility-scale wind project proposed in Florida, USA. We find empirical evidence of a low-dimensional and nonlinear wind-speed attractor characterized by strong temporal patterns that match up well with regular daily and seasonal electricity demand patterns. PMID:25617767

In situ detection of tree root distribution and biomass by multi-electrode resistivity imaging.

PubMed

Amato, Mariana; Basso, Bruno; Celano, Giuseppe; Bitella, Giovanni; Morelli, Gianfranco; Rossi, Roberta

2008-10-01

Traditional methods for studying tree roots are destructive and labor intensive, but available nondestructive techniques are applicable only to small scale studies or are strongly limited by soil conditions and root size. Soil electrical resistivity measured by geoelectrical methods has the potential to detect belowground plant structures, but quantitative relationships of these measurements with root traits have not been assessed. We tested the ability of two-dimensional (2-D) DC resistivity tomography to detect the spatial variability of roots and to quantify their biomass in a tree stand. A high-resolution resistivity tomogram was generated along a 11.75 m transect under an Alnus glutinosa (L.) Gaertn. stand based on an alpha-Wenner configuration with 48 electrodes spaced 0.25 m apart. Data were processed by a 2-D finite-element inversion algorithm, and corrected for soil temperature. Data acquisition, inversion and imaging were completed in the field within 60 min. Root dry mass per unit soil volume (root mass density, RMD) was measured destructively on soil samples collected to a depth of 1.05 m. Soil sand, silt, clay and organic matter contents, electrical conductivity, water content and pH were measured on a subset of samples. The spatial pattern of soil resistivity closely matched the spatial distribution of RMD. Multiple linear regression showed that only RMD and soil water content were related to soil resistivity along the transect. Regression analysis of RMD against soil resistivity revealed a highly significant logistic relationship (n = 97), which was confirmed on a separate dataset (n = 67), showing that soil resistivity was quantitatively related to belowground tree root biomass. This relationship provides a basis for developing quick nondestructive methods for detecting root distribution and quantifying root biomass, as well as for optimizing sampling strategies for studying root-driven phenomena.

Position Information Encoded by Population Activity in Hierarchical Visual Areas

PubMed Central

Majima, Kei; Horikawa, Tomoyasu

2017-01-01

Abstract Neurons in high-level visual areas respond to more complex visual features with broader receptive fields (RFs) compared to those in low-level visual areas. Thus, high-level visual areas are generally considered to carry less information regarding the position of seen objects in the visual field. However, larger RFs may not imply loss of position information at the population level. Here, we evaluated how accurately the position of a seen object could be predicted (decoded) from activity patterns in each of six representative visual areas with different RF sizes [V1–V4, lateral occipital complex (LOC), and fusiform face area (FFA)]. We collected functional magnetic resonance imaging (fMRI) responses while human subjects viewed a ball randomly moving in a two-dimensional field. To estimate population RF sizes of individual fMRI voxels, RF models were fitted for individual voxels in each brain area. The voxels in higher visual areas showed larger estimated RFs than those in lower visual areas. Then, the ball’s position in a separate session was predicted by maximum likelihood estimation using the RF models of individual voxels. We also tested a model-free multivoxel regression (support vector regression, SVR) to predict the position. We found that regardless of the difference in RF size, all visual areas showed similar prediction accuracies, especially on the horizontal dimension. Higher areas showed slightly lower accuracies on the vertical dimension, which appears to be attributed to the narrower spatial distributions of the RF centers. The results suggest that much position information is preserved in population activity through the hierarchical visual pathway regardless of RF sizes and is potentially available in later processing for recognition and behavior. PMID:28451634

Directed liquid phase assembly of highly ordered metallic nanoparticle arrays

DOE PAGES

Wu, Yueying; Dong, Nanyi; Fu, Shaofang; ...

2014-04-01

Directed assembly of nanomaterials is a promising route for the synthesis of advanced materials and devices. We demonstrate the directed-assembly of highly ordered two-dimensional arrays of hierarchical nanostructures with tunable size, spacing and composition. The directed assembly is achieved on lithographically patterned metal films that are subsequently pulse-laser melted; during the brief liquid lifetime, the pattened nanostructures assemble into highly ordered primary and secondary nanoparticles, with sizes below that which was originally patterned. Complementary fluid-dynamics simulations emulate the resultant patterns and show how the competition of capillary forces and liquid metal–solid substrate interaction potential drives the directed assembly. Lastly, asmore » an example of the enhanced functionality, a full-wave electromagnetic analysis has been performed to identify the nature of the supported plasmonic resonances.« less

Capillary sieving electrophoresis and micellar electrokinetic capillary chromatography produce highly correlated separation of tryptic digests

PubMed Central

Dickerson, Jane A.; Dovichi, Norman J.

2011-01-01

We perform two-dimensional capillary electrophoresis on fluorescently labeled proteins and peptides. Capillary sieving electrophoresis was performed in the first dimension and micellar electrokinetic capillary chromatography was performed in the second. A cellular homogenate was labeled with the fluorogenic reagent FQ and separated using the system. This homogenate generated a pair of ridges; the first had essentially constant migration time in the CSE dimension, while the second had essentially constant migration time in the MEKC dimension. In addition a few spots were scattered through the electropherogram. The same homogenate was digested using trypsin, and then labeled and subjected to the two dimensional separation. In this case, the two ridges observed from the original two-dimensional separation disappeared, and were replaced by a set of spots that fell along the diagonal. Those spots were identified using a local-maximum algorithm and each was fit using a two-dimensional Gaussian surface by an unsupervised nonlinear least squares regression algorithm. The migration times of the tryptic digest components were highly correlated (r = 0.862). When the slowest migrating components were eliminated from the analysis, the correlation coefficient improved to r = 0.956. PMID:20564272

Dietary Patterns during Pregnancy Are Associated with Risk of Gestational Diabetes Mellitus.

PubMed

Shin, Dayeon; Lee, Kyung Won; Song, Won O

2015-11-12

Maternal dietary patterns before and during pregnancy play important roles in the development of gestational diabetes mellitus (GDM). We aimed to identify dietary patterns during pregnancy that are associated with GDM risk in pregnant U.S. women. From a 24 h dietary recall of 253 pregnant women (16-41 years) included in the National Health and Nutrition Examination Survey (NHANES) 2003-2012, food items were aggregated into 28 food groups based on Food Patterns Equivalents Database. Three dietary patterns were identified by reduced rank regression with responses including prepregnancy body mass index (BMI), dietary fiber, and ratio of poly- and monounsaturated fatty acids to saturated fatty acid: "high refined grains, fats, oils and fruit juice", "high nuts, seeds, fat and soybean; low milk and cheese", and "high added sugar and organ meats; low fruits, vegetables and seafood". GDM was diagnosed using fasting plasma glucose levels ≥5.1 mmol/L for gestation <24 weeks. Multivariable logistic regression models were used to estimate adjusted odds ratio (AOR) and 95% confidence intervals (CIs) for GDM, after controlling for maternal age, race/ethnicity, education, family poverty income ratio, marital status, prepregnancy BMI, gestational weight gain, energy intake, physical activity, and log-transformed C-reactive protein (CRP). All statistical analyses accounted for the appropriate survey design and sample weights of the NHANES. Of 249 pregnant women, 34 pregnant women (14%) had GDM. Multivariable AOR (95% CIs) of GDM for comparisons between the highest vs. lowest tertiles were 4.9 (1.4-17.0) for "high refined grains, fats, oils and fruit juice" pattern, 7.5 (1.8-32.3) for "high nuts, seeds, fat and soybean; low milk and cheese" pattern, and 22.3 (3.9-127.4) for "high added sugar and organ meats; low fruits, vegetables and seafood" pattern after controlling for maternal sociodemographic variables, prepregnancy BMI, gestational weight gain, energy intake and log-transformed CRP. These findings suggest that dietary patterns during pregnancy are associated with risk of GDM after controlling for potential confounders. The observed connection between a high consumption of refined grains, fat, added sugars and low intake of fruits and vegetables during pregnancy with higher odds for GDM, are consistent with general health benefits of healthy diets, but warrants further research to understand underlying pathophysiology of GDM associated with dietary behaviors during pregnancy.

Stratification for the propensity score compared with linear regression techniques to assess the effect of treatment or exposure.

PubMed

Senn, Stephen; Graf, Erika; Caputo, Angelika

2007-12-30

Stratifying and matching by the propensity score are increasingly popular approaches to deal with confounding in medical studies investigating effects of a treatment or exposure. A more traditional alternative technique is the direct adjustment for confounding in regression models. This paper discusses fundamental differences between the two approaches, with a focus on linear regression and propensity score stratification, and identifies points to be considered for an adequate comparison. The treatment estimators are examined for unbiasedness and efficiency. This is illustrated in an application to real data and supplemented by an investigation on properties of the estimators for a range of underlying linear models. We demonstrate that in specific circumstances the propensity score estimator is identical to the effect estimated from a full linear model, even if it is built on coarser covariate strata than the linear model. As a consequence the coarsening property of the propensity score-adjustment for a one-dimensional confounder instead of a high-dimensional covariate-may be viewed as a way to implement a pre-specified, richly parametrized linear model. We conclude that the propensity score estimator inherits the potential for overfitting and that care should be taken to restrict covariates to those relevant for outcome. Copyright (c) 2007 John Wiley & Sons, Ltd.

Decoding-Accuracy-Based Sequential Dimensionality Reduction of Spatio-Temporal Neural Activities

NASA Astrophysics Data System (ADS)

Funamizu, Akihiro; Kanzaki, Ryohei; Takahashi, Hirokazu

Performance of a brain machine interface (BMI) critically depends on selection of input data because information embedded in the neural activities is highly redundant. In addition, properly selected input data with a reduced dimension leads to improvement of decoding generalization ability and decrease of computational efforts, both of which are significant advantages for the clinical applications. In the present paper, we propose an algorithm of sequential dimensionality reduction (SDR) that effectively extracts motor/sensory related spatio-temporal neural activities. The algorithm gradually reduces input data dimension by dropping neural data spatio-temporally so as not to undermine the decoding accuracy as far as possible. Support vector machine (SVM) was used as the decoder, and tone-induced neural activities in rat auditory cortices were decoded into the test tone frequencies. SDR reduced the input data dimension to a quarter and significantly improved the accuracy of decoding of novel data. Moreover, spatio-temporal neural activity patterns selected by SDR resulted in significantly higher accuracy than high spike rate patterns or conventionally used spatial patterns. These results suggest that the proposed algorithm can improve the generalization ability and decrease the computational effort of decoding.

Characterization of mixing in an electroosmotically stirred continuous micro mixer

NASA Astrophysics Data System (ADS)

Beskok, Ali

2005-11-01

We present theoretical and numerical studies of mixing in a straight micro channel with zeta potential patterned surfaces. A steady pressure driven flow is maintained in the channel in addition to a time dependent electroosmotic flow, generated by a stream-wise AC electric field. The zeta potential patterns are placed critically in the channel to achieve spatially asymmetric time-dependent flow patterns that lead to chaotic stirring. Fixing the geometry, we performed parametric studies of passive particle motion that led to generation of Poincare sections and characterization of chaotic strength by finite time Lyapunov exponents. The parametric studies were performed as a function of the Womersley number (normalized AC frequency) and the ratio of Poiseuille flow and electroosmotic velocities. After determining the non-dimensional parameters that led to high chaotic strength, we performed spectral element simulations of species transport and mixing at high Peclet numbers, and characterized mixing efficiency using the Mixing Index inverse. Mixing lengths proportional to the natural logarithm of the Peclet number are reported. Using the optimum non-dimensional parameters and the typical magnitudes involved in electroosmotic flows, we were able to determine the physical dimensions and operation conditions for a prototype micro-mixer.

Linking basin-scale and pore-scale gas hydrate distribution patterns in diffusion-dominated marine hydrate systems: DIFFUSION-DRIVEN HYDRATE GROWTH IN SANDS

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

Nole, Michael; Daigle, Hugh; Cook, Ann E.

The goal of this study is to computationally determine the potential distribution patterns of diffusion-driven methane hydrate accumulations in coarse-grained marine sediments. Diffusion of dissolved methane in marine gas hydrate systems has been proposed as a potential transport mechanism through which large concentrations of hydrate can preferentially accumulate in coarse-grained sediments over geologic time. Using one-dimensional compositional reservoir simulations, we examine hydrate distribution patterns at the scale of individual sand layers (1 to 20 m thick) that are deposited between microbially active fine-grained material buried through the gas hydrate stability zone (GHSZ). We then extrapolate to two- dimensional and basin-scalemore » three-dimensional simulations, where we model dipping sands and multilayered systems. We find that properties of a sand layer including pore size distribution, layer thickness, dip, and proximity to other layers in multilayered systems all exert control on diffusive methane fluxes toward and within a sand, which in turn impact the distribution of hydrate throughout a sand unit. In all of these simulations, we incorporate data on physical properties and sand layer geometries from the Terrebonne Basin gas hydrate system in the Gulf of Mexico. We demonstrate that diffusion can generate high hydrate saturations (upward of 90%) at the edges of thin sands at shallow depths within the GHSZ, but that it is ineffective at producing high hydrate saturations throughout thick (greater than 10 m) sands buried deep within the GHSZ. As a result, we find that hydrate in fine-grained material can preserve high hydrate saturations in nearby thin sands with burial.« less

Linking basin-scale and pore-scale gas hydrate distribution patterns in diffusion-dominated marine hydrate systems: DIFFUSION-DRIVEN HYDRATE GROWTH IN SANDS

DOE PAGES

Nole, Michael; Daigle, Hugh; Cook, Ann E.; ...

2017-02-01

The goal of this study is to computationally determine the potential distribution patterns of diffusion-driven methane hydrate accumulations in coarse-grained marine sediments. Diffusion of dissolved methane in marine gas hydrate systems has been proposed as a potential transport mechanism through which large concentrations of hydrate can preferentially accumulate in coarse-grained sediments over geologic time. Using one-dimensional compositional reservoir simulations, we examine hydrate distribution patterns at the scale of individual sand layers (1 to 20 m thick) that are deposited between microbially active fine-grained material buried through the gas hydrate stability zone (GHSZ). We then extrapolate to two- dimensional and basin-scalemore » three-dimensional simulations, where we model dipping sands and multilayered systems. We find that properties of a sand layer including pore size distribution, layer thickness, dip, and proximity to other layers in multilayered systems all exert control on diffusive methane fluxes toward and within a sand, which in turn impact the distribution of hydrate throughout a sand unit. In all of these simulations, we incorporate data on physical properties and sand layer geometries from the Terrebonne Basin gas hydrate system in the Gulf of Mexico. We demonstrate that diffusion can generate high hydrate saturations (upward of 90%) at the edges of thin sands at shallow depths within the GHSZ, but that it is ineffective at producing high hydrate saturations throughout thick (greater than 10 m) sands buried deep within the GHSZ. As a result, we find that hydrate in fine-grained material can preserve high hydrate saturations in nearby thin sands with burial.« less

One-Dimensional Scanning Approach to Shock Sensing

NASA Technical Reports Server (NTRS)

Tokars, Roger; Adamovsky, Girgory; Floyd, Bertram

2009-01-01

Measurement tools for high speed air flow are sought both in industry and academia. Particular interest is shown in air flows that exhibit aerodynamic shocks. Shocks are accompanied by sudden changes in density, pressure, and temperature. Optical detection and characterization of such shocks can be difficult because the medium is normally transparent air. A variety of techniques to analyze these flows are available, but they often require large windows and optical components as in the case of Schlieren measurements and/or large operating powers which precludes their use for in-flight monitoring and applications. The one-dimensional scanning approach in this work is a compact low power technique that can be used to non-intrusively detect shocks. The shock is detected by analyzing the optical pattern generated by a small diameter laser beam as it passes through the shock. The optical properties of a shock result in diffraction and spreading of the beam as well as interference fringes. To investigate the feasibility of this technique a shock is simulated by a 426 m diameter optical fiber. Analysis of results revealed a direct correlation between the optical fiber or shock location and the beam s diffraction pattern. A plot of the width of the diffraction pattern vs. optical fiber location reveals that the width of the diffraction pattern was maximized when the laser beam is directed at the center of the optical fiber. This work indicates that the one-dimensional scanning approach may be able to determine the location of an actual shock. Near and far field effects associated with a small diameter laser beam striking an optical fiber used as a simulated shock are investigated allowing a proper one-dimensional scanning beam technique.

LED-based interference-reflection microscopy combined with optical tweezers for quantitative three-dimensional microtubule imaging.

PubMed

Simmert, Steve; Abdosamadi, Mohammad Kazem; Hermsdorf, Gero; Schäffer, Erik

2018-05-28

Optical tweezers combined with various microscopy techniques are a versatile tool for single-molecule force spectroscopy. However, some combinations may compromise measurements. Here, we combined optical tweezers with total-internal-reflection-fluorescence (TIRF) and interference-reflection microscopy (IRM). Using a light-emitting diode (LED) for IRM illumination, we show that single microtubules can be imaged with high contrast. Furthermore, we converted the IRM interference pattern of an upward bent microtubule to its three-dimensional (3D) profile calibrated against the optical tweezers and evanescent TIRF field. In general, LED-based IRM is a powerful method for high-contrast 3D microscopy.

Two-Dimensional Optoelectronic Graphene Nanoprobes for Neural Nerwork

NASA Astrophysics Data System (ADS)

Hong, Tu; Kitko, Kristina; Wang, Rui; Zhang, Qi; Xu, Yaqiong

2014-03-01

Brain is the most complex network created by nature, with billions of neurons connected by trillions of synapses through sophisticated wiring patterns and countless modulatory mechanisms. Current methods to study the neuronal process, either by electrophysiology or optical imaging, have significant limitations on throughput and sensitivity. Here, we use graphene, a monolayer of carbon atoms, as a two-dimensional nanoprobe for neural network. Scanning photocurrent measurement is applied to detect the local integration of electrical and chemical signals in mammalian neurons. Such interface between nanoscale electronic device and biological system provides not only ultra-high sensitivity, but also sub-millisecond temporal resolution, owing to the high carrier mobility of graphene.

Assessing host-specificity of Escherichia coli using a supervised learning logic-regression-based analysis of single nucleotide polymorphisms in intergenic regions.

PubMed

Zhi, Shuai; Li, Qiaozhi; Yasui, Yutaka; Edge, Thomas; Topp, Edward; Neumann, Norman F

2015-11-01

Host specificity in E. coli is widely debated. Herein, we used supervised learning logic-regression-based analysis of intergenic DNA sequence variability in E. coli in an attempt to identify single nucleotide polymorphism (SNP) biomarkers of E. coli that are associated with natural selection and evolution toward host specificity. Seven-hundred and eighty strains of E. coli were isolated from 15 different animal hosts. We utilized logic regression for analyzing DNA sequence data of three intergenic regions (flanked by the genes uspC-flhDC, csgBAC-csgDEFG, and asnS-ompF) to identify genetic biomarkers that could potentially discriminate E. coli based on host sources. Across 15 different animal hosts, logic regression successfully discriminated E. coli based on animal host source with relatively high specificity (i.e., among the samples of the non-target animal host, the proportion that correctly did not have the host-specific marker pattern) and sensitivity (i.e., among the samples from a given animal host, the proportion that correctly had the host-specific marker pattern), even after fivefold cross validation. Permutation tests confirmed that for most animals, host specific intergenic biomarkers identified by logic regression in E. coli were significantly associated with animal host source. The highest level of biomarker sensitivity was observed in deer isolates, with 82% of all deer E. coli isolates displaying a unique SNP pattern that was 98% specific to deer. Fifty-three percent of human isolates displayed a unique biomarker pattern that was 98% specific to humans. Twenty-nine percent of cattle isolates displayed a unique biomarker that was 97% specific to cattle. Interestingly, even within a related host group (i.e., Family: Canidae [domestic dogs and coyotes]), highly specific SNP biomarkers (98% and 99% specificity for dog and coyotes, respectively) were observed, with 21% of dog E. coli isolates displaying a unique dog biomarker and 61% of coyote isolates displaying a unique coyote biomarker. Application of a supervised learning method, such as logic regression, to DNA sequence analysis at certain intergenic regions demonstrates that some E. coli strains may evolve to become host-specific. Copyright © 2015 Elsevier Inc. All rights reserved.

Characteristics of strain-sensitive photonic crystal cavities in a flexible substrate.

PubMed

No, You-Shin; Choi, Jae-Hyuck; Kim, Kyoung-Ho; Park, Hong-Gyu

2016-11-14

High-index semiconductor photonic crystal (PhC) cavities in a flexible substrate support strong and tunable optical resonances that can be used for highly sensitive and spatially localized detection of mechanical deformations in physical systems. Here, we report theoretical studies and fundamental understandings of resonant behavior of an optical mode excited in strain-sensitive rod-type PhC cavities consisting of high-index dielectric nanorods embedded in a low-index flexible polymer substrate. Using the three-dimensional finite-difference time-domain simulation method, we calculated two-dimensional transverse-electric-like photonic band diagrams and the three-dimensional dispersion surfaces near the first Γ-point band edge of unidirectionally strained PhCs. A broken rotational symmetry in the PhCs modifies the photonic band structures and results in the asymmetric distributions and different levels of changes in normalized frequencies near the first Γ-point band edge in the reciprocal space, which consequently reveals strain-dependent directional optical losses and selected emission patterns. The calculated electric fields, resonant wavelengths, and quality factors of the band-edge modes in the strained PhCs show an excellent agreement with the results of qualitative analysis of modified dispersion surfaces. Furthermore, polarization-resolved time-averaged Poynting vectors exhibit characteristic dipole-like emission patterns with preferentially selected linear polarizations, originating from the asymmetric band structures in the strained PhCs.

[Dietary patterns and metabolic syndrome components in women with excess weight 18 to 45 years old].

PubMed

Hernández-Ruiz, Zugey; Rodríguez-Ramírez, Sonia; Hernández-Cordero, Sonia; Monterrubio-Flores, Eric

2018-01-01

To analyze the association between dietary patterns and metabolic syndrome (MS) components in adult women with excess weight. Cross-sectional study with anthropometric, dietary, biochemical and blood pressure data. Dietary patterns were identified by factor analysis and multiple logistic regression models were used to analyze associations. The prevalence of altered glucose was 14.6%, of hypertriglyceridemia 40.4%, of altered concentration of high density lipoprotein cholesterol(HDLc) 45.0%, hypertension 4.6% and MS 30%. The pattern with high consumption of corn tortillas, meats and legumes, was associated with less possibility of hyperglycemia (OR= 0.62; 95%CI 0.39-0.98). The pattern with high consumption of sweet and salty snacks, milk, rice, soaps and pasta, was inversely associated with the possibility of low HDLc concentration (OR= 0.76; 95%CI 0.60-0.97). A dietary pattern with greater consumption of legumes, meats and corn tortillas was associated with less possibility of having hyperglycemia.

Investigating the Performance of Alternate Regression Weights by Studying All Possible Criteria in Regression Models with a Fixed Set of Predictors

ERIC Educational Resources Information Center

Waller, Niels; Jones, Jeff

2011-01-01

We describe methods for assessing all possible criteria (i.e., dependent variables) and subsets of criteria for regression models with a fixed set of predictors, x (where x is an n x 1 vector of independent variables). Our methods build upon the geometry of regression coefficients (hereafter called regression weights) in n-dimensional space. For a…

Etching-free patterning method for electrical characterization of atomically thin MoSe2 films grown by chemical vapor deposition

NASA Astrophysics Data System (ADS)

Utama, M. Iqbal Bakti; Lu, Xin; Zhan, Da; Ha, Son Tung; Yuan, Yanwen; Shen, Zexiang; Xiong, Qihua

2014-10-01

Patterning two-dimensional materials into specific spatial arrangements and geometries is essential for both fundamental studies of materials and practical applications in electronics. However, the currently available patterning methods generally require etching steps that rely on complicated and expensive procedures. We report here a facile patterning method for atomically thin MoSe2 films using stripping with an SU-8 negative resist layer exposed to electron beam lithography. Additional steps of chemical and physical etching were not necessary in this SU-8 patterning method. The SU-8 patterning was used to define a ribbon channel from a field effect transistor of MoSe2 film, which was grown by chemical vapor deposition. The narrowing of the conduction channel area with SU-8 patterning was crucial in suppressing the leakage current within the device, thereby allowing a more accurate interpretation of the electrical characterization results from the sample. An electrical transport study, enabled by the SU-8 patterning, showed a variable range hopping behavior at high temperatures.Patterning two-dimensional materials into specific spatial arrangements and geometries is essential for both fundamental studies of materials and practical applications in electronics. However, the currently available patterning methods generally require etching steps that rely on complicated and expensive procedures. We report here a facile patterning method for atomically thin MoSe2 films using stripping with an SU-8 negative resist layer exposed to electron beam lithography. Additional steps of chemical and physical etching were not necessary in this SU-8 patterning method. The SU-8 patterning was used to define a ribbon channel from a field effect transistor of MoSe2 film, which was grown by chemical vapor deposition. The narrowing of the conduction channel area with SU-8 patterning was crucial in suppressing the leakage current within the device, thereby allowing a more accurate interpretation of the electrical characterization results from the sample. An electrical transport study, enabled by the SU-8 patterning, showed a variable range hopping behavior at high temperatures. Electronic supplementary information (ESI) available: Further experiments on patterning and additional electrical characterizations data. See DOI: 10.1039/c4nr03817g

Coherent frequency bridge between visible and telecommunications band for vortex light.

PubMed

Liu, Shi-Long; Liu, Shi-Kai; Li, Yin-Hai; Shi, Shuai; Zhou, Zhi-Yuan; Shi, Bao-Sen

2017-10-02

In quantum communications, vortex photons can encode higher-dimensional quantum states and build high-dimensional communication networks (HDCNs). The interfaces that connect different wavelengths are significant in HDCNs. We construct a coherent orbital angular momentum (OAM) frequency bridge via difference frequency conversion in a nonlinear bulk crystal for HDCNs. Using a single resonant cavity, maximum quantum conversion efficiencies from visible to infrared are 36%, 15%, and 7.8% for topological charges of 0,1, and 2, respectively. The average fidelity obtained using quantum state tomography for the down-converted infrared OAM-state of topological charge 1 is 96.51%. We also prove that the OAM is conserved in this process by measuring visible and infrared interference patterns. This coherent OAM frequency-down conversion bridge represents a basis for an interface between two high-dimensional quantum systems operating with different spectra.

A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.

PubMed

Saâdaoui, Foued; Bertrand, Pierre R; Boudet, Gil; Rouffiac, Karine; Dutheil, Frédéric; Chamoux, Alain

2015-10-01

Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems. A case study is conducted for an occupational medicine problem with the purpose of analyzing patterns in a population of 813 individuals. To reduce the data set dimensionality, we base our approach on the Principal Component Analysis (PCA), which is the statistical tool most commonly used in factorial analysis. However, the problems in nature, especially in medicine, are often based on heterogeneous-type qualitative-quantitative measurements, whereas PCA only processes quantitative ones. Besides, qualitative data are originally unobservable quantitative responses that are usually binary-coded. Hence, we propose a new set of strategies allowing to simultaneously handle quantitative and qualitative data. The principle of this approach is to perform a projection of the qualitative variables on the subspaces spanned by quantitative ones. Subsequently, an optimal model is allocated to the resulting PCA-regressed subspaces.

Three-dimensional in vitro cancer spheroid models for Photodynamic Therapy: Strengths and Opportunities

NASA Astrophysics Data System (ADS)

Evans, Conor

2015-03-01

Three dimensional, in vitro spheroid cultures offer considerable utility for the development and testing of anticancer photodynamic therapy regimens. More complex than monolayer cultures, three-dimensional spheroid systems replicate many of the important cell-cell and cell-matrix interactions that modulate treatment response in vivo. Simple enough to be grown by the thousands and small enough to be optically interrogated, spheroid cultures lend themselves to high-content and high-throughput imaging approaches. These advantages have enabled studies investigating photosensitizer uptake, spatiotemporal patterns of therapeutic response, alterations in oxygen diffusion and consumption during therapy, and the exploration of mechanisms that underlie therapeutic synergy. The use of quantitative imaging methods, in particular, has accelerated the pace of three-dimensional in vitro photodynamic therapy studies, enabling the rapid compilation of multiple treatment response parameters in a single experiment. Improvements in model cultures, the creation of new molecular probes of cell state and function, and innovations in imaging toolkits will be important for the advancement of spheroid culture systems for future photodynamic therapy studies.

Two Dimensional Movement Patterns of Juvenile Winter Run and Late Fall Run Chinook Salmon at the Fremont Weir, Sacramento River, CA

DTIC Science & Technology

2017-07-01

ER D C/ EL T R- 17 -1 0 Two-Dimensional Movement Patterns of Juvenile Winter- Run and Late-Fall- Run Chinook Salmon at the Fremont Weir...default. ERDC/EL TR-17-10 July 2017 Two-Dimensional Movement Patterns of Juvenile Winter- Run and Late-Fall- Run Chinook Salmon at the Fremont Weir...Sacramento River, smaller winter- run Chinook and larger late-fall- run Chinook salmon were tagged and released into a 2D telemetry array dur- ing the

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

PubMed Central

2014-01-01

Background Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients. Results For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes. Conclusions Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data. PMID:24987463

Three-dimensionally modulated anisotropic structure for diffractive optical elements created by one-step three-beam polarization holographic photoalignment

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

Kawai, Kotaro, E-mail: s135016@stn.nagaokaut.ac.jp; Sakamoto, Moritsugu; Noda, Kohei

2016-03-28

A diffractive optical element with a three-dimensional liquid crystal (LC) alignment structure for advanced control of polarized beams was fabricated by a highly efficient one-step photoalignment method. This study is of great significance because different two-dimensional continuous and complex alignment patterns can be produced on two alignment films by simultaneously irradiating an empty glass cell composed of two unaligned photocrosslinkable polymer LC films with three-beam polarized interference beam. The polarization azimuth, ellipticity, and rotation direction of the diffracted beams from the resultant LC grating widely varied depending on the two-dimensional diffracted position and the polarization states of the incident beams.more » These polarization diffraction properties are well explained by theoretical analysis based on Jones calculus.« less

Retrospective lifetime dietary patterns predict cognitive performance in community-dwelling older Australians.

PubMed

Hosking, Diane E; Nettelbeck, Ted; Wilson, Carlene; Danthiir, Vanessa

2014-07-28

Dietary intake is a modifiable exposure that may have an impact on cognitive outcomes in older age. The long-term aetiology of cognitive decline and dementia, however, suggests that the relevance of dietary intake extends across the lifetime. In the present study, we tested whether retrospective dietary patterns from the life periods of childhood, early adulthood, adulthood and middle age predicted cognitive performance in a cognitively healthy sample of 352 older Australian adults >65 years. Participants completed the Lifetime Diet Questionnaire and a battery of cognitive tests designed to comprehensively assess multiple cognitive domains. In separate regression models, lifetime dietary patterns were the predictors of cognitive factor scores representing ten constructs derived by confirmatory factor analysis of the cognitive test battery. All regression models were progressively adjusted for the potential confounders of current diet, age, sex, years of education, English as native language, smoking history, income level, apoE ɛ4 status, physical activity, other past dietary patterns and health-related variables. In the adjusted models, lifetime dietary patterns predicted cognitive performance in this sample of older adults. In models additionally adjusted for intake from the other life periods and mechanistic health-related variables, dietary patterns from the childhood period alone reached significance. Higher consumption of the 'coffee and high-sugar, high-fat extras' pattern predicted poorer performance on simple/choice reaction time, working memory, retrieval fluency, short-term memory and reasoning. The 'vegetable and non-processed' pattern negatively predicted simple/choice reaction time, and the 'traditional Australian' pattern positively predicted perceptual speed and retrieval fluency. Identifying early-life dietary antecedents of older-age cognitive performance contributes to formulating strategies for delaying or preventing cognitive decline.

MicroCT angiography detects vascular formation and regression in skin wound healing

PubMed Central

Urao, Norifumi; Okonkwo, Uzoagu A.; Fang, Milie M.; Zhuang, Zhen W.; Koh, Timothy J.; DiPietro, Luisa A.

2016-01-01

Properly regulated angiogenesis and arteriogenesis are essential for effective wound healing. Tissue injury induces robust new vessel formation and subsequent vessel maturation, which involves vessel regression and remodeling. Although formation of functional vasculature is essential for healing, alterations in vascular structure over the time course of skin wound healing are not well understood. Here, using high-resolution ex vivo X-ray micro-computed tomography (microCT), we describe the vascular network during healing of skin excisional wounds with highly detailed three-dimensional (3D) reconstructed images and associated quantitative analysis. We found that relative vessel volume, surface area and branching number are significantly decreased in wounds from day 7 to day 14 and 21. Segmentation and skeletonization analysis of selected branches from high-resolution images as small as 2.5 μm voxel size show that branching orders are decreased in the wound vessels during healing. In histological analysis, we found that the contrast agent fills mainly arterioles, but not small capillaries nor large veins. In summary, high-resolution microCT revealed dynamic alterations of vessel structures during wound healing. This technique may be useful as a key tool in the study of the formation and regression of wound vessels. PMID:27009591

Do clinical data and human papilloma virus genotype influence spontaneous regression in grade I cervical intraepithelial neoplasia?

PubMed

Cortés-Alaguero, Caterina; González-Mirasol, Esteban; Morales-Roselló, José; Poblet-Martinez, Enrique

2017-03-15

To determine whether medical history, clinical examination and human papilloma virus (HPV) genotype influence spontaneous regression in cervical intraepithelial neoplasia grade I (CIN-I). We retrospectively evaluated 232 women who were histologically diagnosed as have CIN-I by means of Kaplan-Meier curves, the pattern of spontaneous regression according to the medical history, clinical examination, and HPV genotype. Spontaneous regression occurred in most patients and was influenced by the presence of multiple HPV genotypes but not by the HPV genotype itself. In addition, regression frequency was diminished when more than 50% of the cervix surface was affected or when an abnormal cytology was present at the beginning of follow-up. The frequency of regression in CIN-I is high, making long-term follow-up and conservative management advisable. Data from clinical examination and HPV genotyping might help to anticipate which lesions will regress.

Adherence to the Western, Prudent and Mediterranean dietary patterns and breast cancer risk: MCC-Spain study.

PubMed

Castelló, Adela; Boldo, Elena; Pérez-Gómez, Beatriz; Lope, Virginia; Altzibar, Jone M; Martín, Vicente; Castaño-Vinyals, Gemma; Guevara, Marcela; Dierssen-Sotos, Trinidad; Tardón, Adonina; Moreno, Víctor; Puig-Vives, Montserrat; Llorens-Ivorra, Cristóbal; Alguacil, Juan; Gómez-Acebo, Inés; Castilla, Jesús; Gràcia-Lavedán, Esther; Dávila-Batista, Verónica; Kogevinas, Manolis; Aragonés, Nuria; Amiano, Pilar; Pollán, Marina

2017-09-01

To externally validate the previously identified effect on breast cancer risk of the Western, Prudent and Mediterranean dietary patterns. MCC-Spain is a multicase-control study that collected epidemiological information on 1181 incident cases of female breast cancer and 1682 healthy controls from 10 Spanish provinces. Three dietary patterns derived in another Spanish case-control study were analysed in the MCC-Spain study. These patterns were termed Western (high intakes of fatty and sugary products and red and processed meat), Prudent (high intakes of low-fat dairy products, vegetables, fruits, whole grains and juices) and Mediterranean (high intake of fish, vegetables, legumes, boiled potatoes, fruits, olives, and vegetable oil, and a low intake of juices). Their association with breast cancer was assessed using logistic regression models with random province-specific intercepts considering an interaction with menopausal status. Risk according to tumour subtypes - based on oestrogen (ER), progesterone (PR) and human epidermal growth factor 2 (HER2) receptors (ER+/PR+ & HER2-; HER2+; ER-/PR- & HER2-) - was evaluated with multinomial regression models. Breast cancer and histological subtype. Our results confirm most of the associations found in the previous case-control study. A high adherence to the Western dietary pattern seems to increase breast cancer risk in both premenopausal women (OR 4 th vs.1 st quartile (95% CI):1.68 (1.02;2.79); OR 1SD-increase (95% CI):1.19 (1.02;1.40)) and postmenopausal women (OR 4 th vs.1 st quartile (95% CI):1.48(1.07;2.05); OR 1SD-increase (95% CI): 1.14 (1.01;1.29)). While high adherence to the Prudent pattern did not show any effect on breast cancer, the Mediterranean dietary pattern seemed to be protective, but only among postmenopausal women (OR 4 th vs.1 st quartile (95% CI): 0.72 (95% CI 0.53;0.98); p-int=0.075). There were no significant differences by tumour subtype. Dietary recommendations based on a departure from the Western dietary pattern in favour of the Mediterranean diet could reduce breast cancer risk in the general population. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection of quantum well induced single degenerate-transition-dipoles in ZnO nanorods.

PubMed

Ghosh, Siddharth; Ghosh, Moumita; Seibt, Michael; Rao, G Mohan

2016-02-07

Quantifying and characterising atomic defects in nanocrystals is difficult and low-throughput using the existing methods such as high resolution transmission electron microscopy (HRTEM). In this article, using a defocused wide-field optical imaging technique, we demonstrate that a single ultrahigh-piezoelectric ZnO nanorod contains a single defect site. We model the observed dipole-emission patterns from optical imaging with a multi-dimensional dipole and find that the experimentally observed dipole pattern and model-calculated patterns are in excellent agreement. This agreement suggests the presence of vertically oriented degenerate-transition-dipoles in vertically aligned ZnO nanorods. The HRTEM of the ZnO nanorod shows the presence of a stacking fault, which generates a localised quantum well induced degenerate-transition-dipole. Finally, we elucidate that defocused wide-field imaging can be widely used to characterise defects in nanomaterials to answer many difficult questions concerning the performance of low-dimensional devices, such as in energy harvesting, advanced metal-oxide-semiconductor storage, and nanoelectromechanical and nanophotonic devices.

Instabilities and patterns in an active nematic film

NASA Astrophysics Data System (ADS)

Srivastava, Pragya; Marchetti, Cristina

2015-03-01

Experiments on microtubule bundles confined to an oil-water interface have motivated extensive theoretical studies of two-dimensional active nematics. Theoretical models taking into account the interplay between activity, flow and order have remarkably reproduced several experimentally observed features of the defect-dynamics in these ``living'' nematics. Here, we derive minimal description of a two-dimensional active nematic film confined between walls. At high friction, we eliminate the flow to obtain closed equations for the nematic order parameter, with renormalized Frank elastic constants. Active processes can render the ``Frank'' constants negative, resulting in the instability of the uniformly ordered nematic state. The minimal model yields emergent patterns of growing complexity with increasing activity, including bands and turbulent dynamics with a steady density of topological defects, as obtained with the full hydrodynamic equations. We report on the scaling of the length scales of these patterns and of the steady state number of defects with activity and system size. National Science Foundation grant DMR-1305184 and Syracuse Soft Matter Program.

A three-dimensional bioprinting system for use with a hydrogel-based biomaterial and printing parameter characterization.

PubMed

Song, Seung-Joon; Choi, Jaesoon; Park, Yong-Doo; Lee, Jung-Joo; Hong, So Young; Sun, Kyung

2010-11-01

Bioprinting is an emerging technology for constructing tissue or bioartificial organs with complex three-dimensional (3D) structures. It provides high-precision spatial shape forming ability on a larger scale than conventional tissue engineering methods, and simultaneous multiple components composition ability. Bioprinting utilizes a computer-controlled 3D printer mechanism for 3D biological structure construction. To implement minimal pattern width in a hydrogel-based bioprinting system, a study on printing characteristics was performed by varying printer control parameters. The experimental results showed that printing pattern width depends on associated printer control parameters such as printing flow rate, nozzle diameter, and nozzle velocity. The system under development showed acceptable feasibility of potential use for accurate printing pattern implementation in tissue engineering applications and is another example of novel techniques for regenerative medicine based on computer-aided biofabrication system. © 2010, Copyright the Authors. Artificial Organs © 2010, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Combined Loadings and Cross-Dimensional Loadings Timeliness of Presentation of Financial Statements of Local Government

NASA Astrophysics Data System (ADS)

Muda, I.; Dharsuky, A.; Siregar, H. S.; Sadalia, I.

2017-03-01

This study examines the pattern of readiness dimensional accuracy of financial statements of local government in North Sumatra with a routine pattern of two (2) months after the fiscal year ends and patterns of at least 3 (three) months after the fiscal year ends. This type of research is explanatory survey with quantitative methods. The population and the sample used is of local government officials serving local government financial reports. Combined Analysis And Cross-Loadings Loadings are used with statistical tools WarpPLS. The results showed that there was a pattern that varies above dimensional accuracy of the financial statements of local government in North Sumatra.

Suppression of transient enhanced diffusion in sub-micron patterned silicon template by dislocation loops formation

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

Hu, Kuan-Kan; Woon, Wei Yen; Chang, Ruey-Dar

We investigate the evolution of two dimensional transient enhanced diffusion (TED) of phosphorus in sub-micron scale patterned silicon template. Samples doped with low dose phosphorus with and without high dose silicon self-implantation, were annealed for various durations. Dopant diffusion is probed with plane-view scanning capacitance microscopy. The measurement revealed two phases of TED. Significant suppression in the second phase TED is observed for samples with high dose self-implantation. Transmission electron microscopy suggests the suppressed TED is related to the evolution of end of range defect formed around ion implantation sidewalls.

Suppression of transient enhanced diffusion in sub-micron patterned silicon template by dislocation loops formation

NASA Astrophysics Data System (ADS)

Hu, Kuan-Kan; Chang, Ruey-Dar; Woon, Wei Yen

2015-10-01

We investigate the evolution of two dimensional transient enhanced diffusion (TED) of phosphorus in sub-micron scale patterned silicon template. Samples doped with low dose phosphorus with and without high dose silicon self-implantation, were annealed for various durations. Dopant diffusion is probed with plane-view scanning capacitance microscopy. The measurement revealed two phases of TED. Significant suppression in the second phase TED is observed for samples with high dose self-implantation. Transmission electron microscopy suggests the suppressed TED is related to the evolution of end of range defect formed around ion implantation sidewalls.

Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.

PubMed

Liu, Zhicheng; Wang, Yang; Dontcheva, Mira; Hoffman, Matthew; Walker, Seth; Wilson, Alan

2017-01-01

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

Self-assembly and hierarchical patterning of aligned organic nanowire arrays by solvent evaporation on substrates with patterned wettability.

PubMed

Bao, Rong-Rong; Zhang, Cheng-Yi; Zhang, Xiu-Juan; Ou, Xue-Mei; Lee, Chun-Sing; Jie, Jian-Sheng; Zhang, Xiao-Hong

2013-06-26

The controlled growth and alignment of one-dimensional organic nanostructures at well-defined locations considerably hinders the integration of nanostructures for electronic and optoelectronic applications. Here, we demonstrate a simple process to achieve the growth, alignment, and hierarchical patterning of organic nanowires on substrates with controlled patterns of surface wettability. The first-level pattern is confined by the substrate patterns of wettability. Organic nanostructures are preferentially grown on solvent wettable regions. The second-level pattern is the patterning of aligned organic nanowires deposited by controlling the shape and movement of the solution contact lines during evaporation on the wettable regions. This process is controlled by the cover-hat-controlled method or vertical evaportation method. Therefore, various new patterns of organic nanostructures can be obtained by combing these two levels of patterns. This simple method proves to be a general approach that can be applied to other organic nanostructure systems. Using the as-prepared patterned nanowire arrays, an optoelectronic device (photodetector) is easily fabricated. Hence, the proposed simple, large-scale, low-cost method of preparing patterns of highly ordered organic nanostructures has high potential applications in various electronic and optoelectronic devices.

Laser-induced Forward Transfer of Ag Nanopaste.

PubMed

Breckenfeld, Eric; Kim, Heungsoo; Auyeung, Raymond C Y; Piqué, Alberto

2016-03-31

Over the past decade, there has been much development of non-lithographic methods(1-3) for printing metallic inks or other functional materials. Many of these processes such as inkjet(3) and laser-induced forward transfer (LIFT)(4) have become increasingly popular as interest in printable electronics and maskless patterning has grown. These additive manufacturing processes are inexpensive, environmentally friendly, and well suited for rapid prototyping, when compared to more traditional semiconductor processing techniques. While most direct-write processes are confined to two-dimensional structures and cannot handle materials with high viscosity (particularly inkjet), LIFT can transcend both constraints if performed properly. Congruent transfer of three dimensional pixels (called voxels), also referred to as laser decal transfer (LDT)(5-9), has recently been demonstrated with the LIFT technique using highly viscous Ag nanopastes to fabricate freestanding interconnects, complex voxel shapes, and high-aspect-ratio structures. In this paper, we demonstrate a simple yet versatile process for fabricating a variety of micro- and macroscale Ag structures. Structures include simple shapes for patterning electrical contacts, bridging and cantilever structures, high-aspect-ratio structures, and single-shot, large area transfers using a commercial digital micromirror device (DMD) chip.

Laser-induced Forward Transfer of Ag Nanopaste

PubMed Central

Breckenfeld, Eric; Kim, Heungsoo; Auyeung, Raymond C. Y.; Piqué, Alberto

2016-01-01

Over the past decade, there has been much development of non-lithographic methods1-3 for printing metallic inks or other functional materials. Many of these processes such as inkjet3 and laser-induced forward transfer (LIFT)4 have become increasingly popular as interest in printable electronics and maskless patterning has grown. These additive manufacturing processes are inexpensive, environmentally friendly, and well suited for rapid prototyping, when compared to more traditional semiconductor processing techniques. While most direct-write processes are confined to two-dimensional structures and cannot handle materials with high viscosity (particularly inkjet), LIFT can transcend both constraints if performed properly. Congruent transfer of three dimensional pixels (called voxels), also referred to as laser decal transfer (LDT)5-9, has recently been demonstrated with the LIFT technique using highly viscous Ag nanopastes to fabricate freestanding interconnects, complex voxel shapes, and high-aspect-ratio structures. In this paper, we demonstrate a simple yet versatile process for fabricating a variety of micro- and macroscale Ag structures. Structures include simple shapes for patterning electrical contacts, bridging and cantilever structures, high-aspect-ratio structures, and single-shot, large area transfers using a commercial digital micromirror device (DMD) chip. PMID:27077645

Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression.

PubMed

Kim, Soyeon; Baladandayuthapani, Veerabhadran; Lee, J Jack

2017-06-01

In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on an individual patient's biomarker/genomic profile. Two goals are to choose important biomarkers that accurately predict treatment outcomes and to cull unimportant biomarkers to reduce the cost of biological and clinical verifications. These goals are challenging due to the high dimensionality of genomic data. Variable selection methods based on penalized regression (e.g., the lasso and elastic net) have yielded promising results. However, selecting the right amount of penalization is critical to simultaneously achieving these two goals. Standard approaches based on cross-validation (CV) typically provide high prediction accuracy with high true positive rates but at the cost of too many false positives. Alternatively, stability selection (SS) controls the number of false positives, but at the cost of yielding too few true positives. To circumvent these issues, we propose prediction-oriented marker selection (PROMISE), which combines SS with CV to conflate the advantages of both methods. Our application of PROMISE with the lasso and elastic net in data analysis shows that, compared to CV, PROMISE produces sparse solutions, few false positives, and small type I + type II error, and maintains good prediction accuracy, with a marginal decrease in the true positive rates. Compared to SS, PROMISE offers better prediction accuracy and true positive rates. In summary, PROMISE can be applied in many fields to select regularization parameters when the goals are to minimize false positives and maximize prediction accuracy.

New Patterns of the Two-Dimensional Rogue Waves: (2+1)-Dimensional Maccari System

NASA Astrophysics Data System (ADS)

Wang, Gai-Hua; Wang, Li-Hong; Rao, Ji-Guang; He, Jing-Song

2017-06-01

The ocean rogue wave is one kind of puzzled destructive phenomenon that has not been understood thoroughly so far. The two-dimensional nature of this wave has inspired the vast endeavors on the recognizing new patterns of the rogue waves based on the dynamical equations with two-spatial variables and one-temporal variable, which is a very crucial step to prevent this disaster event at the earliest stage. Along this issue, we present twelve new patterns of the two-dimensional rogue waves, which are reduced from a rational and explicit formula of the solutions for a (2+1)-dimensional Maccari system. The extreme points (lines) of the first-order lumps (rogue waves) are discussed according to their analytical formulas. For the lower-order rogue waves, we show clearly in formula that parameter b 2 plays a significant role to control these patterns. Supported by the National Natural Science Foundation of China under Grant No. 11671219, the K. C. Wong Magna Fund in Ningbo University, Gai-Hua Wang is also supported by the Scientific Research Foundation of Graduate School of Ningbo University

High-efficiency aperiodic two-dimensional high-contrast-grating hologram

NASA Astrophysics Data System (ADS)

Qiao, Pengfei; Zhu, Li; Chang-Hasnain, Connie J.

2016-03-01

High efficiency phase holograms are designed and implemented using aperiodic two-dimensional (2D) high-contrast gratings (HCGs). With our design algorithm and an in-house developed rigorous coupled-wave analysis (RCWA) package for periodic 2D HCGs, the structural parameters are obtained to achieve a full 360-degree phase-tuning range of the reflected or transmitted wave, while maintaining the power efficiency above 90%. For given far-field patterns or 3D objects to reconstruct, we can generate the near-field phase distribution through an iterative process. The aperiodic HCG phase plates we design for holograms are pixelated, and the local geometric parameters for each pixel to achieve desired phase alternation are extracted from our periodic HCG designs. Our aperiodic HCG holograms are simulated using the 3D finite-difference time-domain method. The simulation results confirm that the desired far-field patterns are successfully produced under illumination at the designed wavelength. The HCG holograms are implemented on the quartz wafers, using amorphous silicon as the high-index material. We propose HCG designs at both visible and infrared wavelengths, and our simulation confirms the reconstruction of 3D objects. The high-contrast gratings allow us to realize low-cost, compact, flat, and integrable holograms with sub-micrometer thicknesses.

A Three-Dimensional Kinematic and Kinetic Study of the College-Level Female Softball Swing

PubMed Central

Milanovich, Monica; Nesbit, Steven M.

2014-01-01

This paper quantifies and discusses the three-dimensional kinematic and kinetic characteristics of the female softball swing as performed by fourteen female collegiate amateur subjects. The analyses were performed using a three-dimensional computer model. The model was driven kinematically from subject swings data that were recorded with a multi-camera motion analysis system. Each subject used two distinct bats with significantly different inertial properties. Model output included bat trajectories, subject/bat interaction forces and torques, work, and power. These data formed the basis for a detailed analysis and description of fundamental swing kinematic and kinetic quantities. The analyses revealed that the softball swing is a highly coordinated and individual three-dimensional motion and subject-to-subject variations were significant in all kinematic and kinetic quantities. In addition, the potential effects of bat properties on swing mechanics are discussed. The paths of the hands and the centre-of-curvature of the bat relative to the horizontal plane appear to be important trajectory characteristics of the swing. Descriptions of the swing mechanics and practical implications are offered based upon these findings. Key Points The female softball swing is a highly coordinated and individual three-dimensional motion and subject-to-subject variations were significant in all kinematic and kinetic quantities. The paths of the grip point, bat centre-of-curvature, CG, and COP are complex yet reveal consistent patterns among subjects indicating that these patterns are fundamental components of the swing. The most important mechanical quantity relative to generating bat speed is the total work applied to the bat from the batter. Computer modeling of the softball swing is a viable means for study of the fundamental mechanics of the swing motion, the interactions between the batter and the bat, and the energy transfers between the two. PMID:24570623

A three-dimensional kinematic and kinetic study of the college-level female softball swing.

PubMed

Milanovich, Monica; Nesbit, Steven M

2014-01-01

This paper quantifies and discusses the three-dimensional kinematic and kinetic characteristics of the female softball swing as performed by fourteen female collegiate amateur subjects. The analyses were performed using a three-dimensional computer model. The model was driven kinematically from subject swings data that were recorded with a multi-camera motion analysis system. Each subject used two distinct bats with significantly different inertial properties. Model output included bat trajectories, subject/bat interaction forces and torques, work, and power. These data formed the basis for a detailed analysis and description of fundamental swing kinematic and kinetic quantities. The analyses revealed that the softball swing is a highly coordinated and individual three-dimensional motion and subject-to-subject variations were significant in all kinematic and kinetic quantities. In addition, the potential effects of bat properties on swing mechanics are discussed. The paths of the hands and the centre-of-curvature of the bat relative to the horizontal plane appear to be important trajectory characteristics of the swing. Descriptions of the swing mechanics and practical implications are offered based upon these findings. Key PointsThe female softball swing is a highly coordinated and individual three-dimensional motion and subject-to-subject variations were significant in all kinematic and kinetic quantities.The paths of the grip point, bat centre-of-curvature, CG, and COP are complex yet reveal consistent patterns among subjects indicating that these patterns are fundamental components of the swing.The most important mechanical quantity relative to generating bat speed is the total work applied to the bat from the batter.Computer modeling of the softball swing is a viable means for study of the fundamental mechanics of the swing motion, the interactions between the batter and the bat, and the energy transfers between the two.

Write-Read 3D Patterning with a Dual-Channel Nanopipette.

PubMed

Momotenko, Dmitry; Page, Ashley; Adobes-Vidal, Maria; Unwin, Patrick R

2016-09-27

Nanopipettes are becoming extremely versatile and powerful tools in nanoscience for a wide variety of applications from imaging to nanoscale sensing. Herein, the capabilities of nanopipettes to build complex free-standing three-dimensional (3D) nanostructures are demonstrated using a simple double-barrel nanopipette device. Electrochemical control of ionic fluxes enables highly localized delivery of precursor species from one channel and simultaneous (dynamic and responsive) ion conductance probe-to-substrate distance feedback with the other for reliable high-quality patterning. Nanopipettes with 30-50 nm tip opening dimensions of each channel allowed confinement of ionic fluxes for the fabrication of high aspect ratio copper pillar, zigzag, and Γ-like structures, as well as permitted the subsequent topographical mapping of the patterned features with the same nanopipette probe as used for nanostructure engineering. This approach offers versatility and robustness for high-resolution 3D "printing" (writing) and read-out at the nanoscale.

[Sociodemographic context of homicide in Mexico City: a spatial analysis].

PubMed

Fuentes Flores, César; Sánchez Salinas, Omar

2015-12-01

Investigate the spatial distribution pattern of the homicide rate and its relation to sociodemographic features in the Benito Juárez, Coyoacán, and Cuauhtémoc districts of Mexico City in 2010. Inferential cross-sectional study that uses spatial analysis methods to study the spatial association of the homicide rate and demographic features. Spatial association was determined through the location quotient, multiple regression analysis, and the use of geographically weighted regression. Homicides show a heterogeneous location pattern with high rates in areas with non-residential land use, low population density, and low marginalization. Spatial analysis tools are powerful instruments for the design of prevention- and recreation-focused public safety policies that aim to reduce mortality from external causes such as homicides.

THREE-DIMENSIONAL COMPUTATIONAL FLUID DYNAMICS SIMULATIONS OF LOCAL PARTICLE DEPOSITION PATTERNS IN LUNG AIRWAYS

EPA Science Inventory

EPA has identified respirable particulate matter (PM) as a significant threat to human health, particularly in the elderly, in children, and in persons with respiratory disease. However, deposition of PM in the respiratory system is highly variable, depending upon particle chara...

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

PubMed

Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

2016-12-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Feature extraction and classification algorithms for high dimensional data

NASA Technical Reports Server (NTRS)

Lee, Chulhee; Landgrebe, David

1993-01-01

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

Nanopattern-guided growth of single-crystal silicon on amorphous substrates and high-performance sub-100 nm thin-film transistors for three-dimensional integrated circuits

NASA Astrophysics Data System (ADS)

Gu, Jian

This thesis explores how nanopatterns can be used to control the growth of single-crystal silicon on amorphous substrates at low temperature, with potential applications on flat panel liquid-crystal display and 3-dimensional (3D) integrated circuits. I first present excimer laser annealing of amorphous silicon (a-Si) nanostructures on thermally oxidized silicon wafer for controlled formation of single-crystal silicon islands. Preferential nucleation at pattern center is observed due to substrate enhanced edge heating. Single-grain silicon is obtained in a 50 nm x 100 nm rectangular pattern by super lateral growth (SLG). Narrow lines (such as 20-nm-wide) can serve as artificial heterogeneous nucleation sites during crystallization of large patterns, which could lead to the formation of single-crystal silicon islands in a controlled fashion. In addition to eximer laser annealing, NanoPAtterning and nickel-induced lateral C&barbelow;rystallization (NanoPAC) of a-Si lines is presented. Single-crystal silicon is achieved by NanoPAC. The line width of a-Si affects the grain structure of crystallized silicon lines significantly. Statistics show that single-crystal silicon is formed for all lines with width between 50 nm to 200 nm. Using in situ transmission electron microscopy (TEM), nickel-induced lateral crystallization (Ni-ILC) of a-Si inside a pattern is revealed; lithography-constrained single seeding (LISS) is proposed to explain the single-crystal formation. Intragrain line and two-dimensional defects are also studied. To test the electrical properties of NanoPAC silicon films, sub-100 nm thin-film transistors (TFTs) are fabricated using Patten-controlled crystallization of Ṯhin a-Si channel layer and H&barbelow;igh temperature (850°C) annealing, coined PaTH process. PaTH TFTs show excellent device performance over traditional solid phase crystallized (SPC) TFTs in terms of threshold voltage, threshold voltage roll-off, leakage current, subthreshold swing, on/off current ratio, device-to-device uniformity etc. Two-dimensional device simulations show that PaTH TFTs are comparable to silicon-on-insulator (SOI) devices, making it a promising candidate for the fabrication of future high performance, low-power 3D integrated circuits. Finally, an ultrafast nanolithography technique, laser-assisted direct imprint (LADI) is introduced. LADI shows the ability of patterning nanostructures directly in silicon in nanoseconds with sub-10 nm resolution. The process has potential applications in multiple disciplines, and could be extended to other materials and processes.

Modeling photovoltaic performance in periodic patterned colloidal quantum dot solar cells.

PubMed

Fu, Yulan; Dinku, Abay G; Hara, Yukihiro; Miller, Christopher W; Vrouwenvelder, Kristina T; Lopez, Rene

2015-07-27

Colloidal quantum dot (CQD) solar cells have attracted tremendous attention mostly due to their wide absorption spectrum window and potentially low processability cost. The ultimate efficiency of CQD solar cells is highly limited by their high trap state density. Here we show that the overall device power conversion efficiency could be improved by employing photonic structures that enhance both charge generation and collection efficiencies. By employing a two-dimensional numerical model, we have calculated the characteristics of patterned CQD solar cells based of a simple grating structure. Our calculation predicts a power conversion efficiency as high as 11.2%, with a short circuit current density of 35.2 mA/cm2, a value nearly 1.5 times larger than the conventional flat design, showing the great potential value of patterned quantum dot solar cells.

Depth measurements through controlled aberrations of projected patterns.

PubMed

Birch, Gabriel C; Tyo, J Scott; Schwiegerling, Jim

2012-03-12

Three-dimensional displays have become increasingly present in consumer markets. However, the ability to capture three-dimensional images in space confined environments and without major modifications to current cameras is uncommon. Our goal is to create a simple modification to a conventional camera that allows for three dimensional reconstruction. We require such an imaging system have imaging and illumination paths coincident. Furthermore, we require that any three-dimensional modification to a camera also permits full resolution 2D image capture.Here we present a method of extracting depth information with a single camera and aberrated projected pattern. A commercial digital camera is used in conjunction with a projector system with astigmatic focus to capture images of a scene. By using an astigmatic projected pattern we can create two different focus depths for horizontal and vertical features of a projected pattern, thereby encoding depth. By designing an aberrated projected pattern, we are able to exploit this differential focus in post-processing designed to exploit the projected pattern and optical system. We are able to correlate the distance of an object at a particular transverse position from the camera to ratios of particular wavelet coefficients.We present our information regarding construction, calibration, and images produced by this system. The nature of linking a projected pattern design and image processing algorithms will be discussed.

How do drug market changes affect characteristics of injecting initiation and subsequent patterns of drug use? Findings from a cohort of regular heroin and methamphetamine injectors in Melbourne, Australia.

PubMed

Horyniak, Danielle; Stoové, Mark; Degenhardt, Louisa; Aitken, Campbell; Kerr, Thomas; Dietze, Paul

2015-01-01

Changes in drug market characteristics have been shown to affect drug use patterns but few studies have examined their impacts on injecting initiation experiences and subsequent patterns of injecting drug use (IDU). We collected data on self-reported injecting initiation experiences and past-month patterns of IDU from 688 regular heroin and methamphetamine injectors in Melbourne, Australia, who initiated injecting across three different drug market periods (prior to the Australian heroin shortage ('high heroin')/immediately following the shortage ('low heroin')/'contemporary' markets (fluctuating heroin and methamphetamine availability)). We used univariable and multivariable logistic regression to examine the relationship between period of injecting initiation and first drug injected, and multinomial logistic regression for the relationship between period of injecting initiation and current injecting patterns. 425 participants (62%) reported initiating injecting in the high heroin period, 146 (21%) in the low heroin period, and 117 (17%) in the contemporary period. Participants who initiated injecting during the low heroin period were twice as likely to initiate injecting using a drug other than heroin (AOR: 1.94, 95% CI: 1.27-2.95). The most common patterns of drug use among study participants in the month preceding interview were polydrug use (44%) and primary heroin use (41%). Injecting initiation period was either non-significantly or weakly associated with current drug use pattern, which was more strongly associated with other socio-demographic and drug use characteristics, particularly self-reported drug of choice. The drug market period in which injecting initiation occurred influenced the first drug injected and influenced some aspects of subsequent drug use. In the context of highly dynamic drug markets in which polydrug use is common there is a need for broad harm reduction and drug treatment services which are flexible and responsive to changing patterns of drug use. Copyright © 2014 Elsevier B.V. All rights reserved.

Floating Data and the Problem with Illustrating Multiple Regression.

ERIC Educational Resources Information Center

Sachau, Daniel A.

2000-01-01

Discusses how to introduce basic concepts of multiple regression by creating a large-scale, three-dimensional regression model using the classroom walls and floor. Addresses teaching points that should be covered and reveals student reaction to the model. Finds that the greatest benefit of the model is the low fear, walk-through, nonmathematical…

Enabling complex nanoscale pattern customization using directed self-assembly.

PubMed

Doerk, Gregory S; Cheng, Joy Y; Singh, Gurpreet; Rettner, Charles T; Pitera, Jed W; Balakrishnan, Srinivasan; Arellano, Noel; Sanders, Daniel P

2014-12-16

Block copolymer directed self-assembly is an attractive method to fabricate highly uniform nanoscale features for various technological applications, but the dense periodicity of block copolymer features limits the complexity of the resulting patterns and their potential utility. Therefore, customizability of nanoscale patterns has been a long-standing goal for using directed self-assembly in device fabrication. Here we show that a hybrid organic/inorganic chemical pattern serves as a guiding pattern for self-assembly as well as a self-aligned mask for pattern customization through cotransfer of aligned block copolymer features and an inorganic prepattern. As informed by a phenomenological model, deliberate process engineering is implemented to maintain global alignment of block copolymer features over arbitrarily shaped, 'masking' features incorporated into the chemical patterns. These hybrid chemical patterns with embedded customization information enable deterministic, complex two-dimensional nanoscale pattern customization through directed self-assembly.

A novel multi-target regression framework for time-series prediction of drug efficacy.

PubMed

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-18

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

A novel multi-target regression framework for time-series prediction of drug efficacy

PubMed Central

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-01

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task. PMID:28098186

Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions.

PubMed

Drouard, Vincent; Horaud, Radu; Deleforge, Antoine; Ba, Sileye; Evangelidis, Georgios

2017-03-01

Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose to use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available data sets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.

Nonparametric regression applied to quantitative structure-activity relationships

PubMed

Constans; Hirst

2000-03-01

Several nonparametric regressors have been applied to modeling quantitative structure-activity relationship (QSAR) data. The simplest regressor, the Nadaraya-Watson, was assessed in a genuine multivariate setting. Other regressors, the local linear and the shifted Nadaraya-Watson, were implemented within additive models--a computationally more expedient approach, better suited for low-density designs. Performances were benchmarked against the nonlinear method of smoothing splines. A linear reference point was provided by multilinear regression (MLR). Variable selection was explored using systematic combinations of different variables and combinations of principal components. For the data set examined, 47 inhibitors of dopamine beta-hydroxylase, the additive nonparametric regressors have greater predictive accuracy (as measured by the mean absolute error of the predictions or the Pearson correlation in cross-validation trails) than MLR. The use of principal components did not improve the performance of the nonparametric regressors over use of the original descriptors, since the original descriptors are not strongly correlated. It remains to be seen if the nonparametric regressors can be successfully coupled with better variable selection and dimensionality reduction in the context of high-dimensional QSARs.

Three-dimensional kinematic correlates of ball velocity during maximal instep soccer kicking in males.

PubMed

Sinclair, Jonathan; Fewtrell, David; Taylor, Paul John; Bottoms, Lindsay; Atkins, Stephen; Hobbs, Sarah Jane

2014-01-01

Achieving a high ball velocity is important during soccer shooting, as it gives the goalkeeper less time to react, thus improving a player's chance of scoring. This study aimed to identify important technical aspects of kicking linked to the generation of ball velocity using regression analyses. Maximal instep kicks were obtained from 22 academy-level soccer players using a 10-camera motion capture system sampling at 500 Hz. Three-dimensional kinematics of the lower extremity segments were obtained. Regression analysis was used to identify the kinematic parameters associated with the development of ball velocity. A single biomechanical parameter; knee extension velocity of the kicking limb at ball contact Adjusted R(2) = 0.39, p ≤ 0.01 was obtained as a significant predictor of ball-velocity. This study suggests that sagittal plane knee extension velocity is the strongest contributor to ball velocity and potentially overall kicking performance. It is conceivable therefore that players may benefit from exposure to coaching and strength techniques geared towards the improvement of knee extension angular velocity as highlighted in this study.

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

Zhang, Jiangjiang; Li, Weixuan; Lin, Guang

In decision-making for groundwater management and contamination remediation, it is important to accurately evaluate the probability of the occurrence of a failure event. For small failure probability analysis, a large number of model evaluations are needed in the Monte Carlo (MC) simulation, which is impractical for CPU-demanding models. One approach to alleviate the computational cost caused by the model evaluations is to construct a computationally inexpensive surrogate model instead. However, using a surrogate approximation can cause an extra error in the failure probability analysis. Moreover, constructing accurate surrogates is challenging for high-dimensional models, i.e., models containing many uncertain input parameters.more » To address these issues, we propose an efficient two-stage MC approach for small failure probability analysis in high-dimensional groundwater contaminant transport modeling. In the first stage, a low-dimensional representation of the original high-dimensional model is sought with Karhunen–Loève expansion and sliced inverse regression jointly, which allows for the easy construction of a surrogate with polynomial chaos expansion. Then a surrogate-based MC simulation is implemented. In the second stage, the small number of samples that are close to the failure boundary are re-evaluated with the original model, which corrects the bias introduced by the surrogate approximation. The proposed approach is tested with a numerical case study and is shown to be 100 times faster than the traditional MC approach in achieving the same level of estimation accuracy.« less

Spatiotemporal chaos involving wave instability.

PubMed

Berenstein, Igal; Carballido-Landeira, Jorge

2017-01-01

In this paper, we investigate pattern formation in a model of a reaction confined in a microemulsion, in a regime where both Turing and wave instability occur. In one-dimensional systems, the pattern corresponds to spatiotemporal intermittency where the behavior of the systems alternates in both time and space between stationary Turing patterns and traveling waves. In two-dimensional systems, the behavior initially may correspond to Turing patterns, which then turn into wave patterns. The resulting pattern also corresponds to a chaotic state, where the system alternates in both space and time between standing wave patterns and traveling waves, and the local dynamics may show vanishing amplitude of the variables.

Spatiotemporal chaos involving wave instability

NASA Astrophysics Data System (ADS)

Berenstein, Igal; Carballido-Landeira, Jorge

2017-01-01

In this paper, we investigate pattern formation in a model of a reaction confined in a microemulsion, in a regime where both Turing and wave instability occur. In one-dimensional systems, the pattern corresponds to spatiotemporal intermittency where the behavior of the systems alternates in both time and space between stationary Turing patterns and traveling waves. In two-dimensional systems, the behavior initially may correspond to Turing patterns, which then turn into wave patterns. The resulting pattern also corresponds to a chaotic state, where the system alternates in both space and time between standing wave patterns and traveling waves, and the local dynamics may show vanishing amplitude of the variables.

A Penalized Likelihood Framework For High-Dimensional Phylogenetic Comparative Methods And An Application To New-World Monkeys Brain Evolution.

PubMed

Julien, Clavel; Leandro, Aristide; Hélène, Morlon

2018-06-19

Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.

Three-dimensional reconstruction of the giant mimivirus particle with an x-ray free-electron laser.

PubMed

Ekeberg, Tomas; Svenda, Martin; Abergel, Chantal; Maia, Filipe R N C; Seltzer, Virginie; Claverie, Jean-Michel; Hantke, Max; Jönsson, Olof; Nettelblad, Carl; van der Schot, Gijs; Liang, Mengning; DePonte, Daniel P; Barty, Anton; Seibert, M Marvin; Iwan, Bianca; Andersson, Inger; Loh, N Duane; Martin, Andrew V; Chapman, Henry; Bostedt, Christoph; Bozek, John D; Ferguson, Ken R; Krzywinski, Jacek; Epp, Sascha W; Rolles, Daniel; Rudenko, Artem; Hartmann, Robert; Kimmel, Nils; Hajdu, Janos

2015-03-06

We present a proof-of-concept three-dimensional reconstruction of the giant mimivirus particle from experimentally measured diffraction patterns from an x-ray free-electron laser. Three-dimensional imaging requires the assembly of many two-dimensional patterns into an internally consistent Fourier volume. Since each particle is randomly oriented when exposed to the x-ray pulse, relative orientations have to be retrieved from the diffraction data alone. We achieve this with a modified version of the expand, maximize and compress algorithm and validate our result using new methods.

Verification of a three-dimensional viscous flow analysis for a single stage compressor

NASA Astrophysics Data System (ADS)

Matsuoka, Akinori; Hashimoto, Keisuke; Nozaki, Osamu; Kikuchi, Kazuo; Fukuda, Masahiro; Tamura, Atsuhiro

1992-12-01

A transonic flowfield around rotor blades of a highly loaded single stage axial compressor was numerically analyzed by a three dimensional compressible Navier-Stokes equation code using Chakravarthy and Osher type total variation diminishing (TVD) scheme. A stage analysis which calculates both flowfields around inlet guide vane (IGV) and rotor blades simultaneously was carried out. Comparing with design values and experimental data, computed results show slight difference quantitatively. But the numerical calculation simulates well the pressure rise characteristics of the compressor and its flow pattern including strong shock surface.

Exciton Polaritons in a Two-Dimensional Lieb Lattice with Spin-Orbit Coupling

NASA Astrophysics Data System (ADS)

Whittaker, C. E.; Cancellieri, E.; Walker, P. M.; Gulevich, D. R.; Schomerus, H.; Vaitiekus, D.; Royall, B.; Whittaker, D. M.; Clarke, E.; Iorsh, I. V.; Shelykh, I. A.; Skolnick, M. S.; Krizhanovskii, D. N.

2018-03-01

We study exciton polaritons in a two-dimensional Lieb lattice of micropillars. The energy spectrum of the system features two flat bands formed from S and Px ,y photonic orbitals, into which we trigger bosonic condensation under high power excitation. The symmetry of the orbital wave functions combined with photonic spin-orbit coupling gives rise to emission patterns with pseudospin texture in the flat band condensates. Our Letter shows the potential of polariton lattices for emulating flat band Hamiltonians with spin-orbit coupling, orbital degrees of freedom, and interactions.

Exciton Polaritons in a Two-Dimensional Lieb Lattice with Spin-Orbit Coupling.

PubMed

Whittaker, C E; Cancellieri, E; Walker, P M; Gulevich, D R; Schomerus, H; Vaitiekus, D; Royall, B; Whittaker, D M; Clarke, E; Iorsh, I V; Shelykh, I A; Skolnick, M S; Krizhanovskii, D N

2018-03-02

We study exciton polaritons in a two-dimensional Lieb lattice of micropillars. The energy spectrum of the system features two flat bands formed from S and P_{x,y} photonic orbitals, into which we trigger bosonic condensation under high power excitation. The symmetry of the orbital wave functions combined with photonic spin-orbit coupling gives rise to emission patterns with pseudospin texture in the flat band condensates. Our Letter shows the potential of polariton lattices for emulating flat band Hamiltonians with spin-orbit coupling, orbital degrees of freedom, and interactions.

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

PubMed

Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification

PubMed Central

Feng, Yang; Jiang, Jiancheng; Tong, Xin

2015-01-01

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing. PMID:27185970

Shearlet-based measures of entropy and complexity for two-dimensional patterns

NASA Astrophysics Data System (ADS)

Brazhe, Alexey

2018-06-01

New spatial entropy and complexity measures for two-dimensional patterns are proposed. The approach is based on the notion of disequilibrium and is built on statistics of directional multiscale coefficients of the fast finite shearlet transform. Shannon entropy and Jensen-Shannon divergence measures are employed. Both local and global spatial complexity and entropy estimates can be obtained, thus allowing for spatial mapping of complexity in inhomogeneous patterns. The algorithm is validated in numerical experiments with a gradually decaying periodic pattern and Ising surfaces near critical state. It is concluded that the proposed algorithm can be instrumental in describing a wide range of two-dimensional imaging data, textures, or surfaces, where an understanding of the level of order or randomness is desired.

Fabrication and characterization of hexagonally patterned quasi-1D ZnO nanowire arrays

PubMed Central

2014-01-01

Quasi-one-dimensional (quasi-1D) ZnO nanowire arrays with hexagonal pattern have been successfully synthesized via the vapor transport process without any metal catalyst. By utilizing polystyrene microsphere self-assembled monolayer, sol–gel-derived ZnO thin films were used as the periodic nucleation sites for the growth of ZnO nanowires. High-quality quasi-1D ZnO nanowires were grown from nucleation sites, and the original hexagonal periodicity is well-preserved. According to the experimental results, the vapor transport solid condensation mechanism was proposed, in which the sol–gel-derived ZnO film acting as a seed layer for nucleation. This simple method provides a favorable way to form quasi-1D ZnO nanostructures applicable to diverse fields such as two-dimensional photonic crystal, nanolaser, sensor arrays, and other optoelectronic devices. PMID:24521308

Identification marking by means of laser peening

DOEpatents

Hackel, Lloyd A.; Dane, C. Brent; Harris, Fritz

2002-01-01

The invention is a method and apparatus for marking components by inducing a shock wave on the surface that results in an indented (strained) layer and a residual compressive stress in the surface layer. One embodiment of the laser peenmarking system rapidly imprints, with single laser pulses, a complete identification code or three-dimensional pattern and leaves the surface in a state of deep residual compressive stress. A state of compressive stress in parts made of metal or other materials is highly desirable to make them resistant to fatigue failure and stress corrosion cracking. This process employs a laser peening system and beam spatial modulation hardware or imaging technology that can be setup to impress full three dimensional patterns into metal surfaces at the pulse rate of the laser, a rate that is at least an order of magnitude faster than competing marking technologies.

A dimensional approach to understanding severity estimates and risk correlates of marijuana abuse and dependence in adults

PubMed Central

WU, LI-TZY; WOODY, GEORGE E.; YANG, CHONGMING; PAN, JENG-JONG; REEVE, BRYCE B.; BLAZER, DAN G.

2012-01-01

While item response theory (IRT) research shows a latent severity trait underlying response patterns of substance abuse and dependence symptoms, little is known about IRT-based severity estimates in relation to clinically relevant measures. In response to increased prevalences of marijuana-related treatment admissions, an elevated level of marijuana potency, and the debate on medical marijuana use, we applied dimensional approaches to understand IRT-based severity estimates for marijuana use disorders (MUDs) and their correlates while simultaneously considering gender- and race/ethnicity-related differential item functioning (DIF). Using adult data from the 2008 National Survey on Drug Use and Health (N=37,897), DSM-IV criteria for MUDs among past-year marijuana users were examined by IRT, logistic regression, and multiple indicators–multiple causes (MIMIC) approaches. Among 6,917 marijuana users, 15% met criteria for a MUD; another 24% exhibited subthreshold dependence. Abuse criteria were highly correlated with dependence criteria (correlation=0.90), indicating unidimensionality; item information curves revealed redundancy in multiple criteria. MIMIC analyses showed that MUD criteria were positively associated with weekly marijuana use, early marijuana use, other substance use disorders, substance abuse treatment, and serious psychological distress. African Americans and Hispanics showed higher levels of MUDs than whites, even after adjusting for race/ethnicity-related DIF. The redundancy in multiple criteria suggests an opportunity to improve efficiency in measuring symptom-level manifestations by removing low-informative criteria. Elevated rates of MUDs among African Americans and Hispanics require research to elucidate risk factors and improve assessments of MUDs for different racial/ethnic groups. PMID:22351489

Patterns of morning and evening fatigue among adults with HIV/AIDS.

PubMed

Lerdal, Anners; Gay, Caryl L; Aouizerat, Bradley E; Portillo, Carmen J; Lee, Kathryn A

2011-08-01

Describe patterns of morning and evening fatigue in adults with HIV and examine their relationship to demographic and clinical factors and other symptoms. Most studies of HIV-related fatigue assess average levels of fatigue and do not address its diurnal fluctuations. Patterns of fatigue over the course of the day may have important implications for assessment and treatment. A cross-sectional, correlational design was used with six repeated measures over 72 hours. A convenience sample of 318 HIV-infected adults was recruited in San Francisco. Socio-demographic, clinical and symptom data were collected with questionnaires. CD4+ T-cell count and viral load were obtained from medical records. Participants completed a four-item version of the Lee Fatigue Scale each morning and evening for three consecutive days. Participants were grouped based on their diurnal pattern of fatigue (high evening only, high morning only, high morning and evening and low morning and evening). Group comparisons and logistic regression were used to determine the unique predictors of each fatigue pattern. The high evening fatigue pattern was associated with anxiety and the high morning pattern was associated with anxiety and depression. The morning fatigue pattern showed very little fluctuation between morning and evening, the evening pattern showed the largest fluctuation. The high morning and evening pattern was associated with anxiety, depression and sleep disturbance and this group reported the most fatigue-related distress and interference in functioning. These results provide initial evidence for the importance of assessing the patient's daily pattern of fatigue fluctuation, as different patterns were associated with different symptom experiences and perhaps different aetiologies. Different fatigue patterns may benefit from tailored intervention strategies. Management of depressive symptoms could be tested in patients who experience high levels of morning fatigue. © 2011 Blackwell Publishing Ltd.

Plasma sheath structure surrounding a large powered spacecraft

NASA Technical Reports Server (NTRS)

Mandell, M. J.; Jongeward, G. A.; Katz, I.

1984-01-01

Various factors determining the floating potential of a highly biased (about 4-kV) spacecraft in low earth orbit are discussed. While the common rule of thumb (90 percent negative; 10 percent positive) is usually a good guide, different biasing and grounding patterns can lead to high positive potentials. The NASCAP/LEO code can be used to predict spacecraft floating potential for complex three-dimensional spacecraft.

Two-dimensional transport in structured optical force landscapes

NASA Astrophysics Data System (ADS)

Xiao, Ke

The overdamped transport of a Brownian particle in a structured force landscape has been studied extensively for a century. Even such well-studied examples as Brownian transport in a one-dimensional tilted washboard potential continue to yield surprising results, with recent discoveries including the giant enhancement of diffusion at the depinning transition, and the so-called "thermal ratchet effect". The transport phenomena in higher-dimensional systems should be substantially richer, but remain largely unexplored. In this Thesis we study the biased diffusion of colloidal spheres through two-dimensional force landscapes created with holographic optical tweezers (HOT). These studies take advantage of holographic video microscopy (HVM), which enables us to follow spheres' three-dimensional motions with nanometer resolution while simultaneously measuring their radii and refractive indexes with part-per-thousand resolution. Using these techniques we investigated the kinetically and statistically locked-in transport of colloidal spheres through arrays of optical traps, and confirmed previously untested predictions for kinetically locked-in transport that can be used for sorting applications with previously unheard finesse. Extending this result to highly structured two-dimensional landscapes, we developed prismatic optical fractionation, in which objects with different physical properties are deflected into different directions, a phenomenon analogous to a prism dispersing different wavelengths of light into different directions. Our simulational and experimental studies revealed the important role that thermal fluctuations play in establishing the hierarchy of kinetically locked-in states. We also investigated Brownian motion in a two-dimensional optical force landscape that varies in time. The traps for these studies were arranged in particular pattern called a "Fibonacci spiral" that is both the densest arrangement of circular objects with a circular domain and also particularly endowed with useful and interesting symmetries. Periodically rotating this pattern gives rise to transport in the both radial and azimuthal dimensions, whose direction depends on the angle and speed of rotation as well as the inter-trap separation. This deceptively simple system displays an extremely rich pattern of flux reversals in both dimensions and creates new avenues for studying the departure from equilibrium in noise-driven machines.

Practical limits on muscle synergy identification by non-negative matrix factorization in systems with mechanical constraints.

PubMed

Burkholder, Thomas J; van Antwerp, Keith W

2013-02-01

Statistical decomposition, including non-negative matrix factorization (NMF), is a convenient tool for identifying patterns of structured variability within behavioral motor programs, but it is unclear how the resolved factors relate to actual neural structures. Factors can be extracted from a uniformly sampled, low-dimension command space. In practical application, the command space is limited, either to those activations that perform some task(s) successfully or to activations induced in response to specific perturbations. NMF was applied to muscle activation patterns synthesized from low dimensional, synergy-like control modules mimicking simple task performance or feedback activation from proprioceptive signals. In the task-constrained paradigm, the accuracy of control module recovery was highly dependent on the sampled volume of control space, such that sampling even 50% of control space produced a substantial degradation in factor accuracy. In the feedback paradigm, NMF was not capable of extracting more than four control modules, even in a mechanical model with seven internal degrees of freedom. Reduced access to the low-dimensional control space imposed by physical constraints may result in substantial distortion of an existing low dimensional controller, such that neither the dimensionality nor the composition of the recovered/extracted factors match the original controller.

Maintaining Moore's law: enabling cost-friendly dimensional scaling

NASA Astrophysics Data System (ADS)

Mallik, Arindam; Ryckaert, Julien; Mercha, Abdelkarim; Verkest, Diederik; Ronse, Kurt; Thean, Aaron

2015-03-01

Moore's Law (Moore's Observation) has been driving the progress in semiconductor technology for the past 50 years. The semiconductor industry is at a juncture where significant increase in manufacturing cost is foreseen to sustain the past trend of dimensional scaling. At N10 and N7 technology nodes, the industry is struggling to find a cost-friendly solution. At a device level, technologists have come up with novel devices (finFET, Gate-All-Around), material innovations (SiGe, Ge) to boost performance and reduce power consumption. On the other hand, from the patterning side, the relative slow ramp-up of alternative lithography technologies like EUVL and DSA pushes the industry to adopt a severely multi-patterning-based solution. Both of these technological transformations have a big impact on die yield and eventually die cost. This paper is aimed to analyze the impact on manufacturing cost to keep the Moore's law alive. We have proposed and analyzed various patterning schemes that can enable cost-friendly scaling. We evaluated the impact of EUVL introduction on tackling the high cost of manufacturing. The primary objective of this paper is to maintain Moore's scaling from a patterning perspective and analyzing EUV lithography introduction at a die level.

Dirac electrons in Moiré superlattice: From two to three dimensions

NASA Astrophysics Data System (ADS)

Hu, Chen; Michaud-Rioux, Vincent; Kong, Xianghua; Guo, Hong

2017-11-01

Moiré patterns in van der Waals (vdW) heterostructures bring novel physical effects to the materials. We report theoretical investigations of the Moiré pattern formed by graphene (Gr) on hexagonal boron nitride (h BN). For both the two-dimensional (2D) flat-sheet and the freestanding three-dimensional (3D) wavelike film geometries, the behaviors of Dirac electrons are strongly modulated by the local high-symmetry stacking configurations of the Moiré pattern. In the 2D flat sheet, the secondary Dirac cone (SDC) dispersion emerges due to the stacking-selected localization of SDC wave functions, while the original Dirac cone (ODC) gap is suppressed due to an overall effect of ODC wave functions. In the freestanding 3D wavelike Moiré structure, we predict that a specific local stacking in the Moiré superlattice is promoted at the expense of other local stackings, leading to an electronic structure more similar to that of the perfectly matching flat Gr/h BN than that of the flat-sheet 2D Moiré pattern. To capture the overall picture of the Moiré superlattice, supercells containing 12 322 atoms are simulated by first principles.

Build Angle: Does It Influence the Accuracy of 3D-Printed Dental Restorations Using Digital Light-Processing Technology?

PubMed

Osman, Reham B; Alharbi, Nawal; Wismeijer, Daniel

The aim of this study was to evaluate the effect of the build orientation/build angle on the dimensional accuracy of full-coverage dental restorations manufactured using digital light-processing technology (DLP-AM). A full dental crown was digitally designed and 3D-printed using DLP-AM. Nine build angles were used: 90, 120, 135, 150, 180, 210, 225, 240, and 270 degrees. The specimens were digitally scanned using a high-resolution optical surface scanner (IScan D104i, Imetric). Dimensional accuracy was evaluated using the digital subtraction technique. The 3D digital files of the scanned printed crowns (test model) were exported in standard tessellation language (STL) format and superimposed on the STL file of the designed crown [reference model] using Geomagic Studio 2014 (3D Systems). The root mean square estimate (RMSE) values were evaluated, and the deviation patterns on the color maps were further assessed. The build angle influenced the dimensional accuracy of 3D-printed restorations. The lowest RMSE was recorded for the 135-degree and 210-degree build angles. However, the overall deviation pattern on the color map was more favorable with the 135-degree build angle in contrast with the 210-degree build angle where the deviation was observed around the critical marginal area. Within the limitations of this study, the recommended build angle using the current DLP system was 135 degrees. Among the selected build angles, it offers the highest dimensional accuracy and the most favorable deviation pattern. It also offers a self-supporting crown geometry throughout the building process.

Highly Sensitive Immunoassay Based on Controlled Rehydration of Patterned Reagents in a 2-Dimensional Paper Network

PubMed Central

2015-01-01

We have demonstrated a multistep 2-dimensional paper network immunoassay based on controlled rehydration of patterned, dried reagents. Previous work has shown that signal enhancement improves the limit of detection in 2-dimensional paper network assays, but until now, reagents have only been included as wet or dried in separate conjugate pads placed at the upstream end of the assay device. Wet reagents are not ideal for point-of-care because they must be refrigerated and typically limit automation and require more user steps. Conjugate pads allow drying but do not offer any control of the reagent distribution upon rehydration and can be a source of error when pads do not contact the assay membrane uniformly. Furthermore, each reagent is dried on a separate pad, increasing the fabrication complexity when implementing multistep assays that require several different reagents. Conversely, our novel method allows for consistent, controlled rehydration from patterned reagent storage depots directly within the paper membrane. In this assay demonstration, four separate reagents were patterned in different regions of the assay device: a gold-antibody conjugate used for antigen detection and three different signal enhancement components that must not be mixed until immediately before use. To show the viability of patterning and drying reagents directly onto a paper device for dry reagent storage and subsequent controlled release, we tested this device with the malaria antigen Plasmodium falciparum histidine-rich protein 2 (PfHRP2) as an example of target analyte. In this demonstration, the signal enhancement step increases the visible signal by roughly 3-fold and decreases the analytical limit of detection by 2.75-fold. PMID:24882058

Validation of a selective ensemble-based classification scheme for myoelectric control using a three-dimensional Fitts' Law test.

PubMed

Scheme, Erik J; Englehart, Kevin B

2013-07-01

When controlling a powered upper limb prosthesis it is important not only to know how to move the device, but also when not to move. A novel approach to pattern recognition control, using a selective multiclass one-versus-one classification scheme has been shown to be capable of rejecting unintended motions. This method was shown to outperform other popular classification schemes when presented with muscle contractions that did not correspond to desired actions. In this work, a 3-D Fitts' Law test is proposed as a suitable alternative to using virtual limb environments for evaluating real-time myoelectric control performance. The test is used to compare the selective approach to a state-of-the-art linear discriminant analysis classification based scheme. The framework is shown to obey Fitts' Law for both control schemes, producing linear regression fittings with high coefficients of determination (R(2) > 0.936). Additional performance metrics focused on quality of control are discussed and incorporated in the evaluation. Using this framework the selective classification based scheme is shown to produce significantly higher efficiency and completion rates, and significantly lower overshoot and stopping distances, with no significant difference in throughput.

Dimensional depression severity in women with major depression and post-traumatic stress disorder correlates with fronto-amygdalar hypoconnectivty.

PubMed

Satterthwaite, T D; Cook, P A; Bruce, S E; Conway, C; Mikkelsen, E; Satchell, E; Vandekar, S N; Durbin, T; Shinohara, R T; Sheline, Y I

2016-07-01

Depressive symptoms are common in multiple psychiatric disorders and are frequent sequelae of trauma. A dimensional conceptualization of depression suggests that symptoms should be associated with a continuum of deficits in specific neural circuits. However, most prior investigations of abnormalities in functional connectivity have typically focused on a single diagnostic category using hypothesis-driven seed-based analyses. Here, using a sample of 105 adult female participants from three diagnostic groups (healthy controls, n=17; major depression, n=38; and post-traumatic stress disorder, n=50), we examine the dimensional relationship between resting-state functional dysconnectivity and severity of depressive symptoms across diagnostic categories using a data-driven analysis (multivariate distance-based matrix regression). This connectome-wide analysis identified foci of dysconnectivity associated with depression severity in the bilateral amygdala. Follow-up seed analyses using subject-specific amygdala segmentations revealed that depression severity was associated with amygdalo-frontal hypo-connectivity in a network of regions including bilateral dorsolateral prefrontal cortex, anterior cingulate and anterior insula. In contrast, anxiety was associated with elevated connectivity between the amygdala and the ventromedial prefrontal cortex. Taken together, these results emphasize the centrality of the amygdala in the pathophysiology of depressive symptoms, and suggest that dissociable patterns of amygdalo-frontal dysconnectivity are a critical neurobiological feature across clinical diagnostic categories.

A two-dimensional hydrodynamic model of the St. Clair-Detroit River waterway in the Great Lakes basin

USGS Publications Warehouse

Holtschlag, David J.; Koschik, John A.

2002-01-01

The St. Clair–Detroit River Waterway connects Lake Huron with Lake Erie in the Great Lakes basin to form part of the international boundary between the United States and Canada. A two-dimensional hydrodynamic model is developed to compute flow velocities and water levels as part of a source-water assessment of public water intakes. The model, which uses the generalized finite-element code RMA2, discretizes the waterway into a mesh formed by 13,783 quadratic elements defined by 42,936 nodes. Seven steadystate scenarios are used to calibrate the model by adjusting parameters associated with channel roughness in 25 material zones in sub-areas of the waterway. An inverse modeling code is used to systematically adjust model parameters and to determine their associated uncertainty by use of nonlinear regression. Calibration results show close agreement between simulated and expected flows in major channels and water levels at gaging stations. Sensitivity analyses describe the amount of information available to estimate individual model parameters, and quantify the utility of flow measurements at selected cross sections and water-level measurements at gaging stations. Further data collection, model calibration analysis, and grid refinements are planned to assess and enhance two-dimensional flow simulation capabilities describing the horizontal flow distributions in St. Clair and Detroit Rivers and circulation patterns in Lake St. Clair.

Heat tracing to determine spatial patterns of hyporheic exchange across a river transect

NASA Astrophysics Data System (ADS)

Lu, Chengpeng; Chen, Shuai; Zhang, Ying; Su, Xiaoru; Chen, Guohao

2017-09-01

Significant spatial variability of water fluxes may exist at the water-sediment interface in river channels and has great influence on a variety of water issues. Understanding the complicated flow systems controlling the flux exchanges along an entire river is often limited due to averaging of parameters or the small number of discrete point measurements usually used. This study investigated the spatial pattern of the hyporheic flux exchange across a river transect in China, using the heat tracing approach. This was done with measurements of temperature at high spatial resolution during a 64-h monitoring period and using the data to identify the spatial pattern of the hyporheic exchange flux with the aid of a one-dimensional conduction-advection-dispersion model (VFLUX). The threshold of neutral exchange was considered as 126 L m-2 d-1 in this study and the heat tracing results showed that the change patterns of vertical hyporheic flux varied with buried depth along the river transect; however, the hyporheic flux was not simply controlled by the streambed hydraulic conductivity and water depth in the river transect. Also, lateral flow dominated the hyporheic process within the shallow high-permeability streambed, while the vertical flow was dominant in the deep low-permeability streambed. The spatial pattern of hyporheic exchange across the river transect was naturally controlled by the heterogeneity of the streambed and the bedform of the stream cross-section. Consequently, a two-dimensional conceptual illustration of the hyporheic process across the river transect is proposed, which could be applicable to river transects of similar conditions.

Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population

PubMed Central

Mathew, Boby; Léon, Jens; Sannemann, Wiebke; Sillanpää, Mikko J.

2018-01-01

Gene-by-gene interactions, also known as epistasis, regulate many complex traits in different species. With the availability of low-cost genotyping it is now possible to study epistasis on a genome-wide scale. However, identifying genome-wide epistasis is a high-dimensional multiple regression problem and needs the application of dimensionality reduction techniques. Flowering Time (FT) in crops is a complex trait that is known to be influenced by many interacting genes and pathways in various crops. In this study, we successfully apply Sure Independence Screening (SIS) for dimensionality reduction to identify two-way and three-way epistasis for the FT trait in a Multiparent Advanced Generation Inter-Cross (MAGIC) barley population using the Bayesian multilocus model. The MAGIC barley population was generated from intercrossing among eight parental lines and thus, offered greater genetic diversity to detect higher-order epistatic interactions. Our results suggest that SIS is an efficient dimensionality reduction approach to detect high-order interactions in a Bayesian multilocus model. We also observe that many of our findings (genomic regions with main or higher-order epistatic effects) overlap with known candidate genes that have been already reported in barley and closely related species for the FT trait. PMID:29254994

Three-dimensional flow structure and patterns of bed shear stress in an evolving compound meander bend

USGS Publications Warehouse

Engel, Frank; Rhoads, Bruce L.

2016-01-01

Compound meander bends with multiple lobes of maximum curvature are common in actively evolving lowland rivers. Interaction among spatial patterns of mean flow, turbulence, bed morphology, bank failures and channel migration in compound bends is poorly understood. In this paper, acoustic Doppler current profiler (ADCP) measurements of the three-dimensional (3D) flow velocities in a compound bend are examined to evaluate the influence of channel curvature and hydrologic variability on the structure of flow within the bend. Flow structure at various flow stages is related to changes in bed morphology over the study timeframe. Increases in local curvature within the upstream lobe of the bend reduce outer bank velocities at morphologically significant flows, creating a region that protects the bank from high momentum flow and high bed shear stresses. The dimensionless radius of curvature in the upstream lobe is one-third less than that of the downstream lobe, with average bank erosion rates less than half of the erosion rates for the downstream lobe. Higher bank erosion rates within the downstream lobe correspond to the shift in a core of high velocity and bed shear stresses toward the outer bank as flow moves through the two lobes. These erosion patterns provide a mechanism for continued migration of the downstream lobe in the near future. Bed material size distributions within the bend correspond to spatial patterns of bed shear stress magnitudes, indicating that bed material sorting within the bend is governed by bed shear stress. Results suggest that patterns of flow, sediment entrainment, and planform evolution in compound meander bends are more complex than in simple meander bends. Moreover, interactions among local influences on the flow, such as woody debris, local topographic steering, and locally high curvature, tend to cause compound bends to evolve toward increasing planform complexity over time rather than stable configurations.

3D absolute shape measurement of live rabbit hearts with a superfast two-frequency phase-shifting technique

PubMed Central

Wang, Yajun; Laughner, Jacob I.; Efimov, Igor R.; Zhang, Song

2013-01-01

This paper presents a two-frequency binary phase-shifting technique to measure three-dimensional (3D) absolute shape of beating rabbit hearts. Due to the low contrast of the cardiac surface, the projector and the camera must remain focused, which poses challenges for any existing binary method where the measurement accuracy is low. To conquer this challenge, this paper proposes to utilize the optimal pulse width modulation (OPWM) technique to generate high-frequency fringe patterns, and the error-diffusion dithering technique to produce low-frequency fringe patterns. Furthermore, this paper will show that fringe patterns produced with blue light provide the best quality measurements compared to fringe patterns generated with red or green light; and the minimum data acquisition speed for high quality measurements is around 800 Hz for a rabbit heart beating at 180 beats per minute. PMID:23482151

Towards microscale electrohydrodynamic three-dimensional printing

NASA Astrophysics Data System (ADS)

He, Jiankang; Xu, Fangyuan; Cao, Yi; Liu, Yaxiong; Li, Dichen

2016-02-01

It is challenging for the existing three-dimensional (3D) printing techniques to fabricate high-resolution 3D microstructures with low costs and high efficiency. In this work we present a solvent-based electrohydrodynamic 3D printing technique that allows fabrication of microscale structures like single walls, crossed walls, lattice and concentric circles. Process parameters were optimized to deposit tiny 3D patterns with a wall width smaller than 10 μm and a high aspect ratio of about 60. Tight bonding among neighbour layers could be achieved with a smooth lateral surface. In comparison with the existing microscale 3D printing techniques, the presented method is low-cost, highly efficient and applicable to multiple polymers. It is envisioned that this simple microscale 3D printing strategy might provide an alternative and innovative way for application in MEMS, biosensor and flexible electronics.

Cell Fate Decision as High-Dimensional Critical State Transition

PubMed Central

Zhou, Joseph; Castaño, Ivan G.; Leong-Quong, Rebecca Y. Y.; Chang, Hannah; Trachana, Kalliopi; Giuliani, Alessandro; Huang, Sui

2016-01-01

Cell fate choice and commitment of multipotent progenitor cells to a differentiated lineage requires broad changes of their gene expression profile. But how progenitor cells overcome the stability of their gene expression configuration (attractor) to exit the attractor in one direction remains elusive. Here we show that commitment of blood progenitor cells to the erythroid or myeloid lineage is preceded by the destabilization of their high-dimensional attractor state, such that differentiating cells undergo a critical state transition. Single-cell resolution analysis of gene expression in populations of differentiating cells affords a new quantitative index for predicting critical transitions in a high-dimensional state space based on decrease of correlation between cells and concomitant increase of correlation between genes as cells approach a tipping point. The detection of “rebellious cells” that enter the fate opposite to the one intended corroborates the model of preceding destabilization of a progenitor attractor. Thus, early warning signals associated with critical transitions can be detected in statistical ensembles of high-dimensional systems, offering a formal theory-based approach for analyzing single-cell molecular profiles that goes beyond current computational pattern recognition, does not require knowledge of specific pathways, and could be used to predict impending major shifts in development and disease. PMID:28027308

A plasmonic optical fiber patterned by template transfer as a high-performance flexible nanoprobe for real-time biosensing

NASA Astrophysics Data System (ADS)

Jia, Peipei; Yang, Jun

2014-07-01

Surface plasmon resonance (SPR) on metal nanostructures offers a promising route for manipulation and interrogation of light in the subwavelength regime. However, the utility of SPR structures is largely limited by currently used complex nanofabrication methods and relatively sophisticated optical components. Here to relieve these restrictions, plasmonic optical fibers are constructed by transferring periodic metal nanostructures from patterned templates onto endfaces of optical fibers using an epoxy adhesive. Patterned metal structures are generally extended from two-dimensional (2D) nanohole arrays to one-dimensional (1D) nanoslit arrays. By controlling the viscosity of the adhesive layer, diverse surface topographies of metal structures are realized with the same template. We design a special plasmonic fiber that simultaneously implements multimode refractive index sensing (transmission and reflection) with remarkably narrow linewidth (6.6 nm) and high figure of merit (60.7), which are both among the best reported values for SPR sensors. We further demonstrate a real-time immunoassay relying on our plasmonic fiber integrated with a special flow cell. Plasmonic optical fibers also take advantages of excellent stability during fiber bending and capability of spectrum filtering. These features enable our plasmonic fibers to open up an alternative avenue for the general community in biosensing and nanoplasmonics.

A plasmonic optical fiber patterned by template transfer as a high-performance flexible nanoprobe for real-time biosensing.

PubMed

Jia, Peipei; Yang, Jun

2014-08-07

Surface plasmon resonance (SPR) on metal nanostructures offers a promising route for manipulation and interrogation of light in the subwavelength regime. However, the utility of SPR structures is largely limited by currently used complex nanofabrication methods and relatively sophisticated optical components. Here to relieve these restrictions, plasmonic optical fibers are constructed by transferring periodic metal nanostructures from patterned templates onto endfaces of optical fibers using an epoxy adhesive. Patterned metal structures are generally extended from two-dimensional (2D) nanohole arrays to one-dimensional (1D) nanoslit arrays. By controlling the viscosity of the adhesive layer, diverse surface topographies of metal structures are realized with the same template. We design a special plasmonic fiber that simultaneously implements multimode refractive index sensing (transmission and reflection) with remarkably narrow linewidth (6.6 nm) and high figure of merit (60.7), which are both among the best reported values for SPR sensors. We further demonstrate a real-time immunoassay relying on our plasmonic fiber integrated with a special flow cell. Plasmonic optical fibers also take advantages of excellent stability during fiber bending and capability of spectrum filtering. These features enable our plasmonic fibers to open up an alternative avenue for the general community in biosensing and nanoplasmonics.

Can skull form predict the shape of the temporomandibular joint? A study using geometric morphometrics on the skulls of wolves and domestic dogs.

PubMed

Curth, Stefan; Fischer, Martin S; Kupczik, Kornelius

2017-11-01

The temporomandibular joint (TMJ) conducts and restrains masticatory movements between the mammalian cranium and the mandible. Through this functional integration, TMJ morphology in wild mammals is strongly correlated with diet, resulting in a wide range of TMJ variations. However, in artificially selected and closely related domestic dogs, dietary specialisations between breeds can be ruled out as a diversifying factor although they display an enormous variation in TMJ morphology. This raises the question of the origin of this variation. Here we hypothesise that, even in the face of reduced functional demands, TMJ shape in dogs can be predicted by skull form; i.e. that the TMJ is still highly integrated in the dog skull. If true, TMJ variation in the dog would be a plain by-product of the enormous cranial variation in dogs and its genetic causes. We addressed this hypothesis using geometric morphometry on a data set of 214 dog and 60 wolf skulls. We digitized 53 three-dimensional landmarks of the skull and the TMJ on CT-based segmentations and compared (1) the variation between domestic dog and wolf TMJs (via principal component analysis) and (2) the pattern of covariation of skull size, flexion and rostrum length with TMJ shape (via regression of centroid size on shape and partial least squares analyses). We show that the TMJ in domestic dogs is significantly more diverse than in wolves: its shape covaries significantly with skull size, flexion and rostrum proportions in patterns which resemble those observed in primates. Similar patterns in canids, which are carnivorous, and primates, which are mostly frugivorous imply the existence of basic TMJ integration patterns which are independent of dietary adaptations. However, only limited amounts of TMJ variation in dogs can be explained by simple covariation with overall skull geometry. This implies that the final TMJ shape is gained partially independently of the rest of the skull. Copyright © 2017 Elsevier GmbH. All rights reserved.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

[Spatial patterns and influence factors of specialization in tea cultivation based on geographically weighted regression model: A case study of Anxi County of Fujian Province, China].

PubMed

Shui, Wei; DU, Yong; Chen, Yi Ping; Jian, Xiao Mei; Fan, Bing Xiong

2017-04-18

Anxi County, specializing in tea cultivation, was taken as a case in this research. Pearson correlation analysis, ordinary least squares model (OLS) and geographically weighted regression model (GWR) were used to select four primary influence factors of specialization in tea cultivation (i.e., the average elevation, net income per capita, proportion of agricultural population, and the distance from roads) by analyzing the specialization degree of each town of Anxi County. Meanwhile, the spatial patterns of specialization in tea cultivation of Anxi County were evaluated. The results indicated that specialization in tea cultivation of Anxi County showed an obvious spatial auto-correlation, and a spatial pattern with "low-middle-high" circle structure, which was similar to Von Thünen's circle structure model, appeared from the county town to its surrounding region. Meanwhile, GWR (0.624) had a better fitting degree than OLS (0.595), and GWR could reasonably expound the spatial data. Contrary to the agricultural location theory of Von Thünen's model, which indicated that distance from market was a determination factor, the specialization degree of tea cultivation in Anxi was mainly decided by natural conditions of mountain area, instead of the social factors. Specialization degree of tea cultivation was positively correlated with the average elevation, net income per capita and the proportion of agricultural population, while a negative correlation was found between the distance from roads and specialization degree of tea cultivation. Coefficients of regression between the specialization degree of tea cultivation and two factors (i.e., the average elevation and net income per capita) showed a spatial pattern of higher level in the north direction and lower level in the south direction. On the contrary, the regression coefficients for the proportion of agricultural population increased from south to north of Anxi County. Furthermore, regression coefficient for the distance from roads showed a spatial pattern of higher level in the northeast direction and lower level in the southwest direction of Anxi County.

Quantitative evaluation of the fetal cerebellar vermis using the median view on three-dimensional ultrasound.

PubMed

Zhao, Dan; Liu, Wei; Cai, Ailu; Li, Jingyu; Chen, Lizhu; Wang, Bing

2013-02-01

The purpose of this study was to investigate the effectiveness for quantitative evaluation of cerebellar vermis using three-dimensional (3D) ultrasound and to establish a nomogram for Chinese fetal vermis measurements during gestation. Sonographic examinations were performed in normal fetuses and in cases suspected of the diagnosis of vermian rotation. 3D median planes were obtained with both OMNIVIEW and tomographic ultrasound imaging. Measurements of the cerebellar vermis were highly correlated between two-dimensional and 3D median planes. The diameter of the cerebellar vermis follows growth approximately predicted by the quadratic regression equation. The normal vermis was almost parallel to the brain stem, with the average angle degree to be <2° in normal fetuses. The average angle degree of the 9 cases of vermian rotation was >5°. Three-dimensional median planes are obtained more easily than two-dimensional ones, and allow accurate measurements of the cerebellar vermis. The 3D approach may enable rapid assessment of fetal cerebral anatomy in standard examination. Measurements of cerebellar vermis may provide a quantitative index for prenatal diagnosis of posterior fossa malformations. © 2012 John Wiley & Sons, Ltd.

Dietary Patterns After the Weaning and Lactation Period Are Associated With Celiac Disease Autoimmunity in Children.

PubMed

Barroso, Monica; Beth, Sytske A; Voortman, Trudy; Jaddoe, Vincent W V; van Zelm, Menno C; Moll, Henriette A; Kiefte-de Jong, Jessica C

2018-06-01

There have been many studies of associations between infant feeding practices and development of celiac disease during childhood, but few studies have focused on overall diets of young children after the weaning period. We aimed to examine the association between common dietary patterns in infants and the occurrence of celiac disease autoimmunity during childhood. We performed a prospective analysis of data from the Generation R Study that comprised 1997 children born from April 2002 through January 2006 in Rotterdam, the Netherlands. Food consumption around 1 year of age was assessed with a validated food-frequency questionnaire. Dietary data were examined using a priori (based on existing guidelines) and a posteriori (principal component analysis and reduced rank regression) dietary pattern analyses. Five dietary patterns were compared. Celiac disease autoimmunity, determined on the basis of serum concentration of transglutaminase-2 autoantibody (ie, TG2A) below or above 7 U/mL, was evaluated at 6 years. Associations between dietary pattern adherence scores and celiac disease autoimmunity were examined using multivariable logistic regression models. Higher adherence to the a posteriori-derived prudent dietary pattern (high intake of vegetables, vegetable oils, pasta, and grains and low consumption of refined cereals and sweet beverages) at 1 year was significantly associated with lower odds of celiac disease autoimmunity at 6 years (odds ratio, 0.67; 95% confidence interval, 0.53-0.84). No significant associations were found for the 4 remaining dietary patterns. In a prospective study of dietary patterns of young children in the Netherlands, we associated a dietary pattern characterized by high consumption of vegetables and grains and low consumption of refined cereals and sweet beverages, with lower odds of celiac disease autoimmunity. Early-life dietary patterns might therefore be involved in the development of celiac disease during childhood. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Why credit risk markets are predestined for exhibiting log-periodic power law structures

NASA Astrophysics Data System (ADS)

Wosnitza, Jan Henrik; Leker, Jens

2014-01-01

Recent research has established the existence of log-periodic power law (LPPL) patterns in financial institutions’ credit default swap (CDS) spreads. The main purpose of this paper is to clarify why credit risk markets are predestined for exhibiting LPPL structures. To this end, the credit risk prediction of two variants of logistic regression, i.e. polynomial logistic regression (PLR) and kernel logistic regression (KLR), are firstly compared to the standard logistic regression (SLR). In doing so, the question whether the performances of rating systems based on balance sheet ratios can be improved by nonlinear transformations of the explanatory variables is resolved. Building on the result that nonlinear balance sheet ratio transformations hardly improve the SLR’s predictive power in our case, we secondly compare the classification performance of a multivariate SLR to the discriminative powers of probabilities of default derived from three different capital market data, namely bonds, CDSs, and stocks. Benefiting from the prompt inclusion of relevant information, the capital market data in general and CDSs in particular increasingly outperform the SLR while approaching the time of the credit event. Due to the higher classification performances, it seems plausible for creditors to align their investment decisions with capital market-based default indicators, i.e., to imitate the aggregate opinion of the market participants. Since imitation is considered to be the source of LPPL structures in financial time series, it is highly plausible to scan CDS spread developments for LPPL patterns. By establishing LPPL patterns in governmental CDS spread trajectories of some European crisis countries, the LPPL’s application to credit risk markets is extended. This novel piece of evidence further strengthens the claim that credit risk markets are adequate breeding grounds for LPPL patterns.

Evolutionary fields can explain patterns of high-dimensional complexity in ecology

NASA Astrophysics Data System (ADS)

Wilsenach, James; Landi, Pietro; Hui, Cang

2017-04-01

One of the properties that make ecological systems so unique is the range of complex behavioral patterns that can be exhibited by even the simplest communities with only a few species. Much of this complexity is commonly attributed to stochastic factors that have very high-degrees of freedom. Orthodox study of the evolution of these simple networks has generally been limited in its ability to explain complexity, since it restricts evolutionary adaptation to an inertia-free process with few degrees of freedom in which only gradual, moderately complex behaviors are possible. We propose a model inspired by particle-mediated field phenomena in classical physics in combination with fundamental concepts in adaptation, which suggests that small but high-dimensional chaotic dynamics near to the adaptive trait optimum could help explain complex properties shared by most ecological datasets, such as aperiodicity and pink, fractal noise spectra. By examining a simple predator-prey model and appealing to real ecological data, we show that this type of complexity could be easily confused for or confounded by stochasticity, especially when spurred on or amplified by stochastic factors that share variational and spectral properties with the underlying dynamics.

Nonlinear gamma correction via normed bicoherence minimization in optical fringe projection metrology

NASA Astrophysics Data System (ADS)

Kamagara, Abel; Wang, Xiangzhao; Li, Sikun

2018-03-01

We propose a method to compensate for the projector intensity nonlinearity induced by gamma effect in three-dimensional (3-D) fringe projection metrology by extending high-order spectra analysis and bispectral norm minimization to digital sinusoidal fringe pattern analysis. The bispectrum estimate allows extraction of vital signal information features such as spectral component correlation relationships in fringe pattern images. Our approach exploits the fact that gamma introduces high-order harmonic correlations in the affected fringe pattern image. Estimation and compensation of projector nonlinearity is realized by detecting and minimizing the normed bispectral coherence of these correlations. The proposed technique does not require calibration information and technical knowledge or specification of fringe projection unit. This is promising for developing a modular and calibration-invariant model for intensity nonlinear gamma compensation in digital fringe pattern projection profilometry. Experimental and numerical simulation results demonstrate this method to be efficient and effective in improving the phase measuring accuracies with phase-shifting fringe pattern projection profilometry.

Reusable High Aspect Ratio 3-D Nickel Shadow Mask

PubMed Central

Shandhi, M.M.H.; Leber, M.; Hogan, A.; Warren, D.J.; Bhandari, R.; Negi, S.

2017-01-01

Shadow Mask technology has been used over the years for resistless patterning and to pattern on unconventional surfaces, fragile substrate and biomaterial. In this work, we are presenting a novel method to fabricate high aspect ratio (15:1) three-dimensional (3D) Nickel (Ni) shadow mask with vertical pattern length and width of 1.2 mm and 40 μm respectively. The Ni shadow mask is 1.5 mm tall and 100 μm wide at the base. The aspect ratio of the shadow mask is 15. Ni shadow mask is mechanically robust and hence easy to handle. It is also reusable and used to pattern the sidewalls of unconventional and complex 3D geometries such as microneedles or neural electrodes (such as the Utah array). The standard Utah array has 100 active sites at the tip of the shaft. Using the proposed high aspect ratio Ni shadow mask, the Utah array can accommodate 300 active sites, 200 of which will be along and around the shaft. The robust Ni shadow mask is fabricated using laser patterning and electroplating techniques. The use of Ni 3D shadow mask will lower the fabrication cost, complexity and time for patterning out-of-plane structures. PMID:29056835

Three dimensional fabrication at small size scales

PubMed Central

Leong, Timothy G.; Zarafshar, Aasiyeh M.; Gracias, David H.

2010-01-01

Despite the fact that we live in a three-dimensional (3D) world and macroscale engineering is 3D, conventional sub-mm scale engineering is inherently two-dimensional (2D). New fabrication and patterning strategies are needed to enable truly three-dimensionally-engineered structures at small size scales. Here, we review strategies that have been developed over the last two decades that seek to enable such millimeter to nanoscale 3D fabrication and patterning. A focus of this review is the strategy of self-assembly, specifically in a biologically inspired, more deterministic form known as self-folding. Self-folding methods can leverage the strengths of lithography to enable the construction of precisely patterned 3D structures and “smart” components. This self-assembling approach is compared with other 3D fabrication paradigms, and its advantages and disadvantages are discussed. PMID:20349446

MicroCT angiography detects vascular formation and regression in skin wound healing.

PubMed

Urao, Norifumi; Okonkwo, Uzoagu A; Fang, Milie M; Zhuang, Zhen W; Koh, Timothy J; DiPietro, Luisa A

2016-07-01

Properly regulated angiogenesis and arteriogenesis are essential for effective wound healing. Tissue injury induces robust new vessel formation and subsequent vessel maturation, which involves vessel regression and remodeling. Although formation of functional vasculature is essential for healing, alterations in vascular structure over the time course of skin wound healing are not well understood. Here, using high-resolution ex vivo X-ray micro-computed tomography (microCT), we describe the vascular network during healing of skin excisional wounds with highly detailed three-dimensional (3D) reconstructed images and associated quantitative analysis. We found that relative vessel volume, surface area and branching number are significantly decreased in wounds from day 7 to days 14 and 21. Segmentation and skeletonization analysis of selected branches from high-resolution images as small as 2.5μm voxel size show that branching orders are decreased in the wound vessels during healing. In histological analysis, we found that the contrast agent fills mainly arterioles, but not small capillaries nor large veins. In summary, high-resolution microCT revealed dynamic alterations of vessel structures during wound healing. This technique may be useful as a key tool in the study of the formation and regression of wound vessels. Copyright © 2016 Elsevier Inc. All rights reserved.

A combined high-sugar and high-saturated-fat dietary pattern is associated with more depressive symptoms in a multi-ethnic population: the HELIUS (Healthy Life in an Urban Setting) study.

PubMed

Vermeulen, Esther; Stronks, Karien; Snijder, Marieke B; Schene, Aart H; Lok, Anja; de Vries, Jeanne H; Visser, Marjolein; Brouwer, Ingeborg A; Nicolaou, Mary

2017-09-01

To identify a high-sugar (HS) dietary pattern, a high-saturated-fat (HF) dietary pattern and a combined high-sugar and high-saturated-fat (HSHF) dietary pattern and to explore if these dietary patterns are associated with depressive symptoms. We used data from the HELIUS (Healthy Life in an Urban Setting) study and included 4969 individuals aged 18-70 years. Diet was assessed using four ethnic-specific FFQ. Dietary patterns were derived using reduced rank regression with mono- and disaccharides, saturated fat and total fat as response variables. The nine-item Patient Health Questionnaire (PHQ-9) was used to assess depressive symptoms by using continuous scores and depressed mood (identified using the cut-off point: PHQ-9 sum score ≥10). The Netherlands. Three dietary patterns were identified; an HSHF dietary pattern (including chocolates, red meat, added sugars, high-fat dairy products, fried foods, creamy sauces), an HS dietary pattern (including sugar-sweetened beverages, added sugars, fruit (juices)) and an HF dietary pattern (including high-fat dairy products, butter). When comparing extreme quartiles, consumption of an HSHF dietary pattern was associated with more depressive symptoms (Q1 v. Q4: β=0·18, 95 % CI 0·07, 0·30, P=0·001) and with higher odds of depressed mood (Q1 v. Q4: OR=2·36, 95 % CI 1·19, 4·66, P=0·014). No associations were found between consumption of the remaining dietary patterns and depressive symptoms. Higher consumption of an HSHF dietary pattern is associated with more depressive symptoms and with depressed mood. Our findings reinforce the idea that the focus should be on dietary patterns that are high in both sugar and saturated fat.

Transition of spiral calcium waves between multiple stable patterns can be triggered by a single calcium spark in a fire-diffuse-fire model

PubMed Central

Tang, Ai-Hui; Wang, Shi-Qiang

2009-01-01

Spiral patterns have been found in various nonequilibrium systems. The Ca2+-induced Ca2+ release system in single cardiac cells is unique for highly discrete reaction elements, each giving rise to a Ca2+ spark upon excitation. We imaged the spiral Ca2+ waves in isolated cardiac cells and numerically studied the effect of system excitability on spiral patterns using a two-dimensional fire-diffuse-fire model. We found that under certain conditions, the system was able to display multiple stable patterns of spiral waves, each exhibiting different periods and distinct routines of spiral tips. Transition between these different patterns could be triggered by an internal fluctuation in the form of a single Ca2+ spark. PMID:19792039

Transition of spiral calcium waves between multiple stable patterns can be triggered by a single calcium spark in a fire-diffuse-fire model.

PubMed

Tang, Ai-Hui; Wang, Shi-Qiang

2009-09-01

Spiral patterns have been found in various nonequilibrium systems. The Ca(2+)-induced Ca(2+) release system in single cardiac cells is unique for highly discrete reaction elements, each giving rise to a Ca(2+) spark upon excitation. We imaged the spiral Ca(2+) waves in isolated cardiac cells and numerically studied the effect of system excitability on spiral patterns using a two-dimensional fire-diffuse-fire model. We found that under certain conditions, the system was able to display multiple stable patterns of spiral waves, each exhibiting different periods and distinct routines of spiral tips. Transition between these different patterns could be triggered by an internal fluctuation in the form of a single Ca(2+) spark.

MRI-visible perivascular spaces in cerebral amyloid angiopathy and hypertensive arteriopathy

PubMed Central

Boulouis, Gregoire; Pasi, Marco; Auriel, Eitan; van Etten, Ellis S.; Haley, Kellen; Ayres, Alison; Schwab, Kristin M.; Martinez-Ramirez, Sergi; Goldstein, Joshua N.; Rosand, Jonathan; Viswanathan, Anand; Greenberg, Steven M.; Gurol, M. Edip

2017-01-01

Objective: To assess MRI-visible enlarged perivascular spaces (EPVS) burden and different topographical patterns (in the centrum semiovale [CSO] and basal ganglia [BG]) in 2 common microangiopathies: cerebral amyloid angiopathy (CAA) and hypertensive arteriopathy (HA). Methods: Consecutive patients with spontaneous intracerebral hemorrhage (ICH) from a prospective MRI cohort were included. Small vessel disease MRI markers, including cerebral microbleeds (CMBs), cortical superficial siderosis (cSS), and white matter hyperintensities (WMH), were rated. CSO-EPVS/BG-EPVS were assessed on a validated 4-point visual rating scale (0 = no EPVS, 1 = <10, 2 = 11–20, 3 = 21–40, and 4 = >40 EPVS). We tested associations of predefined high-degree (score >2) CSO-EPVS and BG-EPVS with other MRI markers in multivariable logistic regression. We subsequently evaluated associations with CSO-EPVS predominance (i.e., CSO-EPVS > BG-EPVS) and BG-EPVS predominance pattern (i.e., BG-EPVS > CSO-EPVS) in adjusted multinomial logistic regression (reference group, BG-EPVS = CSO-EPVS). Results: We included 315 patients with CAA-ICH and 137 with HA-ICH. High-degree CSO-EPVS prevalence was greater in CAA-related ICH vs HA-related ICH (43.8% vs 17.5%, p < 0.001). In multivariable logistic regression, high-degree CSO-EPVS was associated with lobar CMB (odds ratio [OR] 1.33, 95% confidence interval [CI] 1.10–1.61, p = 0.003) and cSS (OR 2.08, 95% CI 1.30–3.32, p = 0.002). Deep CMBs (OR 2.85, 95% CI 1.75–4.64, p < 0.0001) and higher WMH volume (OR 1.02, 95% CI 1.01–1.04, p = 0.010) were predictors of high-degree BG-EPVS. A CSO-EPVS–predominant pattern was more common in CAA-ICH than in HA-ICH (75.9% vs 39.4%, respectively, p < 0.0001). CSO-PVS predominance was associated with lobar CMB burden and cSS, while BG-EPVS predominance was associated with HA-ICH and WMH volumes. Conclusions: Different patterns of MRI-visible EPVS provide insights into the dominant underlying microangiopathy type in patients with spontaneous ICH. PMID:28228568

History of family violence, childhood behavior problems, and adolescent high-risk behaviors as predictors of girls' repeated patterns of dating victimization in two developmental periods.

PubMed

Vézina, Johanne; Hébert, Martine; Poulin, François; Lavoie, Francine; Vitaro, Frank; Tremblay, Richard E

2015-04-01

This study aims to document the prevalence of repeated patterns of dating victimization and to examine, within the frameworks of an ecological model and lifestyle/routine activities theories, associations between such patterns and family, peer, and individual factors. Dating victimization in adolescence (age 15) and early adulthood (age 21) was evaluated in 443 female participants. Multinomial logistic regression analyses revealed that history of family violence, childhood behavior problems, and adolescent high-risk behaviors were associated with an increased risk for girls of being victimized (psychologically and/or physically/sexually) in their dating relationships, either in adolescence or early adulthood, or at both developmental periods. © The Author(s) 2015.

Three-dimensional whispering gallery modes in InGaAs nanoneedle lasers on silicon

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

Tran, T.-T. D.; Chen, R.; Ng, K. W.

2014-09-15

As-grown InGaAs nanoneedle lasers, synthesized at complementary metal–oxide–semiconductor compatible temperatures on polycrystalline and crystalline silicon substrates, were studied in photoluminescence experiments. Radiation patterns of three-dimensional whispering gallery modes were observed upon optically pumping the needles above the lasing threshold. Using the radiation patterns as well as finite-difference-time-domain simulations and polarization measurements, all modal numbers of the three-dimensional whispering gallery modes could be identified.

Two-dimensional patterns in bacterial veils arise from self-generated, three-dimensional fluid flows.

PubMed

Cogan, N G; Wolgemuth, C W

2011-01-01

The behavior of collections of oceanic bacteria is controlled by metabolic (chemotaxis) and physical (fluid motion) processes. Some sulfur-oxidizing bacteria, such as Thiovulum majus, unite these two processes via a material interface produced by the bacteria and upon which the bacteria are transiently attached. This interface, termed a bacterial veil, is formed by exo-polymeric substances (EPS) produced by the bacteria. By adhering to the veil while continuing to rotate their flagella, the bacteria are able to exert force on the fluid surroundings. This behavior induces a fluid flow that, in turn, causes the bacteria to aggregate leading to the formation of a physical pattern in the veil. These striking patterns are very similar in flavor to the classic convection instability observed when a shallow fluid is heated from below. However, the physics are very different since the flow around the veil is mediated by the bacteria and affects the bacterial densities. In this study, we extend a model of a one-dimensional veil in a two-dimensional fluid to the more realistic two-dimensional veil in a three-dimensional fluid. The linear stability analysis indicates that the Peclet number serves as a bifurcation parameter, which is consistent with experimental observations. We also solve the nonlinear problem numerically and are able to obtain patterns that are similar to those observed in the experiments.

PATTERNS IN BIOMEDICAL DATA-HOW DO WE FIND THEM?

PubMed

Basile, Anna O; Verma, Anurag; Byrska-Bishop, Marta; Pendergrass, Sarah A; Darabos, Christian; Lester Kirchner, H

2017-01-01

Given the exponential growth of biomedical data, researchers are faced with numerous challenges in extracting and interpreting information from these large, high-dimensional, incomplete, and often noisy data. To facilitate addressing this growing concern, the "Patterns in Biomedical Data-How do we find them?" session of the 2017 Pacific Symposium on Biocomputing (PSB) is devoted to exploring pattern recognition using data-driven approaches for biomedical and precision medicine applications. The papers selected for this session focus on novel machine learning techniques as well as applications of established methods to heterogeneous data. We also feature manuscripts aimed at addressing the current challenges associated with the analysis of biomedical data.

Two-dimensional single-cell patterning with one cell per well driven by surface acoustic waves

PubMed Central

Collins, David J.; Morahan, Belinda; Garcia-Bustos, Jose; Doerig, Christian; Plebanski, Magdalena; Neild, Adrian

2015-01-01

In single-cell analysis, cellular activity and parameters are assayed on an individual, rather than population-average basis. Essential to observing the activity of these cells over time is the ability to trap, pattern and retain them, for which previous single-cell-patterning work has principally made use of mechanical methods. While successful as a long-term cell-patterning strategy, these devices remain essentially single use. Here we introduce a new method for the patterning of multiple spatially separated single particles and cells using high-frequency acoustic fields with one cell per acoustic well. We characterize and demonstrate patterning for both a range of particle sizes and the capture and patterning of cells, including human lymphocytes and red blood cells infected by the malarial parasite Plasmodium falciparum. This ability is made possible by a hitherto unexplored regime where the acoustic wavelength is on the same order as the cell dimensions. PMID:26522429

Annual variation in the atmospheric radon concentration in Japan.

PubMed

Kobayashi, Yuka; Yasuoka, Yumi; Omori, Yasutaka; Nagahama, Hiroyuki; Sanada, Tetsuya; Muto, Jun; Suzuki, Toshiyuki; Homma, Yoshimi; Ihara, Hayato; Kubota, Kazuhito; Mukai, Takahiro

2015-08-01

Anomalous atmospheric variations in radon related to earthquakes have been observed in hourly exhaust-monitoring data from radioisotope institutes in Japan. The extraction of seismic anomalous radon variations would be greatly aided by understanding the normal pattern of variation in radon concentrations. Using atmospheric daily minimum radon concentration data from five sampling sites, we show that a sinusoidal regression curve can be fitted to the data. In addition, we identify areas where the atmospheric radon variation is significantly affected by the variation in atmospheric turbulence and the onshore-offshore pattern of Asian monsoons. Furthermore, by comparing the sinusoidal regression curve for the normal annual (seasonal) variations at the five sites to the sinusoidal regression curve for a previously published dataset of radon values at the five Japanese prefectures, we can estimate the normal annual variation pattern. By fitting sinusoidal regression curves to the previously published dataset containing sites in all Japanese prefectures, we find that 72% of the Japanese prefectures satisfy the requirements of the sinusoidal regression curve pattern. Using the normal annual variation pattern of atmospheric daily minimum radon concentration data, these prefectures are suitable areas for obtaining anomalous radon variations related to earthquakes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Nonlinear dimensionality reduction of electroencephalogram (EEG) for Brain Computer interfaces.

PubMed

Teli, Mohammad Nayeem; Anderson, Charles

2009-01-01

Patterns in electroencephalogram (EEG) signals are analyzed for a Brain Computer Interface (BCI). An important aspect of this analysis is the work on transformations of high dimensional EEG data to low dimensional spaces in which we can classify the data according to mental tasks being performed. In this research we investigate how a Neural Network (NN) in an auto-encoder with bottleneck configuration can find such a transformation. We implemented two approximate second-order methods to optimize the weights of these networks, because the more common first-order methods are very slow to converge for networks like these with more than three layers of computational units. The resulting non-linear projections of time embedded EEG signals show interesting separations that are related to tasks. The bottleneck networks do indeed discover nonlinear transformations to low-dimensional spaces that capture much of the information present in EEG signals. However, the resulting low-dimensional representations do not improve classification rates beyond what is possible using Quadratic Discriminant Analysis (QDA) on the original time-lagged EEG.

Quantitative two-dimensional gel electrophoresis analysis of human fibroblasts transformed by ras oncogenes.

PubMed

Miller, M J; Maher, V M; McCormick, J J

1992-11-01

Quantitative two-dimensional gel electrophoresis was used to compare the cellular protein patterns of a normal foreskin-derived human fibroblasts cell line (LG1) and three immortal derivatives of LG1. One derivative, designated MSU-1.1 VO, was selected for its ability to grow in the absence of serum and is non-tumorigenic in athymic mice. The other two strains were selected for focus-formation following transfection with either Ha-ras or N-ras oncogenes and form high grade malignant tumors. Correspondence and cluster analysis provided a nonbiased estimate of the relative similarity of the different two-dimensional patterns. These techniques separated the gel patterns into three distinct classes: LG1, MSU-1.1 VO, and the ras transformed cell strains. The MSU-1.1 VO cells were more closely related to the parental LG1 than to the ras-transformed cells. The differences between the three classes were primarily quantitative in nature: 16% of the spots demonstrated statistically significant changes (P < 0.01, T test, mean ratio of intensity > 2) in the rate of incorporation of radioactive amino acids. The patterns from the two ras-transformed cell strains were similar, and variations in the expression of proteins that occurred between the separate experiments obscured consistent differences between the Ha-ras and N-ras transformed cells. However, while only 9 out of 758 spots were classified as different (1%), correspondence analysis could consistently separate the two ras transformants. One of these spots was five times more intense in the Ha-ras transformed cells than the N-ras.(ABSTRACT TRUNCATED AT 250 WORDS)

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy

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

Dean, Jamie A., E-mail: jamie.dean@icr.ac.uk; Wong, Kee H.; Gay, Hiram

Purpose: Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue–sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. Methods and Materials: FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogrammore » data. The reduced dose data were input into functional logistic regression models (functional partial least squares–logistic regression [FPLS-LR] and functional principal component–logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate–response associations, assessed using bootstrapping. Results: The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/−0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/−0.96, 0.79/−0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. Conclusions: FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling.« less

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy.

PubMed

Dean, Jamie A; Wong, Kee H; Gay, Hiram; Welsh, Liam C; Jones, Ann-Britt; Schick, Ulrike; Oh, Jung Hun; Apte, Aditya; Newbold, Kate L; Bhide, Shreerang A; Harrington, Kevin J; Deasy, Joseph O; Nutting, Christopher M; Gulliford, Sarah L

2016-11-15

Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue-sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogram data. The reduced dose data were input into functional logistic regression models (functional partial least squares-logistic regression [FPLS-LR] and functional principal component-logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate-response associations, assessed using bootstrapping. The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/-0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/-0.96, 0.79/-0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

The Creation and Varied Applications of Educational Holograms.

ERIC Educational Resources Information Center

Layng, Jacqueline M.

The potential of holograms has been left virtually untapped in the field of education. A hologram can be described as a three-dimensional photographic record of the interference pattern of two superimposed beams of coherent light. Holography requires: (1) high-resolution film; (2) a laser, often a red-beamed helium neon laser; (3) optical…

Dynamic Dimensionality Selection for Bayesian Classifier Ensembles

DTIC Science & Technology

2015-03-19

learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but much more...classifier, Generative learning, Discriminative learning, Naïve Bayes, Feature selection, Logistic regression , higher order attribute independence 16...discriminative learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but

High-resolution line-shape spectroscopy during a laser pulse based on Dual-Broad-Band-CARS interferometry

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

Vereschagin, Konstantin A; Vereschagin, Alexey K; Smirnov, Valery V

2006-07-31

A high-resolution spectroscopic method is developed for recording Raman spectra of molecular transitions in transient objects during a laser pulse with a resolution of {approx}0.1 cm{sup -1}. The method is based on CARS spectroscopy using a Fabry-Perot interferometer for spectral analysis of the CARS signal and detecting a circular interferometric pattern on a two-dimensional multichannel photodetector. It is shown that the use of the Dual-Broad-Band-CARS configuration to obtain the CARS process provides the efficient averaging of the spectral-amplitude noise of the CARS signal generated by a laser pulse and, in combination with the angular integration of the two-dimensional interference pattern,more » considerably improves the quality of interferograms. The method was tested upon diagnostics of the transient oxygen-hydrogen flame where information on the shapes of spectral lines of the Q-branch of hydrogen molecules required for measuring temperature was simultaneously obtained and used. (special issue devoted to the 90th anniversary of a.m. prokhorov)« less

Direct-referencing Two-dimensional-array Digital Microfluidics Using Multi-layer Printed Circuit Board

PubMed Central

Gong, Jian; Kim, Chang-Jin “CJ”

2008-01-01

Digital (i.e. droplet-based) microfluidics, by the electrowetting-on-dielectric (EWOD) mechanism, has shown great potential for a wide range of applications, such as lab-on-a-chip. While most reported EWOD chips use a series of electrode pads essentially in one-dimensional line pattern designed for specific tasks, the desired universal chips allowing user-reconfigurable paths would require the electrode pads in two-dimensional pattern. However, to electrically access the electrode pads independently, conductive lines need to be fabricated underneath the pads in multiple layers, raising a cost issue especially for disposable chip applications. In this article, we report the building of digital microfluidic plates based on a printed-circuit-board (PCB), in which multilayer electrical access lines were created inexpensively using mature PCB technology. However, due to its surface topography and roughness and resulting high resistance against droplet movement, as-fabricated PCB surfaces require unacceptably high (~500 V) voltages unless coated with or immersed in oil. Our goal is EWOD operations of aqueous droplets not only on oil-covered but also on dry surfaces. To meet varying levels of performances, three types of gradually complex post-PCB microfabrication processes are developed and evaluated. By introducing land-grid-array (LGA) sockets in the packaging, a scalable digital microfluidics system with reconfigurable and low-cost chip is also demonstrated. PMID:19234613

An Exploration of Teacher Attrition and Mobility in High Poverty Racially Segregated Schools

ERIC Educational Resources Information Center

Djonko-Moore, Cara M.

2016-01-01

The purpose of this study was to examine the mobility (movement to a new school) and attrition (quitting teaching) patterns of teachers in high poverty, racially segregated (HPRS) schools in the US. Using 2007-9 survey data from the National Center for Education Statistics, a multi-level multinomial logistic regression was performed to examine the…

Depression and incident dementia. An 8-year population-based prospective study.

PubMed

Luppa, Melanie; Luck, Tobias; Ritschel, Franziska; Angermeyer, Matthias C; Villringer, Arno; Riedel-Heller, Steffi G

2013-01-01

The aim of the study was to investigate the impact of depression (categorical diagnosis; major depression, MD) and depressive symptoms (dimensional diagnosis and symptom patterns) on incident dementia in the German general population. Within the Leipzig Longitudinal Study of the Aged (LEILA 75+), a representative sample of 1,265 individuals aged 75 years and older were interviewed every 1.5 years over 8 years (mean observation time 4.3 years; mean number of visits 4.2). Cox proportional hazards and binary logistic regressions were used to estimate the effect of baseline depression and depressive symptoms on incident dementia. The incidence of dementia was 48 per 1,000 person-years (95% confidence interval (CI) 45-51). Depressive symptoms (Hazard ratio HR 1.03, 95% CI 1.01-1.05), and in particular mood-related symptoms (HR 1.08, 95% CI 1.03-1.14), showed a significant impact on the incidence of dementia only in univariate analysis, but not after adjustment for cognitive and functional impairment. MD showed only a significant impact on incidence of dementia in Cox proportional hazards regression, but not in binary logistic regression models. The present study using different diagnostic measures of depression on future dementia found no clear significant associations of depression and incident dementia. Further in-depth investigation would help to understand the nature of depression in the context of incident dementia.

Trans-dimensional joint inversion of seabed scattering and reflection data.

PubMed

Steininger, Gavin; Dettmer, Jan; Dosso, Stan E; Holland, Charles W

2013-03-01

This paper examines joint inversion of acoustic scattering and reflection data to resolve seabed interface roughness parameters (spectral strength, exponent, and cutoff) and geoacoustic profiles. Trans-dimensional (trans-D) Bayesian sampling is applied with both the number of sediment layers and the order (zeroth or first) of auto-regressive parameters in the error model treated as unknowns. A prior distribution that allows fluid sediment layers over an elastic basement in a trans-D inversion is derived and implemented. Three cases are considered: Scattering-only inversion, joint scattering and reflection inversion, and joint inversion with the trans-D auto-regressive error model. Including reflection data improves the resolution of scattering and geoacoustic parameters. The trans-D auto-regressive model further improves scattering resolution and correctly differentiates between strongly and weakly correlated residual errors.

Three-dimensional phase-field simulations of directional solidification

NASA Astrophysics Data System (ADS)

Plapp, Mathis

2007-05-01

The phase-field method has become the method of choice for simulating microstructural pattern formation during solidification. One of its main advantages is that time-dependent three-dimensional simulations become feasible, which makes it possible to address long-standing questions of pattern stability and pattern selection. Here, a brief introduction to the phase-field model and its implementation is given, and its capabilities are illustrated by examples taken from the directional solidification of binary alloys. In particular, the morphological stability of hexagonal cellular arrays and of eutectic lamellar patterns is investigated.

Network patterns in exponentially growing two-dimensional biofilms

NASA Astrophysics Data System (ADS)

Zachreson, Cameron; Yap, Xinhui; Gloag, Erin S.; Shimoni, Raz; Whitchurch, Cynthia B.; Toth, Milos

2017-10-01

Anisotropic collective patterns occur frequently in the morphogenesis of two-dimensional biofilms. These patterns are often attributed to growth regulation mechanisms and differentiation based on gradients of diffusing nutrients and signaling molecules. Here, we employ a model of bacterial growth dynamics to show that even in the absence of growth regulation or differentiation, confinement by an enclosing medium such as agar can itself lead to stable pattern formation over time scales that are employed in experiments. The underlying mechanism relies on path formation through physical deformation of the enclosing environment.

Concentration data and dimensionality in groundwater models: evaluation using inverse modelling

USGS Publications Warehouse

Barlebo, H.C.; Hill, M.C.; Rosbjerg, D.; Jensen, K.H.

1998-01-01

A three-dimensional inverse groundwater flow and transport model that fits hydraulic-head and concentration data simultaneously using nonlinear regression is presented and applied to a layered sand and silt groundwater system beneath the Grindsted Landfill in Denmark. The aquifer is composed of rather homogeneous hydrogeologic layers. Two issues common to groundwater flow and transport modelling are investigated: 1) The accuracy of simulated concentrations in the case of calibration with head data alone; and 2) The advantages and disadvantages of using a two-dimensional cross-sectional model instead of a three-dimensional model to simulate contaminant transport when the source is at the land surface. Results show that using only hydraulic heads in the nonlinear regression produces a simulated plume that is profoundly different from what is obtained in a calibration using both hydraulic-head and concentration data. The present study provides a well-documented example of the differences that can occur. Representing the system as a two-dimensional cross-section obviously omits some of the system dynamics. It was, however, possible to obtain a simulated plume cross-section that matched the actual plume cross-section well. The two-dimensional model execution times were about a seventh of those for the three-dimensional model, but some difficulties were encountered in representing the spatially variable source concentrations and less precise simulated concentrations were calculated by the two-dimensional model compared to the three-dimensional model. Summed up, the present study indicates that three dimensional modelling using both hydraulic heads and concentrations in the calibration should be preferred in the considered type of transport studies.

High-definition polymeric membranes: construction of 3D lithographed channel arrays through control of natural building blocks dynamics.

PubMed

Speranza, Valentina; Trotta, Francesco; Drioli, Enrico; Gugliuzza, Annarosa

2010-02-01

The fabrication of well-defined interfaces is in high demand in many fields of biotechnologies. Here, high-definition membrane-like arrays are developed through the self-assembly of water droplets, which work as natural building blocks for the construction of ordered channels. Solution viscosity together with the dynamics of the water droplets can decide the final formation of three-dimensional well-ordered patterns resembling anodic structures, especially because solvents denser than water are used. Particularly, the polymer solution viscosity is demonstrated to be a powerful tool for control of the mobility of submerged droplets during the microfabrication process. The polymeric patterns are structured at very high levels of organization and exhibit well-established transport-surface property relationships, considered basics for any types of advanced biotechnologies.

Patterns of Movement in Foster Care: An Optimal Matching Analysis

PubMed Central

Havlicek, Judy

2011-01-01

Placement instability remains a vexing problem for child welfare agencies across the country. This study uses child welfare administrative data to retrospectively follow the entire placement histories (birth to age 17.5) of 474 foster youth who reached the age of majority in the state of Illinois and to search for patterns in their movement through the child welfare system. Patterns are identified through optimal matching and hierarchical cluster analyses. Multiple logistic regression is used to analyze administrative and survey data in order to examine covariates related to patterns. Five distinct patterns of movement are differentiated: Late Movers, Settled with Kin, Community Care, Institutionalized, and Early Entry. These patterns suggest high but variable rates of movement. Implications for child welfare policy and service provision are discussed. PMID:20873020

Resolution extension by image summing in serial femtosecond crystallography of two-dimensional membrane-protein crystals

DOE PAGES

Casadei, Cecilia M.; Tsai, Ching-Ju; Barty, Anton; ...

2018-01-01

Previous proof-of-concept measurements on single-layer two-dimensional membrane-protein crystals performed at X-ray free-electron lasers (FELs) have demonstrated that the collection of meaningful diffraction patterns, which is not possible at synchrotrons because of radiation-damage issues, is feasible. Here, the results obtained from the analysis of a thousand single-shot, room-temperature X-ray FEL diffraction images from two-dimensional crystals of a bacteriorhodopsin mutant are reported in detail. The high redundancy in the measurements boosts the intensity signal-to-noise ratio, so that the values of the diffracted intensities can be reliably determined down to the detector-edge resolution of 4 Å. The results show that two-dimensional serial crystallography atmore » X-ray FELs is a suitable method to study membrane proteins to near-atomic length scales at ambient temperature. The method presented here can be extended to pump–probe studies of optically triggered structural changes on submillisecond timescales in two-dimensional crystals, which allow functionally relevant large-scale motions that may be quenched in three-dimensional crystals.« less

Resolution extension by image summing in serial femtosecond crystallography of two-dimensional membrane-protein crystals

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

Casadei, Cecilia M.; Tsai, Ching-Ju; Barty, Anton

Previous proof-of-concept measurements on single-layer two-dimensional membrane-protein crystals performed at X-ray free-electron lasers (FELs) have demonstrated that the collection of meaningful diffraction patterns, which is not possible at synchrotrons because of radiation-damage issues, is feasible. Here, the results obtained from the analysis of a thousand single-shot, room-temperature X-ray FEL diffraction images from two-dimensional crystals of a bacteriorhodopsin mutant are reported in detail. The high redundancy in the measurements boosts the intensity signal-to-noise ratio, so that the values of the diffracted intensities can be reliably determined down to the detector-edge resolution of 4 Å. The results show that two-dimensional serial crystallography atmore » X-ray FELs is a suitable method to study membrane proteins to near-atomic length scales at ambient temperature. The method presented here can be extended to pump–probe studies of optically triggered structural changes on submillisecond timescales in two-dimensional crystals, which allow functionally relevant large-scale motions that may be quenched in three-dimensional crystals.« less

Infant feeding patterns over the first year of life: influence of family characteristics

PubMed Central

Betoko, Aisha; Charles, Marie-Aline; Hankard, Régis; Forhan, Anne; Bonet, Mercedes; Saurel-Cubizolles, Marie-Josephe; Heude, Barbara; De Lauzon-Guillain, Blandine

2013-01-01

Background/Objectives Early eating patterns and behaviors can determine later eating habits and food preferences and they have been related to the development of childhood overweight and obesity. We aimed to identify patterns of feeding in the first year of life and to examine their associations with family characteristics. Subjects/Methods Our analysis included 1004 infants from the EDEN mother-child cohort. Feeding practices were assessed through maternal self-report at birth, 4, 8 and 12 months. Principal component analysis was applied to derive patterns from breastfeeding duration, age at complementary food (CF) introduction and type of food used at 1y. Associations between patterns and family characteristics were analyzed by linear regressions. Results The main source of variability in infant feeding was characterized by a pattern labeled ‘Late CF introduction and use of ready-prepared baby foods’. Older, more educated, primiparous women with high monthly income ranked high on this pattern. The second pattern, labeled ‘Longer breastfeeding, late CF introduction and use of home-made foods’ was the closest to infant feeding guidelines. Mothers ranking high on this pattern were older and more educated. The third pattern, labeled ‘Use of adults’ foods’ suggests a less age-specific diet for the infants. Mothers ranking high on this pattern were often younger and multiparous. Recruitment center was related to all patterns. Conclusion Not only maternal education level and age but also parity and region are important contributors to the variability in patterns. Further studies are needed to describe associations between these patterns and infant growth and later food preferences. PMID:23299715

Infant feeding patterns over the first year of life: influence of family characteristics.

PubMed

Betoko, A; Charles, M-A; Hankard, R; Forhan, A; Bonet, M; Saurel-Cubizolles, M-J; Heude, B; de Lauzon-Guillain, B

2013-06-01

Early eating patterns and behaviors can determine later eating habits and food preferences and they have been related to the development of childhood overweight and obesity. We aimed to identify patterns of feeding in the first year of life and to examine their associations with family characteristics. Our analysis included 1004 infants from the EDEN mother-child cohort. Feeding practices were assessed through maternal self-report at birth, 4, 8 and 12 months. Principal component analysis was applied to derive patterns from breastfeeding duration, age at complementary food (CF) introduction and type of food used at 1 year. Associations between patterns and family characteristics were analyzed by linear regressions. The main source of variability in infant feeding was characterized by a pattern labeled 'late CF introduction and use of ready-prepared baby foods'. Older, more educated, primiparous women with high monthly income ranked high on this pattern. The second pattern, labeled 'longer breastfeeding, late CF introduction and use of home-made foods' was the closest to infant feeding guidelines. Mothers ranking high on this pattern were older and more educated. The third pattern, labeled 'use of adults' foods' suggests a less age-specific diet for the infants. Mothers ranking high on this pattern were often younger and multiparous. Recruitment center was related to all patterns. Not only maternal education level and age, but also parity and region are important contributors to the variability in patterns. Further studies are needed to describe associations between these patterns and infant growth and later food preferences.

Finding gene clusters for a replicated time course study

PubMed Central

2014-01-01

Background Finding genes that share similar expression patterns across samples is an important question that is frequently asked in high-throughput microarray studies. Traditional clustering algorithms such as K-means clustering and hierarchical clustering base gene clustering directly on the observed measurements and do not take into account the specific experimental design under which the microarray data were collected. A new model-based clustering method, the clustering of regression models method, takes into account the specific design of the microarray study and bases the clustering on how genes are related to sample covariates. It can find useful gene clusters for studies from complicated study designs such as replicated time course studies. Findings In this paper, we applied the clustering of regression models method to data from a time course study of yeast on two genotypes, wild type and YOX1 mutant, each with two technical replicates, and compared the clustering results with K-means clustering. We identified gene clusters that have similar expression patterns in wild type yeast, two of which were missed by K-means clustering. We further identified gene clusters whose expression patterns were changed in YOX1 mutant yeast compared to wild type yeast. Conclusions The clustering of regression models method can be a valuable tool for identifying genes that are coordinately transcribed by a common mechanism. PMID:24460656

TripAdvisor^{N-D}: A Tourism-Inspired High-Dimensional Space Exploration Framework with Overview and Detail.

PubMed

Nam, Julia EunJu; Mueller, Klaus

2013-02-01

Gaining a true appreciation of high-dimensional space remains difficult since all of the existing high-dimensional space exploration techniques serialize the space travel in some way. This is not so foreign to us since we, when traveling, also experience the world in a serial fashion. But we typically have access to a map to help with positioning, orientation, navigation, and trip planning. Here, we propose a multivariate data exploration tool that compares high-dimensional space navigation with a sightseeing trip. It decomposes this activity into five major tasks: 1) Identify the sights: use a map to identify the sights of interest and their location; 2) Plan the trip: connect the sights of interest along a specifyable path; 3) Go on the trip: travel along the route; 4) Hop off the bus: experience the location, look around, zoom into detail; and 5) Orient and localize: regain bearings in the map. We describe intuitive and interactive tools for all of these tasks, both global navigation within the map and local exploration of the data distributions. For the latter, we describe a polygonal touchpad interface which enables users to smoothly tilt the projection plane in high-dimensional space to produce multivariate scatterplots that best convey the data relationships under investigation. Motion parallax and illustrative motion trails aid in the perception of these transient patterns. We describe the use of our system within two applications: 1) the exploratory discovery of data configurations that best fit a personal preference in the presence of tradeoffs and 2) interactive cluster analysis via cluster sculpting in N-D.

Measurement of pO2 and pH in Living Breast Tumor Models with Three-Dimensional Resolution by Multiphoton Microscopy during Combined Therapy with Herceptin

DTIC Science & Technology

2007-04-01

contact with a freshly spin-coated NC–titania pre- polymer , which was transferred to a hot plate to initiate polymerization . The pattern of the PDMS stamp...to quantify pO2 and pH in vivo with high three-dimensional resolution (~1 µm3) and significant depth penetration (up to 400 µm) with MPLSM. The...proposed to develop techniques for measuring in vivo pO2 and pH of HER2-positive and negative primary tumors in murine models of breast cancer using

Pattern-set generation algorithm for the one-dimensional multiple stock sizes cutting stock problem

NASA Astrophysics Data System (ADS)

Cui, Yaodong; Cui, Yi-Ping; Zhao, Zhigang

2015-09-01

A pattern-set generation algorithm (PSG) for the one-dimensional multiple stock sizes cutting stock problem (1DMSSCSP) is presented. The solution process contains two stages. In the first stage, the PSG solves the residual problems repeatedly to generate the patterns in the pattern set, where each residual problem is solved by the column-generation approach, and each pattern is generated by solving a single large object placement problem. In the second stage, the integer linear programming model of the 1DMSSCSP is solved using a commercial solver, where only the patterns in the pattern set are considered. The computational results of benchmark instances indicate that the PSG outperforms existing heuristic algorithms and rivals the exact algorithm in solution quality.

High-speed classification of coherent X-ray diffraction patterns on the K computer for high-resolution single biomolecule imaging.

PubMed

Tokuhisa, Atsushi; Arai, Junya; Joti, Yasumasa; Ohno, Yoshiyuki; Kameyama, Toyohisa; Yamamoto, Keiji; Hatanaka, Masayuki; Gerofi, Balazs; Shimada, Akio; Kurokawa, Motoyoshi; Shoji, Fumiyoshi; Okada, Kensuke; Sugimoto, Takashi; Yamaga, Mitsuhiro; Tanaka, Ryotaro; Yokokawa, Mitsuo; Hori, Atsushi; Ishikawa, Yutaka; Hatsui, Takaki; Go, Nobuhiro

2013-11-01

Single-particle coherent X-ray diffraction imaging using an X-ray free-electron laser has the potential to reveal the three-dimensional structure of a biological supra-molecule at sub-nanometer resolution. In order to realise this method, it is necessary to analyze as many as 1 × 10(6) noisy X-ray diffraction patterns, each for an unknown random target orientation. To cope with the severe quantum noise, patterns need to be classified according to their similarities and average similar patterns to improve the signal-to-noise ratio. A high-speed scalable scheme has been developed to carry out classification on the K computer, a 10PFLOPS supercomputer at RIKEN Advanced Institute for Computational Science. It is designed to work on the real-time basis with the experimental diffraction pattern collection at the X-ray free-electron laser facility SACLA so that the result of classification can be feedback for optimizing experimental parameters during the experiment. The present status of our effort developing the system and also a result of application to a set of simulated diffraction patterns is reported. About 1 × 10(6) diffraction patterns were successfully classificatied by running 255 separate 1 h jobs in 385-node mode.

High-speed classification of coherent X-ray diffraction patterns on the K computer for high-resolution single biomolecule imaging

PubMed Central

Tokuhisa, Atsushi; Arai, Junya; Joti, Yasumasa; Ohno, Yoshiyuki; Kameyama, Toyohisa; Yamamoto, Keiji; Hatanaka, Masayuki; Gerofi, Balazs; Shimada, Akio; Kurokawa, Motoyoshi; Shoji, Fumiyoshi; Okada, Kensuke; Sugimoto, Takashi; Yamaga, Mitsuhiro; Tanaka, Ryotaro; Yokokawa, Mitsuo; Hori, Atsushi; Ishikawa, Yutaka; Hatsui, Takaki; Go, Nobuhiro

2013-01-01

Single-particle coherent X-ray diffraction imaging using an X-ray free-electron laser has the potential to reveal the three-dimensional structure of a biological supra-molecule at sub-nanometer resolution. In order to realise this method, it is necessary to analyze as many as 1 × 106 noisy X-ray diffraction patterns, each for an unknown random target orientation. To cope with the severe quantum noise, patterns need to be classified according to their similarities and average similar patterns to improve the signal-to-noise ratio. A high-speed scalable scheme has been developed to carry out classification on the K computer, a 10PFLOPS supercomputer at RIKEN Advanced Institute for Computational Science. It is designed to work on the real-time basis with the experimental diffraction pattern collection at the X-ray free-electron laser facility SACLA so that the result of classification can be feedback for optimizing experimental parameters during the experiment. The present status of our effort developing the system and also a result of application to a set of simulated diffraction patterns is reported. About 1 × 106 diffraction patterns were successfully classificatied by running 255 separate 1 h jobs in 385-node mode. PMID:24121336

Adenocarcinoma predominant pattern subtyping and nuclear grading in cytology: Is there a role in prognostication of advanced pulmonary adenocarcinomas?

PubMed

Nambirajan, A; Kaur, H; Jangra, K; Kaur, K; Madan, K; Mathur, S R; Iyer, V K; Jain, D

2018-04-01

Primary lung adenocarcinomas (ADs) show varied architectural patterns, and pattern-based subtyping of ADs is currently recommended due to prognostic implications. Predicting AD patterns on cytology is challenging; however, cytological nuclear features appear to correlate with histological grade and survival in early stage lung ADs. The feasibility and value of AD pattern prediction and nuclear grading on cytology in advanced lung ADs is not known. We aimed to predict patterns and analyse nuclear features on cytology and evaluate their role in prognostication. One-hundred patients of Stage III/IV lung AD with available matched cytology and histology samples were included. Cyto-patterns based on cell arrangement patterns (flat sheets vs three-dimensional clusters vs papillae) and cyto-nuclear score based on nuclear features (size, shape, contour), nucleoli (macronucleoli vs prominent vs inconspicuous), and nuclear chromatin were determined, and correlated with predominant histological-pattern observed on the matched small biopsy and outcome. Higher cyto-nuclear scores were observed with high-grade histo-patterns (solid, micropapillary and cribriform), while the predicted cyto-patterns did not correspond to the predominant pattern on histology in 77% cases. Highest cyto-histo agreement was observed for solid pattern (72%). High grade histo-patterns and cyto-nuclear scores > 3 showed a trend towards inferior survival (not significant). Nuclear grade scoring on cytology is simple to perform, and is predictive of high grade patterns. Its inclusion in routine reporting of cytology samples of lung ADs may be valuable. © 2018 John Wiley & Sons Ltd.

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

PubMed Central

Liu, Aiming; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

2017-01-01

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems. PMID:29117100

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata.

PubMed

Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

2017-11-08

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.

Decomposition and model selection for large contingency tables.

PubMed

Dahinden, Corinne; Kalisch, Markus; Bühlmann, Peter

2010-04-01

Large contingency tables summarizing categorical variables arise in many areas. One example is in biology, where large numbers of biomarkers are cross-tabulated according to their discrete expression level. Interactions of the variables are of great interest and are generally studied with log-linear models. The structure of a log-linear model can be visually represented by a graph from which the conditional independence structure can then be easily read off. However, since the number of parameters in a saturated model grows exponentially in the number of variables, this generally comes with a heavy computational burden. Even if we restrict ourselves to models of lower-order interactions or other sparse structures, we are faced with the problem of a large number of cells which play the role of sample size. This is in sharp contrast to high-dimensional regression or classification procedures because, in addition to a high-dimensional parameter, we also have to deal with the analogue of a huge sample size. Furthermore, high-dimensional tables naturally feature a large number of sampling zeros which often leads to the nonexistence of the maximum likelihood estimate. We therefore present a decomposition approach, where we first divide the problem into several lower-dimensional problems and then combine these to form a global solution. Our methodology is computationally feasible for log-linear interaction models with many categorical variables each or some of them having many levels. We demonstrate the proposed method on simulated data and apply it to a bio-medical problem in cancer research.

Structured light optical microscopy for three-dimensional reconstruction of technical surfaces

NASA Astrophysics Data System (ADS)

Kettel, Johannes; Reinecke, Holger; Müller, Claas

2016-04-01

In microsystems technology quality control of micro structured surfaces with different surface properties is playing an ever more important role. The process of quality control incorporates three-dimensional (3D) reconstruction of specularand diffusive reflecting technical surfaces. Due to the demand on high measurement accuracy and data acquisition rates, structured light optical microscopy has become a valuable solution to solve this problem providing high vertical and lateral resolution. However, 3D reconstruction of specular reflecting technical surfaces still remains a challenge to optical measurement principles. In this paper we present a measurement principle based on structured light optical microscopy which enables 3D reconstruction of specular- and diffusive reflecting technical surfaces. It is realized using two light paths of a stereo microscope equipped with different magnification levels. The right optical path of the stereo microscope is used to project structured light onto the object surface. The left optical path is used to capture the structured illuminated object surface with a camera. Structured light patterns are generated by a Digital Light Processing (DLP) device in combination with a high power Light Emitting Diode (LED). Structured light patterns are realized as a matrix of discrete light spots to illuminate defined areas on the object surface. The introduced measurement principle is based on multiple and parallel processed point measurements. Analysis of the measured Point Spread Function (PSF) by pattern recognition and model fitting algorithms enables the precise calculation of 3D coordinates. Using exemplary technical surfaces we demonstrate the successful application of our measurement principle.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

PubMed

Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t -test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t -test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

Estimation of Human Body Volume (BV) from Anthropometric Measurements Based on Three-Dimensional (3D) Scan Technique.

PubMed

Liu, Xingguo; Niu, Jianwei; Ran, Linghua; Liu, Taijie

2017-08-01

This study aimed to develop estimation formulae for the total human body volume (BV) of adult males using anthropometric measurements based on a three-dimensional (3D) scanning technique. Noninvasive and reliable methods to predict the total BV from anthropometric measurements based on a 3D scan technique were addressed in detail. A regression analysis of BV based on four key measurements was conducted for approximately 160 adult male subjects. Eight total models of human BV show that the predicted results fitted by the regression models were highly correlated with the actual BV (p < 0.001). Two metrics, the mean value of the absolute difference between the actual and predicted BV (V error ) and the mean value of the ratio between V error and actual BV (RV error ), were calculated. The linear model based on human weight was recommended as the most optimal due to its simplicity and high efficiency. The proposed estimation formulae are valuable for estimating total body volume in circumstances in which traditional underwater weighing or air displacement plethysmography is not applicable or accessible. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

A regression model for calculating the second dimension retention index in comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry.

PubMed

Wang, Bing; Shen, Hao; Fang, Aiqin; Huang, De-Shuang; Jiang, Changjun; Zhang, Jun; Chen, Peng

2016-06-17

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) system has become a key analytical technology in high-throughput analysis. Retention index has been approved to be helpful for compound identification in one-dimensional gas chromatography, which is also true for two-dimensional gas chromatography. In this work, a novel regression model was proposed for calculating the second dimension retention index of target components where n-alkanes were used as reference compounds. This model was developed to depict the relationship among adjusted second dimension retention time, temperature of the second dimension column and carbon number of n-alkanes by an exponential nonlinear function with only five parameters. Three different criteria were introduced to find the optimal values of parameters. The performance of this model was evaluated using experimental data of n-alkanes (C7-C31) at 24 temperatures which can cover all 0-6s adjusted retention time area. The experimental results show that the mean relative error between predicted adjusted retention time and experimental data of n-alkanes was only 2%. Furthermore, our proposed model demonstrates a good extrapolation capability for predicting adjusted retention time of target compounds which located out of the range of the reference compounds in the second dimension adjusted retention time space. Our work shows the deviation was less than 9 retention index units (iu) while the number of alkanes were added up to 5. The performance of our proposed model has also been demonstrated by analyzing a mixture of compounds in temperature programmed experiments. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantile regression applied to spectral distance decay

USGS Publications Warehouse

Rocchini, D.; Cade, B.S.

2008-01-01

Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.01), considering both OLS and quantile regressions. Nonetheless, the OLS regression estimate of the mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when the spectral distance approaches zero, was very low compared with the intercepts of the upper quantiles, which detected high species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.

Spectral distance decay: Assessing species beta-diversity by quantile regression

USGS Publications Warehouse

Rocchinl, D.; Nagendra, H.; Ghate, R.; Cade, B.S.

2009-01-01

Remotely sensed data represents key information for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance may allow us to quantitatively estimate how beta-diversity in species changes with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological datasets are characterized by a high number of zeroes that can add noise to the regression model. Quantile regression can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this paper, we used ordinary least square (ols) and quantile regression to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.05) considering both ols and quantile regression. Nonetheless, ols regression estimate of mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when spectral distance approaches zero, was very low compared with the intercepts of upper quantiles, which detected high species similarity when habitats are more similar. In this paper we demonstrated the power of using quantile regressions applied to spectral distance decay in order to reveal species diversity patterns otherwise lost or underestimated by ordinary least square regression. ?? 2009 American Society for Photogrammetry and Remote Sensing.

High regression rate hybrid rocket fuel grains with helical port structures

NASA Astrophysics Data System (ADS)

Walker, Sean D.

Hybrid rockets are popular in the aerospace industry due to their storage safety, simplicity, and controllability during rocket motor burn. However, they produce fuel regression rates typically 25% lower than solid fuel motors of the same thrust level. These lowered regression rates produce unacceptably high oxidizer-to-fuel (O/F) ratios that produce a potential for motor instability, nozzle erosion, and reduced motor duty cycles. To achieve O/F ratios that produce acceptable combustion characteristics, traditional cylindrical fuel ports are fabricated with very long length-to-diameter ratios to increase the total burning area. These high aspect ratios produce further reduced fuel regression rate and thrust levels, poor volumetric efficiency, and a potential for lateral structural loading issues during high thrust burns. In place of traditional cylindrical fuel ports, it is proposed that by researching the effects of centrifugal flow patterns introduced by embedded helical fuel port structures, a significant increase in fuel regression rates can be observed. The benefits of increasing volumetric efficiencies by lengthening the internal flow path will also be observed. The mechanisms of this increased fuel regression rate are driven by enhancing surface skin friction and reducing the effect of boundary layer "blowing" to enhance convective heat transfer to the fuel surface. Preliminary results using additive manufacturing to fabricate hybrid rocket fuel grains from acrylonitrile-butadiene-styrene (ABS) with embedded helical fuel port structures have been obtained, with burn-rate amplifications up to 3.0x than that of cylindrical fuel ports.

Assortativity Patterns in Multi-dimensional Inter-organizational Networks: A Case Study of the Humanitarian Relief Sector

NASA Astrophysics Data System (ADS)

Zhao, Kang; Ngamassi, Louis-Marie; Yen, John; Maitland, Carleen; Tapia, Andrea

We use computational tools to study assortativity patterns in multi-dimensional inter-organizational networks on the basis of different node attributes. In the case study of an inter-organizational network in the humanitarian relief sector, we consider not only macro-level topological patterns, but also assortativity on the basis of micro-level organizational attributes. Unlike assortative social networks, this inter-organizational network exhibits disassortative or random patterns on three node attributes. We believe organizations' seek of complementarity is one of the main reasons for the special patterns. Our analysis also provides insights on how to promote collaborations among the humanitarian relief organizations.

Patterns of Visual Attention to Faces and Objects in Autism Spectrum Disorder

ERIC Educational Resources Information Center

McPartland, James C.; Webb, Sara Jane; Keehn, Brandon; Dawson, Geraldine

2011-01-01

This study used eye-tracking to examine visual attention to faces and objects in adolescents with autism spectrum disorder (ASD) and typical peers. Point of gaze was recorded during passive viewing of images of human faces, inverted human faces, monkey faces, three-dimensional curvilinear objects, and two-dimensional geometric patterns.…

Childhood temperament predictors of adolescent physical activity.

PubMed

Janssen, James A; Kolacz, Jacek; Shanahan, Lilly; Gangel, Meghan J; Calkins, Susan D; Keane, Susan P; Wideman, Laurie

2017-01-05

Physical inactivity is a leading cause of mortality worldwide. Many patterns of physical activity involvement are established early in life. To date, the role of easily identifiable early-life individual predictors of PA, such as childhood temperament, remains relatively unexplored. Here, we tested whether childhood temperamental activity level, high intensity pleasure, low intensity pleasure, and surgency predicted engagement in physical activity (PA) patterns 11 years later in adolescence. Data came from a longitudinal community study (N = 206 participants, 53% females, 70% Caucasian). Parents reported their children's temperamental characteristics using the Child Behavior Questionnaire (CBQ) when children were 4 & 5 years old. Approximately 11 years later, adolescents completed self-reports of PA using the Godin Leisure Time Exercise Questionnaire and the Youth Risk Behavior Survey. Ordered logistic regression, ordinary least squares linear regression, and Zero-inflated Poisson regression models were used to predict adolescent PA from childhood temperament. Race, socioeconomic status, and adolescent body mass index were used as covariates. Males with greater childhood temperamental activity level engaged in greater adolescent PA volume (B = .42, SE = .13) and a 1 SD difference in childhood temperamental activity level predicted 29.7% more strenuous adolescent PA per week. Males' high intensity pleasure predicted higher adolescent PA volume (B = .28, SE = .12). Males' surgency positively predicted more frequent PA activity (B = .47, SE = .23, OR = 1.61, 95% CI: 1.02, 2.54) and PA volume (B = .31, SE = .12). No predictions from females' childhood temperament to later PA engagement were identified. Childhood temperament may influence the formation of later PA habits, particularly in males. Boys with high temperamental activity level, high intensity pleasure, and surgency may directly seek out pastimes that involve PA. Indirectly, temperament may also influence caregivers' perceptions of optimal activity choices for children. Understanding how temperament influences the development of PA patterns has the potential to inform efforts aimed at promoting long-term PA engagement and physical health.

Neural constraints on learning.

PubMed

Sadtler, Patrick T; Quick, Kristin M; Golub, Matthew D; Chase, Steven M; Ryu, Stephen I; Tyler-Kabara, Elizabeth C; Yu, Byron M; Batista, Aaron P

2014-08-28

Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.

Moiré patterns in doubly differential electron-momentum distributions in atomic ionization by mid-infrared lasers

NASA Astrophysics Data System (ADS)

Dran, Martín; Arbó, Diego G.

2018-05-01

We analyze the doubly differential electron momentum distribution in above-threshold ionization of atomic hydrogen by a linearly polarized mid-infrared laser pulse. We reproduce side rings in the momentum distribution with forward-backward symmetry previously observed by Lemell et al. [Phys. Rev. A 87, 013421 (2013), 10.1103/PhysRevA.87.013421], whose origin, as far as we know, has not been explained so far. By developing a Fourier theory of moiré patterns, we demonstrate that such structures stem from the interplay between intra- and intercycle interference patterns which work as two separate grids in the two-dimensional momentum domain. We use a three-dimensional (3D) description based on the saddle-point approximation (SPA) to unravel the nature of these structures. When the periods of the two grids (intra- and intercycle) are similar, principal moiré patterns arise symmetrically as concentric rings in the forward and backward directions at high electron kinetic energy. Higher order moiré patterns are observed and characterized when the period of one grid is multiple of the other. We find a scale law for the position (in momentum space) of the center of the moiré rings in the tunneling regime. We verify the SPA predictions by comparison with time-dependent distorted-wave strong-field approximation calculations and the solutions of the full 3D time-dependent Schrödinger equation.

Digital Image Restoration Under a Regression Model - The Unconstrained, Linear Equality and Inequality Constrained Approaches

DTIC Science & Technology

1974-01-01

REGRESSION MODEL - THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January 1974 Nelson Delfino d’Avila Mascarenha;? Image...Report 520 DIGITAL IMAGE RESTORATION UNDER A REGRESSION MODEL THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January...a two- dimensional form adequately describes the linear model . A dis- cretization is performed by using quadrature methods. By trans

Variability in total ozone associated with baroclinic waves

NASA Technical Reports Server (NTRS)

Mote, Philip W.; Holton, James R.; Wallace, John M.

1991-01-01

One-point regression maps of total ozone formed by regressing the time series of bandpass-filtered geopotential height data have been analyzed against Total Ozone Mapping Spectrometer data. Results obtained reveal a strong signature of baroclinic waves in the ozone variability. The regressed patterns are found to be similar in extent and behavior to the relative vorticity patterns reported by Lim and Wallace (1991).

Fabrication of cross-shaped Cu-nanowire resistive memory devices using a rapid, scalable, and designable inorganic-nanowire-digital-alignment technique (Conference Presentation)

NASA Astrophysics Data System (ADS)

Xu, Wentao; Lee, Yeongjun; Min, Sung-Yong; Park, Cheolmin; Lee, Tae-Woo

2016-09-01

Resistive random-access memory (RRAM) is a candidate next generation nonvolatile memory due to its high access speed, high density and ease of fabrication. Especially, cross-point-access allows cross-bar arrays that lead to high-density cells in a two-dimensional planar structure. Use of such designs could be compatible with the aggressive scaling down of memory devices, but existing methods such as optical or e-beam lithographic approaches are too complicated. One-dimensional inorganic nanowires (i-NWs) are regarded as ideal components of nanoelectronics to circumvent the limitations of conventional lithographic approaches. However, post-growth alignment of these i-NWs precisely on a large area with individual control is still a difficult challenge. Here, we report a simple, inexpensive, and rapid method to fabricate two-dimensional arrays of perpendicularly-aligned, individually-conductive Cu-NWs with a nanometer-scale CuxO layer sandwiched at each cross point, by using an inorganic-nanowire-digital-alignment technique (INDAT) and a one-step reduction process. In this approach, the oxide layer is self-formed and patterned, so conventional deposition and lithography are not necessary. INDAT eliminates the difficulties of alignment and scalable fabrication that are encountered when using currently-available techniques that use inorganic nanowires. This simple process facilitates fabrication of cross-point nonvolatile memristor arrays. Fabricated arrays had reproducible resistive switching behavior, high on/off current ratio (Ion/Ioff) 10 6 and extensive cycling endurance. This is the first report of memristors with the resistive switching oxide layer self-formed, self-patterned and self-positioned; we envision that the new features of the technique will provide great opportunities for future nano-electronic circuits.

A Novel Deployment Scheme Based on Three-Dimensional Coverage Model for Wireless Sensor Networks

PubMed Central

Xiao, Fu; Yang, Yang; Wang, Ruchuan; Sun, Lijuan

2014-01-01

Coverage pattern and deployment strategy are directly related to the optimum allocation of limited resources for wireless sensor networks, such as energy of nodes, communication bandwidth, and computing power, and quality improvement is largely determined by these for wireless sensor networks. A three-dimensional coverage pattern and deployment scheme are proposed in this paper. Firstly, by analyzing the regular polyhedron models in three-dimensional scene, a coverage pattern based on cuboids is proposed, and then relationship between coverage and sensor nodes' radius is deduced; also the minimum number of sensor nodes to maintain network area's full coverage is calculated. At last, sensor nodes are deployed according to the coverage pattern after the monitor area is subdivided into finite 3D grid. Experimental results show that, compared with traditional random method, sensor nodes number is reduced effectively while coverage rate of monitor area is ensured using our coverage pattern and deterministic deployment scheme. PMID:25045747

Biodiversity patterns along ecological gradients: unifying β-diversity indices.

PubMed

Szava-Kovats, Robert C; Pärtel, Meelis

2014-01-01

Ecologists have developed an abundance of conceptions and mathematical expressions to define β-diversity, the link between local (α) and regional-scale (γ) richness, in order to characterize patterns of biodiversity along ecological (i.e., spatial and environmental) gradients. These patterns are often realized by regression of β-diversity indices against one or more ecological gradients. This practice, however, is subject to two shortcomings that can undermine the validity of the biodiversity patterns. First, many β-diversity indices are constrained to range between fixed lower and upper limits. As such, regression analysis of β-diversity indices against ecological gradients can result in regression curves that extend beyond these mathematical constraints, thus creating an interpretational dilemma. Second, despite being a function of the same measured α- and γ-diversity, the resultant biodiversity pattern depends on the choice of β-diversity index. We propose a simple logistic transformation that rids beta-diversity indices of their mathematical constraints, thus eliminating the possibility of an uninterpretable regression curve. Moreover, this transformation results in identical biodiversity patterns for three commonly used classical beta-diversity indices. As a result, this transformation eliminates the difficulties of both shortcomings, while allowing the researcher to use whichever beta-diversity index deemed most appropriate. We believe this method can help unify the study of biodiversity patterns along ecological gradients.

Biodiversity Patterns along Ecological Gradients: Unifying β-Diversity Indices

PubMed Central

Szava-Kovats, Robert C.; Pärtel, Meelis

2014-01-01

Ecologists have developed an abundance of conceptions and mathematical expressions to define β-diversity, the link between local (α) and regional-scale (γ) richness, in order to characterize patterns of biodiversity along ecological (i.e., spatial and environmental) gradients. These patterns are often realized by regression of β-diversity indices against one or more ecological gradients. This practice, however, is subject to two shortcomings that can undermine the validity of the biodiversity patterns. First, many β-diversity indices are constrained to range between fixed lower and upper limits. As such, regression analysis of β-diversity indices against ecological gradients can result in regression curves that extend beyond these mathematical constraints, thus creating an interpretational dilemma. Second, despite being a function of the same measured α- and γ-diversity, the resultant biodiversity pattern depends on the choice of β-diversity index. We propose a simple logistic transformation that rids beta-diversity indices of their mathematical constraints, thus eliminating the possibility of an uninterpretable regression curve. Moreover, this transformation results in identical biodiversity patterns for three commonly used classical beta-diversity indices. As a result, this transformation eliminates the difficulties of both shortcomings, while allowing the researcher to use whichever beta-diversity index deemed most appropriate. We believe this method can help unify the study of biodiversity patterns along ecological gradients. PMID:25330181

Prediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning

PubMed Central

Casanova, Ramon; Saldana, Santiago; Simpson, Sean L.; Lacy, Mary E.; Subauste, Angela R.; Blackshear, Chad; Wagenknecht, Lynne; Bertoni, Alain G.

2016-01-01

Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in a high-dimensional setting defined by a large set of observational data, and 2) uncover potential predictors of diabetes. The Jackson Heart Study collected data at baseline and in two follow-up visits from 5,301 African Americans. We excluded those with baseline diabetes and no follow-up, leaving 3,633 individuals for analyses. Over a mean 8-year follow-up, 584 participants developed diabetes. The full RF model evaluated 93 variables including demographic, anthropometric, blood biomarker, medical history, and echocardiogram data. We also used RF metrics of variable importance to rank variables according to their contribution to diabetes prediction. We implemented other models based on logistic regression and RF where features were preselected. The RF full model performance was similar (AUC = 0.82) to those more parsimonious models. The top-ranked variables according to RF included hemoglobin A1C, fasting plasma glucose, waist circumference, adiponectin, c-reactive protein, triglycerides, leptin, left ventricular mass, high-density lipoprotein cholesterol, and aldosterone. This work shows the potential of RF for incident diabetes prediction while dealing with high-dimensional data. PMID:27727289

Spectral pattern of urinary water as a biomarker of estrus in the giant panda

NASA Astrophysics Data System (ADS)

Kinoshita, Kodzue; Miyazaki, Mari; Morita, Hiroyuki; Vassileva, Maria; Tang, Chunxiang; Li, Desheng; Ishikawa, Osamu; Kusunoki, Hiroshi; Tsenkova, Roumiana

2012-11-01

Near infrared spectroscopy (NIRS) has been successfully used for non-invasive diagnosis of diseases and abnormalities where water spectral patterns are found to play an important role. The present study investigates water absorbance patterns indicative of estrus in the female giant panda. NIR spectra of urine samples were acquired from the same animal on a daily basis over three consecutive putative estrus periods. Characteristic water absorbance patterns based on 12 specific water absorbance bands were discovered, which displayed high urine spectral variation, suggesting that hydrogen-bonded water structures increase with estrus. Regression analysis of urine spectra and spectra of estrone-3-glucuronide standard concentrations at these water bands showed high correlation with estrogen levels. Cluster analysis of urine spectra grouped together estrus samples from different years. These results open a new avenue for using water structure as a molecular mirror for fast estrus detection.

Spectral pattern of urinary water as a biomarker of estrus in the giant panda.

PubMed

Kinoshita, Kodzue; Miyazaki, Mari; Morita, Hiroyuki; Vassileva, Maria; Tang, Chunxiang; Li, Desheng; Ishikawa, Osamu; Kusunoki, Hiroshi; Tsenkova, Roumiana

2012-01-01

Near infrared spectroscopy (NIRS) has been successfully used for non-invasive diagnosis of diseases and abnormalities where water spectral patterns are found to play an important role. The present study investigates water absorbance patterns indicative of estrus in the female giant panda. NIR spectra of urine samples were acquired from the same animal on a daily basis over three consecutive putative estrus periods. Characteristic water absorbance patterns based on 12 specific water absorbance bands were discovered, which displayed high urine spectral variation, suggesting that hydrogen-bonded water structures increase with estrus. Regression analysis of urine spectra and spectra of estrone-3-glucuronide standard concentrations at these water bands showed high correlation with estrogen levels. Cluster analysis of urine spectra grouped together estrus samples from different years. These results open a new avenue for using water structure as a molecular mirror for fast estrus detection.

Spectral pattern of urinary water as a biomarker of estrus in the giant panda

PubMed Central

Kinoshita, Kodzue; Miyazaki, Mari; Morita, Hiroyuki; Vassileva, Maria; Tang, Chunxiang; Li, Desheng; Ishikawa, Osamu; Kusunoki, Hiroshi; Tsenkova, Roumiana

2012-01-01

Near infrared spectroscopy (NIRS) has been successfully used for non-invasive diagnosis of diseases and abnormalities where water spectral patterns are found to play an important role. The present study investigates water absorbance patterns indicative of estrus in the female giant panda. NIR spectra of urine samples were acquired from the same animal on a daily basis over three consecutive putative estrus periods. Characteristic water absorbance patterns based on 12 specific water absorbance bands were discovered, which displayed high urine spectral variation, suggesting that hydrogen-bonded water structures increase with estrus. Regression analysis of urine spectra and spectra of estrone-3-glucuronide standard concentrations at these water bands showed high correlation with estrogen levels. Cluster analysis of urine spectra grouped together estrus samples from different years. These results open a new avenue for using water structure as a molecular mirror for fast estrus detection. PMID:23181188

Process for Making Ceramic Mold

NASA Technical Reports Server (NTRS)

Buck, Gregory M. (Inventor); Vasquez, Peter (Inventor)

2001-01-01

An improved process for slip casting molds that can be more economically automated and that also exhibits greater dimensional stability is disclosed. The process involves subjecting an investment pattern, preferably made from wax, to successive cycles of wet-dipping in a slurry of colloidal, silica-based binder and dry powder-coating, or stuccoing with plaster of Paris or calcium sulfate mixtures to produce a multi-layer shell over the pattern. The invention as claimed entails applying a primary and a secondary coating to the investment pattern. At least two wet-dipping on in a primary slurry and dry-stuccoing cycles provide the primary coating, and an additional two wet-dippings and dry-stuccoing cycles provide the secondary, or back-up, coating. The primary and secondary coatings produce a multi-layered shell pattern. The multi-layered shell pattern is placed in a furnace first to cure and harden, and then to vaporize the investment pattern, leaving a detailed, high precision shell mold.

Observation of the wing deformation and the CFD study of cicada

NASA Astrophysics Data System (ADS)

Dai, Hu; Mohd Adam Das, Shahrizan; Luo, Haoxiang

2011-11-01

We studied the wing properties and kinematics of cicada when the 13-year species emerged in amazingly large numbers in middle Tennessee during May 2011. Using a high-speed camera, we recorded the wing motion of the insect and then reconstructed the three-dimensional wing kinematics using a video digitization software. Like many other insects, the deformation of the cicada wing is asymmetric between the downstroke and upstroke half cycles, and this particular deformation pattern would benefit production of the lift and propulsive forces. Both two-dimensional and three-dimensional CFD studies are carried out based on the reconstructed wing motion. The implication of the study on the role of the aerodynamic force in the wing deformation will be discussed. This work is sponsored by the NSF.

On mass transport in porosity waves

NASA Astrophysics Data System (ADS)

Jordan, Jacob S.; Hesse, Marc A.; Rudge, John F.

2018-03-01

Porosity waves arise naturally from the equations describing fluid migration in ductile rocks. Here, we show that higher-dimensional porosity waves can transport mass and therefore preserve geochemical signatures, at least partially. Fluid focusing into these high porosity waves leads to recirculation in their center. This recirculating fluid is separated from the background flow field by a circular dividing streamline and transported with the phase velocity of the porosity wave. Unlike models for one-dimensional chromatography in geological porous media, tracer transport in higher-dimensional porosity waves does not produce chromatographic separations between relatively incompatible elements due to the circular flow pattern. This may allow melt that originated from the partial melting of fertile heterogeneities or fluid produced during metamorphism to retain distinct geochemical signatures as they rise buoyantly towards the surface.

Hyperspectral face recognition with spatiospectral information fusion and PLS regression.

PubMed

Uzair, Muhammad; Mahmood, Arif; Mian, Ajmal

2015-03-01

Hyperspectral imaging offers new opportunities for face recognition via improved discrimination along the spectral dimension. However, it poses new challenges, including low signal-to-noise ratio, interband misalignment, and high data dimensionality. Due to these challenges, the literature on hyperspectral face recognition is not only sparse but is limited to ad hoc dimensionality reduction techniques and lacks comprehensive evaluation. We propose a hyperspectral face recognition algorithm using a spatiospectral covariance for band fusion and partial least square regression for classification. Moreover, we extend 13 existing face recognition techniques, for the first time, to perform hyperspectral face recognition.We formulate hyperspectral face recognition as an image-set classification problem and evaluate the performance of seven state-of-the-art image-set classification techniques. We also test six state-of-the-art grayscale and RGB (color) face recognition algorithms after applying fusion techniques on hyperspectral images. Comparison with the 13 extended and five existing hyperspectral face recognition techniques on three standard data sets show that the proposed algorithm outperforms all by a significant margin. Finally, we perform band selection experiments to find the most discriminative bands in the visible and near infrared response spectrum.

Simultaneous grouping pursuit and feature selection over an undirected graph*

PubMed Central

Zhu, Yunzhang; Shen, Xiaotong; Pan, Wei

2013-01-01

Summary In high-dimensional regression, grouping pursuit and feature selection have their own merits while complementing each other in battling the curse of dimensionality. To seek a parsimonious model, we perform simultaneous grouping pursuit and feature selection over an arbitrary undirected graph with each node corresponding to one predictor. When the corresponding nodes are reachable from each other over the graph, regression coefficients can be grouped, whose absolute values are the same or close. This is motivated from gene network analysis, where genes tend to work in groups according to their biological functionalities. Through a nonconvex penalty, we develop a computational strategy and analyze the proposed method. Theoretical analysis indicates that the proposed method reconstructs the oracle estimator, that is, the unbiased least squares estimator given the true grouping, leading to consistent reconstruction of grouping structures and informative features, as well as to optimal parameter estimation. Simulation studies suggest that the method combines the benefit of grouping pursuit with that of feature selection, and compares favorably against its competitors in selection accuracy and predictive performance. An application to eQTL data is used to illustrate the methodology, where a network is incorporated into analysis through an undirected graph. PMID:24098061

Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection.

PubMed

Mohammed, Ameer; Zamani, Majid; Bayford, Richard; Demosthenous, Andreas

2017-12-01

In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

Three-dimensional MRI perfusion maps: a step beyond volumetric analysis in mental disorders

PubMed Central

Fabene, Paolo F; Farace, Paolo; Brambilla, Paolo; Andreone, Nicola; Cerini, Roberto; Pelizza, Luisa; Versace, Amelia; Rambaldelli, Gianluca; Birbaumer, Niels; Tansella, Michele; Sbarbati, Andrea

2007-01-01

A new type of magnetic resonance imaging analysis, based on fusion of three-dimensional reconstructions of time-to-peak parametric maps and high-resolution T1-weighted images, is proposed in order to evaluate the perfusion of selected volumes of interest. Because in recent years a wealth of data have suggested the crucial involvement of vascular alterations in mental diseases, we tested our new method on a restricted sample of schizophrenic patients and matched healthy controls. The perfusion of the whole brain was compared with that of the caudate nucleus by means of intrasubject analysis. As expected, owing to the encephalic vascular pattern, a significantly lower time-to-peak was observed in the caudate nucleus than in the whole brain in all healthy controls, indicating that the suggested method has enough sensitivity to detect subtle perfusion changes even in small volumes of interest. Interestingly, a less uniform pattern was observed in the schizophrenic patients. The latter finding needs to be replicated in an adequate number of subjects. In summary, the three-dimensional analysis method we propose has been shown to be a feasible tool for revealing subtle vascular changes both in normal subjects and in pathological conditions. PMID:17229290

Fabrication and independent control of patterned polymer gate for a few-layer WSe{sub 2} field-effect transistor

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

Hong, Sung Ju; Park, Min; Kang, Hojin

We report the fabrication of a patterned polymer electrolyte for a two-dimensional (2D) semiconductor, few-layer tungsten diselenide (WSe{sub 2}) field-effect transistor (FET). We expose an electron-beam in a desirable region to form the patterned structure. The WSe{sub 2} FET acts as a p-type semiconductor in both bare and polymer-covered devices. We observe a highly efficient gating effect in the polymer-patterned device with independent gate control. The patterned polymer gate operates successfully in a molybdenum disulfide (MoS{sub 2}) FET, indicating the potential for general applications to 2D semiconductors. The results of this study can contribute to large-scale integration and better flexibilitymore » in transition metal dichalcogenide (TMD)-based electronics.« less

Fluorine-containing composition for forming anti-reflection film on resist surface and pattern formation method

DOEpatents

Nishi, Mineo; Makishima, Hideo

1996-01-01

A composition for forming anti-reflection film on resist surface which comprises an aqueous solution of a water soluble fluorine compound, and a pattern formation method which comprises the steps of coating a photoresist composition on a substrate; coating the above-mentioned composition for forming anti-reflection film; exposing the coated film to form a specific pattern; and developing the photoresist, are provided. Since the composition for forming anti-reflection film can be coated on the photoresist in the form of an aqueous solution, not only the anti-reflection film can be formed easily, but also, the film can be removed easily by rinsing with water or alkali development. Therefore, by the pattern formation method according to the present invention, it is possible to form a pattern easily with a high dimensional accuracy.

Patterns of medicinal plant use: an examination of the Ecuadorian Shuar medicinal flora using contingency table and binomial analyses.

PubMed

Bennett, Bradley C; Husby, Chad E

2008-03-28

Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws. We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador). We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families. Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels. Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.

Parametric Study of Flow Patterns behind the Standing Accretion Shock Wave for Core-Collapse Supernovae

NASA Astrophysics Data System (ADS)

Iwakami, Wakana; Nagakura, Hiroki; Yamada, Shoichi

2014-05-01

In this study, we conduct three-dimensional hydrodynamic simulations systematically to investigate the flow patterns behind the accretion shock waves that are commonly formed in the post-bounce phase of core-collapse supernovae. Adding small perturbations to spherically symmetric, steady, shocked accretion flows, we compute the subsequent evolutions to find what flow pattern emerges as a consequence of hydrodynamical instabilities such as convection and standing accretion shock instability for different neutrino luminosities and mass accretion rates. Depending on these two controlling parameters, various flow patterns are indeed realized. We classify them into three basic patterns and two intermediate ones; the former includes sloshing motion (SL), spiral motion (SP), and multiple buoyant bubble formation (BB); the latter consists of spiral motion with buoyant-bubble formation (SPB) and spiral motion with pulsationally changing rotational velocities (SPP). Although the post-shock flow is highly chaotic, there is a clear trend in the pattern realization. The sloshing and spiral motions tend to be dominant for high accretion rates and low neutrino luminosities, and multiple buoyant bubbles prevail for low accretion rates and high neutrino luminosities. It is interesting that the dominant pattern is not always identical between the semi-nonlinear and nonlinear phases near the critical luminosity; the intermediate cases are realized in the latter case. Running several simulations with different random perturbations, we confirm that the realization of flow pattern is robust in most cases.

GATE: software for the analysis and visualization of high-dimensional time series expression data.

PubMed

MacArthur, Ben D; Lachmann, Alexander; Lemischka, Ihor R; Ma'ayan, Avi

2010-01-01

We present Grid Analysis of Time series Expression (GATE), an integrated computational software platform for the analysis and visualization of high-dimensional biomolecular time series. GATE uses a correlation-based clustering algorithm to arrange molecular time series on a two-dimensional hexagonal array and dynamically colors individual hexagons according to the expression level of the molecular component to which they are assigned, to create animated movies of systems-level molecular regulatory dynamics. In order to infer potential regulatory control mechanisms from patterns of correlation, GATE also allows interactive interroga-tion of movies against a wide variety of prior knowledge datasets. GATE movies can be paused and are interactive, allowing users to reconstruct networks and perform functional enrichment analyses. Movies created with GATE can be saved in Flash format and can be inserted directly into PDF manuscript files as interactive figures. GATE is available for download and is free for academic use from http://amp.pharm.mssm.edu/maayan-lab/gate.htm

Electron-beam induced nano-etching of suspended graphene

PubMed Central

Sommer, Benedikt; Sonntag, Jens; Ganczarczyk, Arkadius; Braam, Daniel; Prinz, Günther; Lorke, Axel; Geller, Martin

2015-01-01

Besides its interesting physical properties, graphene as a two-dimensional lattice of carbon atoms promises to realize devices with exceptional electronic properties, where freely suspended graphene without contact to any substrate is the ultimate, truly two-dimensional system. The practical realization of nano-devices from suspended graphene, however, relies heavily on finding a structuring method which is minimally invasive. Here, we report on the first electron beam-induced nano-etching of suspended graphene and demonstrate high-resolution etching down to ~7 nm for line-cuts into the monolayer graphene. We investigate the structural quality of the etched graphene layer using two-dimensional (2D) Raman maps and demonstrate its high electronic quality in a nano-device: A 25 nm-wide suspended graphene nanoribbon (GNR) that shows a transport gap with a corresponding energy of ~60 meV. This is an important step towards fast and reliable patterning of suspended graphene for future ballistic transport, nano-electronic and nano-mechanical devices. PMID:25586495

Analysis of microvascular perfusion with multi-dimensional complete ensemble empirical mode decomposition with adaptive noise algorithm: Processing of laser speckle contrast images recorded in healthy subjects, at rest and during acetylcholine stimulation.

PubMed

Humeau-Heurtier, Anne; Marche, Pauline; Dubois, Severine; Mahe, Guillaume

2015-01-01

Laser speckle contrast imaging (LSCI) is a full-field imaging modality to monitor microvascular blood flow. It is able to give images with high temporal and spatial resolutions. However, when the skin is studied, the interpretation of the bidimensional data may be difficult. This is why an averaging of the perfusion values in regions of interest is often performed and the result is followed in time, reducing the data to monodimensional time series. In order to avoid such a procedure (that leads to a loss of the spatial resolution), we propose to extract patterns from LSCI data and to compare these patterns for two physiological states in healthy subjects: at rest and at the peak of acetylcholine-induced perfusion peak. For this purpose, the recent multi-dimensional complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) algorithm is applied to LSCI data. The results show that the intrinsic mode functions and residue given by MCEEMDAN show different patterns for the two physiological states. The images, as bidimensional data, can therefore be processed to reveal microvascular perfusion patterns, hidden in the images themselves. This work is therefore a feasibility study before analyzing data in patients with microvascular dysfunctions.

Traceable Mueller polarimetry and scatterometry for shape reconstruction of grating structures

NASA Astrophysics Data System (ADS)

Hansen, Poul-Erik; Madsen, Morten H.; Lehtolahti, Joonas; Nielsen, Lars

2017-11-01

Dimensional measurements of multi-patterned transmission gratings with a mixture of long and small periods are great challenges for optical metrology today. It is a further challenge when the aspect ratio of the structures is high, that is, when the height of structures is larger than the pitch. Here we consider a double patterned transmission grating with pitches of 500 nm and 20 000 nm. For measuring the geometrical properties of double patterned transmission grating we use a combined spectroscopic Mueller polarimetry and scatterometry setup. For modelling the experimentally obtained data we rigorously compute the scattering signal by solving Maxwell's equations using the RCWA method on a supercell structure. We also present a new method for analyzing the Mueller polarimetry parameters that performs the analysis in the measured variables. This new inversion method for finding the best fit between measured and calculated values are tested on silicon gratings with periods from 300 to 600 nm. The method is shown to give results within the expanded uncertainty of reference AFM measurements. The application of the new inversion method and the supercell structure to the double patterned transmission grating gives best estimates of dimensional quantities that are in fair agreement with those derived from local AFM measurements

Origami Inspired Self-assembly of Patterned and Reconfigurable Particles

PubMed Central

Pandey, Shivendra; Gultepe, Evin; Gracias, David H.

2013-01-01

There are numerous techniques such as photolithography, electron-beam lithography and soft-lithography that can be used to precisely pattern two dimensional (2D) structures. These technologies are mature, offer high precision and many of them can be implemented in a high-throughput manner. We leverage the advantages of planar lithography and combine them with self-folding methods1-20 wherein physical forces derived from surface tension or residual stress, are used to curve or fold planar structures into three dimensional (3D) structures. In doing so, we make it possible to mass produce precisely patterned static and reconfigurable particles that are challenging to synthesize. In this paper, we detail visualized experimental protocols to create patterned particles, notably, (a) permanently bonded, hollow, polyhedra that self-assemble and self-seal due to the minimization of surface energy of liquefied hinges21-23 and (b) grippers that self-fold due to residual stress powered hinges24,25. The specific protocol described can be used to create particles with overall sizes ranging from the micrometer to the centimeter length scales. Further, arbitrary patterns can be defined on the surfaces of the particles of importance in colloidal science, electronics, optics and medicine. More generally, the concept of self-assembling mechanically rigid particles with self-sealing hinges is applicable, with some process modifications, to the creation of particles at even smaller, 100 nm length scales22, 26 and with a range of materials including metals21, semiconductors9 and polymers27. With respect to residual stress powered actuation of reconfigurable grasping devices, our specific protocol utilizes chromium hinges of relevance to devices with sizes ranging from 100 μm to 2.5 mm. However, more generally, the concept of such tether-free residual stress powered actuation can be used with alternate high-stress materials such as heteroepitaxially deposited semiconductor films5,7 to possibly create even smaller nanoscale grasping devices. PMID:23407436

Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation

NASA Astrophysics Data System (ADS)

Tang, Kunkun; Congedo, Pietro M.; Abgrall, Rémi

2016-06-01

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable for real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.

Direct coupling of tomography and ptychography

DOE PAGES

Gürsoy, Doğa

2017-08-09

We present a generalization of the ptychographic phase problem for recovering refractive properties of a three-dimensional object in a tomography setting. Our approach, which ignores the lateral overlapping probe requirements in existing ptychography algorithms, can enable the reconstruction of objects using highly flexible acquisition patterns and pave the way for sparse and rapid data collection with lower radiation exposure.

Probabilistic Gait Classification in Children with Cerebral Palsy: A Bayesian Approach

ERIC Educational Resources Information Center

Van Gestel, Leen; De Laet, Tinne; Di Lello, Enrico; Bruyninckx, Herman; Molenaers, Guy; Van Campenhout, Anja; Aertbelien, Erwin; Schwartz, Mike; Wambacq, Hans; De Cock, Paul; Desloovere, Kaat

2011-01-01

Three-dimensional gait analysis (3DGA) generates a wealth of highly variable data. Gait classifications help to reduce, simplify and interpret this vast amount of 3DGA data and thereby assist and facilitate clinical decision making in the treatment of CP. CP gait is often a mix of several clinically accepted distinct gait patterns. Therefore,…

Resistant to the recession: low-income adults' maintenance of cooking and away-from-home eating behaviors during times of economic turbulence.

PubMed

Smith, Lindsey P; Ng, Shu Wen; Popkin, Barry M

2014-05-01

We examined the effects of state-level unemployment rates during the recession of 2008 on patterns of home food preparation and away-from-home (AFH) eating among low-income and minority populations. We analyzed pooled cross-sectional data on 118 635 adults aged 18 years or older who took part in the American Time Use Study. Multinomial logistic regression models stratified by gender were used to evaluate the associations between state-level unemployment, poverty, race/ethnicity, and time spent cooking, and log binomial regression was used to assess respondents' AFH consumption patterns. High state-level unemployment was associated with only trivial increases in respondents' cooking patterns and virtually no change in their AFH eating patterns. Low-income and racial/ethnic minority groups were not disproportionately affected by the recession. Even during a major economic downturn, US adults are resistant to food-related behavior change. More work is needed to understand whether this reluctance to change is attributable to time limits, lack of knowledge or skill related to food preparation, or lack of access to fresh produce and raw ingredients.

Resistant to the Recession: Low-Income Adults’ Maintenance of Cooking and Away-From-Home Eating Behaviors During Times of Economic Turbulence

PubMed Central

Smith, Lindsey P.; Ng, Shu Wen

2014-01-01

Objectives. We examined the effects of state-level unemployment rates during the recession of 2008 on patterns of home food preparation and away-from-home (AFH) eating among low-income and minority populations. Methods. We analyzed pooled cross-sectional data on 118 635 adults aged 18 years or older who took part in the American Time Use Study. Multinomial logistic regression models stratified by gender were used to evaluate the associations between state-level unemployment, poverty, race/ethnicity, and time spent cooking, and log binomial regression was used to assess respondents’ AFH consumption patterns. Results. High state-level unemployment was associated with only trivial increases in respondents’ cooking patterns and virtually no change in their AFH eating patterns. Low-income and racial/ethnic minority groups were not disproportionately affected by the recession. Conclusions. Even during a major economic downturn, US adults are resistant to food-related behavior change. More work is needed to understand whether this reluctance to change is attributable to time limits, lack of knowledge or skill related to food preparation, or lack of access to fresh produce and raw ingredients. PMID:24625145

Scanning wave photopolymerization enables dye-free alignment patterning of liquid crystals

PubMed Central

Hisano, Kyohei; Aizawa, Miho; Ishizu, Masaki; Kurata, Yosuke; Nakano, Wataru; Akamatsu, Norihisa; Barrett, Christopher J.; Shishido, Atsushi

2017-01-01

Hierarchical control of two-dimensional (2D) molecular alignment patterns over large areas is essential for designing high-functional organic materials and devices. However, even by the most powerful current methods, dye molecules that discolor and destabilize the materials need to be doped in, complicating the process. We present a dye-free alignment patterning technique, based on a scanning wave photopolymerization (SWaP) concept, that achieves a spatial light–triggered mass flow to direct molecular order using scanning light to propagate the wavefront. This enables one to generate macroscopic, arbitrary 2D alignment patterns in a wide variety of optically transparent polymer films from various polymerizable mesogens with sufficiently high birefringence (>0.1) merely by single-step photopolymerization, without alignment layers or polarized light sources. A set of 150,000 arrays of a radial alignment pattern with a size of 27.4 μm × 27.4 μm were successfully inscribed by SWaP, in which each individual pattern is smaller by a factor of 104 than that achievable by conventional photoalignment methods. This dye-free inscription of microscopic, complex alignment patterns over large areas provides a new pathway for designing higher-performance optical and mechanical devices. PMID:29152567